sagemaker.core.shapes.shapes

sagemaker.core.shapes.shapes#

class sagemaker.core.shapes.shapes.AIBenchmarkEndpoint(*, identifier: str | PipelineVariable, target_container_hostname: str | PipelineVariable | None = Unassigned(), inference_components: List[AIBenchmarkInferenceComponent] | None = Unassigned())[source]#

Bases: Base

The SageMaker endpoint configuration for benchmarking.

identifier#

Type:: The name or Amazon Resource Name (ARN) of the SageMaker endpoint to benchmark.

target_container_hostname#

Type:: The hostname of the specific container to target within a multi-container endpoint.

inference_components#

Type:: The list of inference components to benchmark on the endpoint.

identifier: str | PipelineVariable#

inference_components: List[AIBenchmarkInferenceComponent] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

target_container_hostname: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AIBenchmarkInferenceComponent(*, identifier: str | PipelineVariable)[source]#

Bases: Base

An inference component to benchmark.

identifier#

Type:: The name or Amazon Resource Name (ARN) of the inference component.

identifier: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIBenchmarkJobSummary(*, ai_benchmark_job_name: str | PipelineVariable, ai_benchmark_job_arn: str | PipelineVariable, ai_benchmark_job_status: str | PipelineVariable, creation_time: datetime, end_time: datetime | None = Unassigned(), ai_workload_config_name: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Summary information about an AI benchmark job.

ai_benchmark_job_name#

Type:: The name of the benchmark job.

ai_benchmark_job_arn#

Type:: The Amazon Resource Name (ARN) of the benchmark job.

ai_benchmark_job_status#

Type:: The status of the benchmark job.

creation_time#

Type:: A timestamp that indicates when the benchmark job was created.

end_time#

Type:: A timestamp that indicates when the benchmark job completed.

ai_workload_config_name#

Type:: The name of the AI workload configuration used by the benchmark job.

ai_benchmark_job_arn: str | PipelineVariable#

ai_benchmark_job_name: str | PipelineVariable#

ai_benchmark_job_status: str | PipelineVariable#

ai_workload_config_name: str | PipelineVariable | None#

creation_time: datetime#

end_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIBenchmarkNetworkConfig(*, vpc_config: VpcConfig | None = Unassigned())[source]#

Bases: Base

The network configuration for an AI benchmark job.

vpc_config#

Type:: The VPC configuration, including security group IDs and subnet IDs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

vpc_config: VpcConfig | None#

class sagemaker.core.shapes.shapes.AIBenchmarkOutputConfig(*, s3_output_location: str | PipelineVariable)[source]#

Bases: Base

The output configuration for an AI benchmark job.

s3_output_location#

Type:: The Amazon S3 URI where benchmark results are stored.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_output_location: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AIBenchmarkOutputResult(*, s3_output_location: str | PipelineVariable, cloud_watch_logs: List[AICloudWatchLogs] | None = Unassigned())[source]#

Bases: Base

The output result of an AI benchmark job, including the Amazon S3 location and CloudWatch log information.

s3_output_location#

Type:: The Amazon S3 URI where benchmark results are stored.

cloud_watch_logs#

Type:: The CloudWatch log information for the benchmark job.

cloud_watch_logs: List[AICloudWatchLogs] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_output_location: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AIBenchmarkTarget(*, endpoint: AIBenchmarkEndpoint | None = Unassigned())[source]#

Bases: Base

The target for an AI benchmark job. This is a union type — specify one of the members.

endpoint#

Type:: The SageMaker endpoint to benchmark.

endpoint: AIBenchmarkEndpoint | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AICapacityReservationConfig(*, capacity_reservation_preference: str | PipelineVariable | None = Unassigned(), ml_reservation_arns: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

The capacity reservation configuration for an AI recommendation job.

capacity_reservation_preference#

Type:: The capacity reservation preference. The only valid value is capacity-reservations-only.

ml_reservation_arns#

Type:: The list of ML reservation ARNs to use.

capacity_reservation_preference: str | PipelineVariable | None#

ml_reservation_arns: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AICloudWatchLogs(*, log_group_arn: str | PipelineVariable | None = Unassigned(), log_stream_name: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

CloudWatch log information for an AI benchmark or recommendation job.

log_group_arn#

Type:: The Amazon Resource Name (ARN) of the CloudWatch log group.

log_stream_name#

Type:: The name of the CloudWatch log stream.

log_group_arn: str | PipelineVariable | None#

log_stream_name: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIDatasetConfig(*, input_data_config: List[AIWorkloadInputDataConfig] | None = Unassigned())[source]#

Bases: Base

The dataset configuration for an AI workload. This is a union type — specify one of the members.

input_data_config#

Type:: An array of input data channel configurations for the workload.

input_data_config: List[AIWorkloadInputDataConfig] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIModelSource(*, s3: AIModelSourceS3 | None = Unassigned())[source]#

Bases: Base

The source of the model for an AI recommendation job. This is a union type.

s3#

Type:: The Amazon S3 location of the model artifacts.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3: AIModelSourceS3 | None#

class sagemaker.core.shapes.shapes.AIModelSourceS3(*, s3_uri: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The Amazon S3 model source configuration.

s3_uri#

Type:: The Amazon S3 URI of the model artifacts.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_uri: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AIRecommendation(*, recommendation_description: str | PipelineVariable | None = Unassigned(), optimization_details: List[AIRecommendationOptimizationDetail] | None = Unassigned(), model_details: AIRecommendationModelDetails | None = Unassigned(), deployment_configuration: AIRecommendationDeploymentConfiguration | None = Unassigned(), ai_benchmark_job_arn: str | PipelineVariable | None = Unassigned(), expected_performance: List[AIRecommendationPerformanceMetric] | None = Unassigned())[source]#

Bases: Base

An optimization recommendation generated by an AI recommendation job.

recommendation_description#

Type:: A description of the recommendation.

optimization_details#

Type:: The optimization techniques applied in this recommendation.

model_details#

Type:: Details about the model package associated with this recommendation.

deployment_configuration#

Type:: The deployment configuration for this recommendation, including the container image, instance type, instance count, and environment variables.

ai_benchmark_job_arn#

Type:: The Amazon Resource Name (ARN) of the benchmark job associated with this recommendation.

expected_performance#

Type:: The expected performance metrics for this recommendation.

ai_benchmark_job_arn: str | PipelineVariable | None#

deployment_configuration: AIRecommendationDeploymentConfiguration | None#

expected_performance: List[AIRecommendationPerformanceMetric] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_details: AIRecommendationModelDetails | None#

optimization_details: List[AIRecommendationOptimizationDetail] | None#

recommendation_description: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AIRecommendationComputeSpec(*, instance_types: List[str | PipelineVariable] | None = Unassigned(), capacity_reservation_config: AICapacityReservationConfig | None = Unassigned())[source]#

Bases: Base

The compute resource specification for an AI recommendation job.

instance_types#

Type:: The list of instance types to consider for recommendations. You can specify up to 3 instance types.

capacity_reservation_config#

Type:: The capacity reservation configuration.

capacity_reservation_config: AICapacityReservationConfig | None#

instance_types: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIRecommendationConstraint(*, metric: str | PipelineVariable)[source]#

Bases: Base

A performance constraint for an AI recommendation job.

metric#

Type:: The performance metric. Valid values are ttft-ms (time to first token in milliseconds), throughput, and cost.

metric: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIRecommendationDeploymentConfiguration(*, s3: List[AIRecommendationDeploymentS3Channel] | None = Unassigned(), image_uri: str | PipelineVariable | None = Unassigned(), instance_type: str | PipelineVariable | None = Unassigned(), instance_count: int | None = Unassigned(), copy_count_per_instance: int | None = Unassigned(), environment_variables: Dict[str | PipelineVariable, str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

The deployment configuration for a recommendation.

s3#

Type:: The Amazon S3 data channels for the deployment.

image_uri#

Type:: The URI of the container image for the deployment.

instance_type#

Type:: The recommended instance type for the deployment.

instance_count#

Type:: The recommended number of instances for the deployment.

copy_count_per_instance#

Type:: The number of model copies per instance.

environment_variables#

Type:: The environment variables for the deployment.

copy_count_per_instance: int | None#

environment_variables: Dict[str | PipelineVariable, str | PipelineVariable] | None#

image_uri: str | PipelineVariable | None#

instance_count: int | None#

instance_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3: List[AIRecommendationDeploymentS3Channel] | None#

class sagemaker.core.shapes.shapes.AIRecommendationDeploymentS3Channel(*, channel_name: str | PipelineVariable | None = Unassigned(), uri: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

An Amazon S3 data channel for a recommended deployment configuration, containing model artifacts or optimized model outputs.

channel_name#

Type:: A custom name for this Amazon S3 data channel.

uri#

Type:: The Amazon S3 URI of the data for this channel.

channel_name: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

uri: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AIRecommendationInferenceSpecification(*, framework: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The inference framework for an AI recommendation job.

framework#

Type:: The inference framework. Valid values are LMI and VLLM.

framework: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIRecommendationInstanceDetail(*, instance_type: str | PipelineVariable | None = Unassigned(), instance_count: int | None = Unassigned(), copy_count_per_instance: int | None = Unassigned())[source]#

Bases: Base

Instance details for a recommendation.

instance_type#

Type:: The recommended instance type.

instance_count#

Type:: The recommended number of instances.

copy_count_per_instance#

Type:: The number of model copies per instance.

copy_count_per_instance: int | None#

instance_count: int | None#

instance_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIRecommendationJobSummary(*, ai_recommendation_job_name: str | PipelineVariable, ai_recommendation_job_arn: str | PipelineVariable, ai_recommendation_job_status: str | PipelineVariable, creation_time: datetime, end_time: datetime | None = Unassigned())[source]#

Bases: Base

Summary information about an AI recommendation job.

ai_recommendation_job_name#

Type:: The name of the recommendation job.

ai_recommendation_job_arn#

Type:: The Amazon Resource Name (ARN) of the recommendation job.

ai_recommendation_job_status#

Type:: The status of the recommendation job.

creation_time#

Type:: A timestamp that indicates when the recommendation job was created.

end_time#

Type:: A timestamp that indicates when the recommendation job completed.

ai_recommendation_job_arn: str | PipelineVariable#

ai_recommendation_job_name: str | PipelineVariable#

ai_recommendation_job_status: str | PipelineVariable#

creation_time: datetime#

end_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIRecommendationModelDetails(*, model_package_arn: str | PipelineVariable | None = Unassigned(), inference_specification_name: str | PipelineVariable | None = Unassigned(), instance_details: List[AIRecommendationInstanceDetail] | None = Unassigned())[source]#

Bases: Base

Details about the model package in a recommendation.

model_package_arn#

Type:: The Amazon Resource Name (ARN) of the model package.

inference_specification_name#

Type:: The name of the inference specification within the model package.

instance_details#

Type:: The instance details for this recommendation, including instance type, count, and model copies per instance.

inference_specification_name: str | PipelineVariable | None#

instance_details: List[AIRecommendationInstanceDetail] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_package_arn: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AIRecommendationOptimizationDetail(*, optimization_type: str | PipelineVariable, optimization_config: Dict[str | PipelineVariable, str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

Details about an optimization technique applied in a recommendation.

optimization_type#

Type:: The type of optimization. Valid values are SpeculativeDecoding and KernelTuning.

optimization_config#

Type:: A map of configuration parameters for the optimization technique.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

optimization_config: Dict[str | PipelineVariable, str | PipelineVariable] | None#

optimization_type: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AIRecommendationOutputConfig(*, s3_output_location: str | PipelineVariable | None = Unassigned(), model_package_group_identifier: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The output configuration for an AI recommendation job.

s3_output_location#

Type:: The Amazon S3 URI where recommendation results are stored.

model_package_group_identifier#

Type:: The name or Amazon Resource Name (ARN) of the model package group where the optimized model is registered as a new model package version.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_package_group_identifier: str | PipelineVariable | None#

s3_output_location: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AIRecommendationOutputResult(*, s3_output_location: str | PipelineVariable, model_package_group_identifier: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The output configuration for an AI recommendation job, including the S3 location for results and the model package group for deployment.

s3_output_location#

Type:: The Amazon S3 URI where the recommendation job writes its output results.

model_package_group_identifier#

Type:: The name or Amazon Resource Name (ARN) of the model package group where deployment-ready model packages are registered.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_package_group_identifier: str | PipelineVariable | None#

s3_output_location: str | PipelineVariable#

Bases: Base

An expected performance metric for a recommendation.

metric#

Type:: The name of the performance metric.

stat#

Type:: The statistical measure for the metric.

value#

Type:: The value of the metric.

unit#

Type:: The unit of the metric value.

metric: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

stat: str | PipelineVariable | None#

unit: str | PipelineVariable | None#

value: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AIRecommendationPerformanceTarget(*, constraints: List[AIRecommendationConstraint])[source]#

Bases: Base

The performance targets for an AI recommendation job.

constraints#

Type:: An array of performance constraints that define the optimization objectives.

constraints: List[AIRecommendationConstraint]#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIWorkloadConfigSummary(*, ai_workload_config_name: str | PipelineVariable, ai_workload_config_arn: str | PipelineVariable, creation_time: datetime)[source]#

Bases: Base

Summary information about an AI workload configuration.

ai_workload_config_name#

Type:: The name of the AI workload configuration.

ai_workload_config_arn#

Type:: The Amazon Resource Name (ARN) of the AI workload configuration.

creation_time#

Type:: A timestamp that indicates when the configuration was created.

ai_workload_config_arn: str | PipelineVariable#

ai_workload_config_name: str | PipelineVariable#

creation_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIWorkloadConfigs(*, workload_spec: WorkloadSpec)[source]#

Bases: Base

The benchmark tool configuration for an AI workload.

workload_spec#

Type:: The workload specification that defines benchmark parameters.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

workload_spec: WorkloadSpec#

class sagemaker.core.shapes.shapes.AIWorkloadDataSource(*, s3_data_source: AIWorkloadS3DataSource | None = Unassigned())[source]#

Bases: Base

The data source for an AI workload input data channel.

s3_data_source#

Type:: The Amazon S3 data source configuration.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_data_source: AIWorkloadS3DataSource | None#

class sagemaker.core.shapes.shapes.AIWorkloadInputDataConfig(*, channel_name: str | PipelineVariable, data_source: AIWorkloadDataSource)[source]#

Bases: Base

A channel of input data for an AI workload configuration. Each channel has a name and a data source.

channel_name#

Type:: The logical name for the data channel.

data_source#

Type:: The data source for this channel.

channel_name: str | PipelineVariable#

data_source: AIWorkloadDataSource#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AIWorkloadS3DataSource(*, s3_uri: str | PipelineVariable)[source]#

Bases: Base

The Amazon S3 data source for an AI workload.

s3_uri#

Type:: The Amazon S3 URI of the data.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_uri: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AcceleratorPartitionConfig(*, type: str | PipelineVariable, count: int)[source]#

Bases: Base

Configuration for allocating accelerator partitions.

type#

Type:: The Multi-Instance GPU (MIG) profile type that defines the partition configuration. The profile specifies the compute and memory allocation for each partition instance. The available profile types depend on the instance type specified in the compute quota configuration.

count#

Type:: The number of accelerator partitions to allocate with the specified partition type. If you don’t specify a value for vCPU and MemoryInGiB, SageMaker AI automatically allocates ratio-based values for those parameters based on the accelerator partition count you provide.

count: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AccessForbidden(*, message: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

You do not have permission to perform an action.

message#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable | None

message: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

A structure describing the source of an action.

source_uri#

Type:: The URI of the source.

source_type#

Type:: The type of the source.

source_id#

Type:: The ID of the source.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_id: str | PipelineVariable | None#

source_type: str | PipelineVariable | None#

source_uri: str | PipelineVariable#

Bases: Base

Lists the properties of an action. An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.

action_arn#

Type:: The Amazon Resource Name (ARN) of the action.

action_name#

Type:: The name of the action.

source#

Type:: The source of the action.

action_type#

Type:: The type of the action.

status#

Type:: The status of the action.

creation_time#

Type:: When the action was created.

last_modified_time#

Type:: When the action was last modified.

action_arn: str | PipelineVariable | None#

action_name: str | PipelineVariable | object | None#

action_type: str | PipelineVariable | None#

creation_time: datetime | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source: ActionSource | None#

status: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AddClusterNodeSpecification(*, instance_group_name: str | PipelineVariable, increment_target_count_by: int, availability_zones: List[str | PipelineVariable] | None = Unassigned(), instance_types: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

Specifies an instance group and the number of nodes to add to it.

instance_group_name#

Type:: The name of the instance group to which you want to add nodes.

increment_target_count_by#

Type:: The number of nodes to add to the specified instance group. The total number of nodes across all instance groups in a single request cannot exceed 50.

availability_zones#

Type:: The availability zones in which to add nodes. Use this to target node placement in specific availability zones within a flexible instance group.

instance_types#

Type:: The instance types to use when adding nodes. Use this to target specific instance types within a flexible instance group.

availability_zones: List[str | PipelineVariable] | None#

increment_target_count_by: int#

instance_group_name: str | PipelineVariable#

instance_types: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AdditionalEnis(*, efa_enis: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

Information about additional Elastic Network Interfaces (ENIs) associated with an instance.

efa_enis#

Type:: A list of Elastic Fabric Adapter (EFA) ENIs associated with the instance.

efa_enis: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AdditionalInferenceSpecificationDefinition(*, name: str | PipelineVariable, containers: List[ModelPackageContainerDefinition], description: str | PipelineVariable | None = Unassigned(), supported_transform_instance_types: List[str | PipelineVariable] | None = Unassigned(), supported_realtime_inference_instance_types: List[str | PipelineVariable] | None = Unassigned(), supported_content_types: List[str | PipelineVariable] | None = Unassigned(), supported_response_mime_types: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package

name#

Type:: A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.

description#

Type:: A description of the additional Inference specification

containers#

Type:: The Amazon ECR registry path of the Docker image that contains the inference code.

supported_transform_instance_types#

Type:: A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

supported_realtime_inference_instance_types#

Type:: A list of the instance types that are used to generate inferences in real-time.

supported_content_types#

Type:: The supported MIME types for the input data.

supported_response_mime_types#

Type:: The supported MIME types for the output data.

containers: List[ModelPackageContainerDefinition]#

description: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

supported_content_types: List[str | PipelineVariable] | None#

supported_realtime_inference_instance_types: List[str | PipelineVariable] | None#

supported_response_mime_types: List[str | PipelineVariable] | None#

supported_transform_instance_types: List[str | PipelineVariable] | None#

class sagemaker.core.shapes.shapes.AdditionalModelDataSource(*, channel_name: str | PipelineVariable, s3_data_source: S3ModelDataSource)[source]#

Bases: Base

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

channel_name#

Type:: A custom name for this AdditionalModelDataSource object.

s3_data_source#

Type:: sagemaker.core.shapes.shapes.S3ModelDataSource

channel_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_data_source: S3ModelDataSource#

Bases: Base

A data source used for training or inference that is in addition to the input dataset or model data.

s3_data_type#

Type:: The data type of the additional data source that you specify for use in inference or training.

s3_uri#

Type:: The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

compression_type#

Type:: The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

e_tag#

Type:: The ETag associated with S3 URI.

compression_type: str | PipelineVariable | None#

e_tag: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_data_type: str | PipelineVariable#

s3_uri: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AgentVersion(*, version: str | PipelineVariable, agent_count: int)[source]#

Bases: Base

Edge Manager agent version.

version#

Type:: Version of the agent.

agent_count#

Type:: The number of Edge Manager agents.

agent_count: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

version: str | PipelineVariable#

class sagemaker.core.shapes.shapes.Alarm(*, alarm_name: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

alarm_name#

Type:: The name of a CloudWatch alarm in your account.

alarm_name: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AlarmDetails(*, alarm_name: str | PipelineVariable)[source]#

Bases: Base

The details of the alarm to monitor during the AMI update.

alarm_name#

Type:: The name of the alarm.

alarm_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AlgorithmSpecification(*, training_input_mode: str | PipelineVariable, training_image: str | PipelineVariable | None = Unassigned(), algorithm_name: str | PipelineVariable | object | None = Unassigned(), metric_definitions: List[MetricDefinition] | None = Unassigned(), enable_sage_maker_metrics_time_series: bool | None = Unassigned(), container_entrypoint: List[str | PipelineVariable] | None = Unassigned(), container_arguments: List[str | PipelineVariable] | None = Unassigned(), training_image_config: TrainingImageConfig | None = Unassigned())[source]#

Bases: Base

Specifies the training algorithm to use in a CreateTrainingJob request. SageMaker uses its own SageMaker account credentials to pull and access built-in algorithms so built-in algorithms are universally accessible across all Amazon Web Services accounts. As a result, built-in algorithms have standard, unrestricted access. You cannot restrict built-in algorithms using IAM roles. Use custom algorithms if you require specific access controls. For more information about algorithms provided by SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

training_image#

Type:: The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker. You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. For more information, see the note in the AlgorithmName parameter description.

algorithm_name#

Type:: The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can’t specify a value for TrainingImage, and vice versa. If you specify values for both parameters, the training job might break; if you don’t specify any value for both parameters, the training job might raise a null error.

training_input_mode#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable

metric_definitions#

Type:: A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

enable_sage_maker_metrics_time_series#

Type:: To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren’t generated except in the following cases: You use one of the SageMaker built-in algorithms You use one of the following Prebuilt SageMaker Docker Images: Tensorflow (version >= 1.15) MXNet (version >= 1.6) PyTorch (version >= 1.3) You specify at least one MetricDefinition

container_entrypoint#

Type:: The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

container_arguments#

Type:: The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

training_image_config#

Type:: The configuration to use an image from a private Docker registry for a training job.

algorithm_name: str | PipelineVariable | object | None#

container_arguments: List[str | PipelineVariable] | None#

container_entrypoint: List[str | PipelineVariable] | None#

enable_sage_maker_metrics_time_series: bool | None#

metric_definitions: List[MetricDefinition] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

training_image: str | PipelineVariable | None#

training_image_config: TrainingImageConfig | None#

training_input_mode: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AlgorithmStatusDetails(*, validation_statuses: List[AlgorithmStatusItem] | None = Unassigned(), image_scan_statuses: List[AlgorithmStatusItem] | None = Unassigned())[source]#

Bases: Base

Specifies the validation and image scan statuses of the algorithm.

validation_statuses#

Type:: The status of algorithm validation.

image_scan_statuses#

Type:: The status of the scan of the algorithm’s Docker image container.

image_scan_statuses: List[AlgorithmStatusItem] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

validation_statuses: List[AlgorithmStatusItem] | None#

class sagemaker.core.shapes.shapes.AlgorithmStatusItem(*, name: str | PipelineVariable, status: str | PipelineVariable, failure_reason: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Represents the overall status of an algorithm.

name#

Type:: The name of the algorithm for which the overall status is being reported.

status#

Type:: The current status.

failure_reason#

Type:: if the overall status is Failed, the reason for the failure.

failure_reason: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

status: str | PipelineVariable#

Bases: Base

Provides summary information about an algorithm.

algorithm_name#

Type:: The name of the algorithm that is described by the summary.

algorithm_arn#

Type:: The Amazon Resource Name (ARN) of the algorithm.

algorithm_description#

Type:: A brief description of the algorithm.

creation_time#

Type:: A timestamp that shows when the algorithm was created.

algorithm_status#

Type:: The overall status of the algorithm.

algorithm_arn: str | PipelineVariable#

algorithm_description: str | PipelineVariable | None#

algorithm_name: str | PipelineVariable | object#

algorithm_status: str | PipelineVariable#

creation_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AlgorithmValidationProfile(*, profile_name: str | PipelineVariable, training_job_definition: TrainingJobDefinition, transform_job_definition: TransformJobDefinition | None = Unassigned())[source]#

Bases: Base

Defines a training job and a batch transform job that SageMaker runs to validate your algorithm. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

profile_name#

Type:: The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

training_job_definition#

Type:: The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.

transform_job_definition#

Type:: The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

profile_name: str | PipelineVariable#

training_job_definition: TrainingJobDefinition#

transform_job_definition: TransformJobDefinition | None#

class sagemaker.core.shapes.shapes.AlgorithmValidationSpecification(*, validation_role: str | PipelineVariable, validation_profiles: List[AlgorithmValidationProfile])[source]#

Bases: Base

Specifies configurations for one or more training jobs that SageMaker runs to test the algorithm.

validation_role#

Type:: The IAM roles that SageMaker uses to run the training jobs.

validation_profiles#

Type:: An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

validation_profiles: List[AlgorithmValidationProfile]#

validation_role: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AmazonQSettings(*, status: str | PipelineVariable | None = Unassigned(), q_profile_arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A collection of settings that configure the Amazon Q experience within the domain.

status#

Type:: Whether Amazon Q has been enabled within the domain.

q_profile_arn#

Type:: The ARN of the Amazon Q profile used within the domain.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

q_profile_arn: str | PipelineVariable | None#

status: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AnnotationConsolidationConfig(*, annotation_consolidation_lambda_arn: str | PipelineVariable)[source]#

Bases: Base

Configures how labels are consolidated across human workers and processes output data.

annotation_consolidation_lambda_arn#

Type:: The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn. For custom labeling workflows, see Post-annotation Lambda. Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes. arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as “votes” for the correct label. arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label. arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video. arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking 3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection 3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking 3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation Use the following ARNs for Label Verification and Adjustment Jobs Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels . Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as “votes” for the correct label. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking 3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud. arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection 3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking 3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation Generative AI/Custom - Direct passthrough of output data without any transformation. arn:aws:lambda:us-east-1:432418664414:function:ACS-PassThrough arn:aws:lambda:us-east-2:266458841044:function:ACS-PassThrough arn:aws:lambda:us-west-2:081040173940:function:ACS-PassThrough arn:aws:lambda:eu-west-1:568282634449:function:ACS-PassThrough arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-PassThrough arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-PassThrough arn:aws:lambda:ap-south-1:565803892007:function:ACS-PassThrough arn:aws:lambda:eu-central-1:203001061592:function:ACS-PassThrough arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-PassThrough arn:aws:lambda:eu-west-2:487402164563:function:ACS-PassThrough arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-PassThrough arn:aws:lambda:ca-central-1:918755190332:function:ACS-PassThrough

annotation_consolidation_lambda_arn: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Details about an Amazon SageMaker AI app.

domain_id#

Type:: The domain ID.

user_profile_name#

Type:: The user profile name.

space_name#

Type:: The name of the space.

app_type#

Type:: The type of app.

app_name#

Type:: The name of the app.

status#

Type:: The status.

creation_time#

Type:: The creation time.

resource_spec#

Type:: sagemaker.core.shapes.shapes.ResourceSpec | None

app_name: str | PipelineVariable | object | None#

app_type: str | PipelineVariable | None#

creation_time: datetime | None#

domain_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

resource_spec: ResourceSpec | None#

space_name: str | PipelineVariable | object | None#

status: str | PipelineVariable | None#

user_profile_name: str | PipelineVariable | object | None#

class sagemaker.core.shapes.shapes.AppImageConfigDetails(*, app_image_config_arn: str | PipelineVariable | None = Unassigned(), app_image_config_name: str | PipelineVariable | object | None = Unassigned(), creation_time: datetime | None = Unassigned(), last_modified_time: datetime | None = Unassigned(), kernel_gateway_image_config: KernelGatewayImageConfig | None = Unassigned(), jupyter_lab_app_image_config: JupyterLabAppImageConfig | None = Unassigned(), code_editor_app_image_config: CodeEditorAppImageConfig | None = Unassigned())[source]#

Bases: Base

The configuration for running a SageMaker AI image as a KernelGateway app.

app_image_config_arn#

Type:: The ARN of the AppImageConfig.

app_image_config_name#

Type:: The name of the AppImageConfig. Must be unique to your account.

creation_time#

Type:: When the AppImageConfig was created.

last_modified_time#

Type:: When the AppImageConfig was last modified.

kernel_gateway_image_config#

Type:: The configuration for the file system and kernels in the SageMaker AI image.

jupyter_lab_app_image_config#

Type:: The configuration for the file system and the runtime, such as the environment variables and entry point.

code_editor_app_image_config#

Type:: The configuration for the file system and the runtime, such as the environment variables and entry point.

app_image_config_arn: str | PipelineVariable | None#

app_image_config_name: str | PipelineVariable | object | None#

code_editor_app_image_config: CodeEditorAppImageConfig | None#

creation_time: datetime | None#

jupyter_lab_app_image_config: JupyterLabAppImageConfig | None#

kernel_gateway_image_config: KernelGatewayImageConfig | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AppLifecycleManagement(*, idle_settings: IdleSettings | None = Unassigned())[source]#

Bases: Base

Settings that are used to configure and manage the lifecycle of Amazon SageMaker Studio applications.

idle_settings#

Type:: Settings related to idle shutdown of Studio applications.

idle_settings: IdleSettings | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Configuration to run a processing job in a specified container image.

image_uri#

Type:: The container image to be run by the processing job.

container_entrypoint#

Type:: The entrypoint for a container used to run a processing job.

container_arguments#

Type:: The arguments for a container used to run a processing job.

container_arguments: List[str | PipelineVariable] | None#

container_entrypoint: List[str | PipelineVariable] | None#

image_uri: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ArtifactSource(*, source_uri: str | PipelineVariable, source_types: List[ArtifactSourceType] | None = Unassigned())[source]#

Bases: Base

A structure describing the source of an artifact.

source_uri#

Type:: The URI of the source.

source_types#

Type:: A list of source types.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_types: List[ArtifactSourceType] | None#

source_uri: str | PipelineVariable#

class sagemaker.core.shapes.shapes.ArtifactSourceType(*, source_id_type: str | PipelineVariable, value: str | PipelineVariable)[source]#

Bases: Base

The ID and ID type of an artifact source.

source_id_type#

Type:: The type of ID.

value#

Type:: The ID.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_id_type: str | PipelineVariable#

value: str | PipelineVariable#

Bases: Base

Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model.

artifact_arn#

Type:: The Amazon Resource Name (ARN) of the artifact.

artifact_name#

Type:: The name of the artifact.

source#

Type:: The source of the artifact.

artifact_type#

Type:: The type of the artifact.

creation_time#

Type:: When the artifact was created.

last_modified_time#

Type:: When the artifact was last modified.

artifact_arn: str | PipelineVariable | None#

artifact_name: str | PipelineVariable | object | None#

artifact_type: str | PipelineVariable | None#

creation_time: datetime | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source: ArtifactSource | None#

class sagemaker.core.shapes.shapes.AssociationInfo(*, source_arn: str | PipelineVariable, destination_arn: str | PipelineVariable)[source]#

Bases: Base

The data type used to describe the relationship between different sources.

source_arn#

Type:: The Amazon Resource Name (ARN) of the AssociationInfo source.

destination_arn#

Type:: The Amazon Resource Name (ARN) of the AssociationInfo destination.

destination_arn: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_arn: str | PipelineVariable#

Bases: Base

Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.

source_arn#

Type:: The ARN of the source.

destination_arn#

Type:: The Amazon Resource Name (ARN) of the destination.

source_type#

Type:: The source type.

destination_type#

Type:: The destination type.

association_type#

Type:: The type of the association.

source_name#

Type:: The name of the source.

destination_name#

Type:: The name of the destination.

creation_time#

Type:: When the association was created.

created_by#

Type:: sagemaker.core.shapes.shapes.UserContext | None

association_type: str | PipelineVariable | None#

created_by: UserContext | None#

creation_time: datetime | None#

destination_arn: str | PipelineVariable | None#

destination_name: str | PipelineVariable | None#

destination_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_arn: str | PipelineVariable | None#

source_name: str | PipelineVariable | None#

source_type: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AsyncInferenceClientConfig(*, max_concurrent_invocations_per_instance: int | None = Unassigned())[source]#

Bases: Base

Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

max_concurrent_invocations_per_instance#

Type:: The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

max_concurrent_invocations_per_instance: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AsyncInferenceConfig(*, output_config: AsyncInferenceOutputConfig, client_config: AsyncInferenceClientConfig | None = Unassigned())[source]#

Bases: Base

Specifies configuration for how an endpoint performs asynchronous inference.

client_config#

Type:: Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

output_config#

Type:: Specifies the configuration for asynchronous inference invocation outputs.

client_config: AsyncInferenceClientConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

output_config: AsyncInferenceOutputConfig#

Bases: Base

Specifies the configuration for notifications of inference results for asynchronous inference.

success_topic#

Type:: Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

error_topic#

Type:: Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

include_inference_response_in#

Type:: The Amazon SNS topics where you want the inference response to be included. The inference response is included only if the response size is less than or equal to 128 KB.

error_topic: str | PipelineVariable | None#

include_inference_response_in: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

success_topic: str | PipelineVariable | None#

Bases: Base

Specifies the configuration for asynchronous inference invocation outputs.

kms_key_id#

Type:: The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

s3_output_path#

Type:: The Amazon S3 location to upload inference responses to.

notification_config#

Type:: Specifies the configuration for notifications of inference results for asynchronous inference.

s3_failure_path#

Type:: The Amazon S3 location to upload failure inference responses to.

kms_key_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

notification_config: AsyncInferenceNotificationConfig | None#

s3_failure_path: str | PipelineVariable | None#

s3_output_path: str | PipelineVariable | None#

Bases: Base

Configuration for Athena Dataset Definition input.

catalog#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable

database#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable

query_string#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable

work_group#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable | None

output_s3_uri#

Type:: The location in Amazon S3 where Athena query results are stored.

kms_key_id#

Type:: The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.

output_format#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable

output_compression#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable | None

catalog: str | PipelineVariable#

database: str | PipelineVariable#

kms_key_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

output_compression: str | PipelineVariable | None#

output_format: str | PipelineVariable#

output_s3_uri: str | PipelineVariable#

query_string: str | PipelineVariable#

work_group: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AuthorizedUrl(*, url: str | PipelineVariable | None = Unassigned(), local_path: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Contains a presigned URL and its associated local file path for downloading hub content artifacts.

url#

Type:: The presigned S3 URL that provides temporary, secure access to download the file. URLs expire within 15 minutes for security purposes.

local_path#

Type:: The recommended local file path where the downloaded file should be stored to maintain proper directory structure and file organization.

local_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

url: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AutoMLAlgorithmConfig(*, auto_ml_algorithms: List[str | PipelineVariable])[source]#

Bases: Base

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

auto_ml_algorithms#

Type:: The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job. For the tabular problem type TabularJobConfig: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm. In ENSEMBLING mode: “catboost” “extra-trees” “fastai” “lightgbm” “linear-learner” “nn-torch” “randomforest” “xgboost” In HYPERPARAMETER_TUNING mode: “linear-learner” “mlp” “xgboost” For the time-series forecasting problem type TimeSeriesForecastingJobConfig: Choose your algorithms from this list. “cnn-qr” “deepar” “prophet” “arima” “npts” “ets”

auto_ml_algorithms: List[str | PipelineVariable]#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AutoMLCandidate(*, candidate_name: str | PipelineVariable, objective_status: str | PipelineVariable, candidate_steps: List[AutoMLCandidateStep], candidate_status: str | PipelineVariable, creation_time: datetime, last_modified_time: datetime, final_auto_ml_job_objective_metric: FinalAutoMLJobObjectiveMetric | None = Unassigned(), inference_containers: List[AutoMLContainerDefinition] | None = Unassigned(), end_time: datetime | None = Unassigned(), failure_reason: str | PipelineVariable | None = Unassigned(), candidate_properties: CandidateProperties | None = Unassigned(), inference_container_definitions: Dict[str | PipelineVariable, List[AutoMLContainerDefinition]] | None = Unassigned())[source]#

Bases: Base

Information about a candidate produced by an AutoML training job, including its status, steps, and other properties.

candidate_name#

Type:: The name of the candidate.

final_auto_ml_job_objective_metric#

Type:: sagemaker.core.shapes.shapes.FinalAutoMLJobObjectiveMetric | None

objective_status#

Type:: The objective’s status.

candidate_steps#

Type:: Information about the candidate’s steps.

candidate_status#

Type:: The candidate’s status.

inference_containers#

Type:: Information about the recommended inference container definitions.

creation_time#

Type:: The creation time.

end_time#

Type:: The end time.

last_modified_time#

Type:: The last modified time.

failure_reason#

Type:: The failure reason.

candidate_properties#

Type:: The properties of an AutoML candidate job.

inference_container_definitions#

Type:: The mapping of all supported processing unit (CPU, GPU, etc…) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2) related to image or text classification problem types only.

candidate_name: str | PipelineVariable#

candidate_properties: CandidateProperties | None#

candidate_status: str | PipelineVariable#

candidate_steps: List[AutoMLCandidateStep]#

creation_time: datetime#

end_time: datetime | None#

failure_reason: str | PipelineVariable | None#

final_auto_ml_job_objective_metric: FinalAutoMLJobObjectiveMetric | None#

inference_container_definitions: Dict[str | PipelineVariable, List[AutoMLContainerDefinition]] | None#

inference_containers: List[AutoMLContainerDefinition] | None#

last_modified_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

objective_status: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AutoMLCandidateGenerationConfig(*, feature_specification_s3_uri: str | PipelineVariable | None = Unassigned(), algorithms_config: List[AutoMLAlgorithmConfig] | None = Unassigned())[source]#

Bases: Base

Stores the configuration information for how a candidate is generated (optional).

feature_specification_s3_uri#

Type:: A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below: { “FeatureAttributeNames”:[“col1”, “col2”, …] }. You can also specify the data type of the feature (optional) in the format shown below: { “FeatureDataTypes”:{“col1”:”numeric”, “col2”:”categorical” … } } These column keys may not include the target column. In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence. If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data. If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames. The key name FeatureAttributeNames is fixed. The values listed in [“col1”, “col2”, …] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

algorithms_config#

Type:: Stores the configuration information for the selection of algorithms trained on tabular data. The list of available algorithms to choose from depends on the training mode set in TabularJobConfig.Mode . AlgorithmsConfig should not be set if the training mode is set on AUTO. When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode. When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode. For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig. For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

algorithms_config: List[AutoMLAlgorithmConfig] | None#

feature_specification_s3_uri: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AutoMLCandidateStep(*, candidate_step_type: str | PipelineVariable, candidate_step_arn: str | PipelineVariable, candidate_step_name: str | PipelineVariable)[source]#

Bases: Base

Information about the steps for a candidate and what step it is working on.

candidate_step_type#

Type:: Whether the candidate is at the transform, training, or processing step.

candidate_step_arn#

Type:: The ARN for the candidate’s step.

candidate_step_name#

Type:: The name for the candidate’s step.

candidate_step_arn: str | PipelineVariable#

candidate_step_name: str | PipelineVariable#

candidate_step_type: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel. A validation dataset must contain the same headers as the training dataset.

data_source#

Type:: The data source for an AutoML channel.

compression_type#

Type:: You can use Gzip or None. The default value is None.

target_attribute_name#

Type:: The name of the target variable in supervised learning, usually represented by ‘y’.

content_type#

Type:: The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

channel_type#

Type:: The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.

sample_weight_attribute_name#

Type:: If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation. Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded. Support for sample weights is available in Ensembling mode only.

channel_type: str | PipelineVariable | None#

compression_type: str | PipelineVariable | None#

content_type: str | PipelineVariable | None#

data_source: AutoMLDataSource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

sample_weight_attribute_name: str | PipelineVariable | None#

target_attribute_name: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AutoMLComputeConfig(*, emr_serverless_compute_config: EmrServerlessComputeConfig | None = Unassigned())[source]#

Bases: Base

This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment. Specifies the compute configuration for an AutoML job V2.

emr_serverless_compute_config#

Type:: The configuration for using EMR Serverless to run the AutoML job V2. To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request. By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you. EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.

emr_serverless_compute_config: EmrServerlessComputeConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition.

image#

Type:: The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.

model_data_url#

Type:: The location of the model artifacts. For more information, see ContainerDefinition.

environment#

Type:: The environment variables to set in the container. For more information, see ContainerDefinition.

environment: Dict[str | PipelineVariable, str | PipelineVariable] | None#

image: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_data_url: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AutoMLDataSource(*, s3_data_source: AutoMLS3DataSource)[source]#

Bases: Base

The data source for the Autopilot job.

s3_data_source#

Type:: The Amazon S3 location of the input data.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_data_source: AutoMLS3DataSource#

class sagemaker.core.shapes.shapes.AutoMLDataSplitConfig(*, validation_fraction: float | None = Unassigned())[source]#

Bases: Base

This structure specifies how to split the data into train and validation datasets. The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

validation_fraction#

Type:: The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

validation_fraction: float | None#

class sagemaker.core.shapes.shapes.AutoMLJobArtifacts(*, candidate_definition_notebook_location: str | PipelineVariable | None = Unassigned(), data_exploration_notebook_location: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The artifacts that are generated during an AutoML job.

candidate_definition_notebook_location#

Type:: The URL of the notebook location.

data_exploration_notebook_location#

Type:: The URL of the notebook location.

candidate_definition_notebook_location: str | PipelineVariable | None#

data_exploration_notebook_location: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2).

channel_type#

Type:: The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType The type of channel defaults to training for the time-series forecasting problem type.

content_type#

Type:: The content type of the data from the input source. The following are the allowed content types for different problems: For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present. For image classification: image/png, image/jpeg, or image/. The default value is image/. For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present. For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present. For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

compression_type#

Type:: The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

data_source#

Type:: The data source for an AutoML channel (Required).

channel_type: str | PipelineVariable | None#

compression_type: str | PipelineVariable | None#

content_type: str | PipelineVariable | None#

data_source: AutoMLDataSource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AutoMLJobCompletionCriteria(*, max_candidates: int | None = Unassigned(), max_runtime_per_training_job_in_seconds: int | None = Unassigned(), max_auto_ml_job_runtime_in_seconds: int | None = Unassigned())[source]#

Bases: Base

How long a job is allowed to run, or how many candidates a job is allowed to generate.

max_candidates#

Type:: The maximum number of times a training job is allowed to run. For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

max_runtime_per_training_job_in_seconds#

Type:: The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action. For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate. For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

max_auto_ml_job_runtime_in_seconds#

Type:: The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

max_auto_ml_job_runtime_in_seconds: int | None#

max_candidates: int | None#

max_runtime_per_training_job_in_seconds: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AutoMLJobConfig(*, completion_criteria: AutoMLJobCompletionCriteria | None = Unassigned(), security_config: AutoMLSecurityConfig | None = Unassigned(), candidate_generation_config: AutoMLCandidateGenerationConfig | None = Unassigned(), data_split_config: AutoMLDataSplitConfig | None = Unassigned(), mode: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A collection of settings used for an AutoML job.

completion_criteria#

Type:: How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

security_config#

Type:: The security configuration for traffic encryption or Amazon VPC settings.

candidate_generation_config#

Type:: The configuration for generating a candidate for an AutoML job (optional).

data_split_config#

Type:: The configuration for splitting the input training dataset. Type: AutoMLDataSplitConfig

mode#

Type:: The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones. The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode. The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

candidate_generation_config: AutoMLCandidateGenerationConfig | None#

completion_criteria: AutoMLJobCompletionCriteria | None#

data_split_config: AutoMLDataSplitConfig | None#

mode: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

security_config: AutoMLSecurityConfig | None#

class sagemaker.core.shapes.shapes.AutoMLJobObjective(*, metric_name: str | PipelineVariable)[source]#

Bases: Base

Specifies a metric to minimize or maximize as the objective of an AutoML job.

metric_name#

Type:: The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model’s parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset. The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type. For tabular problem types: List of available metrics: Regression: MAE, MSE, R2, RMSE Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro For a description of each metric, see Autopilot metrics for classification and regression. Default objective metrics: Regression: MSE. Binary classification: F1. Multiclass classification: Accuracy. For image or text classification problem types: List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification. Default objective metrics: Accuracy For time-series forecasting problem types: List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE For a description of each metric, see Autopilot metrics for time-series forecasting. Default objective metrics: AverageWeightedQuantileLoss For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

metric_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AutoMLJobStepMetadata(*, arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Metadata for an AutoML job step.

arn#

Type:: The Amazon Resource Name (ARN) of the AutoML job.

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AutoMLJobSummary(*, auto_ml_job_name: str | PipelineVariable, auto_ml_job_arn: str | PipelineVariable, auto_ml_job_status: str | PipelineVariable, auto_ml_job_secondary_status: str | PipelineVariable, creation_time: datetime, last_modified_time: datetime, end_time: datetime | None = Unassigned(), failure_reason: str | PipelineVariable | None = Unassigned(), partial_failure_reasons: List[AutoMLPartialFailureReason] | None = Unassigned())[source]#

Bases: Base

Provides a summary about an AutoML job.

auto_ml_job_name#

Type:: The name of the AutoML job you are requesting.

auto_ml_job_arn#

Type:: The ARN of the AutoML job.

auto_ml_job_status#

Type:: The status of the AutoML job.

auto_ml_job_secondary_status#

Type:: The secondary status of the AutoML job.

creation_time#

Type:: When the AutoML job was created.

end_time#

Type:: The end time of an AutoML job.

last_modified_time#

Type:: When the AutoML job was last modified.

failure_reason#

Type:: The failure reason of an AutoML job.

partial_failure_reasons#

Type:: The list of reasons for partial failures within an AutoML job.

auto_ml_job_arn: str | PipelineVariable#

auto_ml_job_name: str | PipelineVariable#

auto_ml_job_secondary_status: str | PipelineVariable#

auto_ml_job_status: str | PipelineVariable#

creation_time: datetime#

end_time: datetime | None#

failure_reason: str | PipelineVariable | None#

last_modified_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

partial_failure_reasons: List[AutoMLPartialFailureReason] | None#

class sagemaker.core.shapes.shapes.AutoMLOutputDataConfig(*, s3_output_path: str | PipelineVariable, kms_key_id: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The output data configuration.

kms_key_id#

Type:: The Key Management Service encryption key ID.

s3_output_path#

Type:: The Amazon S3 output path. Must be 512 characters or less.

kms_key_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_output_path: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AutoMLPartialFailureReason(*, partial_failure_message: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The reason for a partial failure of an AutoML job.

partial_failure_message#

Type:: The message containing the reason for a partial failure of an AutoML job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

partial_failure_message: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.AutoMLProblemTypeConfig(*, image_classification_job_config: ImageClassificationJobConfig | None = Unassigned(), text_classification_job_config: TextClassificationJobConfig | None = Unassigned(), time_series_forecasting_job_config: TimeSeriesForecastingJobConfig | None = Unassigned(), tabular_job_config: TabularJobConfig | None = Unassigned(), text_generation_job_config: TextGenerationJobConfig | None = Unassigned())[source]#

Bases: Base

A collection of settings specific to the problem type used to configure an AutoML job V2. There must be one and only one config of the following type.

image_classification_job_config#

Type:: Settings used to configure an AutoML job V2 for the image classification problem type.

text_classification_job_config#

Type:: Settings used to configure an AutoML job V2 for the text classification problem type.

time_series_forecasting_job_config#

Type:: Settings used to configure an AutoML job V2 for the time-series forecasting problem type.

tabular_job_config#

Type:: Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).

text_generation_job_config#

Type:: Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type. The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.

image_classification_job_config: ImageClassificationJobConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

tabular_job_config: TabularJobConfig | None#

text_classification_job_config: TextClassificationJobConfig | None#

text_generation_job_config: TextGenerationJobConfig | None#

time_series_forecasting_job_config: TimeSeriesForecastingJobConfig | None#

class sagemaker.core.shapes.shapes.AutoMLProblemTypeResolvedAttributes(*, tabular_resolved_attributes: TabularResolvedAttributes | None = Unassigned(), text_generation_resolved_attributes: TextGenerationResolvedAttributes | None = Unassigned())[source]#

Bases: Base

Stores resolved attributes specific to the problem type of an AutoML job V2.

tabular_resolved_attributes#

Type:: The resolved attributes for the tabular problem type.

text_generation_resolved_attributes#

Type:: The resolved attributes for the text generation problem type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

tabular_resolved_attributes: TabularResolvedAttributes | None#

text_generation_resolved_attributes: TextGenerationResolvedAttributes | None#

class sagemaker.core.shapes.shapes.AutoMLResolvedAttributes(*, auto_ml_job_objective: AutoMLJobObjective | None = Unassigned(), completion_criteria: AutoMLJobCompletionCriteria | None = Unassigned(), auto_ml_problem_type_resolved_attributes: AutoMLProblemTypeResolvedAttributes | None = Unassigned())[source]#

Bases: Base

The resolved attributes used to configure an AutoML job V2.

auto_ml_job_objective#

Type:: sagemaker.core.shapes.shapes.AutoMLJobObjective | None

completion_criteria#

Type:: sagemaker.core.shapes.shapes.AutoMLJobCompletionCriteria | None

auto_ml_problem_type_resolved_attributes#

Type:: Defines the resolved attributes specific to a problem type.

auto_ml_job_objective: AutoMLJobObjective | None#

auto_ml_problem_type_resolved_attributes: AutoMLProblemTypeResolvedAttributes | None#

completion_criteria: AutoMLJobCompletionCriteria | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AutoMLS3DataSource(*, s3_data_type: str | PipelineVariable, s3_uri: str | PipelineVariable)[source]#

Bases: Base

Describes the Amazon S3 data source.

s3_data_type#

Type:: The data type. If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker AI uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker AI to use for model training. A ManifestFile should have the format shown below: [ {“prefix”: “s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/”}, “DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1”, “DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2”, … “DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N” ] If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2). Here is a minimal, single-record example of an AugmentedManifestFile: {“source-ref”: “s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg”, “label-metadata”: {“class-name”: “cat” } For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.

s3_uri#

Type:: The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_data_type: str | PipelineVariable#

s3_uri: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AutoMLSecurityConfig(*, volume_kms_key_id: str | PipelineVariable | None = Unassigned(), enable_inter_container_traffic_encryption: bool | None = Unassigned(), vpc_config: VpcConfig | None = Unassigned())[source]#

Bases: Base

Security options.

volume_kms_key_id#

Type:: The key used to encrypt stored data.

enable_inter_container_traffic_encryption#

Type:: Whether to use traffic encryption between the container layers.

vpc_config#

Type:: The VPC configuration.

enable_inter_container_traffic_encryption: bool | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

volume_kms_key_id: str | PipelineVariable | None#

vpc_config: VpcConfig | None#

class sagemaker.core.shapes.shapes.AutoParameter(*, name: str | PipelineVariable, value_hint: str | PipelineVariable)[source]#

Bases: Base

The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.

name#

Type:: The name of the hyperparameter to optimize using Autotune.

value_hint#

Type:: An example value of the hyperparameter to optimize using Autotune.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

value_hint: str | PipelineVariable#

class sagemaker.core.shapes.shapes.AutoRollbackConfig(*, alarms: List[Alarm] | None = Unassigned())[source]#

Bases: Base

Automatic rollback configuration for handling endpoint deployment failures and recovery.

alarms#

Type:: List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.

alarms: List[Alarm] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.Autotune(*, mode: str | PipelineVariable)[source]#

Bases: Base

A flag to indicate if you want to use Autotune to automatically find optimal values for the following fields: ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time. TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job. RetryStrategy: The number of times to retry a training job. Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches. ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

mode#

Type:: Set Mode to Enabled if you want to use Autotune.

mode: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.AvailableUpgrade(*, version: str | PipelineVariable | None = Unassigned(), release_notes: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

Contains information about an available upgrade for a SageMaker Partner AI App, including the version number and release notes.

version#

Type:: The semantic version number of the available upgrade for the SageMaker Partner AI App.

release_notes#

Type:: A list of release notes describing the changes and improvements included in the available upgrade version.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

release_notes: List[str | PipelineVariable] | None#

version: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.Base[source]#

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Identifies the foundation model that was used as the starting point for model customization.

hub_content_name#

Type:: The hub content name of the base model.

hub_content_version#

Type:: The hub content version of the base model.

recipe_name#

Type:: The recipe name of the base model.

hub_content_name: str | PipelineVariable | object | None#

hub_content_version: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

recipe_name: str | PipelineVariable | None#

Bases: Base

Information about an error that occurred during the node addition operation.

instance_group_name#

Type:: The name of the instance group for which the error occurred.

error_code#

Type:: The error code associated with the failure. Possible values include InstanceGroupNotFound and InvalidInstanceGroupState.

failed_count#

Type:: The number of nodes that failed to be added to the specified instance group.

availability_zones#

Type:: The availability zones associated with the failed node addition request.

instance_types#

Type:: The instance types associated with the failed node addition request.

message#

Type:: A descriptive message providing additional details about the error.

availability_zones: List[str | PipelineVariable] | None#

error_code: str | PipelineVariable#

failed_count: int#

instance_group_name: str | PipelineVariable#

instance_types: List[str | PipelineVariable] | None#

message: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BatchDataCaptureConfig(*, destination_s3_uri: str | PipelineVariable, kms_key_id: str | PipelineVariable | None = Unassigned(), generate_inference_id: bool | None = Unassigned())[source]#

Bases: Base

Configuration to control how SageMaker captures inference data for batch transform jobs.

destination_s3_uri#

Type:: The Amazon S3 location being used to capture the data.

kms_key_id#

Type:: The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job. The KmsKeyId can be any of the following formats: Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab Alias name: alias/ExampleAlias Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

generate_inference_id#

Type:: Flag that indicates whether to append inference id to the output.

destination_s3_uri: str | PipelineVariable#

generate_inference_id: bool | None#

kms_key_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BatchDeleteClusterNodeLogicalIdsError(*, code: str | PipelineVariable, message: str | PipelineVariable, node_logical_id: str | PipelineVariable)[source]#

Bases: Base

Information about an error that occurred when attempting to delete a node identified by its NodeLogicalId.

code#

Type:: The error code associated with the failure. Possible values include NodeLogicalIdNotFound, InvalidNodeStatus, and InternalError.

message#

Type:: A descriptive message providing additional details about the error.

node_logical_id#

Type:: The NodeLogicalId of the node that could not be deleted.

code: str | PipelineVariable#

message: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_logical_id: str | PipelineVariable#

class sagemaker.core.shapes.shapes.BatchDeleteClusterNodesError(*, code: str | PipelineVariable, message: str | PipelineVariable, node_id: str | PipelineVariable)[source]#

Bases: Base

Represents an error encountered when deleting a node from a SageMaker HyperPod cluster.

code#

Type:: The error code associated with the error encountered when deleting a node. The code provides information about the specific issue encountered, such as the node not being found, the node’s status being invalid for deletion, or the node ID being in use by another process.

message#

Type:: A message describing the error encountered when deleting a node.

node_id#

Type:: The ID of the node that encountered an error during the deletion process.

code: str | PipelineVariable#

message: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_id: str | PipelineVariable#

class sagemaker.core.shapes.shapes.BatchDeleteClusterNodesResponse(*, failed: List[BatchDeleteClusterNodesError] | None = Unassigned(), successful: List[str | PipelineVariable] | None = Unassigned(), failed_node_logical_ids: List[BatchDeleteClusterNodeLogicalIdsError] | None = Unassigned(), successful_node_logical_ids: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

failed#

Type:: A list of errors encountered when deleting the specified nodes.

successful#

Type:: A list of node IDs that were successfully deleted from the specified cluster.

failed_node_logical_ids#

Type:: A list of NodeLogicalIds that could not be deleted, along with error information explaining why the deletion failed.

successful_node_logical_ids#

Type:: A list of NodeLogicalIds that were successfully deleted from the cluster.

failed: List[BatchDeleteClusterNodesError] | None#

failed_node_logical_ids: List[BatchDeleteClusterNodeLogicalIdsError] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

successful: List[str | PipelineVariable] | None#

successful_node_logical_ids: List[str | PipelineVariable] | None#

class sagemaker.core.shapes.shapes.BatchDescribeModelPackageError(*, error_code: str | PipelineVariable, error_response: str | PipelineVariable)[source]#

Bases: Base

The error code and error description associated with the resource.

error_code#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable

error_response#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable

error_code: str | PipelineVariable#

error_response: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BatchDescribeModelPackageOutput(*, model_package_summaries: Dict[str | PipelineVariable, BatchDescribeModelPackageSummary] | None = Unassigned(), batch_describe_model_package_error_map: Dict[str | PipelineVariable, BatchDescribeModelPackageError] | None = Unassigned())[source]#

Bases: Base

model_package_summaries#

Type:: The summaries for the model package versions

batch_describe_model_package_error_map#

Type:: A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.

batch_describe_model_package_error_map: Dict[str | PipelineVariable, BatchDescribeModelPackageError] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_package_summaries: Dict[str | PipelineVariable, BatchDescribeModelPackageSummary] | None#

class sagemaker.core.shapes.shapes.BatchDescribeModelPackageSummary(*, model_package_group_name: str | PipelineVariable | object, model_package_arn: str | PipelineVariable, creation_time: datetime, inference_specification: InferenceSpecification, model_package_status: str | PipelineVariable, model_package_version: int | None = Unassigned(), model_package_description: str | PipelineVariable | None = Unassigned(), model_approval_status: str | PipelineVariable | None = Unassigned(), model_package_registration_type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Provides summary information about the model package.

model_package_group_name#

Type:: The group name for the model package

model_package_version#

Type:: The version number of a versioned model.

model_package_arn#

Type:: The Amazon Resource Name (ARN) of the model package.

model_package_description#

Type:: The description of the model package.

creation_time#

Type:: The creation time of the mortgage package summary.

inference_specification#

Type:: sagemaker.core.shapes.shapes.InferenceSpecification

model_package_status#

Type:: The status of the mortgage package.

model_approval_status#

Type:: The approval status of the model.

model_package_registration_type#

Type:: The package registration type of the model package summary.

creation_time: datetime#

inference_specification: InferenceSpecification#

model_approval_status: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_package_arn: str | PipelineVariable#

model_package_description: str | PipelineVariable | None#

model_package_group_name: str | PipelineVariable | object#

model_package_registration_type: str | PipelineVariable | None#

model_package_status: str | PipelineVariable#

model_package_version: int | None#

class sagemaker.core.shapes.shapes.BatchGetMetricsResponse(*, metric_query_results: List[MetricQueryResult] | None = Unassigned())[source]#

Bases: Base

metric_query_results#

Type:: The results of a query to retrieve training metrics from SageMaker.

metric_query_results: List[MetricQueryResult] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BatchGetRecordError(*, feature_group_name: str | PipelineVariable | object, record_identifier_value_as_string: str | PipelineVariable, error_code: str | PipelineVariable, error_message: str | PipelineVariable)[source]#

Bases: Base

The error that has occurred when attempting to retrieve a batch of Records.

feature_group_name#

Type:: The name of the feature group that the record belongs to.

record_identifier_value_as_string#

Type:: The value for the RecordIdentifier in string format of a Record from a FeatureGroup that is causing an error when attempting to be retrieved.

error_code#

Type:: The error code of an error that has occurred when attempting to retrieve a batch of Records. For more information on errors, see Errors.

error_message#

Type:: The error message of an error that has occurred when attempting to retrieve a record in the batch.

error_code: str | PipelineVariable#

error_message: str | PipelineVariable#

feature_group_name: str | PipelineVariable | object#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

record_identifier_value_as_string: str | PipelineVariable#

class sagemaker.core.shapes.shapes.BatchGetRecordIdentifier(*, feature_group_name: str | PipelineVariable | object, record_identifiers_value_as_string: List[str | PipelineVariable], feature_names: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

The identifier that identifies the batch of Records you are retrieving in a batch.

feature_group_name#

Type:: The name or Amazon Resource Name (ARN) of the FeatureGroup containing the records you are retrieving in a batch.

record_identifiers_value_as_string#

Type:: The value for a list of record identifiers in string format.

feature_names#

Type:: List of names of Features to be retrieved. If not specified, the latest value for all the Features are returned.

feature_group_name: str | PipelineVariable | object#

feature_names: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

record_identifiers_value_as_string: List[str | PipelineVariable]#

class sagemaker.core.shapes.shapes.BatchGetRecordResponse(*, records: List[BatchGetRecordResultDetail], errors: List[BatchGetRecordError], unprocessed_identifiers: List[BatchGetRecordIdentifier])[source]#

Bases: Base

records#

Type:: A list of Records you requested to be retrieved in batch.

errors#

Type:: A list of errors that have occurred when retrieving a batch of Records.

unprocessed_identifiers#

Type:: A unprocessed list of FeatureGroup names, with their corresponding RecordIdentifier value, and Feature name.

errors: List[BatchGetRecordError]#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

records: List[BatchGetRecordResultDetail]#

unprocessed_identifiers: List[BatchGetRecordIdentifier]#

class sagemaker.core.shapes.shapes.BatchGetRecordResultDetail(*, feature_group_name: str | PipelineVariable | object, record_identifier_value_as_string: str | PipelineVariable, record: List[FeatureValue], expires_at: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The output of records that have been retrieved in a batch.

feature_group_name#

Type:: The FeatureGroupName containing Records you retrieved in a batch.

record_identifier_value_as_string#

Type:: The value of the record identifier in string format.

record#

Type:: The Record retrieved.

expires_at#

Type:: The ExpiresAt ISO string of the requested record.

expires_at: str | PipelineVariable | None#

feature_group_name: str | PipelineVariable | object#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

record: List[FeatureValue]#

record_identifier_value_as_string: str | PipelineVariable#

class sagemaker.core.shapes.shapes.BatchPutMetricsError(*, code: str | PipelineVariable | None = Unassigned(), metric_index: int | None = Unassigned())[source]#

Bases: Base

An error that occured when putting the metric data.

code#

Type:: The error code of an error that occured when attempting to put metrics. METRIC_LIMIT_EXCEEDED: The maximum amount of metrics per resource is exceeded. INTERNAL_ERROR: An internal error occured. VALIDATION_ERROR: The metric data failed validation. CONFLICT_ERROR: Multiple requests attempted to modify the same data simultaneously.

metric_index#

Type:: An index that corresponds to the metric in the request.

code: str | PipelineVariable | None#

metric_index: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BatchRebootClusterNodeLogicalIdsError(*, node_logical_id: str | PipelineVariable, error_code: str | PipelineVariable, message: str | PipelineVariable)[source]#

Bases: Base

Represents an error encountered when rebooting a node (identified by its logical node ID) from a SageMaker HyperPod cluster.

node_logical_id#

Type:: The logical node ID of the node that encountered an error during the reboot operation.

error_code#

Type:: The error code associated with the error encountered when rebooting a node by logical node ID. Possible values: InstanceIdNotFound: The node does not exist in the specified cluster. InvalidInstanceStatus: The node is in a state that does not allow rebooting. Wait for the node to finish any ongoing changes before retrying. InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying. InternalServerError: An internal error occurred while processing this node.

message#

Type:: A human-readable message describing the error encountered when rebooting a node by logical node ID.

error_code: str | PipelineVariable#

message: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_logical_id: str | PipelineVariable#

class sagemaker.core.shapes.shapes.BatchRebootClusterNodesError(*, node_id: str | PipelineVariable, error_code: str | PipelineVariable, message: str | PipelineVariable)[source]#

Bases: Base

Represents an error encountered when rebooting a node from a SageMaker HyperPod cluster.

node_id#

Type:: The EC2 instance ID of the node that encountered an error during the reboot operation.

error_code#

Type:: The error code associated with the error encountered when rebooting a node. Possible values: InstanceIdNotFound: The instance does not exist in the specified cluster. InvalidInstanceStatus: The instance is in a state that does not allow rebooting. Wait for the instance to finish any ongoing changes before retrying. InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying. InternalServerError: An internal error occurred while processing this node.

message#

Type:: A human-readable message describing the error encountered when rebooting a node.

error_code: str | PipelineVariable#

message: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_id: str | PipelineVariable#

class sagemaker.core.shapes.shapes.BatchReplaceClusterNodeLogicalIdsError(*, node_logical_id: str | PipelineVariable, error_code: str | PipelineVariable, message: str | PipelineVariable)[source]#

Bases: Base

Represents an error encountered when replacing a node (identified by its logical node ID) in a SageMaker HyperPod cluster.

node_logical_id#

Type:: The logical node ID of the node that encountered an error during the replacement operation.

error_code#

Type:: The error code associated with the error encountered when replacing a node by logical node ID. Possible values: InstanceIdNotFound: The node does not exist in the specified cluster. InvalidInstanceStatus: The node is in a state that does not allow replacement. Wait for the node to finish any ongoing changes before retrying. InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying. InternalServerError: An internal error occurred while processing this node.

message#

Type:: A human-readable message describing the error encountered when replacing a node by logical node ID.

error_code: str | PipelineVariable#

message: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_logical_id: str | PipelineVariable#

class sagemaker.core.shapes.shapes.BatchReplaceClusterNodesError(*, node_id: str | PipelineVariable, error_code: str | PipelineVariable, message: str | PipelineVariable)[source]#

Bases: Base

Represents an error encountered when replacing a node in a SageMaker HyperPod cluster.

node_id#

Type:: The EC2 instance ID of the node that encountered an error during the replacement operation.

error_code#

Type:: The error code associated with the error encountered when replacing a node. Possible values: InstanceIdNotFound: The instance does not exist in the specified cluster. InvalidInstanceStatus: The instance is in a state that does not allow replacement. Wait for the instance to finish any ongoing changes before retrying. InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying. InternalServerError: An internal error occurred while processing this node.

message#

Type:: A human-readable message describing the error encountered when replacing a node.

error_code: str | PipelineVariable#

message: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_id: str | PipelineVariable#

Bases: Base

Input object for the batch transform job.

data_captured_destination_s3_uri#

Type:: The Amazon S3 location being used to capture the data.

dataset_format#

Type:: The dataset format for your batch transform job.

local_path#

Type:: Path to the filesystem where the batch transform data is available to the container.

s3_input_mode#

Type:: Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

s3_data_distribution_type#

Type:: Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

features_attribute#

Type:: The attributes of the input data that are the input features.

inference_attribute#

Type:: The attribute of the input data that represents the ground truth label.

probability_attribute#

Type:: In a classification problem, the attribute that represents the class probability.

probability_threshold_attribute#

Type:: The threshold for the class probability to be evaluated as a positive result.

start_time_offset#

Type:: If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

end_time_offset#

Type:: If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

exclude_features_attribute#

Type:: The attributes of the input data to exclude from the analysis.

data_captured_destination_s3_uri: str | PipelineVariable#

dataset_format: MonitoringDatasetFormat#

end_time_offset: str | PipelineVariable | None#

exclude_features_attribute: str | PipelineVariable | None#

features_attribute: str | PipelineVariable | None#

inference_attribute: str | PipelineVariable | None#

local_path: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

probability_attribute: str | PipelineVariable | None#

probability_threshold_attribute: float | None#

s3_data_distribution_type: str | PipelineVariable | None#

s3_input_mode: str | PipelineVariable | None#

start_time_offset: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.BatchWriteRecordEntry(*, feature_group_name: str | PipelineVariable | object, record: List[FeatureValue], target_stores: List[str | PipelineVariable] | None = Unassigned(), ttl_duration: TtlDuration | None = Unassigned())[source]#

Bases: Base

An entry to write as part of a BatchWriteRecord request.

feature_group_name#

Type:: The name or Amazon Resource Name (ARN) of the FeatureGroup to write the record to.

record#

Type:: List of FeatureValues to be inserted. This will be a full over-write.

target_stores#

Type:: A list of stores to which you’re adding the record. By default, Feature Store adds the record to all of the stores that you’re using for the FeatureGroup.

ttl_duration#

Type:: Time to live duration for this entry, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. This overrides the request level TtlDuration.

feature_group_name: str | PipelineVariable | object#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

record: List[FeatureValue]#

target_stores: List[str | PipelineVariable] | None#

ttl_duration: TtlDuration | None#

class sagemaker.core.shapes.shapes.BatchWriteRecordError(*, entry: BatchWriteRecordEntry, error_code: str | PipelineVariable, error_message: str | PipelineVariable)[source]#

Bases: Base

The error that has occurred when attempting to write a record in a batch.

entry#

Type:: The entry that failed to be written.

error_code#

Type:: The error code for the failed record write.

error_message#

Type:: The error message for the failed record write.

entry: BatchWriteRecordEntry#

error_code: str | PipelineVariable#

error_message: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BatchWriteRecordResponse(*, errors: List[BatchWriteRecordError], unprocessed_entries: List[BatchWriteRecordEntry])[source]#

Bases: Base

errors#

Type:: A list of errors that occurred when writing records in the batch.

unprocessed_entries#

Type:: A list of entries that were not processed. These entries can be retried.

errors: List[BatchWriteRecordError]#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

unprocessed_entries: List[BatchWriteRecordEntry]#

class sagemaker.core.shapes.shapes.BedrockCustomModelDeploymentMetadata(*, arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The metadata of the Amazon Bedrock custom model deployment.

arn#

Type:: The Amazon Resource Name (ARN) for the Amazon Bedrock custom model deployment.

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BedrockCustomModelMetadata(*, arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The metadata of the Amazon Bedrock custom model.

arn#

Type:: The Amazon Resource Name (ARN) of the Amazon Bedrock custom model.

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BedrockModelImportMetadata(*, arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The metadata of the Amazon Bedrock model import.

arn#

Type:: The Amazon Resource Name (ARN) of the Amazon Bedrock model import.

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BedrockProvisionedModelThroughputMetadata(*, arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The metadata of the Amazon Bedrock provisioned model throughput.

arn#

Type:: The Amazon Resource Name (ARN) of the Amazon Bedrock provisioned model throughput.

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.BestObjectiveNotImproving(*, max_number_of_training_jobs_not_improving: int | None = Unassigned())[source]#

Bases: Base

A structure that keeps track of which training jobs launched by your hyperparameter tuning job are not improving model performance as evaluated against an objective function.

max_number_of_training_jobs_not_improving#

Type:: The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.

max_number_of_training_jobs_not_improving: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.Bias(*, report: MetricsSource | None = Unassigned(), pre_training_report: MetricsSource | None = Unassigned(), post_training_report: MetricsSource | None = Unassigned())[source]#

Bases: Base

Contains bias metrics for a model.

report#

Type:: The bias report for a model

pre_training_report#

Type:: The pre-training bias report for a model.

post_training_report#

Type:: The post-training bias report for a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

post_training_report: MetricsSource | None#

pre_training_report: MetricsSource | None#

report: MetricsSource | None#

class sagemaker.core.shapes.shapes.BlueGreenUpdatePolicy(*, traffic_routing_configuration: TrafficRoutingConfig, termination_wait_in_seconds: int | None = Unassigned(), maximum_execution_timeout_in_seconds: int | None = Unassigned())[source]#

Bases: Base

Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.

traffic_routing_configuration#

Type:: Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.

termination_wait_in_seconds#

Type:: Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.

maximum_execution_timeout_in_seconds#

Type:: Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds.

maximum_execution_timeout_in_seconds: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

termination_wait_in_seconds: int | None#

traffic_routing_configuration: TrafficRoutingConfig#

class sagemaker.core.shapes.shapes.CacheHitResult(*, source_pipeline_execution_arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Details on the cache hit of a pipeline execution step.

source_pipeline_execution_arn#

Type:: The Amazon Resource Name (ARN) of the pipeline execution.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_pipeline_execution_arn: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.CallbackStepMetadata(*, callback_token: str | PipelineVariable | None = Unassigned(), sqs_queue_url: str | PipelineVariable | None = Unassigned(), output_parameters: List[OutputParameter] | None = Unassigned())[source]#

Bases: Base

Metadata about a callback step.

callback_token#

Type:: The pipeline generated token from the Amazon SQS queue.

sqs_queue_url#

Type:: The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.

output_parameters#

Type:: A list of the output parameters of the callback step.

callback_token: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

output_parameters: List[OutputParameter] | None#

sqs_queue_url: str | PipelineVariable | None#

Bases: Base

The location of artifacts for an AutoML candidate job.

explainability#

Type:: The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.

model_insights#

Type:: The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.

backtest_results#

Type:: The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.

backtest_results: str | PipelineVariable | None#

explainability: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_insights: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.CandidateGenerationConfig(*, algorithms_config: List[AutoMLAlgorithmConfig] | None = Unassigned())[source]#

Bases: Base

Stores the configuration information for how model candidates are generated using an AutoML job V2.

algorithms_config#

Type:: Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type. AlgorithmsConfig stores the customized selection of algorithms to train on your data. For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode . AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO. When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode. When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode. For the list of all algorithms per training mode, see AlgorithmConfig. For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide. For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig. For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide. When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting. When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

algorithms_config: List[AutoMLAlgorithmConfig] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CandidateProperties(*, candidate_artifact_locations: CandidateArtifactLocations | None = Unassigned(), candidate_metrics: List[MetricDatum] | None = Unassigned())[source]#

Bases: Base

The properties of an AutoML candidate job.

candidate_artifact_locations#

Type:: The Amazon S3 prefix to the artifacts generated for an AutoML candidate.

candidate_metrics#

Type:: Information about the candidate metrics for an AutoML job.

candidate_artifact_locations: CandidateArtifactLocations | None#

candidate_metrics: List[MetricDatum] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CanvasAppSettings(*, time_series_forecasting_settings: TimeSeriesForecastingSettings | None = Unassigned(), model_register_settings: ModelRegisterSettings | None = Unassigned(), workspace_settings: WorkspaceSettings | None = Unassigned(), identity_provider_o_auth_settings: List[IdentityProviderOAuthSetting] | None = Unassigned(), direct_deploy_settings: DirectDeploySettings | None = Unassigned(), kendra_settings: KendraSettings | None = Unassigned(), generative_ai_settings: GenerativeAiSettings | None = Unassigned(), emr_serverless_settings: EmrServerlessSettings | None = Unassigned())[source]#

Bases: Base

The SageMaker Canvas application settings.

time_series_forecasting_settings#

Type:: Time series forecast settings for the SageMaker Canvas application.

model_register_settings#

Type:: The model registry settings for the SageMaker Canvas application.

workspace_settings#

Type:: The workspace settings for the SageMaker Canvas application.

identity_provider_o_auth_settings#

Type:: The settings for connecting to an external data source with OAuth.

direct_deploy_settings#

Type:: The model deployment settings for the SageMaker Canvas application.

kendra_settings#

Type:: The settings for document querying.

generative_ai_settings#

Type:: The generative AI settings for the SageMaker Canvas application.

emr_serverless_settings#

Type:: The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.

direct_deploy_settings: DirectDeploySettings | None#

emr_serverless_settings: EmrServerlessSettings | None#

generative_ai_settings: GenerativeAiSettings | None#

identity_provider_o_auth_settings: List[IdentityProviderOAuthSetting] | None#

kendra_settings: KendraSettings | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_register_settings: ModelRegisterSettings | None#

time_series_forecasting_settings: TimeSeriesForecastingSettings | None#

workspace_settings: WorkspaceSettings | None#

class sagemaker.core.shapes.shapes.CapacityReservation(*, arn: str | PipelineVariable | None = Unassigned(), type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Information about the Capacity Reservation used by an instance or instance group.

arn#

Type:: The Amazon Resource Name (ARN) of the Capacity Reservation.

type#

Type:: The type of Capacity Reservation. Valid values are ODCR (On-Demand Capacity Reservation) or CRG (Capacity Reservation Group).

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.CapacitySize(*, type: str | PipelineVariable, value: int)[source]#

Bases: Base

Specifies the type and size of the endpoint capacity to activate for a blue/green deployment, a rolling deployment, or a rollback strategy. You can specify your batches as either instance count or the overall percentage or your fleet. For a rollback strategy, if you don’t specify the fields in this object, or if you set the Value to 100%, then SageMaker uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.

type#

Type:: Specifies the endpoint capacity type. INSTANCE_COUNT: The endpoint activates based on the number of instances. CAPACITY_PERCENT: The endpoint activates based on the specified percentage of capacity.

value#

Type:: Defines the capacity size, either as a number of instances or a capacity percentage.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: str | PipelineVariable#

value: int#

class sagemaker.core.shapes.shapes.CapacitySizeConfig(*, type: str | PipelineVariable, value: int)[source]#

Bases: Base

The configuration of the size measurements of the AMI update. Using this configuration, you can specify whether SageMaker should update your instance group by an amount or percentage of instances.

type#

Type:: Specifies whether SageMaker should process the update by amount or percentage of instances.

value#

Type:: Specifies the amount or percentage of instances SageMaker updates at a time.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: str | PipelineVariable#

value: int#

class sagemaker.core.shapes.shapes.CaptureContentTypeHeader(*, csv_content_types: List[str | PipelineVariable] | None = Unassigned(), json_content_types: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.

csv_content_types#

Type:: The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly.

json_content_types#

Type:: The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly.

csv_content_types: List[str | PipelineVariable] | None#

json_content_types: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CaptureOption(*, capture_mode: str | PipelineVariable)[source]#

Bases: Base

Specifies data Model Monitor will capture.

capture_mode#

Type:: Specify the boundary of data to capture.

capture_mode: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CategoricalParameter(*, name: str | PipelineVariable, value: List[str | PipelineVariable])[source]#

Bases: Base

Environment parameters you want to benchmark your load test against.

name#

Type:: The Name of the environment variable.

value#

Type:: The list of values you can pass.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

value: List[str | PipelineVariable]#

class sagemaker.core.shapes.shapes.CategoricalParameterRange(*, name: str | PipelineVariable, values: List[str | PipelineVariable])[source]#

Bases: Base

A list of categorical hyperparameters to tune.

name#

Type:: The name of the categorical hyperparameter to tune.

values#

Type:: A list of the categories for the hyperparameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

values: List[str | PipelineVariable]#

class sagemaker.core.shapes.shapes.CategoricalParameterRangeSpecification(*, values: List[str | PipelineVariable])[source]#

Bases: Base

Defines the possible values for a categorical hyperparameter.

values#

Type:: The allowed categories for the hyperparameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

values: List[str | PipelineVariable]#

class sagemaker.core.shapes.shapes.CfnCreateTemplateProvider(*, template_name: str | PipelineVariable, template_url: str | PipelineVariable, role_arn: str | PipelineVariable | None = Unassigned(), parameters: List[CfnStackCreateParameter] | None = Unassigned())[source]#

Bases: Base

The CloudFormation template provider configuration for creating infrastructure resources.

template_name#

Type:: A unique identifier for the template within the project.

template_url#

Type:: The Amazon S3 URL of the CloudFormation template.

role_arn#

Type:: The IAM role that CloudFormation assumes when creating the stack.

parameters#

Type:: An array of CloudFormation stack parameters.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

parameters: List[CfnStackCreateParameter] | None#

role_arn: str | PipelineVariable | None#

template_name: str | PipelineVariable#

template_url: str | PipelineVariable#

class sagemaker.core.shapes.shapes.CfnStackCreateParameter(*, key: str | PipelineVariable, value: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A key-value pair that represents a parameter for the CloudFormation stack.

key#

Type:: The name of the CloudFormation parameter.

value#

Type:: The value of the CloudFormation parameter.

key: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

value: str | PipelineVariable | None#

Bases: Base

Details about the CloudFormation stack.

name#

Type:: The name of the CloudFormation stack.

id#

Type:: The unique identifier of the CloudFormation stack.

status_message#

Type:: A human-readable message about the stack’s current status.

id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable | None#

status_message: str | PipelineVariable#

class sagemaker.core.shapes.shapes.CfnStackParameter(*, key: str | PipelineVariable, value: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A key-value pair representing a parameter used in the CloudFormation stack.

key#

Type:: The name of the CloudFormation parameter.

value#

Type:: The value of the CloudFormation parameter.

key: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

value: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.CfnStackUpdateParameter(*, key: str | PipelineVariable, value: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A key-value pair representing a parameter used in the CloudFormation stack.

key#

Type:: The name of the CloudFormation parameter.

value#

Type:: The value of the CloudFormation parameter.

key: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

value: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.CfnTemplateProviderDetail(*, template_name: str | PipelineVariable, template_url: str | PipelineVariable, role_arn: str | PipelineVariable | None = Unassigned(), parameters: List[CfnStackParameter] | None = Unassigned(), stack_detail: CfnStackDetail | None = Unassigned())[source]#

Bases: Base

Details about a CloudFormation template provider configuration and associated provisioning information.

template_name#

Type:: The unique identifier of the template within the project.

template_url#

Type:: The Amazon S3 URL of the CloudFormation template.

role_arn#

Type:: The IAM role used by CloudFormation to create the stack.

parameters#

Type:: An array of CloudFormation stack parameters.

stack_detail#

Type:: Information about the CloudFormation stack created by the template provider.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

parameters: List[CfnStackParameter] | None#

role_arn: str | PipelineVariable | None#

stack_detail: CfnStackDetail | None#

template_name: str | PipelineVariable#

template_url: str | PipelineVariable#

class sagemaker.core.shapes.shapes.CfnUpdateTemplateProvider(*, template_name: str | PipelineVariable, template_url: str | PipelineVariable, parameters: List[CfnStackUpdateParameter] | None = Unassigned())[source]#

Bases: Base

Contains configuration details for updating an existing CloudFormation template provider in the project.

template_name#

Type:: The unique identifier of the template to update within the project.

template_url#

Type:: The Amazon S3 URL of the CloudFormation template.

parameters#

Type:: An array of CloudFormation stack parameters.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

parameters: List[CfnStackUpdateParameter] | None#

template_name: str | PipelineVariable#

template_url: str | PipelineVariable#

Bases: Base

A channel is a named input source that training algorithms can consume.

channel_name#

Type:: The name of the channel.

data_source#

Type:: The location of the channel data.

content_type#

Type:: The MIME type of the data.

compression_type#

Type:: If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

record_wrapper_type#

Type:: Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don’t need to set this attribute. For more information, see Create a Dataset Using RecordIO. In File mode, leave this field unset or set it to None.

input_mode#

Type:: (Optional) The input mode to use for the data channel in a training job. If you don’t set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job’s general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode. To use a model for incremental training, choose File input model.

shuffle_config#

Type:: A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value. For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

channel_name: str | PipelineVariable#

compression_type: str | PipelineVariable | None#

content_type: str | PipelineVariable | None#

data_source: DataSource#

input_mode: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

record_wrapper_type: str | PipelineVariable | None#

shuffle_config: ShuffleConfig | None#

class sagemaker.core.shapes.shapes.ChannelSpecification(*, name: str | PipelineVariable, supported_content_types: List[str | PipelineVariable], supported_input_modes: List[str | PipelineVariable], description: str | PipelineVariable | None = Unassigned(), is_required: bool | None = Unassigned(), supported_compression_types: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

Defines a named input source, called a channel, to be used by an algorithm.

name#

Type:: The name of the channel.

description#

Type:: A brief description of the channel.

is_required#

Type:: Indicates whether the channel is required by the algorithm.

supported_content_types#

Type:: The supported MIME types for the data.

supported_compression_types#

Type:: The allowed compression types, if data compression is used.

supported_input_modes#

Type:: The allowed input mode, either FILE or PIPE. In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

description: str | PipelineVariable | None#

is_required: bool | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

supported_compression_types: List[str | PipelineVariable] | None#

supported_content_types: List[str | PipelineVariable]#

supported_input_modes: List[str | PipelineVariable]#

class sagemaker.core.shapes.shapes.CheckpointConfig(*, s3_uri: str | PipelineVariable, local_path: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Contains information about the output location for managed spot training checkpoint data.

s3_uri#

Type:: Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.

local_path#

Type:: (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.

local_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_uri: str | PipelineVariable#

Bases: Base

The container for the metadata for the ClarifyCheck step. For more information, see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.

check_type#

Type:: The type of the Clarify Check step

baseline_used_for_drift_check_constraints#

Type:: The Amazon S3 URI of baseline constraints file to be used for the drift check.

calculated_baseline_constraints#

Type:: The Amazon S3 URI of the newly calculated baseline constraints file.

model_package_group_name#

Type:: The model package group name.

violation_report#

Type:: The Amazon S3 URI of the violation report if violations are detected.

check_job_arn#

Type:: The Amazon Resource Name (ARN) of the check processing job that was run by this step’s execution.

skip_check#

Type:: This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to False, the previous baseline of the configured check type must be available.

register_new_baseline#

Type:: This flag indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints property.

baseline_used_for_drift_check_constraints: str | PipelineVariable | None#

calculated_baseline_constraints: str | PipelineVariable | None#

check_job_arn: str | PipelineVariable | None#

check_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_package_group_name: str | PipelineVariable | object | None#

register_new_baseline: bool | None#

skip_check: bool | None#

violation_report: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClarifyExplainerConfig(*, shap_config: ClarifyShapConfig, enable_explanations: str | PipelineVariable | None = Unassigned(), inference_config: ClarifyInferenceConfig | None = Unassigned())[source]#

Bases: Base

The configuration parameters for the SageMaker Clarify explainer.

enable_explanations#

Type:: A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See EnableExplanations for additional information.

inference_config#

Type:: The inference configuration parameter for the model container.

shap_config#

Type:: The configuration for SHAP analysis.

enable_explanations: str | PipelineVariable | None#

inference_config: ClarifyInferenceConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shap_config: ClarifyShapConfig#

Bases: Base

The inference configuration parameter for the model container.

features_attribute#

Type:: Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression ‘myfeatures’, it extracts a list of features [1,2,3] from request data ‘{“myfeatures”:[1,2,3]}’.

content_template#

Type:: A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string ‘{“myfeatures”:$features}’ will format a list of features [1,2,3] into the record string ‘{“myfeatures”:[1,2,3]}’. Required only when the model container input is in JSON Lines format.

max_record_count#

Type:: The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container’s capacity at runtime.

max_payload_in_mb#

Type:: The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

probability_index#

Type:: A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list. Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: ‘1,0.6’, set ProbabilityIndex to 1 to select the probability value 0.6. Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: ‘”[‘cat’,’dog’,’fish’]”,”[0.1,0.6,0.3]”’, set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3].

label_index#

Type:: A zero-based index used to extract a label header or list of label headers from model container output in CSV format. Example for a multiclass model: If the model container output consists of label headers followed by probabilities: ‘”[‘cat’,’dog’,’fish’]”,”[0.1,0.6,0.3]”’, set LabelIndex to 0 to select the label headers [‘cat’,’dog’,’fish’].

probability_attribute#

Type:: A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format. Example: If the model container output of a single request is ‘{“predicted_label”:1,”probability”:0.6}’, then set ProbabilityAttribute to ‘probability’.

label_attribute#

Type:: A JMESPath expression used to locate the list of label headers in the model container output. Example: If the model container output of a batch request is ‘{“labels”:[“cat”,”dog”,”fish”],”probability”:[0.6,0.3,0.1]}’, then set LabelAttribute to ‘labels’ to extract the list of label headers [“cat”,”dog”,”fish”]

label_headers#

Type:: For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

feature_headers#

Type:: The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

feature_types#

Type:: A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one ‘text’ string (for example, [‘text’]). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

content_template: str | PipelineVariable | None#

feature_headers: List[str | PipelineVariable] | None#

feature_types: List[str | PipelineVariable] | None#

features_attribute: str | PipelineVariable | None#

label_attribute: str | PipelineVariable | None#

label_headers: List[str | PipelineVariable] | None#

label_index: int | None#

max_payload_in_mb: int | None#

max_record_count: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

probability_attribute: str | PipelineVariable | None#

probability_index: int | None#

Bases: Base

The configuration for the SHAP baseline (also called the background or reference dataset) of the Kernal SHAP algorithm. The number of records in the baseline data determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint. ShapBaseline and ShapBaselineUri are mutually exclusive parameters. One or the either is required to configure a SHAP baseline.

mime_type#

Type:: The MIME type of the baseline data. Choose from ‘text/csv’ or ‘application/jsonlines’. Defaults to ‘text/csv’.

shap_baseline#

Type:: The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

shap_baseline_uri#

Type:: The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.

mime_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shap_baseline: str | PipelineVariable | None#

shap_baseline_uri: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClarifyShapConfig(*, shap_baseline_config: ClarifyShapBaselineConfig, number_of_samples: int | None = Unassigned(), use_logit: bool | None = Unassigned(), seed: int | None = Unassigned(), text_config: ClarifyTextConfig | None = Unassigned())[source]#

Bases: Base

The configuration for SHAP analysis using SageMaker Clarify Explainer.

shap_baseline_config#

Type:: The configuration for the SHAP baseline of the Kernal SHAP algorithm.

number_of_samples#

Type:: The number of samples to be used for analysis by the Kernal SHAP algorithm. The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.

use_logit#

Type:: A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

seed#

Type:: The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

text_config#

Type:: A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

number_of_samples: int | None#

seed: int | None#

shap_baseline_config: ClarifyShapBaselineConfig#

text_config: ClarifyTextConfig | None#

use_logit: bool | None#

class sagemaker.core.shapes.shapes.ClarifyTextConfig(*, language: str | PipelineVariable, granularity: str | PipelineVariable)[source]#

Bases: Base

A parameter used to configure the SageMaker Clarify explainer to treat text features as text so that explanations are provided for individual units of text. Required only for natural language processing (NLP) explainability.

language#

Type:: Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language. For a mix of multiple languages, use code ‘xx’.

granularity#

Type:: The unit of granularity for the analysis of text features. For example, if the unit is ‘token’, then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

granularity: str | PipelineVariable#

language: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterAutoScalingConfig(*, mode: str | PipelineVariable, auto_scaler_type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Specifies the autoscaling configuration for a HyperPod cluster.

mode#

Type:: Describes whether autoscaling is enabled or disabled for the cluster. Valid values are Enable and Disable.

auto_scaler_type#

Type:: The type of autoscaler to use. Currently supported value is Karpenter.

auto_scaler_type: str | PipelineVariable | None#

mode: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

The autoscaling configuration and status information for a HyperPod cluster.

mode#

Type:: Describes whether autoscaling is enabled or disabled for the cluster.

auto_scaler_type#

Type:: The type of autoscaler configured for the cluster.

status#

Type:: The current status of the autoscaling configuration. Valid values are InService, Failed, Creating, and Deleting.

failure_message#

Type:: If the autoscaling status is Failed, this field contains a message describing the failure.

auto_scaler_type: str | PipelineVariable | None#

failure_message: str | PipelineVariable | None#

mode: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

status: str | PipelineVariable#

class sagemaker.core.shapes.shapes.ClusterCapacityRequirements(*, spot: ClusterSpotOptions | None = Unassigned(), on_demand: ClusterOnDemandOptions | None = Unassigned())[source]#

Bases: Base

Defines the instance capacity requirements for an instance group, including configurations for both Spot and On-Demand capacity types.

spot#

Type:: Configuration options specific to Spot instances.

on_demand#

Type:: Configuration options specific to On-Demand instances.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

on_demand: ClusterOnDemandOptions | None#

spot: ClusterSpotOptions | None#

class sagemaker.core.shapes.shapes.ClusterEbsVolumeConfig(*, volume_size_in_gb: int | None = Unassigned(), volume_kms_key_id: str | PipelineVariable | None = Unassigned(), root_volume: bool | None = Unassigned())[source]#

Bases: Base

Defines the configuration for attaching an additional Amazon Elastic Block Store (EBS) volume to each instance of the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.

volume_size_in_gb#

Type:: The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.

volume_kms_key_id#

Type:: The ID of a KMS key to encrypt the Amazon EBS volume.

root_volume#

Type:: Specifies whether the configuration is for the cluster’s root or secondary Amazon EBS volume. You can specify two ClusterEbsVolumeConfig fields to configure both the root and secondary volumes. Set the value to True if you’d like to provide your own customer managed Amazon Web Services KMS key to encrypt the root volume. When True: The configuration is applied to the root volume. You can’t specify the VolumeSizeInGB field. The size of the root volume is determined for you. You must specify a KMS key ID for VolumeKmsKeyId to encrypt the root volume with your own KMS key instead of an Amazon Web Services owned KMS key. Otherwise, by default, the value is False, and the following applies: The configuration is applied to the secondary volume, while the root volume is encrypted with an Amazon Web Services owned key. You must specify the VolumeSizeInGB field. You can optionally specify the VolumeKmsKeyId to encrypt the secondary volume with your own KMS key instead of an Amazon Web Services owned KMS key.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

root_volume: bool | None#

volume_kms_key_id: str | PipelineVariable | None#

volume_size_in_gb: int | None#

Bases: Base

Detailed information about a specific event in a HyperPod cluster.

event_id#

Type:: The unique identifier (UUID) of the event.

cluster_arn#

Type:: The Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.

cluster_name#

Type:: The name of the HyperPod cluster associated with the event.

instance_group_name#

Type:: The name of the instance group associated with the event, if applicable.

instance_id#

Type:: The EC2 instance ID associated with the event, if applicable.

resource_type#

Type:: The type of resource associated with the event. Valid values are Cluster, InstanceGroup, or Instance.

event_time#

Type:: The timestamp when the event occurred.

event_details#

Type:: Additional details about the event, including event-specific metadata.

description#

Type:: A human-readable description of the event.

event_level#

Type:: The severity level of the event. Valid values are Info, Warn, and Error.

cluster_arn: str | PipelineVariable#

cluster_name: str | PipelineVariable | object#

description: str | PipelineVariable | None#

event_details: EventDetails | None#

event_id: str | PipelineVariable#

event_level: str | PipelineVariable | None#

event_time: datetime#

instance_group_name: str | PipelineVariable | None#

instance_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

resource_type: str | PipelineVariable#

Bases: Base

A summary of an event in a HyperPod cluster.

event_id#

Type:: The unique identifier (UUID) of the event.

cluster_arn#

Type:: The Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.

cluster_name#

Type:: The name of the HyperPod cluster associated with the event.

instance_group_name#

Type:: The name of the instance group associated with the event, if applicable.

instance_id#

Type:: The Amazon Elastic Compute Cloud (EC2) instance ID associated with the event, if applicable.

resource_type#

Type:: The type of resource associated with the event. Valid values are Cluster, InstanceGroup, or Instance.

event_time#

Type:: The timestamp when the event occurred.

description#

Type:: A brief, human-readable description of the event.

event_level#

Type:: The severity level of the event. Valid values are Info, Warn, and Error.

cluster_arn: str | PipelineVariable#

cluster_name: str | PipelineVariable | object#

description: str | PipelineVariable | None#

event_id: str | PipelineVariable#

event_level: str | PipelineVariable | None#

event_time: datetime#

instance_group_name: str | PipelineVariable | None#

instance_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

resource_type: str | PipelineVariable#

class sagemaker.core.shapes.shapes.ClusterFsxLustreConfig(*, dns_name: str | PipelineVariable, mount_name: str | PipelineVariable, mount_path: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Defines the configuration for attaching an Amazon FSx for Lustre file system to instances in a SageMaker HyperPod cluster instance group.

dns_name#

Type:: The DNS name of the Amazon FSx for Lustre file system.

mount_name#

Type:: The mount name of the Amazon FSx for Lustre file system.

mount_path#

Type:: The local path where the Amazon FSx for Lustre file system is mounted on instances.

dns_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

mount_name: str | PipelineVariable#

mount_path: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterFsxOpenZfsConfig(*, dns_name: str | PipelineVariable, mount_path: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Defines the configuration for attaching an Amazon FSx for OpenZFS file system to instances in a SageMaker HyperPod cluster instance group.

dns_name#

Type:: The DNS name of the Amazon FSx for OpenZFS file system.

mount_path#

Type:: The local path where the Amazon FSx for OpenZFS file system is mounted on instances.

dns_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

mount_path: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterInstanceGroupDetails(*, current_count: int | None = Unassigned(), target_count: int | None = Unassigned(), min_count: int | None = Unassigned(), instance_group_name: str | PipelineVariable | None = Unassigned(), instance_type: str | PipelineVariable | None = Unassigned(), instance_requirements: ClusterInstanceRequirementDetails | None = Unassigned(), instance_type_details: List[ClusterInstanceTypeDetail] | None = Unassigned(), life_cycle_config: ClusterLifeCycleConfig | None = Unassigned(), execution_role: str | PipelineVariable | None = Unassigned(), threads_per_core: int | None = Unassigned(), instance_storage_configs: List[ClusterInstanceStorageConfig] | None = Unassigned(), on_start_deep_health_checks: List[str | PipelineVariable] | None = Unassigned(), status: str | PipelineVariable | None = Unassigned(), training_plan_arn: str | PipelineVariable | None = Unassigned(), training_plan_status: str | PipelineVariable | None = Unassigned(), override_vpc_config: VpcConfig | None = Unassigned(), scheduled_update_config: ScheduledUpdateConfig | None = Unassigned(), current_image_id: str | PipelineVariable | None = Unassigned(), desired_image_id: str | PipelineVariable | None = Unassigned(), image_version_status: str | PipelineVariable | None = Unassigned(), active_operations: Dict[str | PipelineVariable, int] | None = Unassigned(), kubernetes_config: ClusterKubernetesConfigDetails | None = Unassigned(), capacity_requirements: ClusterCapacityRequirements | None = Unassigned(), target_state_count: int | None = Unassigned(), software_update_status: str | PipelineVariable | None = Unassigned(), active_software_update_config: DeploymentConfiguration | None = Unassigned(), slurm_config: ClusterSlurmConfigDetails | None = Unassigned(), network_interface: ClusterNetworkInterfaceDetails | None = Unassigned())[source]#

Bases: Base

Details of an instance group in a SageMaker HyperPod cluster.

current_count#

Type:: The number of instances that are currently in the instance group of a SageMaker HyperPod cluster.

target_count#

Type:: The number of instances you specified to add to the instance group of a SageMaker HyperPod cluster.

min_count#

Type:: The minimum number of instances that must be available in the instance group of a SageMaker HyperPod cluster before it transitions to InService status.

instance_group_name#

Type:: The name of the instance group of a SageMaker HyperPod cluster.

instance_type#

Type:: The instance type of the instance group of a SageMaker HyperPod cluster.

instance_requirements#

Type:: The instance requirements for the instance group, including the current and desired instance types. This field is present for flexible instance groups that support multiple instance types.

instance_type_details#

Type:: Details about the instance types in the instance group, including the count and configuration of each instance type. This field is present for flexible instance groups that support multiple instance types.

life_cycle_config#

Type:: Details of LifeCycle configuration for the instance group.

execution_role#

Type:: The execution role for the instance group to assume.

threads_per_core#

Type:: The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.

instance_storage_configs#

Type:: The additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.

on_start_deep_health_checks#

Type:: A flag indicating whether deep health checks should be performed when the cluster instance group is created or updated.

status#

Type:: The current status of the cluster instance group. InService: The instance group is active and healthy. Creating: The instance group is being provisioned. Updating: The instance group is being updated. Failed: The instance group has failed to provision or is no longer healthy. Degraded: The instance group is degraded, meaning that some instances have failed to provision or are no longer healthy. Deleting: The instance group is being deleted.

training_plan_arn#

Type:: The Amazon Resource Name (ARN); of the training plan associated with this cluster instance group. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

training_plan_status#

Type:: The current status of the training plan associated with this cluster instance group.

override_vpc_config#

Type:: The customized Amazon VPC configuration at the instance group level that overrides the default Amazon VPC configuration of the SageMaker HyperPod cluster.

scheduled_update_config#

Type:: The configuration object of the schedule that SageMaker follows when updating the AMI.

current_image_id#

Type:: The ID of the Amazon Machine Image (AMI) currently in use by the instance group.

desired_image_id#

Type:: The ID of the Amazon Machine Image (AMI) desired for the instance group.

image_version_status#

Type:: The status of the image version for the instance group. Indicates whether the instance group is running the latest image version or if an update is available.

active_operations#

Type:: A map indicating active operations currently in progress for the instance group of a SageMaker HyperPod cluster. When there is a scaling operation in progress, this map contains a key Scaling with value 1.

kubernetes_config#

Type:: The Kubernetes configuration for the instance group that contains labels and taints to be applied for the nodes in this instance group.

capacity_requirements#

Type:: The instance capacity requirements for the instance group.

target_state_count#

Type:: Represents the number of running nodes using the desired Image ID. During software update operations: This count shows the number of nodes running on the desired Image ID. If a rollback occurs, the current image ID and desired image ID (both included in the describe cluster response) swap values. The TargetStateCount then shows the number of nodes running on the newly designated desired image ID (which was previously the current image ID). During simultaneous scaling and software update operations: This count shows the number of instances running on the desired image ID, including any new instances created as part of the scaling request. New nodes are always created using the desired image ID, so TargetStateCount reflects the total count of nodes running on the desired image ID, even during rollback scenarios.

software_update_status#

Type:: Status of the last software udpate request. Status transitions follow these possible sequences: Pending -> InProgress -> Succeeded Pending -> InProgress -> RollbackInProgress -> RollbackComplete Pending -> InProgress -> RollbackInProgress -> Failed

active_software_update_config#

Type:: sagemaker.core.shapes.shapes.DeploymentConfiguration | None

slurm_config#

Type:: The Slurm configuration for the instance group.

network_interface#

Type:: The network interface configuration for the instance group.

active_operations: Dict[str | PipelineVariable, int] | None#

active_software_update_config: DeploymentConfiguration | None#

capacity_requirements: ClusterCapacityRequirements | None#

current_count: int | None#

current_image_id: str | PipelineVariable | None#

desired_image_id: str | PipelineVariable | None#

execution_role: str | PipelineVariable | None#

image_version_status: str | PipelineVariable | None#

instance_group_name: str | PipelineVariable | None#

instance_requirements: ClusterInstanceRequirementDetails | None#

instance_storage_configs: List[ClusterInstanceStorageConfig] | None#

instance_type: str | PipelineVariable | None#

instance_type_details: List[ClusterInstanceTypeDetail] | None#

kubernetes_config: ClusterKubernetesConfigDetails | None#

life_cycle_config: ClusterLifeCycleConfig | None#

min_count: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

network_interface: ClusterNetworkInterfaceDetails | None#

on_start_deep_health_checks: List[str | PipelineVariable] | None#

override_vpc_config: VpcConfig | None#

scheduled_update_config: ScheduledUpdateConfig | None#

slurm_config: ClusterSlurmConfigDetails | None#

software_update_status: str | PipelineVariable | None#

status: str | PipelineVariable | None#

target_count: int | None#

target_state_count: int | None#

threads_per_core: int | None#

training_plan_arn: str | PipelineVariable | None#

training_plan_status: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterInstanceGroupSpecification(*, instance_count: int, instance_group_name: str | PipelineVariable, execution_role: str | PipelineVariable, min_instance_count: int | None = Unassigned(), instance_type: str | PipelineVariable | None = Unassigned(), instance_requirements: ClusterInstanceRequirements | None = Unassigned(), life_cycle_config: ClusterLifeCycleConfig | None = Unassigned(), threads_per_core: int | None = Unassigned(), instance_storage_configs: List[ClusterInstanceStorageConfig] | None = Unassigned(), on_start_deep_health_checks: List[str | PipelineVariable] | None = Unassigned(), training_plan_arn: str | PipelineVariable | None = Unassigned(), override_vpc_config: VpcConfig | None = Unassigned(), scheduled_update_config: ScheduledUpdateConfig | None = Unassigned(), image_id: str | PipelineVariable | None = Unassigned(), kubernetes_config: ClusterKubernetesConfig | None = Unassigned(), slurm_config: ClusterSlurmConfig | None = Unassigned(), capacity_requirements: ClusterCapacityRequirements | None = Unassigned(), network_interface: ClusterNetworkInterface | None = Unassigned())[source]#

Bases: Base

The specifications of an instance group that you need to define.

instance_count#

Type:: Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.

min_instance_count#

Type:: Defines the minimum number of instances required for an instance group to become InService. If this threshold isn’t met within 3 hours, the instance group rolls back to its previous state - zero instances for new instance groups, or previous settings for existing instance groups. MinInstanceCount only affects the initial transition to InService and does not guarantee maintaining this minimum afterward.

instance_group_name#

Type:: Specifies the name of the instance group.

instance_type#

Type:: Specifies the instance type of the instance group.

instance_requirements#

Type:: The instance requirements for the instance group, including the instance types to use. Use this to create a flexible instance group that supports multiple instance types. The InstanceType and InstanceRequirements properties are mutually exclusive.

life_cycle_config#

Type:: Specifies the LifeCycle configuration for the instance group.

execution_role#

Type:: Specifies an IAM execution role to be assumed by the instance group.

threads_per_core#

Type:: Specifies the value for Threads per core. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For instance types that doesn’t support multithreading, specify 1. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.

instance_storage_configs#

Type:: Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.

on_start_deep_health_checks#

Type:: A flag indicating whether deep health checks should be performed when the cluster instance group is created or updated.

training_plan_arn#

Type:: The Amazon Resource Name (ARN); of the training plan to use for this cluster instance group. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

override_vpc_config#

Type:: To configure multi-AZ deployments, customize the Amazon VPC configuration at the instance group level. You can specify different subnets and security groups across different AZs in the instance group specification to override a SageMaker HyperPod cluster’s default Amazon VPC configuration. For more information about deploying a cluster in multiple AZs, see Setting up SageMaker HyperPod clusters across multiple AZs. When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform: Slurm-orchestrated clusters automatically configure nodes with dual IPv6 and IPv4 addresses, allowing immediate IPv6 network communications. In Amazon EKS-orchestrated clusters, nodes receive dual-stack addressing, but pods can only use IPv6 when the Amazon EKS cluster is explicitly IPv6-enabled. For information about deploying an IPv6 Amazon EKS cluster, see Amazon EKS IPv6 Cluster Deployment. Additional resources for IPv6 configuration: For information about adding IPv6 support to your VPC, see to IPv6 Support for VPC. For information about creating a new IPv6-compatible VPC, see Amazon VPC Creation Guide. To configure SageMaker HyperPod with a custom Amazon VPC, see Custom Amazon VPC Setup for SageMaker HyperPod.

scheduled_update_config#

Type:: The configuration object of the schedule that SageMaker uses to update the AMI.

image_id#

Type:: When configuring your HyperPod cluster, you can specify an image ID using one of the following options: HyperPodPublicAmiId: Use a HyperPod public AMI CustomAmiId: Use your custom AMI default: Use the default latest system image If you choose to use a custom AMI (CustomAmiId), ensure it meets the following requirements: Encryption: The custom AMI must be unencrypted. Ownership: The custom AMI must be owned by the same Amazon Web Services account that is creating the HyperPod cluster. Volume support: Only the primary AMI snapshot volume is supported; additional AMI volumes are not supported. When updating the instance group’s AMI through the UpdateClusterSoftware operation, if an instance group uses a custom AMI, you must provide an ImageId or use the default as input. Note that if you don’t specify an instance group in your UpdateClusterSoftware request, then all of the instance groups are patched with the specified image.

kubernetes_config#

Type:: Specifies the Kubernetes configuration for the instance group. You describe what you want the labels and taints to look like, and the cluster works to reconcile the actual state with the declared state for nodes in this instance group.

slurm_config#

Type:: Specifies the Slurm configuration for the instance group.

capacity_requirements#

Type:: Specifies the capacity requirements for the instance group.

network_interface#

Type:: The network interface configuration for the instance group.

capacity_requirements: ClusterCapacityRequirements | None#

execution_role: str | PipelineVariable#

image_id: str | PipelineVariable | None#

instance_count: int#

instance_group_name: str | PipelineVariable#

instance_requirements: ClusterInstanceRequirements | None#

instance_storage_configs: List[ClusterInstanceStorageConfig] | None#

instance_type: str | PipelineVariable | None#

kubernetes_config: ClusterKubernetesConfig | None#

life_cycle_config: ClusterLifeCycleConfig | None#

min_instance_count: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

network_interface: ClusterNetworkInterface | None#

on_start_deep_health_checks: List[str | PipelineVariable] | None#

override_vpc_config: VpcConfig | None#

scheduled_update_config: ScheduledUpdateConfig | None#

slurm_config: ClusterSlurmConfig | None#

threads_per_core: int | None#

training_plan_arn: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterInstancePlacement(*, availability_zone: str | PipelineVariable | None = Unassigned(), availability_zone_id: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Specifies the placement details for the node in the SageMaker HyperPod cluster, including the Availability Zone and the unique identifier (ID) of the Availability Zone.

availability_zone#

Type:: The Availability Zone where the node in the SageMaker HyperPod cluster is launched.

availability_zone_id#

Type:: The unique identifier (ID) of the Availability Zone where the node in the SageMaker HyperPod cluster is launched.

availability_zone: str | PipelineVariable | None#

availability_zone_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterInstanceRequirementDetails(*, current_instance_types: List[str | PipelineVariable] | None = Unassigned(), desired_instance_types: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

The instance requirement details for a flexible instance group, including the current and desired instance types.

current_instance_types#

Type:: The instance types currently in use by the instance group.

desired_instance_types#

Type:: The desired instance types for the instance group, as specified in the most recent update request.

current_instance_types: List[str | PipelineVariable] | None#

desired_instance_types: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterInstanceRequirements(*, instance_types: List[str | PipelineVariable])[source]#

Bases: Base

The instance requirements for a flexible instance group. Use this to specify multiple instance types that the instance group can use. The order of instance types in the list determines the priority for instance provisioning.

instance_types#

Type:: The list of instance types that the instance group can use. The order of instance types determines the priority—HyperPod attempts to provision instances using the first instance type in the list and falls back to subsequent types if capacity is unavailable.

instance_types: List[str | PipelineVariable]#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterInstanceStatusDetails(*, status: str | PipelineVariable, message: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Details of an instance in a SageMaker HyperPod cluster.

status#

Type:: The status of an instance in a SageMaker HyperPod cluster.

message#

Type:: The message from an instance in a SageMaker HyperPod cluster.

message: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

status: str | PipelineVariable#

class sagemaker.core.shapes.shapes.ClusterInstanceStorageConfig(*, ebs_volume_config: ClusterEbsVolumeConfig | None = Unassigned(), fsx_lustre_config: ClusterFsxLustreConfig | None = Unassigned(), fsx_open_zfs_config: ClusterFsxOpenZfsConfig | None = Unassigned())[source]#

Bases: Base

Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.

ebs_volume_config#

Type:: Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.

fsx_lustre_config#

Type:: Defines the configuration for attaching an Amazon FSx for Lustre file system to the instances in the SageMaker HyperPod cluster instance group.

fsx_open_zfs_config#

Type:: Defines the configuration for attaching an Amazon FSx for OpenZFS file system to the instances in the SageMaker HyperPod cluster instance group.

ebs_volume_config: ClusterEbsVolumeConfig | None#

fsx_lustre_config: ClusterFsxLustreConfig | None#

fsx_open_zfs_config: ClusterFsxOpenZfsConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterInstanceTypeDetail(*, instance_type: str | PipelineVariable | None = Unassigned(), current_count: int | None = Unassigned(), threads_per_core: int | None = Unassigned())[source]#

Bases: Base

Details about a specific instance type within a flexible instance group, including the count and configuration.

instance_type#

Type:: The instance type.

current_count#

Type:: The number of instances of this type currently running in the instance group.

threads_per_core#

Type:: The number of threads per CPU core for this instance type.

current_count: int | None#

instance_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

threads_per_core: int | None#

class sagemaker.core.shapes.shapes.ClusterKubernetesConfig(*, labels: Dict[str | PipelineVariable, str | PipelineVariable] | None = Unassigned(), taints: List[ClusterKubernetesTaint] | None = Unassigned())[source]#

Bases: Base

Kubernetes configuration that specifies labels and taints to be applied to cluster nodes in an instance group.

labels#

Type:: Key-value pairs of labels to be applied to cluster nodes.

taints#

Type:: List of taints to be applied to cluster nodes.

labels: Dict[str | PipelineVariable, str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

taints: List[ClusterKubernetesTaint] | None#

class sagemaker.core.shapes.shapes.ClusterKubernetesConfigDetails(*, current_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None = Unassigned(), desired_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None = Unassigned(), current_taints: List[ClusterKubernetesTaint] | None = Unassigned(), desired_taints: List[ClusterKubernetesTaint] | None = Unassigned())[source]#

Bases: Base

Detailed Kubernetes configuration showing both the current and desired state of labels and taints for cluster nodes.

current_labels#

Type:: The current labels applied to cluster nodes of an instance group.

desired_labels#

Type:: The desired labels to be applied to cluster nodes of an instance group.

current_taints#

Type:: The current taints applied to cluster nodes of an instance group.

desired_taints#

Type:: The desired taints to be applied to cluster nodes of an instance group.

current_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None#

current_taints: List[ClusterKubernetesTaint] | None#

desired_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None#

desired_taints: List[ClusterKubernetesTaint] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterKubernetesConfigNodeDetails(*, current_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None = Unassigned(), desired_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None = Unassigned(), current_taints: List[ClusterKubernetesTaint] | None = Unassigned(), desired_taints: List[ClusterKubernetesTaint] | None = Unassigned())[source]#

Bases: Base

Node-specific Kubernetes configuration showing both current and desired state of labels and taints for an individual cluster node.

current_labels#

Type:: The current labels applied to the cluster node.

desired_labels#

Type:: The desired labels to be applied to the cluster node.

current_taints#

Type:: The current taints applied to the cluster node.

desired_taints#

Type:: The desired taints to be applied to the cluster node.

current_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None#

current_taints: List[ClusterKubernetesTaint] | None#

desired_labels: Dict[str | PipelineVariable, str | PipelineVariable] | None#

desired_taints: List[ClusterKubernetesTaint] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterKubernetesTaint(*, key: str | PipelineVariable, effect: str | PipelineVariable, value: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A Kubernetes taint that can be applied to cluster nodes.

key#

Type:: The key of the taint.

value#

Type:: The value of the taint.

effect#

Type:: The effect of the taint. Valid values are NoSchedule, PreferNoSchedule, and NoExecute.

effect: str | PipelineVariable#

key: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

value: str | PipelineVariable | None#

Bases: Base

The lifecycle configuration for a SageMaker HyperPod cluster.

source_s3_uri#

Type:: An Amazon S3 bucket path where your lifecycle scripts are stored. Make sure that the S3 bucket path starts with s3://sagemaker-. The IAM role for SageMaker HyperPod has the managed AmazonSageMakerClusterInstanceRolePolicy attached, which allows access to S3 buckets with the specific prefix sagemaker-.

on_create#

Type:: The file name of the entrypoint script of lifecycle scripts under SourceS3Uri. This entrypoint script runs during cluster creation.

on_init_complete#

Type:: The file name of the entrypoint script of lifecycle scripts under SourceS3Uri. This script runs on the node after the AMI-based initialization is complete.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

on_create: str | PipelineVariable | None#

on_init_complete: str | PipelineVariable | None#

source_s3_uri: str | PipelineVariable | None#

Bases: Base

Metadata information about a HyperPod cluster showing information about the cluster level operations, such as creating, updating, and deleting.

failure_message#

Type:: An error message describing why the cluster level operation (such as creating, updating, or deleting) failed.

eks_role_access_entries#

Type:: A list of Amazon EKS IAM role ARNs associated with the cluster. This is created by HyperPod on your behalf and only applies for EKS orchestrated clusters.

slr_access_entry#

Type:: The Service-Linked Role (SLR) associated with the cluster. This is created by HyperPod on your behalf and only applies for EKS orchestrated clusters.

eks_role_access_entries: List[str | PipelineVariable] | None#

failure_message: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

slr_access_entry: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterNetworkInterface(*, interface_type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The network interface configuration for a Amazon SageMaker HyperPod cluster instance group.

interface_type#

Type:: The type of network interface for the instance group. Valid values: efa – An EFA with ENA interface, which provides both the EFA device for low-latency, high-throughput communication and the ENA device for IP networking. efa-only – An EFA-only interface, which provides only the EFA device capabilities without the ENA device for traditional IP networking. For more information, see Elastic Fabric Adapter.

interface_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterNetworkInterfaceDetails(*, interface_type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The network interface configuration details for a Amazon SageMaker HyperPod cluster instance group.

interface_type#

Type:: The type of network interface for the instance group. Valid values are efa and efa-only.

interface_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterNodeDetails(*, instance_group_name: str | PipelineVariable | None = Unassigned(), instance_id: str | PipelineVariable | None = Unassigned(), node_logical_id: str | PipelineVariable | None = Unassigned(), instance_status: ClusterInstanceStatusDetails | None = Unassigned(), instance_type: str | PipelineVariable | None = Unassigned(), launch_time: datetime | None = Unassigned(), last_software_update_time: datetime | None = Unassigned(), life_cycle_config: ClusterLifeCycleConfig | None = Unassigned(), override_vpc_config: VpcConfig | None = Unassigned(), threads_per_core: int | None = Unassigned(), instance_storage_configs: List[ClusterInstanceStorageConfig] | None = Unassigned(), private_primary_ip: str | PipelineVariable | None = Unassigned(), private_primary_ipv6: str | PipelineVariable | None = Unassigned(), private_dns_hostname: str | PipelineVariable | None = Unassigned(), placement: ClusterInstancePlacement | None = Unassigned(), current_image_id: str | PipelineVariable | None = Unassigned(), desired_image_id: str | PipelineVariable | None = Unassigned(), image_version_status: str | PipelineVariable | None = Unassigned(), ultra_server_info: UltraServerInfo | None = Unassigned(), kubernetes_config: ClusterKubernetesConfigNodeDetails | None = Unassigned(), capacity_type: str | PipelineVariable | None = Unassigned(), network_interface: ClusterNetworkInterfaceDetails | None = Unassigned())[source]#

Bases: Base

Details of an instance (also called a node interchangeably) in a SageMaker HyperPod cluster.

instance_group_name#

Type:: The instance group name in which the instance is.

instance_id#

Type:: The ID of the instance.

node_logical_id#

Type:: A unique identifier for the node that persists throughout its lifecycle, from provisioning request to termination. This identifier can be used to track the node even before it has an assigned InstanceId.

instance_status#

Type:: The status of the instance.

instance_type#

Type:: The type of the instance.

launch_time#

Type:: The time when the instance is launched.

last_software_update_time#

Type:: The time when the cluster was last updated.

life_cycle_config#

Type:: The LifeCycle configuration applied to the instance.

override_vpc_config#

Type:: The customized Amazon VPC configuration at the instance group level that overrides the default Amazon VPC configuration of the SageMaker HyperPod cluster.

threads_per_core#

Type:: The number of threads per CPU core you specified under CreateCluster.

instance_storage_configs#

Type:: The configurations of additional storage specified to the instance group where the instance (node) is launched.

private_primary_ip#

Type:: The private primary IP address of the SageMaker HyperPod cluster node.

private_primary_ipv6#

Type:: The private primary IPv6 address of the SageMaker HyperPod cluster node when configured with an Amazon VPC that supports IPv6 and includes subnets with IPv6 addressing enabled in either the cluster Amazon VPC configuration or the instance group Amazon VPC configuration.

private_dns_hostname#

Type:: The private DNS hostname of the SageMaker HyperPod cluster node.

placement#

Type:: The placement details of the SageMaker HyperPod cluster node.

current_image_id#

Type:: The ID of the Amazon Machine Image (AMI) currently in use by the node.

desired_image_id#

Type:: The ID of the Amazon Machine Image (AMI) desired for the node.

image_version_status#

Type:: The status of the image version for the cluster node.

ultra_server_info#

Type:: Contains information about the UltraServer.

kubernetes_config#

Type:: The Kubernetes configuration applied to this node, showing both the current and desired state of labels and taints. The cluster works to reconcile the actual state with the declared state.

capacity_type#

Type:: The capacity type of the node. Valid values are OnDemand and Spot. When set to OnDemand, the node is launched as an On-Demand instance. When set to Spot, the node is launched as a Spot instance.

network_interface#

Type:: The network interface configuration for the cluster node.

capacity_type: str | PipelineVariable | None#

current_image_id: str | PipelineVariable | None#

desired_image_id: str | PipelineVariable | None#

image_version_status: str | PipelineVariable | None#

instance_group_name: str | PipelineVariable | None#

instance_id: str | PipelineVariable | None#

instance_status: ClusterInstanceStatusDetails | None#

instance_storage_configs: List[ClusterInstanceStorageConfig] | None#

instance_type: str | PipelineVariable | None#

kubernetes_config: ClusterKubernetesConfigNodeDetails | None#

last_software_update_time: datetime | None#

launch_time: datetime | None#

life_cycle_config: ClusterLifeCycleConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

network_interface: ClusterNetworkInterfaceDetails | None#

node_logical_id: str | PipelineVariable | None#

override_vpc_config: VpcConfig | None#

placement: ClusterInstancePlacement | None#

private_dns_hostname: str | PipelineVariable | None#

private_primary_ip: str | PipelineVariable | None#

private_primary_ipv6: str | PipelineVariable | None#

threads_per_core: int | None#

ultra_server_info: UltraServerInfo | None#

class sagemaker.core.shapes.shapes.ClusterNodeSummary(*, instance_group_name: str | PipelineVariable, instance_id: str | PipelineVariable, instance_type: str | PipelineVariable, launch_time: datetime, instance_status: ClusterInstanceStatusDetails, node_logical_id: str | PipelineVariable | None = Unassigned(), last_software_update_time: datetime | None = Unassigned(), ultra_server_info: UltraServerInfo | None = Unassigned(), private_dns_hostname: str | PipelineVariable | None = Unassigned(), image_version_status: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Lists a summary of the properties of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.

instance_group_name#

Type:: The name of the instance group in which the instance is.

instance_id#

Type:: The ID of the instance.

node_logical_id#

Type:: A unique identifier for the node that persists throughout its lifecycle, from provisioning request to termination. This identifier can be used to track the node even before it has an assigned InstanceId. This field is only included when IncludeNodeLogicalIds is set to True in the ListClusterNodes request.

instance_type#

Type:: The type of the instance.

launch_time#

Type:: The time when the instance is launched.

last_software_update_time#

Type:: The time when SageMaker last updated the software of the instances in the cluster.

instance_status#

Type:: The status of the instance.

ultra_server_info#

Type:: Contains information about the UltraServer.

private_dns_hostname#

Type:: The private DNS hostname of the SageMaker HyperPod cluster node.

image_version_status#

Type:: The status of the image version for the cluster node.

image_version_status: str | PipelineVariable | None#

instance_group_name: str | PipelineVariable#

instance_id: str | PipelineVariable#

instance_status: ClusterInstanceStatusDetails#

instance_type: str | PipelineVariable#

last_software_update_time: datetime | None#

launch_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_logical_id: str | PipelineVariable | None#

private_dns_hostname: str | PipelineVariable | None#

ultra_server_info: UltraServerInfo | None#

class sagemaker.core.shapes.shapes.ClusterOnDemandOptions[source]#

Bases: Base

Configuration options specific to On-Demand instances.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterOrchestrator(*, eks: ClusterOrchestratorEksConfig | None = Unassigned(), slurm: ClusterOrchestratorSlurmConfig | None = Unassigned())[source]#

Bases: Base

The type of orchestrator used for the SageMaker HyperPod cluster.

eks#

Type:: The Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster.

slurm#

Type:: The Slurm orchestrator configuration for the SageMaker HyperPod cluster.

eks: ClusterOrchestratorEksConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

slurm: ClusterOrchestratorSlurmConfig | None#

class sagemaker.core.shapes.shapes.ClusterOrchestratorEksConfig(*, cluster_arn: str | PipelineVariable)[source]#

Bases: Base

The configuration settings for the Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster.

cluster_arn#

Type:: The Amazon Resource Name (ARN) of the Amazon EKS cluster associated with the SageMaker HyperPod cluster.

cluster_arn: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterOrchestratorSlurmConfig(*, slurm_config_strategy: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The configuration settings for the Slurm orchestrator used with the SageMaker HyperPod cluster.

slurm_config_strategy#

Type:: The strategy for managing partitions for the Slurm configuration. Valid values are Managed, Overwrite, and Merge.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

slurm_config_strategy: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterRestrictedInstanceGroupDetails(*, current_count: int | None = Unassigned(), target_count: int | None = Unassigned(), instance_group_name: str | PipelineVariable | None = Unassigned(), instance_type: str | PipelineVariable | None = Unassigned(), execution_role: str | PipelineVariable | None = Unassigned(), threads_per_core: int | None = Unassigned(), instance_storage_configs: List[ClusterInstanceStorageConfig] | None = Unassigned(), on_start_deep_health_checks: List[str | PipelineVariable] | None = Unassigned(), status: str | PipelineVariable | None = Unassigned(), training_plan_arn: str | PipelineVariable | None = Unassigned(), training_plan_status: str | PipelineVariable | None = Unassigned(), override_vpc_config: VpcConfig | None = Unassigned(), scheduled_update_config: ScheduledUpdateConfig | None = Unassigned(), environment_config: EnvironmentConfigDetails | None = Unassigned())[source]#

Bases: Base

The instance group details of the restricted instance group (RIG).

current_count#

Type:: The number of instances that are currently in the restricted instance group of a SageMaker HyperPod cluster.

target_count#

Type:: The number of instances you specified to add to the restricted instance group of a SageMaker HyperPod cluster.

instance_group_name#

Type:: The name of the restricted instance group of a SageMaker HyperPod cluster.

instance_type#

Type:: The instance type of the restricted instance group of a SageMaker HyperPod cluster.

execution_role#

Type:: The execution role for the restricted instance group to assume.

threads_per_core#

Type:: The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.

instance_storage_configs#

Type:: The additional storage configurations for the instances in the SageMaker HyperPod cluster restricted instance group.

on_start_deep_health_checks#

Type:: A flag indicating whether deep health checks should be performed when the cluster’s restricted instance group is created or updated.

status#

Type:: The current status of the cluster’s restricted instance group. InService: The restricted instance group is active and healthy. Creating: The restricted instance group is being provisioned. Updating: The restricted instance group is being updated. Failed: The restricted instance group has failed to provision or is no longer healthy. Degraded: The restricted instance group is degraded, meaning that some instances have failed to provision or are no longer healthy. Deleting: The restricted instance group is being deleted.

training_plan_arn#

Type:: The Amazon Resource Name (ARN) of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

training_plan_status#

Type:: The current status of the training plan associated with this cluster restricted instance group.

override_vpc_config#

Type:: sagemaker.core.shapes.shapes.VpcConfig | None

scheduled_update_config#

Type:: sagemaker.core.shapes.shapes.ScheduledUpdateConfig | None

environment_config#

Type:: The configuration for the restricted instance groups (RIG) environment.

current_count: int | None#

environment_config: EnvironmentConfigDetails | None#

execution_role: str | PipelineVariable | None#

instance_group_name: str | PipelineVariable | None#

instance_storage_configs: List[ClusterInstanceStorageConfig] | None#

instance_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

on_start_deep_health_checks: List[str | PipelineVariable] | None#

override_vpc_config: VpcConfig | None#

scheduled_update_config: ScheduledUpdateConfig | None#

status: str | PipelineVariable | None#

target_count: int | None#

threads_per_core: int | None#

training_plan_arn: str | PipelineVariable | None#

training_plan_status: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterRestrictedInstanceGroupSpecification(*, instance_count: int, instance_group_name: str | PipelineVariable, instance_type: str | PipelineVariable, execution_role: str | PipelineVariable, threads_per_core: int | None = Unassigned(), instance_storage_configs: List[ClusterInstanceStorageConfig] | None = Unassigned(), on_start_deep_health_checks: List[str | PipelineVariable] | None = Unassigned(), training_plan_arn: str | PipelineVariable | None = Unassigned(), override_vpc_config: VpcConfig | None = Unassigned(), scheduled_update_config: ScheduledUpdateConfig | None = Unassigned(), environment_config: EnvironmentConfig | None = Unassigned())[source]#

Bases: Base

The specifications of a restricted instance group that you need to define.

instance_count#

Type:: Specifies the number of instances to add to the restricted instance group of a SageMaker HyperPod cluster.

instance_group_name#

Type:: Specifies the name of the restricted instance group.

instance_type#

Type:: Specifies the instance type of the restricted instance group.

execution_role#

Type:: Specifies an IAM execution role to be assumed by the restricted instance group.

threads_per_core#

Type:: The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.

instance_storage_configs#

Type:: Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster restricted instance group.

on_start_deep_health_checks#

Type:: A flag indicating whether deep health checks should be performed when the cluster restricted instance group is created or updated.

training_plan_arn#

Type:: The Amazon Resource Name (ARN) of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

override_vpc_config#

Type:: sagemaker.core.shapes.shapes.VpcConfig | None

scheduled_update_config#

Type:: sagemaker.core.shapes.shapes.ScheduledUpdateConfig | None

environment_config#

Type:: The configuration for the restricted instance groups (RIG) environment.

environment_config: EnvironmentConfig | None#

execution_role: str | PipelineVariable#

instance_count: int#

instance_group_name: str | PipelineVariable#

instance_storage_configs: List[ClusterInstanceStorageConfig] | None#

instance_type: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

on_start_deep_health_checks: List[str | PipelineVariable] | None#

override_vpc_config: VpcConfig | None#

scheduled_update_config: ScheduledUpdateConfig | None#

threads_per_core: int | None#

training_plan_arn: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ClusterRestrictedInstanceGroupsConfig(*, shared_environment_config: ClusterSharedEnvironmentConfig)[source]#

Bases: Base

The configuration for the restricted instance groups (RIG) in the SageMaker HyperPod cluster.

shared_environment_config#

Type:: The shared environment configuration for the restricted instance groups (RIG).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shared_environment_config: ClusterSharedEnvironmentConfig#

class sagemaker.core.shapes.shapes.ClusterRestrictedInstanceGroupsConfigOutput(*, shared_environment_config: ClusterSharedEnvironmentConfigDetails)[source]#

Bases: Base

The output configuration for the restricted instance groups (RIG) in the SageMaker HyperPod cluster.

shared_environment_config#

Type:: The shared environment configuration details for the restricted instance groups (RIG).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shared_environment_config: ClusterSharedEnvironmentConfigDetails#

class sagemaker.core.shapes.shapes.ClusterSchedulerConfigSummary(*, cluster_scheduler_config_arn: str | PipelineVariable, cluster_scheduler_config_id: str | PipelineVariable, name: str | PipelineVariable, creation_time: datetime, status: str | PipelineVariable, cluster_scheduler_config_version: int | None = Unassigned(), last_modified_time: datetime | None = Unassigned(), cluster_arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Summary of the cluster policy.

cluster_scheduler_config_arn#

Type:: ARN of the cluster policy.

cluster_scheduler_config_id#

Type:: ID of the cluster policy.

cluster_scheduler_config_version#

Type:: Version of the cluster policy.

name#

Type:: Name of the cluster policy.

creation_time#

Type:: Creation time of the cluster policy.

last_modified_time#

Type:: Last modified time of the cluster policy.

status#

Type:: Status of the cluster policy.

cluster_arn#

Type:: ARN of the cluster.

cluster_arn: str | PipelineVariable | None#

cluster_scheduler_config_arn: str | PipelineVariable#

cluster_scheduler_config_id: str | PipelineVariable#

cluster_scheduler_config_version: int | None#

creation_time: datetime#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

status: str | PipelineVariable#

class sagemaker.core.shapes.shapes.ClusterSharedEnvironmentConfig(*, f_sx_lustre_deletion_policy: str | PipelineVariable, f_sx_lustre_config: FSxLustreConfig)[source]#

Bases: Base

The shared environment configuration for the restricted instance groups (RIG).

f_sx_lustre_deletion_policy#

Type:: The deletion policy for the Amazon FSx for Lustre file system in the shared environment.

f_sx_lustre_config#

Type:: Configuration settings for an Amazon FSx for Lustre file system in the shared environment.

f_sx_lustre_config: FSxLustreConfig#

f_sx_lustre_deletion_policy: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterSharedEnvironmentConfigDetails(*, current_f_sx_lustre_config: FSxLustreConfig | None = Unassigned(), desired_f_sx_lustre_config: FSxLustreConfig | None = Unassigned(), current_f_sx_lustre_deletion_policy: str | PipelineVariable | None = Unassigned(), desired_f_sx_lustre_deletion_policy: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The shared environment configuration details for the restricted instance groups (RIG).

current_f_sx_lustre_config#

Type:: The current Amazon FSx for Lustre file system configuration in the shared environment.

desired_f_sx_lustre_config#

Type:: The desired Amazon FSx for Lustre file system configuration in the shared environment.

current_f_sx_lustre_deletion_policy#

Type:: The current deletion policy for the Amazon FSx for Lustre file system in the shared environment.

desired_f_sx_lustre_deletion_policy#

Type:: The desired deletion policy for the Amazon FSx for Lustre file system in the shared environment.

current_f_sx_lustre_config: FSxLustreConfig | None#

current_f_sx_lustre_deletion_policy: str | PipelineVariable | None#

desired_f_sx_lustre_config: FSxLustreConfig | None#

desired_f_sx_lustre_deletion_policy: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ClusterSlurmConfig(*, node_type: str | PipelineVariable, partition_names: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

The Slurm configuration for an instance group in a SageMaker HyperPod cluster.

node_type#

Type:: The type of Slurm node for the instance group. Valid values are Controller, Worker, and Login.

partition_names#

Type:: The list of Slurm partition names that the instance group belongs to.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_type: str | PipelineVariable#

partition_names: List[str | PipelineVariable] | None#

class sagemaker.core.shapes.shapes.ClusterSlurmConfigDetails(*, node_type: str | PipelineVariable, partition_names: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

The Slurm configuration details for an instance group in a SageMaker HyperPod cluster.

node_type#

Type:: The type of Slurm node for the instance group. Valid values are Controller, Worker, and Login.

partition_names#

Type:: The list of Slurm partition names that the instance group belongs to.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_type: str | PipelineVariable#

partition_names: List[str | PipelineVariable] | None#

class sagemaker.core.shapes.shapes.ClusterSpotOptions[source]#

Bases: Base

Configuration options specific to Spot instances.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Lists a summary of the properties of a SageMaker HyperPod cluster.

cluster_arn#

Type:: The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

cluster_name#

Type:: The name of the SageMaker HyperPod cluster.

creation_time#

Type:: The time when the SageMaker HyperPod cluster is created.

cluster_status#

Type:: The status of the SageMaker HyperPod cluster.

training_plan_arns#

Type:: A list of Amazon Resource Names (ARNs) of the training plans associated with this cluster. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

cluster_arn: str | PipelineVariable#

cluster_name: str | PipelineVariable | object#

cluster_status: str | PipelineVariable#

creation_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

training_plan_arns: List[str | PipelineVariable] | None#

class sagemaker.core.shapes.shapes.ClusterTieredStorageConfig(*, mode: str | PipelineVariable, instance_memory_allocation_percentage: int | None = Unassigned())[source]#

Bases: Base

Defines the configuration for managed tier checkpointing in a HyperPod cluster. Managed tier checkpointing uses multiple storage tiers, including cluster CPU memory, to provide faster checkpoint operations and improved fault tolerance for large-scale model training. The system automatically saves checkpoints at high frequency to memory and periodically persists them to durable storage, like Amazon S3.

mode#

Type:: Specifies whether managed tier checkpointing is enabled or disabled for the HyperPod cluster. When set to Enable, the system installs a memory management daemon that provides disaggregated memory as a service for checkpoint storage. When set to Disable, the feature is turned off and the memory management daemon is removed from the cluster.

instance_memory_allocation_percentage#

Type:: The percentage (int) of cluster memory to allocate for checkpointing.

instance_memory_allocation_percentage: int | None#

mode: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CodeEditorAppImageConfig(*, file_system_config: FileSystemConfig | None = Unassigned(), container_config: ContainerConfig | None = Unassigned())[source]#

Bases: Base

The configuration for the file system and kernels in a SageMaker image running as a Code Editor app. The FileSystemConfig object is not supported.

file_system_config#

Type:: sagemaker.core.shapes.shapes.FileSystemConfig | None

container_config#

Type:: sagemaker.core.shapes.shapes.ContainerConfig | None

container_config: ContainerConfig | None#

file_system_config: FileSystemConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CodeEditorAppSettings(*, default_resource_spec: ResourceSpec | None = Unassigned(), custom_images: List[CustomImage] | None = Unassigned(), lifecycle_config_arns: List[str | PipelineVariable] | None = Unassigned(), app_lifecycle_management: AppLifecycleManagement | None = Unassigned(), built_in_lifecycle_config_arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The Code Editor application settings. For more information about Code Editor, see Get started with Code Editor in Amazon SageMaker.

default_resource_spec#

Type:: sagemaker.core.shapes.shapes.ResourceSpec | None

custom_images#

Type:: A list of custom SageMaker images that are configured to run as a Code Editor app.

lifecycle_config_arns#

Type:: The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.

app_lifecycle_management#

Type:: Settings that are used to configure and manage the lifecycle of CodeEditor applications.

built_in_lifecycle_config_arn#

Type:: The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.

app_lifecycle_management: AppLifecycleManagement | None#

built_in_lifecycle_config_arn: str | PipelineVariable | None#

custom_images: List[CustomImage] | None#

default_resource_spec: ResourceSpec | None#

lifecycle_config_arns: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CodeRepository(*, repository_url: str | PipelineVariable)[source]#

Bases: Base

A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.

repository_url#

Type:: The URL of the Git repository.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

repository_url: str | PipelineVariable#

class sagemaker.core.shapes.shapes.CodeRepositorySummary(*, code_repository_name: str | PipelineVariable | object, code_repository_arn: str | PipelineVariable, creation_time: datetime, last_modified_time: datetime, git_config: GitConfig | None = Unassigned())[source]#

Bases: Base

Specifies summary information about a Git repository.

code_repository_name#

Type:: The name of the Git repository.

code_repository_arn#

Type:: The Amazon Resource Name (ARN) of the Git repository.

creation_time#

Type:: The date and time that the Git repository was created.

last_modified_time#

Type:: The date and time that the Git repository was last modified.

git_config#

Type:: Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.

code_repository_arn: str | PipelineVariable#

code_repository_name: str | PipelineVariable | object#

creation_time: datetime#

git_config: GitConfig | None#

last_modified_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CognitoConfig(*, user_pool: str | PipelineVariable, client_id: str | PipelineVariable)[source]#

Bases: Base

Use this parameter to configure your Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.

user_pool#

Type:: A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.

client_id#

Type:: The client ID for your Amazon Cognito user pool.

client_id: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

user_pool: str | PipelineVariable#

class sagemaker.core.shapes.shapes.CognitoMemberDefinition(*, user_pool: str | PipelineVariable, user_group: str | PipelineVariable, client_id: str | PipelineVariable)[source]#

Bases: Base

Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.

user_pool#

Type:: An identifier for a user pool. The user pool must be in the same region as the service that you are calling.

user_group#

Type:: An identifier for a user group.

client_id#

Type:: An identifier for an application client. You must create the app client ID using Amazon Cognito.

client_id: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

user_group: str | PipelineVariable#

user_pool: str | PipelineVariable#

class sagemaker.core.shapes.shapes.CollectionConfig(*, vector_config: VectorConfig | None = Unassigned())[source]#

Bases: Base

Configuration for your collection.

vector_config#

Type:: Configuration for your vector collection type. Dimension: The number of elements in your vector.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

vector_config: VectorConfig | None#

Bases: Base

Configuration information for the Amazon SageMaker Debugger output tensor collections.

collection_name#

Type:: The name of the tensor collection. The name must be unique relative to other rule configuration names.

collection_parameters#

Type:: Parameter values for the tensor collection. The allowed parameters are “name”, “include_regex”, “reduction_config”, “save_config”, “tensor_names”, and “save_histogram”.

collection_name: str | PipelineVariable | None#

collection_parameters: Dict[str | PipelineVariable, str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CompilationJobSummary(*, compilation_job_name: str | PipelineVariable | object, compilation_job_arn: str | PipelineVariable, creation_time: datetime, compilation_job_status: str | PipelineVariable, compilation_start_time: datetime | None = Unassigned(), compilation_end_time: datetime | None = Unassigned(), compilation_target_device: str | PipelineVariable | None = Unassigned(), compilation_target_platform_os: str | PipelineVariable | None = Unassigned(), compilation_target_platform_arch: str | PipelineVariable | None = Unassigned(), compilation_target_platform_accelerator: str | PipelineVariable | None = Unassigned(), last_modified_time: datetime | None = Unassigned())[source]#

Bases: Base

A summary of a model compilation job.

compilation_job_name#

Type:: The name of the model compilation job that you want a summary for.

compilation_job_arn#

Type:: The Amazon Resource Name (ARN) of the model compilation job.

creation_time#

Type:: The time when the model compilation job was created.

compilation_start_time#

Type:: The time when the model compilation job started.

compilation_end_time#

Type:: The time when the model compilation job completed.

compilation_target_device#

Type:: The type of device that the model will run on after the compilation job has completed.

compilation_target_platform_os#

Type:: The type of OS that the model will run on after the compilation job has completed.

compilation_target_platform_arch#

Type:: The type of architecture that the model will run on after the compilation job has completed.

compilation_target_platform_accelerator#

Type:: The type of accelerator that the model will run on after the compilation job has completed.

last_modified_time#

Type:: The time when the model compilation job was last modified.

compilation_job_status#

Type:: The status of the model compilation job.

compilation_end_time: datetime | None#

compilation_job_arn: str | PipelineVariable#

compilation_job_name: str | PipelineVariable | object#

compilation_job_status: str | PipelineVariable#

compilation_start_time: datetime | None#

compilation_target_device: str | PipelineVariable | None#

compilation_target_platform_accelerator: str | PipelineVariable | None#

compilation_target_platform_arch: str | PipelineVariable | None#

compilation_target_platform_os: str | PipelineVariable | None#

creation_time: datetime#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ComputeQuotaConfig(*, compute_quota_resources: List[ComputeQuotaResourceConfig] | None = Unassigned(), resource_sharing_config: ResourceSharingConfig | None = Unassigned(), preempt_team_tasks: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Configuration of the compute allocation definition for an entity. This includes the resource sharing option and the setting to preempt low priority tasks.

compute_quota_resources#

Type:: Allocate compute resources by instance types.

resource_sharing_config#

Type:: Resource sharing configuration. This defines how an entity can lend and borrow idle compute with other entities within the cluster.

preempt_team_tasks#

Type:: Allows workloads from within an entity to preempt same-team workloads. When set to LowerPriority, the entity’s lower priority tasks are preempted by their own higher priority tasks. Default is LowerPriority.

compute_quota_resources: List[ComputeQuotaResourceConfig] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

preempt_team_tasks: str | PipelineVariable | None#

resource_sharing_config: ResourceSharingConfig | None#

class sagemaker.core.shapes.shapes.ComputeQuotaResourceConfig(*, instance_type: str | PipelineVariable, count: int | None = Unassigned(), accelerators: int | None = Unassigned(), v_cpu: float | None = Unassigned(), memory_in_gi_b: float | None = Unassigned(), accelerator_partition: AcceleratorPartitionConfig | None = Unassigned())[source]#

Bases: Base

Configuration of the resources used for the compute allocation definition.

instance_type#

Type:: The instance type of the instance group for the cluster.

count#

Type:: The number of instances to add to the instance group of a SageMaker HyperPod cluster.

accelerators#

Type:: The number of accelerators to allocate. If you don’t specify a value for vCPU and MemoryInGiB, SageMaker AI automatically allocates ratio-based values for those parameters based on the number of accelerators you provide. For example, if you allocate 16 out of 32 total accelerators, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU and MemoryInGiB.

v_cpu#

Type:: The number of vCPU to allocate. If you specify a value only for vCPU, SageMaker AI automatically allocates ratio-based values for MemoryInGiB based on this vCPU parameter. For example, if you allocate 20 out of 40 total vCPU, SageMaker AI uses the ratio of 0.5 and allocates values to MemoryInGiB. Accelerators are set to 0.

memory_in_gi_b#

Type:: The amount of memory in GiB to allocate. If you specify a value only for this parameter, SageMaker AI automatically allocates a ratio-based value for vCPU based on this memory that you provide. For example, if you allocate 200 out of 400 total memory in GiB, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU. Accelerators are set to 0.

accelerator_partition#

Type:: The accelerator partition configuration for fractional GPU allocation.

accelerator_partition: AcceleratorPartitionConfig | None#

accelerators: int | None#

count: int | None#

instance_type: str | PipelineVariable#

memory_in_gi_b: float | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

v_cpu: float | None#

class sagemaker.core.shapes.shapes.ComputeQuotaSummary(*, compute_quota_arn: str | PipelineVariable, compute_quota_id: str | PipelineVariable, name: str | PipelineVariable, status: str | PipelineVariable, compute_quota_target: ComputeQuotaTarget, creation_time: datetime, compute_quota_version: int | None = Unassigned(), cluster_arn: str | PipelineVariable | None = Unassigned(), compute_quota_config: ComputeQuotaConfig | None = Unassigned(), activation_state: str | PipelineVariable | None = Unassigned(), last_modified_time: datetime | None = Unassigned())[source]#

Bases: Base

Summary of the compute allocation definition.

compute_quota_arn#

Type:: ARN of the compute allocation definition.

compute_quota_id#

Type:: ID of the compute allocation definition.

name#

Type:: Name of the compute allocation definition.

compute_quota_version#

Type:: Version of the compute allocation definition.

status#

Type:: Status of the compute allocation definition.

cluster_arn#

Type:: ARN of the cluster.

compute_quota_config#

Type:: Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

compute_quota_target#

Type:: The target entity to allocate compute resources to.

activation_state#

Type:: The state of the compute allocation being described. Use to enable or disable compute allocation. Default is Enabled.

creation_time#

Type:: Creation time of the compute allocation definition.

last_modified_time#

Type:: Last modified time of the compute allocation definition.

activation_state: str | PipelineVariable | None#

cluster_arn: str | PipelineVariable | None#

compute_quota_arn: str | PipelineVariable#

compute_quota_config: ComputeQuotaConfig | None#

compute_quota_id: str | PipelineVariable#

compute_quota_target: ComputeQuotaTarget#

compute_quota_version: int | None#

creation_time: datetime#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

status: str | PipelineVariable#

class sagemaker.core.shapes.shapes.ComputeQuotaTarget(*, team_name: str | PipelineVariable, fair_share_weight: int | None = Unassigned())[source]#

Bases: Base

The target entity to allocate compute resources to.

team_name#

Type:: Name of the team to allocate compute resources to.

fair_share_weight#

Type:: Assigned entity fair-share weight. Idle compute will be shared across entities based on these assigned weights. This weight is only used when FairShare is enabled. A weight of 0 is the lowest priority and 100 is the highest. Weight 0 is the default.

fair_share_weight: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

team_name: str | PipelineVariable#

class sagemaker.core.shapes.shapes.ConditionStepMetadata(*, outcome: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Metadata for a Condition step.

outcome#

Type:: The outcome of the Condition step evaluation.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

outcome: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ConflictException(*, message: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

message#

Type:: str | sagemaker.core.helper.pipeline_variable.PipelineVariable | None

message: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

The configuration used to run the application image container.

container_arguments#

Type:: The arguments for the container when you’re running the application.

container_entrypoint#

Type:: The entrypoint used to run the application in the container.

container_environment_variables#

Type:: The environment variables to set in the container

container_arguments: List[str | PipelineVariable] | None#

container_entrypoint: List[str | PipelineVariable] | None#

container_environment_variables: Dict[str | PipelineVariable, str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Describes the container, as part of model definition.

container_hostname#

Type:: This parameter is ignored for models that contain only a PrimaryContainer. When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don’t specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

image#

Type:: The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker. The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

image_config#

Type:: Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers. The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

mode#

Type:: Whether the container hosts a single model or multiple models.

model_data_url#

Type:: The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters. The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating. If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide. If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

model_data_source#

Type:: Specifies the location of ML model data to deploy. Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

additional_model_data_sources#

Type:: Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

environment#

Type:: The environment variables to set in the Docker container. Don’t include any sensitive data in your environment variables. The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

model_package_name#

Type:: The name or Amazon Resource Name (ARN) of the model package to use to create the model.

inference_specification_name#

Type:: The inference specification name in the model package version.

multi_model_config#

Type:: Specifies additional configuration for multi-model endpoints.

additional_model_data_sources: List[AdditionalModelDataSource] | None#

container_hostname: str | PipelineVariable | None#

environment: Dict[str | PipelineVariable, str | PipelineVariable] | None#

image: str | PipelineVariable | None#

image_config: ImageConfig | None#

inference_specification_name: str | PipelineVariable | None#

mode: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_data_source: ModelDataSource | None#

model_data_url: str | PipelineVariable | None#

model_package_name: str | PipelineVariable | object | None#

multi_model_config: MultiModelConfig | None#

Bases: Base

A structure describing the source of a context.

source_uri#

Type:: The URI of the source.

source_type#

Type:: The type of the source.

source_id#

Type:: The ID of the source.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_id: str | PipelineVariable | None#

source_type: str | PipelineVariable | None#

source_uri: str | PipelineVariable#

Bases: Base

Lists a summary of the properties of a context. A context provides a logical grouping of other entities.

context_arn#

Type:: The Amazon Resource Name (ARN) of the context.

context_name#

Type:: The name of the context.

source#

Type:: The source of the context.

context_type#

Type:: The type of the context.

creation_time#

Type:: When the context was created.

last_modified_time#

Type:: When the context was last modified.

context_arn: str | PipelineVariable | None#

context_name: str | PipelineVariable | object | None#

context_type: str | PipelineVariable | None#

creation_time: datetime | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source: ContextSource | None#

Bases: Base

A list of continuous hyperparameters to tune.

name#

Type:: The name of the continuous hyperparameter to tune.

min_value#

Type:: The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.

max_value#

Type:: The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

scaling_type#

Type:: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values: Auto SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. Linear Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. ReverseLogarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

max_value: str | PipelineVariable#

min_value: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

scaling_type: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.ContinuousParameterRangeSpecification(*, min_value: str | PipelineVariable, max_value: str | PipelineVariable)[source]#

Bases: Base

Defines the possible values for a continuous hyperparameter.

min_value#

Type:: The minimum floating-point value allowed.

max_value#

Type:: The maximum floating-point value allowed.

max_value: str | PipelineVariable#

min_value: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ConvergenceDetected(*, complete_on_convergence: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A flag to indicating that automatic model tuning (AMT) has detected model convergence, defined as a lack of significant improvement (1% or less) against an objective metric.

complete_on_convergence#

Type:: A flag to stop a tuning job once AMT has detected that the job has converged.

complete_on_convergence: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CreateTemplateProvider(*, cfn_template_provider: CfnCreateTemplateProvider | None = Unassigned())[source]#

Bases: Base

Contains configuration details for a template provider. Only one type of template provider can be specified.

cfn_template_provider#

Type:: The CloudFormation template provider configuration for creating infrastructure resources.

cfn_template_provider: CfnCreateTemplateProvider | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CustomFileSystem(*, efs_file_system: EFSFileSystem | None = Unassigned(), f_sx_lustre_file_system: FSxLustreFileSystem | None = Unassigned(), s3_file_system: S3FileSystem | None = Unassigned())[source]#

Bases: Base

A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

efs_file_system#

Type:: A custom file system in Amazon EFS.

f_sx_lustre_file_system#

Type:: A custom file system in Amazon FSx for Lustre.

s3_file_system#

Type:: A custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.

efs_file_system: EFSFileSystem | None#

f_sx_lustre_file_system: FSxLustreFileSystem | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_file_system: S3FileSystem | None#

class sagemaker.core.shapes.shapes.CustomFileSystemConfig(*, efs_file_system_config: EFSFileSystemConfig | None = Unassigned(), f_sx_lustre_file_system_config: FSxLustreFileSystemConfig | None = Unassigned(), s3_file_system_config: S3FileSystemConfig | None = Unassigned())[source]#

Bases: Base

The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

efs_file_system_config#

Type:: The settings for a custom Amazon EFS file system.

f_sx_lustre_file_system_config#

Type:: The settings for a custom Amazon FSx for Lustre file system.

s3_file_system_config#

Type:: Configuration settings for a custom Amazon S3 file system.

efs_file_system_config: EFSFileSystemConfig | None#

f_sx_lustre_file_system_config: FSxLustreFileSystemConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_file_system_config: S3FileSystemConfig | None#

Bases: Base

A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.

image_name#

Type:: The name of the CustomImage. Must be unique to your account.

image_version_number#

Type:: The version number of the CustomImage.

app_image_config_name#

Type:: The name of the AppImageConfig.

app_image_config_name: str | PipelineVariable | object#

image_name: str | PipelineVariable | object#

image_version_number: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.CustomPosixUserConfig(*, uid: int, gid: int)[source]#

Bases: Base

Details about the POSIX identity that is used for file system operations.

uid#

Type:: The POSIX user ID.

gid#

Type:: The POSIX group ID.

gid: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

uid: int#

Bases: Base

A customized metric.

metric_name#

Type:: The name of the customized metric.

namespace#

Type:: The namespace of the customized metric.

statistic#

Type:: The statistic of the customized metric.

metric_name: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

namespace: str | PipelineVariable | None#

statistic: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.DataCaptureConfig(*, initial_sampling_percentage: int, destination_s3_uri: str | PipelineVariable, capture_options: List[CaptureOption], enable_capture: bool | None = Unassigned(), kms_key_id: str | PipelineVariable | None = Unassigned(), capture_content_type_header: CaptureContentTypeHeader | None = Unassigned())[source]#

Bases: Base

Configuration to control how SageMaker AI captures inference data.

enable_capture#

Type:: Whether data capture should be enabled or disabled (defaults to enabled).

initial_sampling_percentage#

Type:: The percentage of requests SageMaker AI will capture. A lower value is recommended for Endpoints with high traffic.

destination_s3_uri#

Type:: The Amazon S3 location used to capture the data.

kms_key_id#

Type:: The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker AI uses to encrypt the captured data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats: Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab Alias name: alias/ExampleAlias Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

capture_options#

Type:: Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both

capture_content_type_header#

Type:: Configuration specifying how to treat different headers. If no headers are specified SageMaker AI will by default base64 encode when capturing the data.

capture_content_type_header: CaptureContentTypeHeader | None#

capture_options: List[CaptureOption]#

destination_s3_uri: str | PipelineVariable#

enable_capture: bool | None#

initial_sampling_percentage: int#

kms_key_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.DataCaptureConfigSummary(*, enable_capture: bool, capture_status: str | PipelineVariable, current_sampling_percentage: int, destination_s3_uri: str | PipelineVariable, kms_key_id: str | PipelineVariable)[source]#

Bases: Base

The currently active data capture configuration used by your Endpoint.

enable_capture#

Type:: Whether data capture is enabled or disabled.

capture_status#

Type:: Whether data capture is currently functional.

current_sampling_percentage#

Type:: The percentage of requests being captured by your Endpoint.

destination_s3_uri#

Type:: The Amazon S3 location being used to capture the data.

kms_key_id#

Type:: The KMS key being used to encrypt the data in Amazon S3.

capture_status: str | PipelineVariable#

current_sampling_percentage: int#

destination_s3_uri: str | PipelineVariable#

enable_capture: bool#

kms_key_id: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.DataCatalogConfig(*, table_name: str | PipelineVariable, catalog: str | PipelineVariable, database: str | PipelineVariable)[source]#

Bases: Base

The meta data of the Glue table which serves as data catalog for the OfflineStore.

table_name#

Type:: The name of the Glue table.

catalog#

Type:: The name of the Glue table catalog.

database#

Type:: The name of the Glue table database.

catalog: str | PipelineVariable#

database: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

table_name: str | PipelineVariable#

Bases: Base

The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

input_filter#

Type:: A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $. Examples: “$”, “$[1:]”, “$.features”

output_filter#

Type:: A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $. If you specify indexes that aren’t within the dimension size of the joined dataset, you get an error. Examples: “$”, “$[0,5:]”, “$[‘id’,’SageMakerOutput’]”

join_source#

Type:: Specifies the source of the data to join with the transformed data. The valid values are None and Input. The default value is None, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input. You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file. For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput. For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file. For information on how joining in applied, see Workflow for Associating Inferences with Input Records.

input_filter: str | PipelineVariable | None#

join_source: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

output_filter: str | PipelineVariable | None#

Bases: Base

Information about the container that a data quality monitoring job runs.

image_uri#

Type:: The container image that the data quality monitoring job runs.

container_entrypoint#

Type:: The entrypoint for a container used to run a monitoring job.

container_arguments#

Type:: The arguments to send to the container that the monitoring job runs.

record_preprocessor_source_uri#

Type:: An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

post_analytics_processor_source_uri#

Type:: An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

environment#

Type:: Sets the environment variables in the container that the monitoring job runs.

container_arguments: List[str | PipelineVariable] | None#

container_entrypoint: List[str | PipelineVariable] | None#

environment: Dict[str | PipelineVariable, str | PipelineVariable] | None#

image_uri: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

post_analytics_processor_source_uri: str | PipelineVariable | None#

record_preprocessor_source_uri: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.DataQualityBaselineConfig(*, baselining_job_name: str | PipelineVariable | None = Unassigned(), constraints_resource: MonitoringConstraintsResource | None = Unassigned(), statistics_resource: MonitoringStatisticsResource | None = Unassigned())[source]#

Bases: Base

Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

baselining_job_name#

Type:: The name of the job that performs baselining for the data quality monitoring job.

constraints_resource#

Type:: sagemaker.core.shapes.shapes.MonitoringConstraintsResource | None

statistics_resource#

Type:: sagemaker.core.shapes.shapes.MonitoringStatisticsResource | None

baselining_job_name: str | PipelineVariable | None#

constraints_resource: MonitoringConstraintsResource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

statistics_resource: MonitoringStatisticsResource | None#

class sagemaker.core.shapes.shapes.DataQualityJobInput(*, endpoint_input: EndpointInput | None = Unassigned(), batch_transform_input: BatchTransformInput | None = Unassigned())[source]#

Bases: Base

The input for the data quality monitoring job. Currently endpoints are supported for input.

endpoint_input#

Type:: sagemaker.core.shapes.shapes.EndpointInput | None

batch_transform_input#

Type:: Input object for the batch transform job.

batch_transform_input: BatchTransformInput | None#

endpoint_input: EndpointInput | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.DataSource(*, s3_data_source: S3DataSource | None = Unassigned(), file_system_data_source: FileSystemDataSource | None = Unassigned(), dataset_source: DatasetSource | None = Unassigned())[source]#

Bases: Base

Describes the location of the channel data.

s3_data_source#

Type:: The S3 location of the data source that is associated with a channel.

file_system_data_source#

Type:: The file system that is associated with a channel.

dataset_source#

Type:: The dataset resource that’s associated with a channel.

dataset_source: DatasetSource | None#

file_system_data_source: FileSystemDataSource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_data_source: S3DataSource | None#

class sagemaker.core.shapes.shapes.DatasetDefinition(*, athena_dataset_definition: AthenaDatasetDefinition | None = Unassigned(), redshift_dataset_definition: RedshiftDatasetDefinition | None = Unassigned(), local_path: str | PipelineVariable | None = Unassigned(), data_distribution_type: str | PipelineVariable | None = Unassigned(), input_mode: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Configuration for Dataset Definition inputs. The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.

athena_dataset_definition#

Type:: sagemaker.core.shapes.shapes.AthenaDatasetDefinition | None

redshift_dataset_definition#

Type:: sagemaker.core.shapes.shapes.RedshiftDatasetDefinition | None

local_path#

Type:: The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).

data_distribution_type#

Type:: Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).

input_mode#

Type:: Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

athena_dataset_definition: AthenaDatasetDefinition | None#

data_distribution_type: str | PipelineVariable | None#

input_mode: str | PipelineVariable | None#

local_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

redshift_dataset_definition: RedshiftDatasetDefinition | None#

class sagemaker.core.shapes.shapes.DatasetSource(*, dataset_arn: str | PipelineVariable)[source]#

Bases: Base

Specifies a dataset source for a channel.

dataset_arn#

Type:: The Amazon Resource Name (ARN) of the dataset resource.

dataset_arn: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

local_path#

Type:: Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/.

s3_output_path#

Type:: Path to Amazon S3 storage location for metrics and tensors.

hook_parameters#

Type:: Configuration information for the Amazon SageMaker Debugger hook parameters.

collection_configurations#

Type:: Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

collection_configurations: List[CollectionConfiguration] | None#

hook_parameters: Dict[str | PipelineVariable, str | PipelineVariable] | None#

local_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_output_path: str | PipelineVariable#

Bases: Base

Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

rule_configuration_name#

Type:: The name of the rule configuration. It must be unique relative to other rule configuration names.

local_path#

Type:: Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.

s3_output_path#

Type:: Path to Amazon S3 storage location for rules.

rule_evaluator_image#

Type:: The Amazon Elastic Container (ECR) Image for the managed rule evaluation.

instance_type#

Type:: The instance type to deploy a custom rule for debugging a training job.

volume_size_in_gb#

Type:: The size, in GB, of the ML storage volume attached to the processing instance.

rule_parameters#

Type:: Runtime configuration for rule container.

instance_type: str | PipelineVariable | None#

local_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rule_configuration_name: str | PipelineVariable#

rule_evaluator_image: str | PipelineVariable#

rule_parameters: Dict[str | PipelineVariable, str | PipelineVariable] | None#

s3_output_path: str | PipelineVariable | None#

volume_size_in_gb: int | None#

Bases: Base

Information about the status of the rule evaluation.

rule_configuration_name#

Type:: The name of the rule configuration.

rule_evaluation_job_arn#

Type:: The Amazon Resource Name (ARN) of the rule evaluation job.

rule_evaluation_status#

Type:: Status of the rule evaluation.

status_details#

Type:: Details from the rule evaluation.

last_modified_time#

Type:: Timestamp when the rule evaluation status was last modified.

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rule_configuration_name: str | PipelineVariable | None#

rule_evaluation_job_arn: str | PipelineVariable | None#

rule_evaluation_status: str | PipelineVariable | None#

status_details: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.DefaultEbsStorageSettings(*, default_ebs_volume_size_in_gb: int, maximum_ebs_volume_size_in_gb: int)[source]#

Bases: Base

A collection of default EBS storage settings that apply to spaces created within a domain or user profile.

default_ebs_volume_size_in_gb#

Type:: The default size of the EBS storage volume for a space.

maximum_ebs_volume_size_in_gb#

Type:: The maximum size of the EBS storage volume for a space.

default_ebs_volume_size_in_gb: int#

maximum_ebs_volume_size_in_gb: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.DefaultSpaceSettings(*, execution_role: str | PipelineVariable | None = Unassigned(), security_groups: List[str | PipelineVariable] | None = Unassigned(), jupyter_server_app_settings: JupyterServerAppSettings | None = Unassigned(), kernel_gateway_app_settings: KernelGatewayAppSettings | None = Unassigned(), jupyter_lab_app_settings: JupyterLabAppSettings | None = Unassigned(), space_storage_settings: DefaultSpaceStorageSettings | None = Unassigned(), custom_posix_user_config: CustomPosixUserConfig | None = Unassigned(), custom_file_system_configs: List[CustomFileSystemConfig] | None = Unassigned())[source]#

Bases: Base

The default settings for shared spaces that users create in the domain. SageMaker applies these settings only to shared spaces. It doesn’t apply them to private spaces.

execution_role#

Type:: The ARN of the execution role for the space.

security_groups#

Type:: The security group IDs for the Amazon VPC that the space uses for communication.

jupyter_server_app_settings#

Type:: sagemaker.core.shapes.shapes.JupyterServerAppSettings | None

kernel_gateway_app_settings#

Type:: sagemaker.core.shapes.shapes.KernelGatewayAppSettings | None

jupyter_lab_app_settings#

Type:: sagemaker.core.shapes.shapes.JupyterLabAppSettings | None

space_storage_settings#

Type:: sagemaker.core.shapes.shapes.DefaultSpaceStorageSettings | None

custom_posix_user_config#

Type:: sagemaker.core.shapes.shapes.CustomPosixUserConfig | None

custom_file_system_configs#

Type:: The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

custom_file_system_configs: List[CustomFileSystemConfig] | None#

custom_posix_user_config: CustomPosixUserConfig | None#

execution_role: str | PipelineVariable | None#

jupyter_lab_app_settings: JupyterLabAppSettings | None#

jupyter_server_app_settings: JupyterServerAppSettings | None#

kernel_gateway_app_settings: KernelGatewayAppSettings | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

security_groups: List[str | PipelineVariable] | None#

space_storage_settings: DefaultSpaceStorageSettings | None#

class sagemaker.core.shapes.shapes.DefaultSpaceStorageSettings(*, default_ebs_storage_settings: DefaultEbsStorageSettings | None = Unassigned())[source]#

Bases: Base

The default storage settings for a space.

default_ebs_storage_settings#

Type:: The default EBS storage settings for a space.

default_ebs_storage_settings: DefaultEbsStorageSettings | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant. If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

specified_image#

Type:: The image path you specified when you created the model.

resolved_image#

Type:: The specific digest path of the image hosted in this ProductionVariant.

resolution_time#

Type:: The date and time when the image path for the model resolved to the ResolvedImage

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

resolution_time: datetime | None#

resolved_image: str | PipelineVariable | None#

specified_image: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.DeploymentConfig(*, blue_green_update_policy: BlueGreenUpdatePolicy | None = Unassigned(), rolling_update_policy: RollingUpdatePolicy | None = Unassigned(), auto_rollback_configuration: AutoRollbackConfig | None = Unassigned())[source]#

Bases: Base

The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

blue_green_update_policy#

Type:: Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.

rolling_update_policy#

Type:: Specifies a rolling deployment strategy for updating a SageMaker endpoint.

auto_rollback_configuration#

Type:: Automatic rollback configuration for handling endpoint deployment failures and recovery.

auto_rollback_configuration: AutoRollbackConfig | None#

blue_green_update_policy: BlueGreenUpdatePolicy | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rolling_update_policy: RollingUpdatePolicy | None#

class sagemaker.core.shapes.shapes.DeploymentConfiguration(*, rolling_update_policy: RollingDeploymentPolicy | None = Unassigned(), wait_interval_in_seconds: int | None = Unassigned(), auto_rollback_configuration: List[AlarmDetails] | None = Unassigned())[source]#

Bases: Base

The configuration to use when updating the AMI versions.

rolling_update_policy#

Type:: The policy that SageMaker uses when updating the AMI versions of the cluster.

wait_interval_in_seconds#

Type:: The duration in seconds that SageMaker waits before updating more instances in the cluster.

auto_rollback_configuration#

Type:: An array that contains the alarms that SageMaker monitors to know whether to roll back the AMI update.

auto_rollback_configuration: List[AlarmDetails] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rolling_update_policy: RollingDeploymentPolicy | None#

wait_interval_in_seconds: int | None#

class sagemaker.core.shapes.shapes.DeploymentRecommendation(*, recommendation_status: str | PipelineVariable, real_time_inference_recommendations: List[RealTimeInferenceRecommendation] | None = Unassigned())[source]#

Bases: Base

A set of recommended deployment configurations for the model. To get more advanced recommendations, see CreateInferenceRecommendationsJob to create an inference recommendation job.

recommendation_status#

Type:: Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS, retry your API call after a few seconds to get a COMPLETED deployment recommendation.

real_time_inference_recommendations#

Type:: A list of RealTimeInferenceRecommendation items.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

real_time_inference_recommendations: List[RealTimeInferenceRecommendation] | None#

recommendation_status: str | PipelineVariable#

class sagemaker.core.shapes.shapes.DeploymentStage(*, stage_name: str | PipelineVariable, device_selection_config: DeviceSelectionConfig, deployment_config: EdgeDeploymentConfig | None = Unassigned())[source]#

Bases: Base

Contains information about a stage in an edge deployment plan.

stage_name#

Type:: The name of the stage.

device_selection_config#

Type:: Configuration of the devices in the stage.

deployment_config#

Type:: Configuration of the deployment details.

deployment_config: EdgeDeploymentConfig | None#

device_selection_config: DeviceSelectionConfig#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

stage_name: str | PipelineVariable#

class sagemaker.core.shapes.shapes.DeploymentStageStatusSummary(*, stage_name: str | PipelineVariable, device_selection_config: DeviceSelectionConfig, deployment_config: EdgeDeploymentConfig, deployment_status: EdgeDeploymentStatus)[source]#

Bases: Base

Contains information summarizing the deployment stage results.

stage_name#

Type:: The name of the stage.

device_selection_config#

Type:: Configuration of the devices in the stage.

deployment_config#

Type:: Configuration of the deployment details.

deployment_status#

Type:: General status of the current state.

deployment_config: EdgeDeploymentConfig#

deployment_status: EdgeDeploymentStatus#

device_selection_config: DeviceSelectionConfig#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

stage_name: str | PipelineVariable#

class sagemaker.core.shapes.shapes.DerivedInformation(*, derived_data_input_config: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Information that SageMaker Neo automatically derived about the model.

derived_data_input_config#

Type:: The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don’t need to specify the data input configuration when you create a compilation job.

derived_data_input_config: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.DescribePipelineDefinitionForExecutionResponse(*, pipeline_definition: str | PipelineVariable | None = Unassigned(), creation_time: datetime | None = Unassigned())[source]#

Bases: Base

pipeline_definition#

Type:: The JSON pipeline definition.

creation_time#

Type:: The time when the pipeline was created.

creation_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

pipeline_definition: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.DesiredWeightAndCapacity(*, variant_name: str | PipelineVariable, desired_weight: float | None = Unassigned(), desired_instance_count: int | None = Unassigned(), serverless_update_config: ProductionVariantServerlessUpdateConfig | None = Unassigned())[source]#

Bases: Base

Specifies weight and capacity values for a production variant.

variant_name#

Type:: The name of the variant to update.

desired_weight#

Type:: The variant’s weight.

desired_instance_count#

Type:: The variant’s capacity.

serverless_update_config#

Type:: Specifies the serverless update concurrency configuration for an endpoint variant.

desired_instance_count: int | None#

desired_weight: float | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

serverless_update_config: ProductionVariantServerlessUpdateConfig | None#

variant_name: str | PipelineVariable#

Bases: Base

Information of a particular device.

device_name#

Type:: The name of the device.

description#

Type:: Description of the device.

iot_thing_name#

Type:: Amazon Web Services Internet of Things (IoT) object name.

description: str | PipelineVariable | None#

device_name: str | PipelineVariable | object#

iot_thing_name: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Contains information summarizing device details and deployment status.

edge_deployment_plan_arn#

Type:: The ARN of the edge deployment plan.

edge_deployment_plan_name#

Type:: The name of the edge deployment plan.

stage_name#

Type:: The name of the stage in the edge deployment plan.

deployed_stage_name#

Type:: The name of the deployed stage.

device_fleet_name#

Type:: The name of the fleet to which the device belongs to.

device_name#

Type:: The name of the device.

device_arn#

Type:: The ARN of the device.

device_deployment_status#

Type:: The deployment status of the device.

device_deployment_status_message#

Type:: The detailed error message for the deployoment status result.

description#

Type:: The description of the device.

deployment_start_time#

Type:: The time when the deployment on the device started.

deployed_stage_name: str | PipelineVariable | None#

deployment_start_time: datetime | None#

description: str | PipelineVariable | None#

device_arn: str | PipelineVariable#

device_deployment_status: str | PipelineVariable | None#

device_deployment_status_message: str | PipelineVariable | None#

device_fleet_name: str | PipelineVariable | object | None#

device_name: str | PipelineVariable | object#

edge_deployment_plan_arn: str | PipelineVariable#

edge_deployment_plan_name: str | PipelineVariable | object#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

stage_name: str | PipelineVariable#

class sagemaker.core.shapes.shapes.DeviceFleetSummary(*, device_fleet_arn: str | PipelineVariable, device_fleet_name: str | PipelineVariable | object, creation_time: datetime | None = Unassigned(), last_modified_time: datetime | None = Unassigned())[source]#

Bases: Base

Summary of the device fleet.

device_fleet_arn#

Type:: Amazon Resource Name (ARN) of the device fleet.

device_fleet_name#

Type:: Name of the device fleet.

creation_time#

Type:: Timestamp of when the device fleet was created.

last_modified_time#

Type:: Timestamp of when the device fleet was last updated.

creation_time: datetime | None#

device_fleet_arn: str | PipelineVariable#

device_fleet_name: str | PipelineVariable | object#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Contains information about the configurations of selected devices.

device_subset_type#

Type:: Type of device subsets to deploy to the current stage.

percentage#

Type:: Percentage of devices in the fleet to deploy to the current stage.

device_names#

Type:: List of devices chosen to deploy.

device_name_contains#

Type:: A filter to select devices with names containing this name.

device_name_contains: str | PipelineVariable | None#

device_names: List[str | PipelineVariable] | None#

device_subset_type: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

percentage: int | None#

class sagemaker.core.shapes.shapes.DeviceStats(*, connected_device_count: int, registered_device_count: int)[source]#

Bases: Base

Status of devices.

connected_device_count#

Type:: The number of devices connected with a heartbeat.

registered_device_count#

Type:: The number of registered devices.

connected_device_count: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

registered_device_count: int#

Bases: Base

Summary of the device.

device_name#

Type:: The unique identifier of the device.

device_arn#

Type:: Amazon Resource Name (ARN) of the device.

description#

Type:: A description of the device.

device_fleet_name#

Type:: The name of the fleet the device belongs to.

iot_thing_name#

Type:: The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..

registration_time#

Type:: The timestamp of the last registration or de-reregistration.

latest_heartbeat#

Type:: The last heartbeat received from the device.

models#

Type:: Models on the device.

agent_version#

Type:: Edge Manager agent version.

agent_version: str | PipelineVariable | None#

description: str | PipelineVariable | None#

device_arn: str | PipelineVariable#

device_fleet_name: str | PipelineVariable | object | None#

device_name: str | PipelineVariable | object#

iot_thing_name: str | PipelineVariable | None#

latest_heartbeat: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

models: List[EdgeModelSummary] | None#

registration_time: datetime | None#

class sagemaker.core.shapes.shapes.DirectDeploySettings(*, status: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The model deployment settings for the SageMaker Canvas application. In order to enable model deployment for Canvas, the SageMaker Domain’s or user profile’s Amazon Web Services IAM execution role must have the AmazonSageMakerCanvasDirectDeployAccess policy attached. You can also turn on model deployment permissions through the SageMaker Domain’s or user profile’s settings in the SageMaker console.

status#

Type:: Describes whether model deployment permissions are enabled or disabled in the Canvas application.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

status: str | PipelineVariable | None#

Bases: Base

A collection of settings that configure the domain’s Docker interaction.

enable_docker_access#

Type:: Indicates whether the domain can access Docker.

vpc_only_trusted_accounts#

Type:: The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.

rootless_docker#

Type:: Indicates whether to use rootless Docker.

enable_docker_access: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rootless_docker: str | PipelineVariable | None#

vpc_only_trusted_accounts: List[str | PipelineVariable] | None#

Bases: Base

The domain’s details.

domain_arn#

Type:: The domain’s Amazon Resource Name (ARN).

domain_id#

Type:: The domain ID.

domain_name#

Type:: The domain name.

status#

Type:: The status.

creation_time#

Type:: The creation time.

last_modified_time#

Type:: The last modified time.

url#

Type:: The domain’s URL.

creation_time: datetime | None#

domain_arn: str | PipelineVariable | None#

domain_id: str | PipelineVariable | None#

domain_name: str | PipelineVariable | object | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

status: str | PipelineVariable | None#

url: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.DomainSettings(*, security_group_ids: List[str | PipelineVariable] | None = Unassigned(), r_studio_server_pro_domain_settings: RStudioServerProDomainSettings | None = Unassigned(), execution_role_identity_config: str | PipelineVariable | None = Unassigned(), trusted_identity_propagation_settings: TrustedIdentityPropagationSettings | None = Unassigned(), docker_settings: DockerSettings | None = Unassigned(), amazon_q_settings: AmazonQSettings | None = Unassigned(), unified_studio_settings: UnifiedStudioSettings | None = Unassigned(), ip_address_type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A collection of settings that apply to the SageMaker Domain. These settings are specified through the CreateDomain API call.

security_group_ids#

Type:: The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.

r_studio_server_pro_domain_settings#

Type:: A collection of settings that configure the RStudioServerPro Domain-level app.

execution_role_identity_config#

Type:: The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key.

trusted_identity_propagation_settings#

Type:: The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.

docker_settings#

Type:: A collection of settings that configure the domain’s Docker interaction.

amazon_q_settings#

Type:: A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO.

unified_studio_settings#

Type:: The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.

ip_address_type#

Type:: The IP address type for the domain. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks. If not specified, defaults to ipv4.

amazon_q_settings: AmazonQSettings | None#

docker_settings: DockerSettings | None#

execution_role_identity_config: str | PipelineVariable | None#

ip_address_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

r_studio_server_pro_domain_settings: RStudioServerProDomainSettings | None#

security_group_ids: List[str | PipelineVariable] | None#

trusted_identity_propagation_settings: TrustedIdentityPropagationSettings | None#

unified_studio_settings: UnifiedStudioSettings | None#

class sagemaker.core.shapes.shapes.DomainSettingsForUpdate(*, r_studio_server_pro_domain_settings_for_update: RStudioServerProDomainSettingsForUpdate | None = Unassigned(), execution_role_identity_config: str | PipelineVariable | None = Unassigned(), security_group_ids: List[str | PipelineVariable] | None = Unassigned(), trusted_identity_propagation_settings: TrustedIdentityPropagationSettings | None = Unassigned(), docker_settings: DockerSettings | None = Unassigned(), amazon_q_settings: AmazonQSettings | None = Unassigned(), unified_studio_settings: UnifiedStudioSettings | None = Unassigned(), ip_address_type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A collection of Domain configuration settings to update.

r_studio_server_pro_domain_settings_for_update#

Type:: A collection of RStudioServerPro Domain-level app settings to update. A single RStudioServerPro application is created for a domain.

execution_role_identity_config#

Type:: The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key. This configuration can only be modified if there are no apps in the InService or Pending state.

security_group_ids#

Type:: The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.

trusted_identity_propagation_settings#

Type:: The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.

docker_settings#

Type:: A collection of settings that configure the domain’s Docker interaction.

amazon_q_settings#

Type:: A collection of settings that configure the Amazon Q experience within the domain.

unified_studio_settings#

Type:: The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.

ip_address_type#

Type:: The IP address type for the domain. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks.

amazon_q_settings: AmazonQSettings | None#

docker_settings: DockerSettings | None#

execution_role_identity_config: str | PipelineVariable | None#

ip_address_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

r_studio_server_pro_domain_settings_for_update: RStudioServerProDomainSettingsForUpdate | None#

security_group_ids: List[str | PipelineVariable] | None#

trusted_identity_propagation_settings: TrustedIdentityPropagationSettings | None#

unified_studio_settings: UnifiedStudioSettings | None#

class sagemaker.core.shapes.shapes.DriftCheckBaselines(*, bias: DriftCheckBias | None = Unassigned(), explainability: DriftCheckExplainability | None = Unassigned(), model_quality: DriftCheckModelQuality | None = Unassigned(), model_data_quality: DriftCheckModelDataQuality | None = Unassigned())[source]#

Bases: Base

Represents the drift check baselines that can be used when the model monitor is set using the model package.

bias#

Type:: Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

explainability#

Type:: Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

model_quality#

Type:: Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

model_data_quality#

Type:: Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.

bias: DriftCheckBias | None#

explainability: DriftCheckExplainability | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_data_quality: DriftCheckModelDataQuality | None#

model_quality: DriftCheckModelQuality | None#

class sagemaker.core.shapes.shapes.DriftCheckBias(*, config_file: FileSource | None = Unassigned(), pre_training_constraints: MetricsSource | None = Unassigned(), post_training_constraints: MetricsSource | None = Unassigned())[source]#

Bases: Base

Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

config_file#

Type:: The bias config file for a model.

pre_training_constraints#

Type:: The pre-training constraints.

post_training_constraints#

Type:: The post-training constraints.

config_file: FileSource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

post_training_constraints: MetricsSource | None#

pre_training_constraints: MetricsSource | None#

class sagemaker.core.shapes.shapes.DriftCheckExplainability(*, constraints: MetricsSource | None = Unassigned(), config_file: FileSource | None = Unassigned())[source]#

Bases: Base

Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

constraints#

Type:: The drift check explainability constraints.

config_file#

Type:: The explainability config file for the model.

config_file: FileSource | None#

constraints: MetricsSource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.DriftCheckModelDataQuality(*, statistics: MetricsSource | None = Unassigned(), constraints: MetricsSource | None = Unassigned())[source]#

Bases: Base

Represents the drift check data quality baselines that can be used when the model monitor is set using the model package.

statistics#

Type:: The drift check model data quality statistics.

constraints#

Type:: The drift check model data quality constraints.

constraints: MetricsSource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

statistics: MetricsSource | None#

class sagemaker.core.shapes.shapes.DriftCheckModelQuality(*, statistics: MetricsSource | None = Unassigned(), constraints: MetricsSource | None = Unassigned())[source]#

Bases: Base

Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

statistics#

Type:: The drift check model quality statistics.

constraints#

Type:: The drift check model quality constraints.

constraints: MetricsSource | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

statistics: MetricsSource | None#

class sagemaker.core.shapes.shapes.DynamicScalingConfiguration(*, min_capacity: int | None = Unassigned(), max_capacity: int | None = Unassigned(), scale_in_cooldown: int | None = Unassigned(), scale_out_cooldown: int | None = Unassigned(), scaling_policies: List[ScalingPolicy] | None = Unassigned())[source]#

Bases: Base

An object with the recommended values for you to specify when creating an autoscaling policy.

min_capacity#

Type:: The recommended minimum capacity to specify for your autoscaling policy.

max_capacity#

Type:: The recommended maximum capacity to specify for your autoscaling policy.

scale_in_cooldown#

Type:: The recommended scale in cooldown time for your autoscaling policy.

scale_out_cooldown#

Type:: The recommended scale out cooldown time for your autoscaling policy.

scaling_policies#

Type:: An object of the scaling policies for each metric.

max_capacity: int | None#

min_capacity: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

scale_in_cooldown: int | None#

scale_out_cooldown: int | None#

scaling_policies: List[ScalingPolicy] | None#

class sagemaker.core.shapes.shapes.EFSFileSystem(*, file_system_id: str | PipelineVariable)[source]#

Bases: Base

A file system, created by you in Amazon EFS, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

file_system_id#

Type:: The ID of your Amazon EFS file system.

file_system_id: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EFSFileSystemConfig(*, file_system_id: str | PipelineVariable, file_system_path: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The settings for assigning a custom Amazon EFS file system to a user profile or space for an Amazon SageMaker AI Domain.

file_system_id#

Type:: The ID of your Amazon EFS file system.

file_system_path#

Type:: The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.

file_system_id: str | PipelineVariable#

file_system_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

The configurations and outcomes of an Amazon EMR step execution.

cluster_id#

Type:: The identifier of the EMR cluster.

step_id#

Type:: The identifier of the EMR cluster step.

step_name#

Type:: The name of the EMR cluster step.

log_file_path#

Type:: The path to the log file where the cluster step’s failure root cause is recorded.

cluster_id: str | PipelineVariable | None#

log_file_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

step_id: str | PipelineVariable | None#

step_name: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.EbsStorageSettings(*, ebs_volume_size_in_gb: int)[source]#

Bases: Base

A collection of EBS storage settings that apply to both private and shared spaces.

ebs_volume_size_in_gb#

Type:: The size of an EBS storage volume for a space.

ebs_volume_size_in_gb: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.Ec2CapacityReservation(*, ec2_capacity_reservation_id: str | PipelineVariable | None = Unassigned(), total_instance_count: int | None = Unassigned(), available_instance_count: int | None = Unassigned(), used_by_current_endpoint: int | None = Unassigned())[source]#

Bases: Base

The EC2 capacity reservations that are shared to an ML capacity reservation.

ec2_capacity_reservation_id#

Type:: The unique identifier for an EC2 capacity reservation that’s part of the ML capacity reservation.

total_instance_count#

Type:: The number of instances that you allocated to the EC2 capacity reservation.

available_instance_count#

Type:: The number of instances that are currently available in the EC2 capacity reservation.

used_by_current_endpoint#

Type:: The number of instances from the EC2 capacity reservation that are being used by the endpoint.

available_instance_count: int | None#

ec2_capacity_reservation_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

total_instance_count: int | None#

used_by_current_endpoint: int | None#

Bases: Base

A directed edge connecting two lineage entities.

source_arn#

Type:: The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.

destination_arn#

Type:: The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.

association_type#

Type:: The type of the Association(Edge) between the source and destination. For example ContributedTo, Produced, or DerivedFrom.

association_type: str | PipelineVariable | None#

destination_arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_arn: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.EdgeDeploymentConfig(*, failure_handling_policy: str | PipelineVariable)[source]#

Bases: Base

Contains information about the configuration of a deployment.

failure_handling_policy#

Type:: Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.

failure_handling_policy: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EdgeDeploymentModelConfig(*, model_handle: str | PipelineVariable, edge_packaging_job_name: str | PipelineVariable | object)[source]#

Bases: Base

Contains information about the configuration of a model in a deployment.

model_handle#

Type:: The name the device application uses to reference this model.

edge_packaging_job_name#

Type:: The edge packaging job associated with this deployment.

edge_packaging_job_name: str | PipelineVariable | object#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_handle: str | PipelineVariable#

class sagemaker.core.shapes.shapes.EdgeDeploymentPlanSummary(*, edge_deployment_plan_arn: str | PipelineVariable, edge_deployment_plan_name: str | PipelineVariable | object, device_fleet_name: str | PipelineVariable | object, edge_deployment_success: int, edge_deployment_pending: int, edge_deployment_failed: int, creation_time: datetime | None = Unassigned(), last_modified_time: datetime | None = Unassigned())[source]#

Bases: Base

Contains information summarizing an edge deployment plan.

edge_deployment_plan_arn#

Type:: The ARN of the edge deployment plan.

edge_deployment_plan_name#

Type:: The name of the edge deployment plan.

device_fleet_name#

Type:: The name of the device fleet used for the deployment.

edge_deployment_success#

Type:: The number of edge devices with the successful deployment.

edge_deployment_pending#

Type:: The number of edge devices yet to pick up the deployment, or in progress.

edge_deployment_failed#

Type:: The number of edge devices that failed the deployment.

creation_time#

Type:: The time when the edge deployment plan was created.

last_modified_time#

Type:: The time when the edge deployment plan was last updated.

creation_time: datetime | None#

device_fleet_name: str | PipelineVariable | object#

edge_deployment_failed: int#

edge_deployment_pending: int#

edge_deployment_plan_arn: str | PipelineVariable#

edge_deployment_plan_name: str | PipelineVariable | object#

edge_deployment_success: int#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EdgeDeploymentStatus(*, stage_status: str | PipelineVariable, edge_deployment_success_in_stage: int, edge_deployment_pending_in_stage: int, edge_deployment_failed_in_stage: int, edge_deployment_status_message: str | PipelineVariable | None = Unassigned(), edge_deployment_stage_start_time: datetime | None = Unassigned())[source]#

Bases: Base

Contains information summarizing the deployment stage results.

stage_status#

Type:: The general status of the current stage.

edge_deployment_success_in_stage#

Type:: The number of edge devices with the successful deployment in the current stage.

edge_deployment_pending_in_stage#

Type:: The number of edge devices yet to pick up the deployment in current stage, or in progress.

edge_deployment_failed_in_stage#

Type:: The number of edge devices that failed the deployment in current stage.

edge_deployment_status_message#

Type:: A detailed message about deployment status in current stage.

edge_deployment_stage_start_time#

Type:: The time when the deployment API started.

edge_deployment_failed_in_stage: int#

edge_deployment_pending_in_stage: int#

edge_deployment_stage_start_time: datetime | None#

edge_deployment_status_message: str | PipelineVariable | None#

edge_deployment_success_in_stage: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

stage_status: str | PipelineVariable#

Bases: Base

The model on the edge device.

model_name#

Type:: The name of the model.

model_version#

Type:: The model version.

latest_sample_time#

Type:: The timestamp of the last data sample taken.

latest_inference#

Type:: The timestamp of the last inference that was made.

latest_inference: datetime | None#

latest_sample_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_name: str | PipelineVariable | object#

model_version: str | PipelineVariable#

class sagemaker.core.shapes.shapes.EdgeModelStat(*, model_name: str | PipelineVariable | object, model_version: str | PipelineVariable, offline_device_count: int, connected_device_count: int, active_device_count: int, sampling_device_count: int)[source]#

Bases: Base

Status of edge devices with this model.

model_name#

Type:: The name of the model.

model_version#

Type:: The model version.

offline_device_count#

Type:: The number of devices that have this model version and do not have a heart beat.

connected_device_count#

Type:: The number of devices that have this model version and have a heart beat.

active_device_count#

Type:: The number of devices that have this model version, a heart beat, and are currently running.

sampling_device_count#

Type:: The number of devices with this model version and are producing sample data.

active_device_count: int#

connected_device_count: int#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_name: str | PipelineVariable | object#

model_version: str | PipelineVariable#

offline_device_count: int#

sampling_device_count: int#

class sagemaker.core.shapes.shapes.EdgeModelSummary(*, model_name: str | PipelineVariable | object, model_version: str | PipelineVariable)[source]#

Bases: Base

Summary of model on edge device.

model_name#

Type:: The name of the model.

model_version#

Type:: The version model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_name: str | PipelineVariable | object#

model_version: str | PipelineVariable#

Bases: Base

The output configuration.

s3_output_location#

Type:: The Amazon Simple Storage (S3) bucker URI.

kms_key_id#

Type:: The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don’t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account.

preset_deployment_type#

Type:: The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

preset_deployment_config#

Type:: The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are: ComponentName (optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of “SagemakerEdgeManager” and the name of your SageMaker Edge Manager packaging job. ComponentDescription (optional) - Description of the component. ComponentVersion (optional) - The version of the component. Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. PlatformOS (optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. PlatformArchitecture (optional) - The processor architecture for the platform. Supported architectures Windows include: Windows32_x86, Windows64_x64. Supported architectures for Linux include: Linux x86_64, Linux ARMV8.

kms_key_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

preset_deployment_config: str | PipelineVariable | None#

preset_deployment_type: str | PipelineVariable | None#

s3_output_location: str | PipelineVariable#

Bases: Base

Summary of edge packaging job.

edge_packaging_job_arn#

Type:: The Amazon Resource Name (ARN) of the edge packaging job.

edge_packaging_job_name#

Type:: The name of the edge packaging job.

edge_packaging_job_status#

Type:: The status of the edge packaging job.

compilation_job_name#

Type:: The name of the SageMaker Neo compilation job.

model_name#

Type:: The name of the model.

model_version#

Type:: The version of the model.

creation_time#

Type:: The timestamp of when the job was created.

last_modified_time#

Type:: The timestamp of when the edge packaging job was last updated.

compilation_job_name: str | PipelineVariable | object | None#

creation_time: datetime | None#

edge_packaging_job_arn: str | PipelineVariable#

edge_packaging_job_name: str | PipelineVariable | object#

edge_packaging_job_status: str | PipelineVariable#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_name: str | PipelineVariable | object | None#

model_version: str | PipelineVariable | None#

Bases: Base

The output of a SageMaker Edge Manager deployable resource.

type#

Type:: The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

artifact#

Type:: The Amazon Resource Name (ARN) of the generated deployable resource.

status#

Type:: The status of the deployable resource.

status_message#

Type:: Returns a message describing the status of the deployed resource.

artifact: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

status: str | PipelineVariable | None#

status_message: str | PipelineVariable | None#

type: str | PipelineVariable#

class sagemaker.core.shapes.shapes.EmrServerlessComputeConfig(*, execution_role_arn: str | PipelineVariable)[source]#

Bases: Base

This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment. Specifies the compute configuration for the EMR Serverless job.

execution_role_arn#

Type:: The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see “How to configure AutoML to initiate a remote job on EMR Serverless for large datasets” in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.

execution_role_arn: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EmrServerlessSettings(*, execution_role_arn: str | PipelineVariable | None = Unassigned(), status: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The settings for running Amazon EMR Serverless jobs in SageMaker Canvas.

execution_role_arn#

Type:: The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.

status#

Type:: Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.

execution_role_arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

status: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.EmrSettings(*, assumable_role_arns: List[str | PipelineVariable] | None = Unassigned(), execution_role_arns: List[str | PipelineVariable] | None = Unassigned())[source]#

Bases: Base

The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.

assumable_role_arns#

Type:: An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.

execution_role_arns#

Type:: An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.

assumable_role_arns: List[str | PipelineVariable] | None#

execution_role_arns: List[str | PipelineVariable] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.Endpoint(*, endpoint_name: str | PipelineVariable | object, endpoint_arn: str | PipelineVariable, endpoint_config_name: str | PipelineVariable | object, endpoint_status: str | PipelineVariable, creation_time: datetime, last_modified_time: datetime, production_variants: List[ProductionVariantSummary] | None = Unassigned(), data_capture_config: DataCaptureConfigSummary | None = Unassigned(), failure_reason: str | PipelineVariable | None = Unassigned(), monitoring_schedules: List[MonitoringSchedule] | None = Unassigned(), tags: List[Tag] | None = Unassigned(), shadow_production_variants: List[ProductionVariantSummary] | None = Unassigned())[source]#

Bases: Base

A hosted endpoint for real-time inference.

endpoint_name#

Type:: The name of the endpoint.

endpoint_arn#

Type:: The Amazon Resource Name (ARN) of the endpoint.

endpoint_config_name#

Type:: The endpoint configuration associated with the endpoint.

production_variants#

Type:: A list of the production variants hosted on the endpoint. Each production variant is a model.

data_capture_config#

Type:: sagemaker.core.shapes.shapes.DataCaptureConfigSummary | None

endpoint_status#

Type:: The status of the endpoint.

failure_reason#

Type:: If the endpoint failed, the reason it failed.

creation_time#

Type:: The time that the endpoint was created.

last_modified_time#

Type:: The last time the endpoint was modified.

monitoring_schedules#

Type:: A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor.

tags#

Type:: A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

shadow_production_variants#

Type:: A list of the shadow variants hosted on the endpoint. Each shadow variant is a model in shadow mode with production traffic replicated from the production variant.

creation_time: datetime#

data_capture_config: DataCaptureConfigSummary | None#

endpoint_arn: str | PipelineVariable#

endpoint_config_name: str | PipelineVariable | object#

endpoint_name: str | PipelineVariable | object#

endpoint_status: str | PipelineVariable#

failure_reason: str | PipelineVariable | None#

last_modified_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

monitoring_schedules: List[MonitoringSchedule] | None#

production_variants: List[ProductionVariantSummary] | None#

shadow_production_variants: List[ProductionVariantSummary] | None#

tags: List[Tag] | None#

class sagemaker.core.shapes.shapes.EndpointConfigStepMetadata(*, arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Metadata for an endpoint configuration step.

arn#

Type:: The Amazon Resource Name (ARN) of the endpoint configuration used in the step.

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EndpointConfigSummary(*, endpoint_config_name: str | PipelineVariable | object, endpoint_config_arn: str | PipelineVariable, creation_time: datetime)[source]#

Bases: Base

Provides summary information for an endpoint configuration.

endpoint_config_name#

Type:: The name of the endpoint configuration.

endpoint_config_arn#

Type:: The Amazon Resource Name (ARN) of the endpoint configuration.

creation_time#

Type:: A timestamp that shows when the endpoint configuration was created.

creation_time: datetime#

endpoint_config_arn: str | PipelineVariable#

endpoint_config_name: str | PipelineVariable | object#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EndpointInfo(*, endpoint_name: str | PipelineVariable | object | None = Unassigned())[source]#

Bases: Base

Details about a customer endpoint that was compared in an Inference Recommender job.

endpoint_name#

Type:: The name of a customer’s endpoint.

endpoint_name: str | PipelineVariable | object | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Input object for the endpoint

endpoint_name#

Type:: An endpoint in customer’s account which has enabled DataCaptureConfig enabled.

local_path#

Type:: Path to the filesystem where the endpoint data is available to the container.

s3_input_mode#

Type:: Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

s3_data_distribution_type#

Type:: Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated

features_attribute#

Type:: The attributes of the input data that are the input features.

inference_attribute#

Type:: The attribute of the input data that represents the ground truth label.

probability_attribute#

Type:: In a classification problem, the attribute that represents the class probability.

probability_threshold_attribute#

Type:: The threshold for the class probability to be evaluated as a positive result.

start_time_offset#

Type:: If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

end_time_offset#

Type:: If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

exclude_features_attribute#

Type:: The attributes of the input data to exclude from the analysis.

end_time_offset: str | PipelineVariable | None#

endpoint_name: str | PipelineVariable | object#

exclude_features_attribute: str | PipelineVariable | None#

features_attribute: str | PipelineVariable | None#

inference_attribute: str | PipelineVariable | None#

local_path: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

probability_attribute: str | PipelineVariable | None#

probability_threshold_attribute: float | None#

s3_data_distribution_type: str | PipelineVariable | None#

s3_input_mode: str | PipelineVariable | None#

start_time_offset: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.EndpointInputConfiguration(*, instance_type: str | PipelineVariable | None = Unassigned(), serverless_config: ProductionVariantServerlessConfig | None = Unassigned(), inference_specification_name: str | PipelineVariable | None = Unassigned(), environment_parameter_ranges: EnvironmentParameterRanges | None = Unassigned())[source]#

Bases: Base

The endpoint configuration for the load test.

instance_type#

Type:: The instance types to use for the load test.

serverless_config#

Type:: sagemaker.core.shapes.shapes.ProductionVariantServerlessConfig | None

inference_specification_name#

Type:: The inference specification name in the model package version.

environment_parameter_ranges#

Type:: The parameter you want to benchmark against.

environment_parameter_ranges: EnvironmentParameterRanges | None#

inference_specification_name: str | PipelineVariable | None#

instance_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

serverless_config: ProductionVariantServerlessConfig | None#

Bases: Base

The metadata of the endpoint.

endpoint_name#

Type:: The name of the endpoint.

endpoint_config_name#

Type:: The name of the endpoint configuration.

endpoint_status#

Type:: The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary.

failure_reason#

Type:: If the status of the endpoint is Failed, or the status is InService but update operation fails, this provides the reason why it failed.

endpoint_config_name: str | PipelineVariable | object | None#

endpoint_name: str | PipelineVariable | object#

endpoint_status: str | PipelineVariable | None#

failure_reason: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EndpointOutputConfiguration(*, endpoint_name: str | PipelineVariable | object, variant_name: str | PipelineVariable, instance_type: str | PipelineVariable | None = Unassigned(), initial_instance_count: int | None = Unassigned(), serverless_config: ProductionVariantServerlessConfig | None = Unassigned())[source]#

Bases: Base

The endpoint configuration made by Inference Recommender during a recommendation job.

endpoint_name#

Type:: The name of the endpoint made during a recommendation job.

variant_name#

Type:: The name of the production variant (deployed model) made during a recommendation job.

instance_type#

Type:: The instance type recommended by Amazon SageMaker Inference Recommender.

initial_instance_count#

Type:: The number of instances recommended to launch initially.

serverless_config#

Type:: sagemaker.core.shapes.shapes.ProductionVariantServerlessConfig | None

endpoint_name: str | PipelineVariable | object#

initial_instance_count: int | None#

instance_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

serverless_config: ProductionVariantServerlessConfig | None#

variant_name: str | PipelineVariable#

class sagemaker.core.shapes.shapes.EndpointPerformance(*, metrics: InferenceMetrics, endpoint_info: EndpointInfo)[source]#

Bases: Base

The performance results from running an Inference Recommender job on an existing endpoint.

metrics#

Type:: The metrics for an existing endpoint.

endpoint_info#

Type:: sagemaker.core.shapes.shapes.EndpointInfo

endpoint_info: EndpointInfo#

metrics: InferenceMetrics#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EndpointStepMetadata(*, arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Metadata for an endpoint step.

arn#

Type:: The Amazon Resource Name (ARN) of the endpoint in the step.

arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EndpointSummary(*, endpoint_name: str | PipelineVariable | object, endpoint_arn: str | PipelineVariable, creation_time: datetime, last_modified_time: datetime, endpoint_status: str | PipelineVariable)[source]#

Bases: Base

Provides summary information for an endpoint.

endpoint_name#

Type:: The name of the endpoint.

endpoint_arn#

Type:: The Amazon Resource Name (ARN) of the endpoint.

creation_time#

Type:: A timestamp that shows when the endpoint was created.

last_modified_time#

Type:: A timestamp that shows when the endpoint was last modified.

endpoint_status#

Type:: The status of the endpoint. OutOfService: Endpoint is not available to take incoming requests. Creating: CreateEndpoint is executing. Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. InService: Endpoint is available to process incoming requests. Deleting: DeleteEndpoint is executing. Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint. To get a list of endpoints with a specified status, use the StatusEquals filter with a call to ListEndpoints.

creation_time: datetime#

endpoint_arn: str | PipelineVariable#

endpoint_name: str | PipelineVariable | object#

endpoint_status: str | PipelineVariable#

last_modified_time: datetime#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EnvironmentConfig(*, f_sx_lustre_config: FSxLustreConfig | None = Unassigned())[source]#

Bases: Base

The configuration for the restricted instance groups (RIG) environment.

f_sx_lustre_config#

Type:: Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster.

f_sx_lustre_config: FSxLustreConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EnvironmentConfigDetails(*, f_sx_lustre_config: FSxLustreConfig | None = Unassigned(), s3_output_path: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The configuration details for the restricted instance groups (RIG) environment.

f_sx_lustre_config#

Type:: Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster.

s3_output_path#

Type:: The Amazon S3 path where output data from the restricted instance group (RIG) environment will be stored.

f_sx_lustre_config: FSxLustreConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_output_path: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.EnvironmentParameter(*, key: str | PipelineVariable, value_type: str | PipelineVariable, value: str | PipelineVariable)[source]#

Bases: Base

A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.

key#

Type:: The environment key suggested by the Amazon SageMaker Inference Recommender.

value_type#

Type:: The value type suggested by the Amazon SageMaker Inference Recommender.

value#

Type:: The value suggested by the Amazon SageMaker Inference Recommender.

key: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

value: str | PipelineVariable#

value_type: str | PipelineVariable#

class sagemaker.core.shapes.shapes.EnvironmentParameterRanges(*, categorical_parameter_ranges: List[CategoricalParameter] | None = Unassigned())[source]#

Bases: Base

Specifies the range of environment parameters

categorical_parameter_ranges#

Type:: Specified a list of parameters for each category.

categorical_parameter_ranges: List[CategoricalParameter] | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.ErrorInfo(*, code: str | PipelineVariable | None = Unassigned(), reason: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

This is an error field object that contains the error code and the reason for an operation failure.

code#

Type:: The error code for an invalid or failed operation.

reason#

Type:: The failure reason for the operation.

code: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

reason: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.EventDetails(*, event_metadata: EventMetadata | None = Unassigned())[source]#

Bases: Base

Detailed information about a specific event, including event metadata.

event_metadata#

Type:: Metadata specific to the event, which may include information about the cluster, instance group, or instance involved.

event_metadata: EventMetadata | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.EventMetadata(*, cluster: ClusterMetadata | None = Unassigned(), instance_group: InstanceGroupMetadata | None = Unassigned(), instance_group_scaling: InstanceGroupScalingMetadata | None = Unassigned(), instance: InstanceMetadata | None = Unassigned())[source]#

Bases: Base

Metadata associated with a cluster event, which may include details about various resource types.

cluster#

Type:: Metadata specific to cluster-level events.

instance_group#

Type:: Metadata specific to instance group-level events.

instance_group_scaling#

Type:: Metadata related to instance group scaling events.

instance#

Type:: Metadata specific to instance-level events.

cluster: ClusterMetadata | None#

instance: InstanceMetadata | None#

instance_group: InstanceGroupMetadata | None#

instance_group_scaling: InstanceGroupScalingMetadata | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

The properties of an experiment as returned by the Search API. For information about experiments, see the CreateExperiment API.

experiment_name#

Type:: The name of the experiment.

experiment_arn#

Type:: The Amazon Resource Name (ARN) of the experiment.

display_name#

Type:: The name of the experiment as displayed. If DisplayName isn’t specified, ExperimentName is displayed.

source#

Type:: sagemaker.core.shapes.shapes.ExperimentSource | None

description#

Type:: The description of the experiment.

creation_time#

Type:: When the experiment was created.

created_by#

Type:: Who created the experiment.

last_modified_time#

Type:: When the experiment was last modified.

last_modified_by#

Type:: sagemaker.core.shapes.shapes.UserContext | None

tags#

Type:: The list of tags that are associated with the experiment. You can use Search API to search on the tags.

created_by: UserContext | None#

creation_time: datetime | None#

description: str | PipelineVariable | None#

display_name: str | PipelineVariable | None#

experiment_arn: str | PipelineVariable | None#

experiment_name: str | PipelineVariable | object | None#

last_modified_by: UserContext | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source: ExperimentSource | None#

tags: List[Tag] | None#

Bases: Base

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: CreateProcessingJob CreateTrainingJob CreateTransformJob

experiment_name#

Type:: The name of an existing experiment to associate with the trial component.

trial_name#

Type:: The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

trial_component_display_name#

Type:: The display name for the trial component. If this key isn’t specified, the display name is the trial component name.

run_name#

Type:: The name of the experiment run to associate with the trial component.

experiment_name: str | PipelineVariable | object | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

run_name: str | PipelineVariable | None#

trial_component_display_name: str | PipelineVariable | None#

trial_name: str | PipelineVariable | object | None#

class sagemaker.core.shapes.shapes.ExperimentSource(*, source_arn: str | PipelineVariable, source_type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The source of the experiment.

source_arn#

Type:: The Amazon Resource Name (ARN) of the source.

source_type#

Type:: The source type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

source_arn: str | PipelineVariable#

source_type: str | PipelineVariable | None#

Bases: Base

A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the ExperimentName.

experiment_arn#

Type:: The Amazon Resource Name (ARN) of the experiment.

experiment_name#

Type:: The name of the experiment.

display_name#

Type:: The name of the experiment as displayed. If DisplayName isn’t specified, ExperimentName is displayed.

experiment_source#

Type:: sagemaker.core.shapes.shapes.ExperimentSource | None

creation_time#

Type:: When the experiment was created.

last_modified_time#

Type:: When the experiment was last modified.

creation_time: datetime | None#

display_name: str | PipelineVariable | None#

experiment_arn: str | PipelineVariable | None#

experiment_name: str | PipelineVariable | object | None#

experiment_source: ExperimentSource | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.Explainability(*, report: MetricsSource | None = Unassigned())[source]#

Bases: Base

Contains explainability metrics for a model.

report#

Type:: The explainability report for a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

report: MetricsSource | None#

class sagemaker.core.shapes.shapes.ExplainerConfig(*, clarify_explainer_config: ClarifyExplainerConfig | None = Unassigned())[source]#

Bases: Base

A parameter to activate explainers.

clarify_explainer_config#

Type:: A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

clarify_explainer_config: ClarifyExplainerConfig | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.FSxLustreConfig(*, size_in_gi_b: int, per_unit_storage_throughput: int)[source]#

Bases: Base

Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster.

size_in_gi_b#

Type:: The storage capacity of the Amazon FSx for Lustre file system, specified in gibibytes (GiB).

per_unit_storage_throughput#

Type:: The throughput capacity of the Amazon FSx for Lustre file system, measured in MB/s per TiB of storage.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

per_unit_storage_throughput: int#

size_in_gi_b: int#

class sagemaker.core.shapes.shapes.FSxLustreFileSystem(*, file_system_id: str | PipelineVariable)[source]#

Bases: Base

A custom file system in Amazon FSx for Lustre.

file_system_id#

Type:: Amazon FSx for Lustre file system ID.

file_system_id: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.FSxLustreFileSystemConfig(*, file_system_id: str | PipelineVariable, file_system_path: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The settings for assigning a custom Amazon FSx for Lustre file system to a user profile or space for an Amazon SageMaker Domain.

file_system_id#

Type:: The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.

file_system_path#

Type:: The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.

file_system_id: str | PipelineVariable#

file_system_path: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.FailStepMetadata(*, error_message: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The container for the metadata for Fail step.

error_message#

Type:: A message that you define and then is processed and rendered by the Fail step when the error occurs.

error_message: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.FeatureDefinition(*, feature_name: str | PipelineVariable, feature_type: str | PipelineVariable, collection_type: str | PipelineVariable | None = Unassigned(), collection_config: CollectionConfig | None = Unassigned())[source]#

Bases: Base

A list of features. You must include FeatureName and FeatureType. Valid feature FeatureTypes are Integral, Fractional and String.

feature_name#

Type:: The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted, write_time, api_invocation_time. The name: Must start with an alphanumeric character. Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.

feature_type#

Type:: The value type of a feature. Valid values are Integral, Fractional, or String.

collection_type#

Type:: A grouping of elements where each element within the collection must have the same feature type (String, Integral, or Fractional). List: An ordered collection of elements. Set: An unordered collection of unique elements. Vector: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.

collection_config#

Type:: Configuration for your collection.

collection_config: CollectionConfig | None#

collection_type: str | PipelineVariable | None#

feature_name: str | PipelineVariable#

feature_type: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.

feature_group_arn#

Type:: The Amazon Resource Name (ARN) of a FeatureGroup.

feature_group_name#

Type:: The name of the FeatureGroup.

record_identifier_feature_name#

Type:: The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions.

event_time_feature_name#

Type:: The name of the feature that stores the EventTime of a Record in a FeatureGroup. A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime.

feature_definitions#

Type:: A list of Features. Each Feature must include a FeatureName and a FeatureType. Valid FeatureTypes are Integral, Fractional and String. FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time. You can create up to 2,500 FeatureDefinitions per FeatureGroup.

creation_time#

Type:: The time a FeatureGroup was created.

last_modified_time#

Type:: A timestamp indicating the last time you updated the feature group.

online_store_config#

Type:: sagemaker.core.shapes.shapes.OnlineStoreConfig | None

offline_store_config#

Type:: sagemaker.core.shapes.shapes.OfflineStoreConfig | None

role_arn#

Type:: The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.

feature_group_status#

Type:: A FeatureGroup status.

offline_store_status#

Type:: sagemaker.core.shapes.shapes.OfflineStoreStatus | None

last_update_status#

Type:: A value that indicates whether the feature group was updated successfully.

failure_reason#

Type:: The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore.

description#

Type:: A free form description of a FeatureGroup.

tags#

Type:: Tags used to define a FeatureGroup.

creation_time: datetime | None#

description: str | PipelineVariable | None#

event_time_feature_name: str | PipelineVariable | None#

failure_reason: str | PipelineVariable | None#

feature_definitions: List[FeatureDefinition] | None#

feature_group_arn: str | PipelineVariable | None#

feature_group_name: str | PipelineVariable | object | None#

feature_group_status: str | PipelineVariable | None#

last_modified_time: datetime | None#

last_update_status: LastUpdateStatus | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

offline_store_config: OfflineStoreConfig | None#

offline_store_status: OfflineStoreStatus | None#

online_store_config: OnlineStoreConfig | None#

record_identifier_feature_name: str | PipelineVariable | None#

role_arn: str | PipelineVariable | None#

tags: List[Tag] | None#

class sagemaker.core.shapes.shapes.FeatureGroupSummary(*, feature_group_name: str | PipelineVariable | object, feature_group_arn: str | PipelineVariable, creation_time: datetime, feature_group_status: str | PipelineVariable | None = Unassigned(), offline_store_status: OfflineStoreStatus | None = Unassigned())[source]#

Bases: Base

The name, ARN, CreationTime, FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup.

feature_group_name#

Type:: The name of FeatureGroup.

feature_group_arn#

Type:: Unique identifier for the FeatureGroup.

creation_time#

Type:: A timestamp indicating the time of creation time of the FeatureGroup.

feature_group_status#

Type:: The status of a FeatureGroup. The status can be any of the following: Creating, Created, CreateFail, Deleting or DetailFail.

offline_store_status#

Type:: Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked.

creation_time: datetime#

feature_group_arn: str | PipelineVariable#

feature_group_name: str | PipelineVariable | object#

feature_group_status: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

offline_store_status: OfflineStoreStatus | None#

Bases: Base

The metadata for a feature. It can either be metadata that you specify, or metadata that is updated automatically.

feature_group_arn#

Type:: The Amazon Resource Number (ARN) of the feature group.

feature_group_name#

Type:: The name of the feature group containing the feature.

feature_name#

Type:: The name of feature.

feature_type#

Type:: The data type of the feature.

creation_time#

Type:: A timestamp indicating when the feature was created.

last_modified_time#

Type:: A timestamp indicating when the feature was last modified.

description#

Type:: An optional description that you specify to better describe the feature.

parameters#

Type:: Optional key-value pairs that you specify to better describe the feature.

creation_time: datetime | None#

description: str | PipelineVariable | None#

feature_group_arn: str | PipelineVariable | None#

feature_group_name: str | PipelineVariable | object | None#

feature_name: str | PipelineVariable | None#

feature_type: str | PipelineVariable | None#

last_modified_time: datetime | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

parameters: List[FeatureParameter] | None#

class sagemaker.core.shapes.shapes.FeatureParameter(*, key: str | PipelineVariable | None = Unassigned(), value: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

A key-value pair that you specify to describe the feature.

key#

Type:: A key that must contain a value to describe the feature.

value#

Type:: The value that belongs to a key.

key: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

value: str | PipelineVariable | None#

Bases: Base

The value associated with a feature.

feature_name#

Type:: The name of a feature that a feature value corresponds to.

value_as_string#

Type:: The value in string format associated with a feature. Used when your CollectionType is None. Note that features types can be String, Integral, or Fractional. This value represents all three types as a string.

value_as_string_list#

Type:: The list of values in string format associated with a feature. Used when your CollectionType is a List, Set, or Vector. Note that features types can be String, Integral, or Fractional. These values represents all three types as a string.

feature_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

value_as_string: str | PipelineVariable | None#

value_as_string_list: List[str | PipelineVariable] | None#

Bases: Base

Contains details regarding the file source.

content_type#

Type:: The type of content stored in the file source.

content_digest#

Type:: The digest of the file source.

s3_uri#

Type:: The Amazon S3 URI for the file source.

content_digest: str | PipelineVariable | None#

content_type: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_uri: str | PipelineVariable#

class sagemaker.core.shapes.shapes.FileSystemConfig(*, mount_path: str | PipelineVariable | None = Unassigned(), default_uid: int | None = Unassigned(), default_gid: int | None = Unassigned())[source]#

Bases: Base

The Amazon Elastic File System storage configuration for a SageMaker AI image.

mount_path#

Type:: The path within the image to mount the user’s EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.

default_uid#

Type:: The default POSIX user ID (UID). If not specified, defaults to 1000.

default_gid#

Type:: The default POSIX group ID (GID). If not specified, defaults to 100.

default_gid: int | None#

default_uid: int | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

mount_path: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.FileSystemDataSource(*, file_system_id: str | PipelineVariable, file_system_access_mode: str | PipelineVariable, file_system_type: str | PipelineVariable, directory_path: str | PipelineVariable)[source]#

Bases: Base

Specifies a file system data source for a channel.

file_system_id#

Type:: The file system id.

file_system_access_mode#

Type:: The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

file_system_type#

Type:: The file system type.

directory_path#

Type:: The full path to the directory to associate with the channel.

directory_path: str | PipelineVariable#

file_system_access_mode: str | PipelineVariable#

file_system_id: str | PipelineVariable#

file_system_type: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: Base

A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API. If you specify a Value, but not an Operator, SageMaker uses the equals operator. In search, there are several property types: Metrics To define a metric filter, enter a value using the form “Metrics.<name>”, where <name> is a metric name. For example, the following filter searches for training jobs with an “accuracy” metric greater than “0.9”: { “Name”: “Metrics.accuracy”, “Operator”: “GreaterThan”, “Value”: “0.9” } HyperParameters To define a hyperparameter filter, enter a value with the form “HyperParameters.<name>”. Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a “learning_rate” hyperparameter that is less than “0.5”: { “Name”: “HyperParameters.learning_rate”, “Operator”: “LessThan”, “Value”: “0.5” } Tags To define a tag filter, enter a value with the form Tags.<key>.

name#

Type:: A resource property name. For example, TrainingJobName. For valid property names, see SearchRecord. You must specify a valid property for the resource.

operator#

Type:: A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values: Equals The value of Name equals Value. NotEquals The value of Name doesn’t equal Value. Exists The Name property exists. NotExists The Name property does not exist. GreaterThan The value of Name is greater than Value. Not supported for text properties. GreaterThanOrEqualTo The value of Name is greater than or equal to Value. Not supported for text properties. LessThan The value of Name is less than Value. Not supported for text properties. LessThanOrEqualTo The value of Name is less than or equal to Value. Not supported for text properties. In The value of Name is one of the comma delimited strings in Value. Only supported for text properties. Contains The value of Name contains the string Value. Only supported for text properties. A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following: Experiment.DisplayName Experiment.ExperimentName Experiment.Tags Trial.DisplayName Trial.TrialName Trial.Tags TrialComponent.DisplayName TrialComponent.TrialComponentName TrialComponent.Tags TrialComponent.InputArtifacts TrialComponent.OutputArtifacts A SearchExpression can include only one Contains operator for all other values of Name. In these cases, if you include multiple Contains operators in the SearchExpression, the result is the following error message: “‘CONTAINS’ operator usage limit of 1 exceeded.”

value#

Type:: A value used with Name and Operator to determine which resources satisfy the filter’s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd’T’HH:MM:SS.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | PipelineVariable#

operator: str | PipelineVariable | None#

value: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.FinalAutoMLJobObjectiveMetric(*, metric_name: str | PipelineVariable, value: float, type: str | PipelineVariable | None = Unassigned(), standard_metric_name: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The best candidate result from an AutoML training job.

type#

Type:: The type of metric with the best result.

metric_name#

Type:: The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.

value#

Type:: The value of the metric with the best result.

standard_metric_name#

Type:: The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.

metric_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

standard_metric_name: str | PipelineVariable | None#

type: str | PipelineVariable | None#

value: float#

class sagemaker.core.shapes.shapes.FinalHyperParameterTuningJobObjectiveMetric(*, metric_name: str | PipelineVariable, value: float, type: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Shows the latest objective metric emitted by a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

type#

Type:: Select if you want to minimize or maximize the objective metric during hyperparameter tuning.

metric_name#

Type:: The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.

value#

Type:: The value of the objective metric.

metric_name: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: str | PipelineVariable | None#

value: float#

class sagemaker.core.shapes.shapes.FlowDefinitionOutputConfig(*, s3_output_path: str | PipelineVariable, kms_key_id: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Contains information about where human output will be stored.

s3_output_path#

Type:: The Amazon S3 path where the object containing human output will be made available. To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.

kms_key_id#

Type:: The Amazon Key Management Service (KMS) key ID for server-side encryption.

kms_key_id: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s3_output_path: str | PipelineVariable#

class sagemaker.core.shapes.shapes.FlowDefinitionSummary(*, flow_definition_name: str | PipelineVariable | object, flow_definition_arn: str | PipelineVariable, flow_definition_status: str | PipelineVariable, creation_time: datetime, failure_reason: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

Contains summary information about the flow definition.

flow_definition_name#

Type:: The name of the flow definition.

flow_definition_arn#

Type:: The Amazon Resource Name (ARN) of the flow definition.

flow_definition_status#

Type:: The status of the flow definition. Valid values:

creation_time#

Type:: The timestamp when SageMaker created the flow definition.

failure_reason#

Type:: The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed.

creation_time: datetime#

failure_reason: str | PipelineVariable | None#

flow_definition_arn: str | PipelineVariable#

flow_definition_name: str | PipelineVariable | object#

flow_definition_status: str | PipelineVariable#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.GenerativeAiSettings(*, amazon_bedrock_role_arn: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

The generative AI settings for the SageMaker Canvas application. Configure these settings for Canvas users starting chats with generative AI foundation models. For more information, see Use generative AI with foundation models.

amazon_bedrock_role_arn#

Type:: The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.

amazon_bedrock_role_arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker.core.shapes.shapes.GetDeviceFleetReportResponse(*, device_fleet_arn: str | PipelineVariable, device_fleet_name: str | PipelineVariable | object, output_config: EdgeOutputConfig | None = Unassigned(), description: str | PipelineVariable | None = Unassigned(), report_generated: datetime | None = Unassigned(), device_stats: DeviceStats | None = Unassigned(), agent_versions: List[AgentVersion] | None = Unassigned(), model_stats: List[EdgeModelStat] | None = Unassigned())[source]#

Bases: Base

device_fleet_arn#

Type:: The Amazon Resource Name (ARN) of the device.

device_fleet_name#

Type:: The name of the fleet.

output_config#

Type:: The output configuration for storing sample data collected by the fleet.

description#

Type:: Description of the fleet.

report_generated#

Type:: Timestamp of when the report was generated.

device_stats#

Type:: Status of devices.

agent_versions#

Type:: The versions of Edge Manager agent deployed on the fleet.

model_stats#

Type:: Status of model on device.

agent_versions: List[AgentVersion] | None#

description: str | PipelineVariable | None#

device_fleet_arn: str | PipelineVariable#

device_fleet_name: str | PipelineVariable | object#

device_stats: DeviceStats | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_stats: List[EdgeModelStat] | None#

output_config: EdgeOutputConfig | None#

report_generated: datetime | None#

class sagemaker.core.shapes.shapes.GetLineageGroupPolicyResponse(*, lineage_group_arn: str | PipelineVariable | None = Unassigned(), resource_policy: str | PipelineVariable | None = Unassigned())[source]#

Bases: Base

lineage_group_arn#

Type:: The Amazon Resource Name (ARN) of the lineage group.

resource_policy#

Type:: The resource policy that gives access to the lineage group in another account.

lineage_group_arn: str | PipelineVariable | None#

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

resource_policy: str | PipelineVariable | None#

class sagemaker.core.shapes.shapes.GetRecordResponse(*, record: List[FeatureValue

sagemaker.core.shapes.shapes

Contents

sagemaker.core.shapes.shapes#