Session

Placeholder docstring

class sagemaker.session.LogState

Bases: object

Placeholder docstring

STARTING = 1
WAIT_IN_PROGRESS = 2
TAILING = 3
JOB_COMPLETE = 4
COMPLETE = 5
class sagemaker.session.Session(boto_session=None, sagemaker_client=None, sagemaker_runtime_client=None, sagemaker_featurestore_runtime_client=None, default_bucket=None, settings=<sagemaker.session_settings.SessionSettings object>, sagemaker_metrics_client=None, sagemaker_config=None, default_bucket_prefix=None)

Bases: object

Manage interactions with the Amazon SageMaker APIs and any other AWS services needed.

This class provides convenient methods for manipulating entities and resources that Amazon SageMaker uses, such as training jobs, endpoints, and input datasets in S3. AWS service calls are delegated to an underlying Boto3 session, which by default is initialized using the AWS configuration chain. When you make an Amazon SageMaker API call that accesses an S3 bucket location and one is not specified, the Session creates a default bucket based on a naming convention which includes the current AWS account ID.

Initialize a SageMaker Session.

Parameters
  • boto_session (boto3.session.Session) – The underlying Boto3 session which AWS service calls are delegated to (default: None). If not provided, one is created with default AWS configuration chain.

  • sagemaker_client (boto3.SageMaker.Client) – Client which makes Amazon SageMaker service calls other than InvokeEndpoint (default: None). Estimators created using this Session use this client. If not provided, one will be created using this instance’s boto_session.

  • sagemaker_runtime_client (boto3.SageMakerRuntime.Client) – Client which makes InvokeEndpoint calls to Amazon SageMaker (default: None). Predictors created using this Session use this client. If not provided, one will be created using this instance’s boto_session.

  • sagemaker_featurestore_runtime_client (boto3.SageMakerFeatureStoreRuntime.Client) – Client which makes SageMaker FeatureStore record related calls to Amazon SageMaker (default: None). If not provided, one will be created using this instance’s boto_session.

  • default_bucket (str) – The default Amazon S3 bucket to be used by this session. This will be created the next time an Amazon S3 bucket is needed (by calling default_bucket()). If not provided, it will be fetched from the sagemaker_config. If not configured there either, a default bucket will be created based on the following format: “sagemaker-{region}-{aws-account-id}”. Example: “sagemaker-my-custom-bucket”.

  • settings (sagemaker.session_settings.SessionSettings) – Optional. Set of optional parameters to apply to the session.

  • sagemaker_metrics_client (boto3.SageMakerMetrics.Client) – Client which makes SageMaker Metrics related calls to Amazon SageMaker (default: None). If not provided, one will be created using this instance’s boto_session.

  • sagemaker_config (dict) – A dictionary containing default values for the SageMaker Python SDK. (default: None). The dictionary must adhere to the schema defined at ~sagemaker.config.config_schema.SAGEMAKER_PYTHON_SDK_CONFIG_SCHEMA. If sagemaker_config is not provided and configuration files exist (at the default paths for admins and users, or paths set through the environment variables SAGEMAKER_ADMIN_CONFIG_OVERRIDE and SAGEMAKER_USER_CONFIG_OVERRIDE), a new dictionary will be generated from those configuration files. Alternatively, this dictionary can be generated by calling load_sagemaker_config() and then be provided to the Session.

  • default_bucket_prefix (str) – The default prefix to use for S3 Object Keys. (default: None). If provided and where applicable, it will be used by the SDK to construct default S3 URIs, in the format: s3://{default_bucket}/{default_bucket_prefix}/<rest of object key> This parameter can also be specified via {sagemaker_config} instead of here. If not provided here or within {sagemaker_config}, default S3 URIs will have the format: s3://{default_bucket}/<rest of object key>

property boto_region_name

Placeholder docstring

upload_data(path, bucket=None, key_prefix='data', extra_args=None)

Upload local file or directory to S3.

If a single file is specified for upload, the resulting S3 object key is {key_prefix}/{filename} (filename does not include the local path, if any specified). If a directory is specified for upload, the API uploads all content, recursively, preserving relative structure of subdirectories. The resulting object key names are: {key_prefix}/{relative_subdirectory_path}/filename.

Parameters
  • path (str) – Path (absolute or relative) of local file or directory to upload.

  • bucket (str) – Name of the S3 Bucket to upload to (default: None). If not specified, the default bucket of the Session is used (if default bucket does not exist, the Session creates it).

  • key_prefix (str) – Optional S3 object key name prefix (default: ‘data’). S3 uses the prefix to create a directory structure for the bucket content that it display in the S3 console.

  • extra_args (dict) – Optional extra arguments that may be passed to the upload operation. Similar to ExtraArgs parameter in S3 upload_file function. Please refer to the ExtraArgs parameter documentation here: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-uploading-files.html#the-extraargs-parameter

Returns

The S3 URI of the uploaded file(s). If a file is specified in the path argument,

the URI format is: s3://{bucket name}/{key_prefix}/{original_file_name}. If a directory is specified in the path argument, the URI format is s3://{bucket name}/{key_prefix}.

Return type

str

upload_string_as_file_body(body, bucket, key, kms_key=None)

Upload a string as a file body.

Parameters
  • body (str) – String representing the body of the file.

  • bucket (str) – Name of the S3 Bucket to upload to (default: None). If not specified, the default bucket of the Session is used (if default bucket does not exist, the Session creates it).

  • key (str) – S3 object key. This is the s3 path to the file.

  • kms_key (str) – The KMS key to use for encrypting the file.

Returns

The S3 URI of the uploaded file.

The URI format is: s3://{bucket name}/{key}.

Return type

str

download_data(path, bucket, key_prefix='', extra_args=None)

Download file or directory from S3.

Parameters
Returns

List of local paths of downloaded files

Return type

list[str]

read_s3_file(bucket, key_prefix)

Read a single file from S3.

Parameters
  • bucket (str) – Name of the S3 Bucket to download from.

  • key_prefix (str) – S3 object key name prefix.

Returns

The body of the s3 file as a string.

Return type

str

list_s3_files(bucket, key_prefix)

Lists the S3 files given an S3 bucket and key.

Parameters
  • bucket (str) – Name of the S3 Bucket to download from.

  • key_prefix (str) – S3 object key name prefix.

Returns

The list of files at the S3 path.

Return type

[str]

default_bucket()

Return the name of the default bucket to use in relevant Amazon SageMaker interactions.

This function will create the s3 bucket if it does not exist.

Returns

The name of the default bucket. If the name was not explicitly specified through

the Session or sagemaker_config, the bucket will take the form: sagemaker-{region}-{AWS account ID}.

Return type

str

train(input_mode, input_config, role=None, job_name=None, output_config=None, resource_config=None, vpc_config=None, hyperparameters=None, stop_condition=None, tags=None, metric_definitions=None, enable_network_isolation=None, image_uri=None, training_image_config=None, algorithm_arn=None, encrypt_inter_container_traffic=None, use_spot_instances=False, checkpoint_s3_uri=None, checkpoint_local_path=None, experiment_config=None, debugger_rule_configs=None, debugger_hook_config=None, tensorboard_output_config=None, enable_sagemaker_metrics=None, profiler_rule_configs=None, profiler_config=None, environment=None, retry_strategy=None)

Create an Amazon SageMaker training job.

Parameters
  • input_mode (str) – The input mode that the algorithm supports. Valid modes: * ‘File’ - Amazon SageMaker copies the training dataset from the S3 location to a directory in the Docker container. * ‘Pipe’ - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe. * ‘FastFile’ - Amazon SageMaker streams data from S3 on demand instead of downloading the entire dataset before training begins.

  • input_config (list) – A list of Channel objects. Each channel is a named input source. Please refer to the format details described: https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role.

  • job_name (str) – Name of the training job being created.

  • output_config (dict) – The S3 URI where you want to store the training results and optional KMS key ID.

  • resource_config (dict) – Contains values for ResourceConfig: * instance_count (int): Number of EC2 instances to use for training. The key in resource_config is ‘InstanceCount’. * instance_type (str): Type of EC2 instance to use for training, for example, ‘ml.c4.xlarge’. The key in resource_config is ‘InstanceType’.

  • vpc_config (dict) – Contains values for VpcConfig: * subnets (list[str]): List of subnet ids. The key in vpc_config is ‘Subnets’. * security_group_ids (list[str]): List of security group ids. The key in vpc_config is ‘SecurityGroupIds’.

  • hyperparameters (dict) – Hyperparameters for model training. The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker. For convenience, this accepts other types for keys and values, but str() will be called to convert them before training.

  • stop_condition (dict) – Defines when training shall finish. Contains entries that can be understood by the service like MaxRuntimeInSeconds.

  • tags (list[dict]) – List of tags for labeling a training job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

  • metric_definitions (list[dict]) – A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: ‘Name’ for the name of the metric, and ‘Regex’ for the regular expression used to extract the metric from the logs.

  • enable_network_isolation (bool) – Whether to request for the training job to run with network isolation or not.

  • image_uri (str) – Docker image containing training code.

  • training_image_config (dict) –

    Training image configuration. Optionally, the dict can contain ‘TrainingRepositoryAccessMode’ and ‘TrainingRepositoryCredentialsProviderArn’ (under ‘TrainingRepositoryAuthConfig’). For example,

    training_image_config = {
        "TrainingRepositoryAccessMode": "Vpc",
        "TrainingRepositoryAuthConfig": {
            "TrainingRepositoryCredentialsProviderArn":
              "arn:aws:lambda:us-west-2:1234567890:function:test"
        },
    }
    

    If TrainingRepositoryAccessMode is set to Vpc, the training image is accessed through a private Docker registry in customer Vpc. If it’s set to Platform or None, the training image is accessed through ECR. If TrainingRepositoryCredentialsProviderArn is provided, the credentials to authenticate to the private Docker registry will be retrieved from this AWS Lambda function. (default: None). When it’s set to None, SageMaker will not do authentication before pulling the image in the private Docker registry.

  • algorithm_arn (str) – Algorithm Arn from Marketplace.

  • encrypt_inter_container_traffic (bool) – Specifies whether traffic between training containers is encrypted for the training job (default: False).

  • use_spot_instances (bool) – whether to use spot instances for training.

  • checkpoint_s3_uri (str) – The S3 URI in which to persist checkpoints that the algorithm persists (if any) during training. (default: None).

  • checkpoint_local_path (str) – The local path that the algorithm writes its checkpoints to. SageMaker will persist all files under this path to checkpoint_s3_uri continually during training. On job startup the reverse happens - data from the s3 location is downloaded to this path before the algorithm is started. If the path is unset then SageMaker assumes the checkpoints will be provided under /opt/ml/checkpoints/. (default: None).

  • experiment_config (dict[str, str]) – Experiment management configuration. Optionally, the dict can contain four keys: ‘ExperimentName’, ‘TrialName’, ‘TrialComponentDisplayName’ and ‘RunName’. The behavior of setting these keys is as follows: * If ExperimentName is supplied but TrialName is not a Trial will be automatically created and the job’s Trial Component associated with the Trial. * If TrialName is supplied and the Trial already exists the job’s Trial Component will be associated with the Trial. * If both ExperimentName and TrialName are not supplied the trial component will be unassociated. * TrialComponentDisplayName is used for display in Studio. * RunName is used to record an experiment run.

  • enable_sagemaker_metrics (bool) – enable SageMaker Metrics Time Series. For more information see: https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries (default: None).

  • profiler_rule_configs (list[dict]) – A list of profiler rule configurations.src/sagemaker/lineage/artifact.py:285

  • profiler_config (dict) – Configuration for how profiling information is emitted with SageMaker Profiler. (default: None).

  • environment (dict[str, str]) – Environment variables to be set for use during training job (default: None)

  • retry_strategy (dict) – Defines RetryStrategy for InternalServerFailures. * max_retry_attsmpts (int): Number of times a job should be retried. The key in RetryStrategy is ‘MaxRetryAttempts’.

Returns

ARN of the training job, if it is created.

Return type

str

update_training_job(job_name, profiler_rule_configs=None, profiler_config=None, resource_config=None)

Calls the UpdateTrainingJob API for the given job name and returns the response.

Parameters
  • job_name (str) – Name of the training job being updated.

  • profiler_rule_configs (list) – List of profiler rule configurations. (default: None).

  • profiler_config (dict) – Configuration for how profiling information is emitted with SageMaker Profiler. (default: None).

  • resource_config (dict) – Configuration of the resources for the training job. You can update the keep-alive period if the warm pool status is Available. No other fields can be updated. (default: None).

process(inputs, output_config, job_name, resources, stopping_condition, app_specification, environment, network_config=None, role_arn=None, tags=None, experiment_config=None)

Create an Amazon SageMaker processing job.

Parameters
  • inputs ([dict]) – List of up to 10 ProcessingInput dictionaries.

  • output_config (dict) – A config dictionary, which contains a list of up to 10 ProcessingOutput dictionaries, as well as an optional KMS key ID.

  • job_name (str) – The name of the processing job. The name must be unique within an AWS Region in an AWS account. Names should have minimum length of 1 and maximum length of 63 characters.

  • resources (dict) – Encapsulates the resources, including ML instances and storage, to use for the processing job.

  • stopping_condition (dict[str,int]) – Specifies a limit to how long the processing job can run, in seconds.

  • app_specification (dict[str,str]) – Configures the processing job to run the given image. Details are in the processing container specification.

  • environment (dict) – Environment variables to start the processing container with.

  • network_config (dict) – Specifies networking options, such as network traffic encryption between processing containers, whether to allow inbound and outbound network calls to and from processing containers, and VPC subnets and security groups to use for VPC-enabled processing jobs.

  • role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • tags ([dict[str,str]]) – A list of dictionaries containing key-value pairs.

  • experiment_config (dict[str, str]) – Experiment management configuration. Optionally, the dict can contain three keys: ‘ExperimentName’, ‘TrialName’, and ‘TrialComponentDisplayName’. The behavior of setting these keys is as follows: * If ExperimentName is supplied but TrialName is not a Trial will be automatically created and the job’s Trial Component associated with the Trial. * If TrialName is supplied and the Trial already exists the job’s Trial Component will be associated with the Trial. * If both ExperimentName and TrialName are not supplied the trial component will be unassociated. * TrialComponentDisplayName is used for display in Studio.

create_monitoring_schedule(monitoring_schedule_name, schedule_expression, statistics_s3_uri, constraints_s3_uri, monitoring_inputs, monitoring_output_config, instance_count, instance_type, volume_size_in_gb, volume_kms_key=None, image_uri=None, entrypoint=None, arguments=None, record_preprocessor_source_uri=None, post_analytics_processor_source_uri=None, max_runtime_in_seconds=None, environment=None, network_config=None, role_arn=None, tags=None)

Create an Amazon SageMaker monitoring schedule.

Parameters
  • monitoring_schedule_name (str) – The name of the monitoring schedule. The name must be unique within an AWS Region in an AWS account. Names should have a minimum length of 1 and a maximum length of 63 characters.

  • schedule_expression (str) – The cron expression that dictates the monitoring execution schedule.

  • statistics_s3_uri (str) – The S3 uri of the statistics file to use.

  • constraints_s3_uri (str) – The S3 uri of the constraints file to use.

  • monitoring_inputs ([dict]) – List of MonitoringInput dictionaries.

  • monitoring_output_config (dict) – A config dictionary, which contains a list of MonitoringOutput dictionaries, as well as an optional KMS key ID.

  • instance_count (int) – The number of instances to run.

  • instance_type (str) – The type of instance to run.

  • volume_size_in_gb (int) – Size of the volume in GB.

  • volume_kms_key (str) – KMS key to use when encrypting the volume.

  • image_uri (str) – The image uri to use for monitoring executions.

  • entrypoint (str) – The entrypoint to the monitoring execution image.

  • arguments (str) – The arguments to pass to the monitoring execution image.

  • record_preprocessor_source_uri (str or None) – The S3 uri that points to the script that pre-processes the dataset (only applicable to first-party images).

  • post_analytics_processor_source_uri (str or None) – The S3 uri that points to the script that post-processes the dataset (only applicable to first-party images).

  • max_runtime_in_seconds (int) – Specifies a limit to how long the processing job can run, in seconds.

  • environment (dict) – Environment variables to start the monitoring execution container with.

  • network_config (dict) – Specifies networking options, such as network traffic encryption between processing containers, whether to allow inbound and outbound network calls to and from processing containers, and VPC subnets and security groups to use for VPC-enabled processing jobs.

  • role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • tags ([dict[str,str]]) – A list of dictionaries containing key-value pairs.

update_monitoring_schedule(monitoring_schedule_name, schedule_expression=None, statistics_s3_uri=None, constraints_s3_uri=None, monitoring_inputs=None, monitoring_output_config=None, instance_count=None, instance_type=None, volume_size_in_gb=None, volume_kms_key=None, image_uri=None, entrypoint=None, arguments=None, record_preprocessor_source_uri=None, post_analytics_processor_source_uri=None, max_runtime_in_seconds=None, environment=None, network_config=None, role_arn=None)

Update an Amazon SageMaker monitoring schedule.

Parameters
  • monitoring_schedule_name (str) – The name of the monitoring schedule. The name must be unique within an AWS Region in an AWS account. Names should have a minimum length of 1 and a maximum length of 63 characters.

  • schedule_expression (str) – The cron expression that dictates the monitoring execution schedule.

  • statistics_s3_uri (str) – The S3 uri of the statistics file to use.

  • constraints_s3_uri (str) – The S3 uri of the constraints file to use.

  • monitoring_inputs ([dict]) – List of MonitoringInput dictionaries.

  • monitoring_output_config (dict) – A config dictionary, which contains a list of MonitoringOutput dictionaries, as well as an optional KMS key ID.

  • instance_count (int) – The number of instances to run.

  • instance_type (str) – The type of instance to run.

  • volume_size_in_gb (int) – Size of the volume in GB.

  • volume_kms_key (str) – KMS key to use when encrypting the volume.

  • image_uri (str) – The image uri to use for monitoring executions.

  • entrypoint (str) – The entrypoint to the monitoring execution image.

  • arguments (str) – The arguments to pass to the monitoring execution image.

  • record_preprocessor_source_uri (str or None) – The S3 uri that points to the script that pre-processes the dataset (only applicable to first-party images).

  • post_analytics_processor_source_uri (str or None) – The S3 uri that points to the script that post-processes the dataset (only applicable to first-party images).

  • max_runtime_in_seconds (int) – Specifies a limit to how long the processing job can run, in seconds.

  • environment (dict) – Environment variables to start the monitoring execution container with.

  • network_config (dict) – Specifies networking options, such as network traffic encryption between processing containers, whether to allow inbound and outbound network calls to and from processing containers, and VPC subnets and security groups to use for VPC-enabled processing jobs.

  • role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • tags ([dict[str,str]]) – A list of dictionaries containing key-value pairs.

start_monitoring_schedule(monitoring_schedule_name)

Starts a monitoring schedule.

Parameters

monitoring_schedule_name (str) – The name of the Amazon SageMaker Monitoring Schedule to start.

stop_monitoring_schedule(monitoring_schedule_name)

Stops a monitoring schedule.

Parameters

monitoring_schedule_name (str) – The name of the Amazon SageMaker Monitoring Schedule to stop.

delete_monitoring_schedule(monitoring_schedule_name)

Deletes a monitoring schedule.

Parameters

monitoring_schedule_name (str) – The name of the Amazon SageMaker Monitoring Schedule to delete.

describe_monitoring_schedule(monitoring_schedule_name)

Calls the DescribeMonitoringSchedule API for given name and returns the response.

Parameters

monitoring_schedule_name (str) – The name of the processing job to describe.

Returns

A dictionary response with the processing job description.

Return type

dict

list_monitoring_executions(monitoring_schedule_name, sort_by='ScheduledTime', sort_order='Descending', max_results=100)

Lists the monitoring executions associated with the given monitoring_schedule_name.

Parameters
  • monitoring_schedule_name (str) – The monitoring_schedule_name for which to retrieve the monitoring executions.

  • sort_by (str) – The field to sort by. Can be one of: “CreationTime”, “ScheduledTime”, “Status”. Default: “ScheduledTime”.

  • sort_order (str) – The sort order. Can be one of: “Ascending”, “Descending”. Default: “Descending”.

  • max_results (int) – The maximum number of results to return. Must be between 1 and 100.

Returns

Dictionary of monitoring schedule executions.

Return type

dict

list_monitoring_schedules(endpoint_name=None, sort_by='CreationTime', sort_order='Descending', max_results=100)

Lists the monitoring executions associated with the given monitoring_schedule_name.

Parameters
  • endpoint_name (str) – The name of the endpoint to filter on. If not provided, does not filter on it. Default: None.

  • sort_by (str) – The field to sort by. Can be one of: “Name”, “CreationTime”, “Status”. Default: “CreationTime”.

  • sort_order (str) – The sort order. Can be one of: “Ascending”, “Descending”. Default: “Descending”.

  • max_results (int) – The maximum number of results to return. Must be between 1 and 100.

Returns

Dictionary of monitoring schedule executions.

Return type

dict

update_monitoring_alert(monitoring_schedule_name, monitoring_alert_name, data_points_to_alert, evaluation_period)

Update the monitoring alerts associated with the given schedule_name and alert_name

Parameters
  • monitoring_schedule_name (str) – The name of the monitoring schedule to update.

  • monitoring_alert_name (str) – The name of the monitoring alert to update.

  • data_points_to_alert (int) – The data point to alert.

  • evaluation_period (int) – The period to evaluate the alert status.

Returns

A dict represents the update alert response.

Return type

dict

list_monitoring_alerts(monitoring_schedule_name, next_token=None, max_results=10)

Lists the monitoring alerts associated with the given monitoring_schedule_name.

Parameters
  • monitoring_schedule_name (str) – The name of the monitoring schedule to filter on. If not provided, does not filter on it.

  • next_token (Optional[str]) – The pagination token. Default: None

  • max_results (Optional[int]) – The maximum number of results to return. Must be between 1 and 100. Default: 10

Returns

list of monitoring alerts.

Return type

dict

list_monitoring_alert_history(monitoring_schedule_name=None, monitoring_alert_name=None, sort_by='CreationTime', sort_order='Descending', next_token=None, max_results=10, creation_time_before=None, creation_time_after=None, status_equals=None)

Lists the alert history associated with the given schedule_name and alert_name.

Parameters
  • monitoring_schedule_name (Optional[str]) – The name of the monitoring_schedule_name to filter on. If not provided, does not filter on it. Default: None.

  • monitoring_alert_name (Optional[str]) – The name of the monitoring_alert_name to filter on. If not provided, does not filter on it. Default: None.

  • sort_by (Optional[str]) – sort_by (str): The field to sort by. Can be one of: “Name”, “CreationTime” Default: “CreationTime”.

  • sort_order (Optional[str]) – The sort order. Can be one of: “Ascending”, “Descending”. Default: “Descending”.

  • next_token (Optional[str]) – The pagination token. Default: None

  • max_results (Optional[int]) – The maximum number of results to return. Must be between 1 and 100. Default: 10.

  • creation_time_before (Optional[str]) – A filter to filter alert history before a time

  • creation_time_after (Optional[str]) – A filter to filter alert history after a time Default: None.

  • status_equals (Optional[str]) – A filter to filter alert history by status Default: None.

Returns

list of monitoring alert history.

Return type

dict

was_processing_job_successful(job_name)

Calls the DescribeProcessingJob API for the given job name.

It returns True if job was successful.

Parameters

job_name (str) – The name of the processing job to describe.

Returns

Whether the processing job was successful.

Return type

bool

describe_processing_job(job_name)

Calls the DescribeProcessingJob API for the given job name and returns the response.

Parameters

job_name (str) – The name of the processing job to describe.

Returns

A dictionary response with the processing job description.

Return type

dict

stop_processing_job(job_name)

Calls the StopProcessingJob API for the given job name.

Parameters

job_name (str) – The name of the processing job to stop.

stop_training_job(job_name)

Calls the StopTrainingJob API for the given job name.

Parameters

job_name (str) – The name of the training job to stop.

describe_training_job(job_name)

Calls the DescribeTrainingJob API for the given job name and returns the response.

Parameters

job_name (str) – The name of the training job to describe.

Returns

A dictionary response with the training job description.

Return type

dict

auto_ml(input_config, output_config, auto_ml_job_config, role=None, job_name=None, problem_type=None, job_objective=None, generate_candidate_definitions_only=False, tags=None, model_deploy_config=None)

Create an Amazon SageMaker AutoML job.

Parameters
  • input_config (list[dict]) – A list of Channel objects. Each channel contains “DataSource” and “TargetAttributeName”, “CompressionType” and “SampleWeightAttributeName” are optional fields.

  • output_config (dict) – The S3 URI where you want to store the training results and optional KMS key ID.

  • auto_ml_job_config (dict) – A dict of AutoMLJob config, containing “StoppingCondition”, “SecurityConfig”, optionally contains “VolumeKmsKeyId”.

  • role (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • job_name (str) – A string that can be used to identify an AutoMLJob. Each AutoMLJob should have a unique job name.

  • problem_type (str) – The type of problem of this AutoMLJob. Valid values are “Regression”, “BinaryClassification”, “MultiClassClassification”. If None, SageMaker AutoMLJob will infer the problem type automatically.

  • job_objective (dict) – AutoMLJob objective, contains “AutoMLJobObjectiveType” (optional), “MetricName” and “Value”.

  • generate_candidate_definitions_only (bool) – Indicates whether to only generate candidate definitions. If True, AutoML.list_candidates() cannot be called. Default: False.

  • tags ([dict[str,str]]) – A list of dictionaries containing key-value pairs.

  • model_deploy_config (dict) – Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment. Contains “AutoGenerateEndpointName” and “EndpointName”

describe_auto_ml_job(job_name)

Calls the DescribeAutoMLJob API for the given job name and returns the response.

Parameters

job_name (str) – The name of the AutoML job to describe.

Returns

A dictionary response with the AutoML Job description.

Return type

dict

list_candidates(job_name, status_equals=None, candidate_name=None, candidate_arn=None, sort_order=None, sort_by=None, max_results=None)

Returns the list of candidates of an AutoML job for a given name.

Parameters
  • job_name (str) – The name of the AutoML job. If None, will use object’s latest_auto_ml_job name.

  • status_equals (str) – Filter the result with candidate status, values could be “Completed”, “InProgress”, “Failed”, “Stopped”, “Stopping”

  • candidate_name (str) – The name of a specified candidate to list. Default to None.

  • candidate_arn (str) – The Arn of a specified candidate to list. Default to None.

  • sort_order (str) – The order that the candidates will be listed in result. Default to None.

  • sort_by (str) – The value that the candidates will be sorted by. Default to None.

  • max_results (int) – The number of candidates will be listed in results, between 1 to 100. Default to None. If None, will return all the candidates.

Returns

A list of dictionaries with candidates information

Return type

list

wait_for_auto_ml_job(job, poll=5)

Wait for an Amazon SageMaker AutoML job to complete.

Parameters
  • job (str) – Name of the auto ml job to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeAutoMLJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the auto ml job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the auto ml job fails.

logs_for_auto_ml_job(job_name, wait=False, poll=10)

Display logs for a given AutoML job, optionally tailing them until job is complete.

If the output is a tty or a Jupyter cell, it will be color-coded based on which instance the log entry is from.

Parameters
  • job_name (str) – Name of the Auto ML job to display the logs for.

  • wait (bool) – Whether to keep looking for new log entries until the job completes (default: False).

  • poll (int) – The interval in seconds between polling for new log entries and job completion (default: 5).

Raises
  • exceptions.CapacityError – If waiting and auto ml job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If waiting and auto ml job fails.

compile_model(input_model_config, output_model_config, role=None, job_name=None, stop_condition=None, tags=None)

Create an Amazon SageMaker Neo compilation job.

Parameters
  • input_model_config (dict) – the trained model and the Amazon S3 location where it is stored.

  • output_model_config (dict) – Identifies the Amazon S3 location where you want Amazon SageMaker Neo to save the results of compilation job

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker Neo compilation jobs use this role to access model artifacts. You must grant sufficient permissions to this role.

  • job_name (str) – Name of the compilation job being created.

  • stop_condition (dict) – Defines when compilation job shall finish. Contains entries that can be understood by the service like MaxRuntimeInSeconds.

  • tags (list[dict]) – List of tags for labeling a compile model job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

Returns

ARN of the compile model job, if it is created.

Return type

str

package_model_for_edge(output_model_config, role=None, job_name=None, compilation_job_name=None, model_name=None, model_version=None, resource_key=None, tags=None)

Create an Amazon SageMaker Edge packaging job.

Parameters
  • output_model_config (dict) – Identifies the Amazon S3 location where you want Amazon SageMaker Edge to save the results of edge packaging job

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker Edge edge packaging jobs use this role to access model artifacts. You must grant sufficient permissions to this role.

  • job_name (str) – Name of the edge packaging job being created.

  • compilation_job_name (str) – Name of the compilation job being created.

  • resource_key (str) – KMS key to encrypt the disk used to package the job

  • tags (list[dict]) – List of tags for labeling a compile model job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

tune(job_name, strategy, objective_type, objective_metric_name, max_jobs, max_parallel_jobs, parameter_ranges, static_hyperparameters, input_mode, metric_definitions, role, input_config, output_config, resource_config, stop_condition, tags, warm_start_config, max_runtime_in_seconds=None, strategy_config=None, completion_criteria_config=None, enable_network_isolation=False, image_uri=None, algorithm_arn=None, early_stopping_type='Off', encrypt_inter_container_traffic=False, vpc_config=None, use_spot_instances=False, checkpoint_s3_uri=None, checkpoint_local_path=None, random_seed=None, environment=None, hpo_resource_config=None)

Create an Amazon SageMaker hyperparameter tuning job.

Parameters
  • job_name (str) – Name of the tuning job being created.

  • strategy (str) – Strategy to be used for hyperparameter estimations.

  • strategy_config (dict) – A configuration for the hyperparameter tuning job optimisation strategy.

  • objective_type (str) – The type of the objective metric for evaluating training jobs. This value can be either ‘Minimize’ or ‘Maximize’.

  • objective_metric_name (str) – Name of the metric for evaluating training jobs.

  • max_jobs (int) – Maximum total number of training jobs to start for the hyperparameter tuning job.

  • max_parallel_jobs (int) – Maximum number of parallel training jobs to start.

  • parameter_ranges (dict) – Dictionary of parameter ranges. These parameter ranges can be one of three types: Continuous, Integer, or Categorical.

  • static_hyperparameters (dict) – Hyperparameters for model training. These hyperparameters remain unchanged across all of the training jobs for the hyperparameter tuning job. The hyperparameters are made accessible as a dictionary for the training code on SageMaker.

  • image_uri (str) – Docker image URI containing training code.

  • algorithm_arn (str) – Resource ARN for training algorithm created on or subscribed from AWS Marketplace (default: None).

  • input_mode (str) – The input mode that the algorithm supports. Valid modes: * ‘File’ - Amazon SageMaker copies the training dataset from the S3 location to a directory in the Docker container. * ‘Pipe’ - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe. * ‘FastFile’ - Amazon SageMaker streams data from S3 on demand instead of downloading the entire dataset before training begins.

  • metric_definitions (list[dict]) – A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: ‘Name’ for the name of the metric, and ‘Regex’ for the regular expression used to extract the metric from the logs. This should be defined only for jobs that don’t use an Amazon algorithm.

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role.

  • input_config (list) – A list of Channel objects. Each channel is a named input source. Please refer to the format details described: https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job

  • output_config (dict) – The S3 URI where you want to store the training results and optional KMS key ID.

  • resource_config (dict) – Contains values for ResourceConfig: * instance_count (int): Number of EC2 instances to use for training. The key in resource_config is ‘InstanceCount’. * instance_type (str): Type of EC2 instance to use for training, for example, ‘ml.c4.xlarge’. The key in resource_config is ‘InstanceType’.

  • stop_condition (dict) – When training should finish, e.g. MaxRuntimeInSeconds.

  • tags (list[dict]) – List of tags for labeling the tuning job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

  • warm_start_config (dict) – Configuration defining the type of warm start and other required configurations.

  • max_runtime_in_seconds (int or PipelineVariable) – The maximum time in seconds that a training job launched by a hyperparameter tuning job can run.

  • completion_criteria_config (sagemaker.tuner.TuningJobCompletionCriteriaConfig) – A configuration for the completion criteria.

  • early_stopping_type (str) – Specifies whether early stopping is enabled for the job. Can be either ‘Auto’ or ‘Off’. If set to ‘Off’, early stopping will not be attempted. If set to ‘Auto’, early stopping of some training jobs may happen, but is not guaranteed to.

  • enable_network_isolation (bool) – Specifies whether to isolate the training container (default: False).

  • encrypt_inter_container_traffic (bool) – Specifies whether traffic between training containers is encrypted for the training jobs started for this hyperparameter tuning job (default: False).

  • vpc_config (dict) – Contains values for VpcConfig (default: None): * subnets (list[str]): List of subnet ids. The key in vpc_config is ‘Subnets’. * security_group_ids (list[str]): List of security group ids. The key in vpc_config is ‘SecurityGroupIds’.

  • use_spot_instances (bool) – whether to use spot instances for training.

  • checkpoint_s3_uri (str) – The S3 URI in which to persist checkpoints that the algorithm persists (if any) during training. (default: None).

  • checkpoint_local_path (str) – The local path that the algorithm writes its checkpoints to. SageMaker will persist all files under this path to checkpoint_s3_uri continually during training. On job startup the reverse happens - data from the s3 location is downloaded to this path before the algorithm is started. If the path is unset then SageMaker assumes the checkpoints will be provided under /opt/ml/checkpoints/. (default: None).

  • random_seed (int) – An initial value used to initialize a pseudo-random number generator. Setting a random seed will make the hyperparameter tuning search strategies to produce more consistent configurations for the same tuning job. (default: None).

  • environment (dict[str, str]) – Environment variables to be set for use during training jobs (default: None)

  • hpo_resource_config (dict) – The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job, where you must specify either instance_configs or instance_count + instance_type + volume_size: * instance_count (int): Number of EC2 instances to use for training. The key in resource_config is ‘InstanceCount’. * instance_type (str): Type of EC2 instance to use for training, for example, ‘ml.c4.xlarge’. The key in resource_config is ‘InstanceType’. * volume_size (int or PipelineVariable): The volume size in GB of the data to be processed for hyperparameter optimisation * instance_configs (List[InstanceConfig]): A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs. * volume_kms_key_id: The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

create_tuning_job(job_name, tuning_config, training_config=None, training_config_list=None, warm_start_config=None, tags=None)

Create an Amazon SageMaker hyperparameter tuning job.

This method supports creating tuning jobs with single or multiple training algorithms (estimators), while the tune() method above only supports creating tuning jobs with single training algorithm.

Parameters
  • job_name (str) – Name of the tuning job being created.

  • tuning_config (dict) – Configuration to launch the tuning job.

  • training_config (dict) – Configuration to launch training jobs under the tuning job using a single algorithm.

  • training_config_list (list[dict]) – A list of configurations to launch training jobs under the tuning job using one or multiple algorithms. Either training_config or training_config_list should be provided, but not both.

  • warm_start_config (dict) – Configuration defining the type of warm start and other required configurations.

  • tags (list[dict]) – List of tags for labeling the tuning job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

describe_tuning_job(job_name)

Calls DescribeHyperParameterTuningJob API for the given job name, returns the response.

Parameters

job_name (str) – The name of the hyperparameter tuning job to describe.

Returns

A dictionary response with the hyperparameter tuning job description.

Return type

dict

stop_tuning_job(name)

Stop the Amazon SageMaker hyperparameter tuning job with the specified name.

Parameters

name (str) – Name of the Amazon SageMaker hyperparameter tuning job.

Raises

ClientError – If an error occurs while trying to stop the hyperparameter tuning job.

transform(job_name, model_name, strategy, max_concurrent_transforms, max_payload, env, input_config, output_config, resource_config, experiment_config, tags=None, data_processing=None, model_client_config=None, batch_data_capture_config=None)

Create an Amazon SageMaker transform job.

Parameters
  • job_name (str) – Name of the transform job being created.

  • model_name (str) – Name of the SageMaker model being used for the transform job.

  • strategy (str) – The strategy used to decide how to batch records in a single request. Possible values are ‘MultiRecord’ and ‘SingleRecord’.

  • max_concurrent_transforms (int) – The maximum number of HTTP requests to be made to each individual transform container at one time.

  • max_payload (int) – Maximum size of the payload in a single HTTP request to the container in MB.

  • env (dict) – Environment variables to be set for use during the transform job.

  • input_config (dict) – A dictionary describing the input data (and its location) for the job.

  • output_config (dict) – A dictionary describing the output location for the job.

  • resource_config (dict) – A dictionary describing the resources to complete the job.

  • experiment_config (dict[str, str]) – Experiment management configuration. Optionally, the dict can contain three keys: ‘ExperimentName’, ‘TrialName’, and ‘TrialComponentDisplayName’. The behavior of setting these keys is as follows: * If ExperimentName is supplied but TrialName is not a Trial will be automatically created and the job’s Trial Component associated with the Trial. * If TrialName is supplied and the Trial already exists the job’s Trial Component will be associated with the Trial. * If both ExperimentName and TrialName are not supplied the trial component will be unassociated. * TrialComponentDisplayName is used for display in Studio.

  • tags (list[dict]) – List of tags for labeling a transform job.

  • data_processing (dict) – A dictionary describing config for combining the input data and transformed data. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

  • model_client_config (dict) – A dictionary describing the model configuration for the job. Dictionary contains two optional keys, ‘InvocationsTimeoutInSeconds’, and ‘InvocationsMaxRetries’.

  • batch_data_capture_config (BatchDataCaptureConfig) – Configuration object which specifies the configurations related to the batch data capture for the transform job

create_model(name, role=None, container_defs=None, vpc_config=None, enable_network_isolation=None, primary_container=None, tags=None)

Create an Amazon SageMaker Model.

Specify the S3 location of the model artifacts and Docker image containing the inference code. Amazon SageMaker uses this information to deploy the model in Amazon SageMaker. This method can also be used to create a Model for an Inference Pipeline if you pass the list of container definitions through the containers parameter.

Parameters
  • name (str) – Name of the Amazon SageMaker Model to create.

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role.

  • container_defs (list[dict[str, str]] or [dict[str, str]]) – A single container definition or a list of container definitions which will be invoked sequentially while performing the prediction. If the list contains only one container, then it’ll be passed to SageMaker Hosting as the PrimaryContainer and otherwise, it’ll be passed as Containers.You can also specify the return value of sagemaker.get_container_def() or sagemaker.pipeline_container_def(), which will used to create more advanced container configurations, including model containers which need artifacts from S3.

  • vpc_config (dict[str, list[str]]) – The VpcConfig set on the model (default: None) * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.

  • enable_network_isolation (bool) – Whether the model requires network isolation or not.

  • primary_container (str or dict[str, str]) – Docker image which defines the inference code. You can also specify the return value of sagemaker.container_def(), which is used to create more advanced container configurations, including model containers which need artifacts from S3. This field is deprecated, please use container_defs instead.

  • tags (List[dict[str, str]]) – Optional. The list of tags to add to the model.

Example

>>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
For more information about tags, see https://boto3.amazonaws.com/v1/documentation            /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
Returns

Name of the Amazon SageMaker Model created.

Return type

str

create_model_from_job(training_job_name, name=None, role=None, image_uri=None, model_data_url=None, env=None, enable_network_isolation=None, vpc_config_override='VPC_CONFIG_DEFAULT', tags=None)

Create an Amazon SageMaker Model from a SageMaker Training Job.

Parameters
  • training_job_name (str) – The Amazon SageMaker Training Job name.

  • name (str) – The name of the SageMaker Model to create (default: None). If not specified, the training job name is used.

  • role (str) – The ExecutionRoleArn IAM Role ARN for the Model, specified either by an IAM role name or role ARN. If None, the RoleArn from the SageMaker Training Job will be used.

  • image_uri (str) – The Docker image URI (default: None). If None, it defaults to the training image URI from training_job_name.

  • model_data_url (str) – S3 location of the model data (default: None). If None, defaults to the ModelS3Artifacts of training_job_name.

  • env (dict[string,string]) – Model environment variables (default: {}).

  • enable_network_isolation (bool) – Whether the model requires network isolation or not.

  • vpc_config_override (dict[str, list[str]]) – Optional override for VpcConfig set on the model. Default: use VpcConfig from training job. * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.

  • tags (List[dict[str, str]]) – Optional. The list of tags to add to the model. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

Returns

The name of the created Model.

Return type

str

create_model_package_from_algorithm(name, description, algorithm_arn, model_data)

Create a SageMaker Model Package from the results of training with an Algorithm Package.

Parameters
  • name (str) – ModelPackage name

  • description (str) – Model Package description

  • algorithm_arn (str) – arn or name of the algorithm used for training.

  • model_data (str) – s3 URI to the model artifacts produced by training

create_model_package_from_containers(containers=None, content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status='PendingManualApproval', description=None, drift_check_baselines=None, customer_metadata_properties=None, validation_specification=None, domain=None, sample_payload_url=None, task=None)

Get request dictionary for CreateModelPackage API.

Parameters
  • containers (list) – A list of inference containers that can be used for inference specifications of Model Package (default: None).

  • content_types (list) – The supported MIME types for the input data (default: None).

  • response_types (list) – The supported MIME types for the output data (default: None).

  • inference_instances (list) – A list of the instance types that are used to generate inferences in real-time (default: None).

  • transform_instances (list) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None).

  • model_package_name (str) – Model Package name, exclusive to model_package_group_name, using model_package_name makes the Model Package un-versioned (default: None).

  • model_package_group_name (str) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned (default: None).

  • model_metrics (ModelMetrics) – ModelMetrics object (default: None).

  • metadata_properties (MetadataProperties) – MetadataProperties object (default: None)

  • marketplace_cert (bool) – A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False).

  • approval_status (str) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval” (default: “PendingManualApproval”).

  • description (str) – Model Package description (default: None).

  • drift_check_baselines (DriftCheckBaselines) – DriftCheckBaselines object (default: None).

  • customer_metadata_properties (dict[str, str]) – A dictionary of key-value paired metadata properties (default: None).

  • domain (str) – Domain values can be “COMPUTER_VISION”, “NATURAL_LANGUAGE_PROCESSING”, “MACHINE_LEARNING” (default: None).

  • sample_payload_url (str) – The S3 path where the sample payload is stored (default: None).

  • task (str) – Task values which are supported by Inference Recommender are “FILL_MASK”, “IMAGE_CLASSIFICATION”, “OBJECT_DETECTION”, “TEXT_GENERATION”, “IMAGE_SEGMENTATION”, “CLASSIFICATION”, “REGRESSION”, “OTHER” (default: None).

wait_for_model_package(model_package_name, poll=5)

Wait for an Amazon SageMaker endpoint deployment to complete.

Parameters
  • endpoint (str) – Name of the Endpoint to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeEndpoint API.

Return type

dict

Raises
  • exceptions.CapacityError – If the Model Package job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If waiting and the Model Package job fails.

describe_model(name)

Calls the DescribeModel API for the given model name.

Parameters

name (str) – The name of the SageMaker model.

Returns

A dictionary response with the model description.

Return type

dict

create_endpoint_config(name, model_name, initial_instance_count, instance_type, accelerator_type=None, tags=None, kms_key=None, data_capture_config_dict=None, volume_size=None, model_data_download_timeout=None, container_startup_health_check_timeout=None, explainer_config_dict=None)

Create an Amazon SageMaker endpoint configuration.

The endpoint configuration identifies the Amazon SageMaker model (created using the CreateModel API) and the hardware configuration on which to deploy the model. Provide this endpoint configuration to the CreateEndpoint API, which then launches the hardware and deploys the model.

Parameters
  • name (str) – Name of the Amazon SageMaker endpoint configuration to create.

  • model_name (str) – Name of the Amazon SageMaker Model.

  • initial_instance_count (int) – Minimum number of EC2 instances to launch. The actual number of active instances for an endpoint at any given time varies due to autoscaling.

  • instance_type (str) – Type of EC2 instance to launch, for example, ‘ml.c4.xlarge’.

  • accelerator_type (str) – Type of Elastic Inference accelerator to attach to the instance. For example, ‘ml.eia1.medium’. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

  • tags (List[dict[str, str]]) – Optional. The list of tags to add to the endpoint config.

  • kms_key (str) – The KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint.

  • data_capture_config_dict (dict) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

  • volume_size (int) – The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currenly only Amazon EBS gp2 storage volumes are supported.

  • model_data_download_timeout (int) – The timeout value, in seconds, to download and extract model data from Amazon S3 to the individual inference instance associated with this production variant.

  • container_startup_health_check_timeout (int) – The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check see: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests

  • explainer_config_dict (dict) – Specifies configuration to enable explainers. Default: None.

Example

>>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
For more information about tags, see
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
Returns

Name of the endpoint point configuration created.

Return type

str

create_endpoint_config_from_existing(existing_config_name, new_config_name, new_tags=None, new_kms_key=None, new_data_capture_config_dict=None, new_production_variants=None, new_explainer_config_dict=None)

Create an Amazon SageMaker endpoint configuration from an existing one.

It also updates any values that were passed in. The endpoint configuration identifies the Amazon SageMaker model (created using the CreateModel API) and the hardware configuration on which to deploy the model. Provide this endpoint configuration to the CreateEndpoint API, which then launches the hardware and deploys the model.

Parameters
  • new_config_name (str) – Name of the Amazon SageMaker endpoint configuration to create.

  • existing_config_name (str) – Name of the existing Amazon SageMaker endpoint configuration.

  • new_tags (list[dict[str, str]]) – Optional. The list of tags to add to the endpoint config. If not specified, the tags of the existing endpoint configuration are used. If any of the existing tags are reserved AWS ones (i.e. begin with “aws”), they are not carried over to the new endpoint configuration.

  • new_kms_key (str) – The KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint (default: None). If not specified, the KMS key of the existing endpoint configuration is used.

  • new_data_capture_config_dict (dict) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring (default: None). If not specified, the data capture configuration of the existing endpoint configuration is used.

  • new_production_variants (list[dict]) – The configuration for which model(s) to host and the resources to deploy for hosting the model(s). If not specified, the ProductionVariants of the existing endpoint configuration is used.

  • new_explainer_config_dict (dict) – Specifies configuration to enable explainers. (default: None). If not specified, the explainer configuration of the existing endpoint configuration is used.

Returns

Name of the endpoint point configuration created.

Return type

str

create_endpoint(endpoint_name, config_name, tags=None, wait=True)

Create an Amazon SageMaker Endpoint according to the configuration in the request.

Once the Endpoint is created, client applications can send requests to obtain inferences. The endpoint configuration is created using the CreateEndpointConfig API.

Parameters
  • endpoint_name (str) – Name of the Amazon SageMaker Endpoint being created.

  • config_name (str) – Name of the Amazon SageMaker endpoint configuration to deploy.

  • wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

Returns

Name of the Amazon SageMaker Endpoint created.

Return type

str

update_endpoint(endpoint_name, endpoint_config_name, wait=True)

Update an Amazon SageMaker Endpoint , Raise an error endpoint_name does not exist.

Parameters
  • endpoint_name (str) – Name of the Amazon SageMaker Endpoint to update.

  • endpoint_config_name (str) – Name of the Amazon SageMaker endpoint configuration to deploy.

  • wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

Returns

Name of the Amazon SageMaker Endpoint being updated.

Return type

str

Raises

ValueError – if the endpoint does not already exist

delete_endpoint(endpoint_name)

Delete an Amazon SageMaker Endpoint.

Parameters

endpoint_name (str) – Name of the Amazon SageMaker Endpoint to delete.

delete_endpoint_config(endpoint_config_name)

Delete an Amazon SageMaker endpoint configuration.

Parameters

endpoint_config_name (str) – Name of the Amazon SageMaker endpoint configuration to delete.

delete_model(model_name)

Delete an Amazon SageMaker Model.

Parameters

model_name (str) – Name of the Amazon SageMaker model to delete.

list_group_resources(group, filters, next_token='')

To list group resources with given filters

Parameters
  • group (str) – The name or the ARN of the group.

  • filters (list) – Filters that needs to be applied to the list operation.

  • next_token (str) –

delete_resource_group(group)

To delete a resource group

Parameters

group (str) – The name or the ARN of the resource group to delete.

get_resource_group_query(group)

To get the group query for an AWS Resource Group

Parameters

group (str) – The name or the ARN of the resource group to query.

get_tagging_resources(tag_filters, resource_type_filters)

To list the complete resources for a particular resource group tag

tag_filters: filters for the tag resource_type_filters: resource filter for the tag

create_group(name, resource_query, tags)

To create a AWS Resource Group

Parameters
  • name (str) – The name of the group, which is also the identifier of the group.

  • resource_query (str) – The resource query that determines which AWS resources are members of this group

  • tags (dict) – The Tags to be attached to the Resource Group

list_tags(resource_arn, max_results=50)

List the tags given an Amazon Resource Name.

Parameters
  • resource_arn (str) – The Amazon Resource Name (ARN) for which to get the tags list.

  • max_results (int) – The maximum number of results to include in a single page. This method takes care of that abstraction and returns a full list.

wait_for_job(job, poll=5)

Wait for an Amazon SageMaker training job to complete.

Parameters
  • job (str) – Name of the training job to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeTrainingJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the training job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the training job fails.

wait_for_processing_job(job, poll=5)

Wait for an Amazon SageMaker Processing job to complete.

Parameters
  • job (str) – Name of the processing job to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeProcessingJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the processing job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the processing job fails.

wait_for_compilation_job(job, poll=5)

Wait for an Amazon SageMaker Neo compilation job to complete.

Parameters
  • job (str) – Name of the compilation job to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeCompilationJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the compilation job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the compilation job fails.

wait_for_edge_packaging_job(job, poll=5)

Wait for an Amazon SageMaker Edge packaging job to complete.

Parameters
  • job (str) – Name of the edge packaging job to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeEdgePackagingJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the edge packaging job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the edge packaging job fails.

wait_for_tuning_job(job, poll=5)

Wait for an Amazon SageMaker hyperparameter tuning job to complete.

Parameters
  • job (str) – Name of the tuning job to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeHyperParameterTuningJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the hyperparameter tuning job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the hyperparameter tuning job fails.

describe_transform_job(job_name)

Calls the DescribeTransformJob API for the given job name and returns the response.

Parameters

job_name (str) – The name of the transform job to describe.

Returns

A dictionary response with the transform job description.

Return type

dict

wait_for_transform_job(job, poll=5)

Wait for an Amazon SageMaker transform job to complete.

Parameters
  • job (str) – Name of the transform job to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Returns

Return value from the DescribeTransformJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the transform job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the transform job fails.

stop_transform_job(name)

Stop the Amazon SageMaker hyperparameter tuning job with the specified name.

Parameters

name (str) – Name of the Amazon SageMaker batch transform job.

Raises

ClientError – If an error occurs while trying to stop the batch transform job.

wait_for_endpoint(endpoint, poll=30)

Wait for an Amazon SageMaker endpoint deployment to complete.

Parameters
  • endpoint (str) – Name of the Endpoint to wait for.

  • poll (int) – Polling interval in seconds (default: 5).

Raises
  • exceptions.CapacityError – If the endpoint creation job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the endpoint creation job fails.

Returns

Return value from the DescribeEndpoint API.

Return type

dict

endpoint_from_job(job_name, initial_instance_count, instance_type, image_uri=None, name=None, role=None, wait=True, model_environment_vars=None, vpc_config_override='VPC_CONFIG_DEFAULT', accelerator_type=None, data_capture_config=None)

Create an Endpoint using the results of a successful training job.

Specify the job name, Docker image containing the inference code, and hardware configuration to deploy the model. Internally the API, creates an Amazon SageMaker model (that describes the model artifacts and the Docker image containing inference code), endpoint configuration (describing the hardware to deploy for hosting the model), and creates an Endpoint (launches the EC2 instances and deploys the model on them). In response, the API returns the endpoint name to which you can send requests for inferences.

Parameters
  • job_name (str) – Name of the training job to deploy the results of.

  • initial_instance_count (int) – Minimum number of EC2 instances to launch. The actual number of active instances for an endpoint at any given time varies due to autoscaling.

  • instance_type (str) – Type of EC2 instance to deploy to an endpoint for prediction, for example, ‘ml.c4.xlarge’.

  • image_uri (str) – The Docker image which defines the inference code to be used as the entry point for accepting prediction requests. If not specified, uses the image used for the training job.

  • name (str) – Name of the Endpoint to create. If not specified, uses the training job name.

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role.

  • wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

  • model_environment_vars (dict[str, str]) – Environment variables to set on the model container (default: None).

  • vpc_config_override (dict[str, list[str]]) – Overrides VpcConfig set on the model. Default: use VpcConfig from training job. * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.

  • accelerator_type (str) – Type of Elastic Inference accelerator to attach to the instance. For example, ‘ml.eia1.medium’. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

  • data_capture_config (sagemaker.model_monitor.DataCaptureConfig) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

Returns

Name of the Endpoint that is created.

Return type

str

endpoint_from_model_data(model_s3_location, image_uri, initial_instance_count, instance_type, name=None, role=None, wait=True, model_environment_vars=None, model_vpc_config=None, accelerator_type=None, data_capture_config=None)

Create and deploy to an Endpoint using existing model data stored in S3.

Parameters
  • model_s3_location (str) – S3 URI of the model artifacts to use for the endpoint.

  • image_uri (str) – The Docker image URI which defines the runtime code to be used as the entry point for accepting prediction requests.

  • initial_instance_count (int) – Minimum number of EC2 instances to launch. The actual number of active instances for an endpoint at any given time varies due to autoscaling.

  • instance_type (str) – Type of EC2 instance to deploy to an endpoint for prediction, e.g. ‘ml.c4.xlarge’.

  • name (str) – Name of the Endpoint to create. If not specified, uses a name generated by combining the image name with a timestamp.

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role.

  • wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

  • model_environment_vars (dict[str, str]) – Environment variables to set on the model container (default: None).

  • model_vpc_config (dict[str, list[str]]) – The VpcConfig set on the model (default: None) * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.

  • accelerator_type (str) – Type of Elastic Inference accelerator to attach to the instance. For example, ‘ml.eia1.medium’. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

  • data_capture_config (sagemaker.model_monitor.DataCaptureConfig) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

Returns

Name of the Endpoint that is created.

Return type

str

endpoint_from_production_variants(name, production_variants, tags=None, kms_key=None, wait=True, data_capture_config_dict=None, async_inference_config_dict=None, explainer_config_dict=None)

Create an SageMaker Endpoint from a list of production variants.

Parameters
  • name (str) – The name of the Endpoint to create.

  • production_variants (list[dict[str, str]]) – The list of production variants to deploy.

  • tags (list[dict[str, str]]) – A list of key-value pairs for tagging the endpoint (default: None).

  • kms_key (str) – The KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint.

  • wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

  • data_capture_config_dict (dict) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

  • async_inference_config_dict (dict) – specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference (default: None)

  • explainer_config_dict (dict) – Specifies configuration related to explainer. Use this configuration when trying to use online explainability. (default: None)

Returns

The name of the created Endpoint.

Return type

str

expand_role(role)

Expand an IAM role name into an ARN.

If the role is already in the form of an ARN, then the role is simply returned. Otherwise we retrieve the full ARN and return it.

Parameters

role (str) – An AWS IAM role (either name or full ARN).

Returns

The corresponding AWS IAM role ARN.

Return type

str

get_caller_identity_arn()

Returns the ARN user or role whose credentials are used to call the API.

Returns

The ARN user or role

Return type

str

logs_for_job(job_name, wait=False, poll=10, log_type='All', timeout=None)

Display logs for a given training job, optionally tailing them until job is complete.

If the output is a tty or a Jupyter cell, it will be color-coded based on which instance the log entry is from.

Parameters
  • job_name (str) – Name of the training job to display the logs for.

  • wait (bool) – Whether to keep looking for new log entries until the job completes (default: False).

  • poll (int) – The interval in seconds between polling for new log entries and job completion (default: 5).

  • log_type ([str]) – A list of strings specifying which logs to print. Acceptable strings are “All”, “None”, “Training”, or “Rules”. To maintain backwards compatibility, boolean values are also accepted and converted to strings.

  • timeout (int) – Timeout in seconds to wait until the job is completed. None by default.

Raises
  • exceptions.CapacityError – If the training job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If waiting and the training job fails.

logs_for_processing_job(job_name, wait=False, poll=10)

Display logs for a given processing job, optionally tailing them until the is complete.

Parameters
  • job_name (str) – Name of the processing job to display the logs for.

  • wait (bool) – Whether to keep looking for new log entries until the job completes (default: False).

  • poll (int) – The interval in seconds between polling for new log entries and job completion (default: 5).

Raises

ValueError – If the processing job fails.

logs_for_transform_job(job_name, wait=False, poll=10)

Display logs for a given training job, optionally tailing them until job is complete.

If the output is a tty or a Jupyter cell, it will be color-coded based on which instance the log entry is from.

Parameters
  • job_name (str) – Name of the transform job to display the logs for.

  • wait (bool) – Whether to keep looking for new log entries until the job completes (default: False).

  • poll (int) – The interval in seconds between polling for new log entries and job completion (default: 5).

Raises

ValueError – If the transform job fails.

delete_feature_group(feature_group_name)

Deletes a FeatureGroup in the FeatureStore service.

Parameters

feature_group_name (str) – name of the feature group to be deleted.

create_feature_group(feature_group_name, record_identifier_name, event_time_feature_name, feature_definitions, role_arn=None, online_store_config=None, offline_store_config=None, description=None, tags=None)

Creates a FeatureGroup in the FeatureStore service.

Parameters
  • feature_group_name (str) – name of the FeatureGroup.

  • record_identifier_name (str) – name of the record identifier feature.

  • event_time_feature_name (str) – name of the event time feature.

  • feature_definitions (Sequence[Dict[str, str]]) – list of feature definitions.

  • role_arn (str) – ARN of the role will be used to execute the api.

  • online_store_config (Dict[str, str]) – dict contains configuration of the feature online store.

  • offline_store_config (Dict[str, str]) – dict contains configuration of the feature offline store.

  • description (str) – description of the FeatureGroup.

  • tags (List[Dict[str, str]]) – list of tags for labeling a FeatureGroup.

Returns

Response dict from service.

Return type

Dict[str, Any]

describe_feature_group(feature_group_name, next_token=None)

Describe a FeatureGroup by name in FeatureStore service.

Parameters
  • feature_group_name (str) – name of the FeatureGroup to describe.

  • next_token (str) – next_token to get next page of features.

Returns

Response dict from service.

Return type

Dict[str, Any]

update_feature_group(feature_group_name, feature_additions)

Update a FeatureGroup and add new features from the given feature definitions.

Parameters
  • feature_group_name (str) – name of the FeatureGroup to update.

  • feature_additions (Sequence[Dict[str, str]) – list of feature definitions to be updated.

Returns

Response dict from service.

Return type

Dict[str, Any]

list_feature_groups(name_contains, feature_group_status_equals, offline_store_status_equals, creation_time_after, creation_time_before, sort_order, sort_by, max_results, next_token)

List all FeatureGroups satisfying given filters.

Parameters
  • name_contains (str) – A string that partially matches one or more FeatureGroups’ names. Filters FeatureGroups by name.

  • feature_group_status_equals (str) – A FeatureGroup status. Filters FeatureGroups by FeatureGroup status.

  • offline_store_status_equals (str) – An OfflineStore status. Filters FeatureGroups by OfflineStore status.

  • creation_time_after (datetime.datetime) – Use this parameter to search for FeatureGroups created after a specific date and time.

  • creation_time_before (datetime.datetime) – Use this parameter to search for FeatureGroups created before a specific date and time.

  • sort_order (str) – The order in which FeatureGroups are listed.

  • sort_by (str) – The value on which the FeatureGroup list is sorted.

  • max_results (int) – The maximum number of results returned by ListFeatureGroups.

  • next_token (str) – A token to resume pagination of ListFeatureGroups results.

Returns

Response dict from service.

Return type

Dict[str, Any]

update_feature_metadata(feature_group_name, feature_name, description=None, parameter_additions=None, parameter_removals=None)

Update a feature metadata and add/remove metadata.

Parameters
  • feature_group_name (str) – name of the FeatureGroup to update.

  • feature_name (str) – name of the feature to update.

  • description (str) – description of the feature to update.

  • parameter_additions (Sequence[Dict[str, str]) – list of feature parameter to be added.

  • parameter_removals (Sequence[Dict[str, str]) – list of feature parameter to be removed.

Returns

Response dict from service.

Return type

Dict[str, Any]

describe_feature_metadata(feature_group_name, feature_name)

Describe feature metadata by feature name in FeatureStore service.

Parameters
  • feature_group_name (str) – name of the FeatureGroup.

  • feature_name (str) – name of the feature.

Returns

Response dict from service.

Return type

Dict[str, Any]

search(resource, search_expression=None, sort_by=None, sort_order=None, next_token=None, max_results=None)

Search for SageMaker resources satisfying given filters.

Parameters
  • resource (str) – The name of the Amazon SageMaker resource to search for.

  • search_expression (Dict[str, any]) – A Boolean conditional statement. Resources must satisfy this condition to be included in search results.

  • sort_by (str) – The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.

  • sort_order (str) – How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending. next_token (str): If more than MaxResults resources match the specified SearchExpression, the response includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results.

  • max_results (int) – The maximum number of results to return.

  • next_token (str) –

Returns

Response dict from service.

Return type

Dict[str, Any]

put_record(feature_group_name, record)

Puts a single record in the FeatureGroup.

Parameters
  • feature_group_name (str) – name of the FeatureGroup.

  • record (Sequence[Dict[str, str]]) – list of FeatureValue dicts to be ingested into FeatureStore.

delete_record(feature_group_name, record_identifier_value_as_string, event_time, deletion_mode=None)

Deletes a single record from the FeatureGroup.

Parameters
  • feature_group_name (str) – name of the FeatureGroup.

  • record_identifier_value_as_string (str) – name of the record identifier.

  • event_time (str) – a timestamp indicating when the deletion event occurred.

  • deletion_mode (str) – (str): deletion mode for deleting record.

get_record(record_identifier_value_as_string, feature_group_name, feature_names)

Gets a single record in the FeatureGroup.

Parameters
  • record_identifier_value_as_string (str) – name of the record identifier.

  • feature_group_name (str) – name of the FeatureGroup.

  • feature_names (Sequence[str]) – list of feature names.

Return type

Dict[str, Sequence[Dict[str, str]]]

batch_get_record(identifiers)

Gets a batch of record from FeatureStore.

Parameters

identifiers (Sequence[Dict[str, Any]]) – list of identifiers to uniquely identify records in FeatureStore.

Returns

Response dict from service.

Return type

Dict[str, Any]

start_query_execution(catalog, database, query_string, output_location, kms_key=None, workgroup=None)

Start Athena query execution.

Parameters
  • catalog (str) – name of the data catalog.

  • database (str) – name of the data catalog database.

  • query_string (str) – SQL expression.

  • output_location (str) – S3 location of the output file.

  • kms_key (str) – KMS key id will be used to encrypt the result if given.

  • workgroup (str) – The name of the workgroup in which the query is being started.

  • the workgroup is not specified, the default workgroup is used. (If) –

Returns

Response dict from the service.

Return type

Dict[str, str]

get_query_execution(query_execution_id)

Get execution status of the Athena query.

Parameters

query_execution_id (str) – execution ID of the Athena query.

Return type

Dict[str, Any]

wait_for_athena_query(query_execution_id, poll=5)

Wait for Athena query to finish.

Parameters
  • query_execution_id (str) – execution ID of the Athena query.

  • poll (int) – time interval to poll get_query_execution API.

download_athena_query_result(bucket, prefix, query_execution_id, filename)

Download query result file from S3.

Parameters
  • bucket (str) – name of the S3 bucket where the result file is stored.

  • prefix (str) – S3 prefix of the result file.

  • query_execution_id (str) – execution ID of the Athena query.

  • filename (str) – name of the downloaded file.

account_id()

Get the AWS account id of the caller.

Returns

AWS account ID.

Return type

str

create_inference_recommendations_job(role, sample_payload_url, supported_content_types, job_name=None, job_type='Default', model_name=None, model_package_version_arn=None, job_duration_in_seconds=None, nearest_model_name=None, supported_instance_types=None, framework=None, framework_version=None, endpoint_configurations=None, traffic_pattern=None, stopping_conditions=None, resource_limit=None)

Creates an Inference Recommendations Job

Parameters
  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role.

  • sample_payload_url (str) – The S3 path where the sample payload is stored.

  • supported_content_types (List[str]) – The supported MIME types for the input data.

  • model_name (str) – Name of the Amazon SageMaker Model to be used.

  • model_package_version_arn (str) – The Amazon Resource Name (ARN) of a versioned model package.

  • job_name (str) – The name of the job being run.

  • job_type (str) – The type of job being run. Must either be Default or Advanced.

  • job_duration_in_seconds (int) – The maximum job duration that a job can run for. Will be used for Advanced jobs.

  • nearest_model_name (str) – The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.

  • supported_instance_types (List[str]) – A list of the instance types that are used to generate inferences in real-time.

  • framework (str) – The machine learning framework of the Image URI.

  • framework_version (str) – The framework version of the Image URI.

  • endpoint_configurations (List[Dict[str, any]]) – Specifies the endpoint configurations to use for a job. Will be used for Advanced jobs.

  • traffic_pattern (Dict[str, any]) – Specifies the traffic pattern for the job. Will be used for Advanced jobs.

  • stopping_conditions (Dict[str, any]) – A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped. Will be used for Advanced jobs.

  • resource_limit (Dict[str, any]) – Defines the resource limit for the job. Will be used for Advanced jobs.

Returns

The name of the job created. In the form of SMPYTHONSDK-<timestamp>

Return type

str

wait_for_inference_recommendations_job(job_name, poll=120, log_level='Verbose')

Wait for an Amazon SageMaker Inference Recommender job to complete.

Parameters
  • job_name (str) – Name of the Inference Recommender job to wait for.

  • poll (int) – Polling interval in seconds (default: 120).

  • log_level (str) – The level of verbosity for the logs.

  • be "Quiet" or "Verbose" (default (Can) – “Quiet”).

Returns

Return value from the DescribeInferenceRecommendationsJob API.

Return type

(dict)

Raises
  • exceptions.CapacityError – If the Inference Recommender job fails with CapacityError.

  • exceptions.UnexpectedStatusException – If the Inference Recommender job fails.

sagemaker.session.get_model_package_args(content_types, response_types, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, model_data=None, image_uri=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status=None, description=None, tags=None, container_def_list=None, drift_check_baselines=None, customer_metadata_properties=None, validation_specification=None, domain=None, sample_payload_url=None, task=None)

Get arguments for create_model_package method.

Parameters
  • content_types (list) – The supported MIME types for the input data.

  • response_types (list) – The supported MIME types for the output data.

  • inference_instances (list) – A list of the instance types that are used to generate inferences in real-time (default: None).

  • transform_instances (list) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None).

  • model_package_name (str) – Model Package name, exclusive to model_package_group_name, using model_package_name makes the Model Package un-versioned (default: None).

  • model_package_group_name (str) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package

  • model_data (str) – s3 URI to the model artifacts from training (default: None).

  • image_uri (str) – Inference image uri for the container. Model class’ self.image will be used if it is None (default: None).

  • model_metrics (ModelMetrics) – ModelMetrics object (default: None).

  • metadata_properties (MetadataProperties) – MetadataProperties object (default: None).

  • marketplace_cert (bool) – A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False).

  • approval_status (str) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval” (default: “PendingManualApproval”).

  • description (str) – Model Package description (default: None).

  • tags (List[dict[str, str]]) – A list of dictionaries containing key-value pairs (default: None).

  • container_def_list (list) – A list of container defintiions (default: None).

  • drift_check_baselines (DriftCheckBaselines) – DriftCheckBaselines object (default: None).

  • customer_metadata_properties (dict[str, str]) – A dictionary of key-value paired metadata properties (default: None).

  • domain (str) – Domain values can be “COMPUTER_VISION”, “NATURAL_LANGUAGE_PROCESSING”, “MACHINE_LEARNING” (default: None).

  • sample_payload_url (str) – The S3 path where the sample payload is stored (default: None).

  • task (str) – Task values which are supported by Inference Recommender are “FILL_MASK”, “IMAGE_CLASSIFICATION”, “OBJECT_DETECTION”, “TEXT_GENERATION”, “IMAGE_SEGMENTATION”, “CLASSIFICATION”, “REGRESSION”, “OTHER” (default: None).

Returns

A dictionary of method argument names and values.

Return type

dict

sagemaker.session.get_create_model_package_request(model_package_name=None, model_package_group_name=None, containers=None, content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status='PendingManualApproval', description=None, tags=None, drift_check_baselines=None, customer_metadata_properties=None, validation_specification=None, domain=None, sample_payload_url=None, task=None)

Get request dictionary for CreateModelPackage API.

Parameters
  • model_package_name (str) – Model Package name, exclusive to model_package_group_name, using model_package_name makes the Model Package un-versioned (default: None).

  • model_package_group_name (str) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package

  • containers (list) – A list of inference containers that can be used for inference specifications of Model Package (default: None).

  • content_types (list) – The supported MIME types for the input data (default: None).

  • response_types (list) – The supported MIME types for the output data (default: None).

  • inference_instances (list) – A list of the instance types that are used to generate inferences in real-time (default: None).

  • transform_instances (list) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None).

  • model_metrics (ModelMetrics) – ModelMetrics object (default: None).

  • metadata_properties (MetadataProperties) – MetadataProperties object (default: None).

  • marketplace_cert (bool) – A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False).

  • approval_status (str) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval” (default: “PendingManualApproval”).

  • description (str) – Model Package description (default: None).

  • tags (List[dict[str, str]]) – A list of dictionaries containing key-value pairs (default: None).

  • drift_check_baselines (DriftCheckBaselines) – DriftCheckBaselines object (default: None).

  • customer_metadata_properties (dict[str, str]) – A dictionary of key-value paired metadata properties (default: None).

  • domain (str) – Domain values can be “COMPUTER_VISION”, “NATURAL_LANGUAGE_PROCESSING”, “MACHINE_LEARNING” (default: None).

  • sample_payload_url (str) – The S3 path where the sample payload is stored (default: None).

  • task (str) – Task values which are supported by Inference Recommender are “FILL_MASK”, “IMAGE_CLASSIFICATION”, “OBJECT_DETECTION”, “TEXT_GENERATION”, “IMAGE_SEGMENTATION”, “CLASSIFICATION”, “REGRESSION”, “OTHER” (default: None).

sagemaker.session.update_args(args, **kwargs)

Updates the request arguments dict with the value if populated.

This is to handle the case that the service API doesn’t like NoneTypes for argument values.

Parameters
  • request_args (Dict[str, Any]) – the request arguments dict

  • kwargs – key, value pairs to update the args dict

  • args (Dict[str, Any]) –

sagemaker.session.container_def(image_uri, model_data_url=None, env=None, container_mode=None, image_config=None)

Create a definition for executing a container as part of a SageMaker model.

Parameters
  • image_uri (str) – Docker image URI to run for this container.

  • model_data_url (str) – S3 URI of data required by this container, e.g. SageMaker training job model artifacts (default: None).

  • env (dict[str, str]) – Environment variables to set inside the container (default: None).

  • container_mode (str) – The model container mode. Valid modes: * MultiModel: Indicates that model container can support hosting multiple models * SingleModel: Indicates that model container can support hosting a single model This is the default model container mode when container_mode = None

  • image_config (dict[str, str]) – Specifies whether the image of model container is pulled from ECR, or private registry in your VPC. By default it is set to pull model container image from ECR. (default: None).

Returns

A complete container definition object usable with the CreateModel API if passed via PrimaryContainers field.

Return type

dict[str, str]

sagemaker.session.pipeline_container_def(models, instance_type=None)

Create a definition for executing a pipeline of containers as part of a SageMaker model.

Parameters
  • models (list[sagemaker.Model]) – this will be a list of sagemaker.Model objects in the order the inference should be invoked.

  • instance_type (str) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’ (default: None).

Returns

list of container definition objects usable with with the

CreateModel API for inference pipelines if passed via Containers field.

Return type

list[dict[str, str]]

sagemaker.session.production_variant(model_name, instance_type=None, initial_instance_count=None, variant_name='AllTraffic', initial_weight=1, accelerator_type=None, serverless_inference_config=None, volume_size=None, model_data_download_timeout=None, container_startup_health_check_timeout=None)

Create a production variant description suitable for use in a ProductionVariant list.

This is also part of a CreateEndpointConfig request.

Parameters
  • model_name (str) – The name of the SageMaker model this production variant references.

  • instance_type (str) – The EC2 instance type for this production variant. For example, ‘ml.c4.8xlarge’.

  • initial_instance_count (int) – The initial instance count for this production variant (default: 1).

  • variant_name (string) – The VariantName of this production variant (default: ‘AllTraffic’).

  • initial_weight (int) – The relative InitialVariantWeight of this production variant (default: 1).

  • accelerator_type (str) – Type of Elastic Inference accelerator for this production variant. For example, ‘ml.eia1.medium’. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

  • serverless_inference_config (dict) – Specifies configuration dict related to serverless endpoint. The dict is converted from sagemaker.model_monitor.ServerlessInferenceConfig object (default: None)

  • volume_size (int) – The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currenly only Amazon EBS gp2 storage volumes are supported.

  • model_data_download_timeout (int) – The timeout value, in seconds, to download and extract model data from Amazon S3 to the individual inference instance associated with this production variant.

  • container_startup_health_check_timeout (int) – The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check see: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests

Returns

An SageMaker ProductionVariant description

Return type

dict[str, str]

sagemaker.session.get_execution_role(sagemaker_session=None)

Return the role ARN whose credentials are used to call the API.

Throws an exception if role doesn’t exist.

Parameters

sagemaker_session (Session) – Current sagemaker session

Returns

The role ARN

Return type

(str)

sagemaker.session.generate_default_sagemaker_bucket_name(boto_session)

Generates a name for the default sagemaker S3 bucket.

Parameters

boto_session (boto3.session.Session) – The underlying Boto3 session which AWS service

sagemaker.session.get_log_events_for_inference_recommender(cw_client, log_group_name, log_stream_name)

Retrieves log events from the specified CloudWatch log group and log stream.

Parameters
  • cw_client (boto3.client) – A boto3 CloudWatch client.

  • log_group_name (str) – The name of the CloudWatch log group.

  • log_stream_name (str) – The name of the CloudWatch log stream.

Returns

A dictionary containing log events from CloudWatch log group and log stream.

Return type

(dict)