Model

class sagemaker.model.Model(image_uri, model_data=None, role=None, predictor_cls=None, env=None, name=None, vpc_config=None, sagemaker_session=None, enable_network_isolation=False, model_kms_key=None, image_config=None, source_dir=None, code_location=None, entry_point=None, container_log_level=20, dependencies=None, git_config=None)

Bases: sagemaker.model.ModelBase

A SageMaker Model that can be deployed to an Endpoint.

Initialize an SageMaker Model.

Parameters
  • image_uri (str or PipelineVariable) – A Docker image URI.

  • model_data (str or PipelineVariable) – The S3 location of a SageMaker model data .tar.gz file (default: None).

  • 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. After the endpoint is created, the inference code might use the IAM role if it needs to access some AWS resources. It can be null if this is being used to create a Model to pass to a PipelineModel which has its own Role field. (default: None)

  • predictor_cls (callable[string, sagemaker.session.Session]) – A function to call to create a predictor (default: None). If not None, deploy will return the result of invoking this function on the created endpoint name.

  • env (dict[str, str] or dict[str, PipelineVariable]) – Environment variables to run with image_uri when hosted in SageMaker (default: None).

  • name (str) – The model name. If None, a default model name will be selected on each deploy.

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

  • sagemaker_session (sagemaker.session.Session) – A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

  • enable_network_isolation (Boolean or PipelineVariable) – Default False. if True, enables network isolation in the endpoint, isolating the model container. No inbound or outbound network calls can be made to or from the model container.

  • model_kms_key (str) – KMS key ARN used to encrypt the repacked model archive file if the model is repacked

  • image_config (dict[str, str] or dict[str, PipelineVariable]) – 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).

  • source_dir (str) –

    The absolute, relative, or S3 URI Path to a directory with any other training source code dependencies aside from the entry point file (default: None). If source_dir is an S3 URI, it must point to a tar.gz file. Structure within this directory is preserved when training on Amazon SageMaker. If ‘git_config’ is provided, ‘source_dir’ should be a relative location to a directory in the Git repo. If the directory points to S3, no code is uploaded and the S3 location is used instead.

    Example

    With the following GitHub repo directory structure:

    >>> |----- README.md
    >>> |----- src
    >>>         |----- inference.py
    >>>         |----- test.py
    

    You can assign entry_point=’inference.py’, source_dir=’src’.

  • code_location (str) – Name of the S3 bucket where custom code is uploaded (default: None). If not specified, the default bucket created by sagemaker.session.Session is used.

  • entry_point (str) –

    The absolute or relative path to the local Python source file that should be executed as the entry point to model hosting. (Default: None). If source_dir is specified, then entry_point must point to a file located at the root of source_dir. If ‘git_config’ is provided, ‘entry_point’ should be a relative location to the Python source file in the Git repo.

    Example

    With the following GitHub repo directory structure:

    >>> |----- README.md
    >>> |----- src
    >>>         |----- inference.py
    >>>         |----- test.py
    

    You can assign entry_point=’src/inference.py’.

  • container_log_level (int or PipelineVariable) – Log level to use within the container (default: logging.INFO). Valid values are defined in the Python logging module.

  • dependencies (list[str]) –

    A list of absolute or relative paths to directories with any additional libraries that should be exported to the container (default: []). The library folders are copied to SageMaker in the same folder where the entrypoint is copied. If ‘git_config’ is provided, ‘dependencies’ should be a list of relative locations to directories with any additional libraries needed in the Git repo. If the `source_dir` points to S3, code will be uploaded and the S3 location will be used instead.

    Example

    The following call

    >>> Model(entry_point='inference.py',
    ...       dependencies=['my/libs/common', 'virtual-env'])
    

    results in the following structure inside the container:

    >>> $ ls
    
    >>> opt/ml/code
    >>>     |------ inference.py
    >>>     |------ common
    >>>     |------ virtual-env
    

    This is not supported with “local code” in Local Mode.

  • git_config (dict[str, str]) –

    Git configurations used for cloning files, including repo, branch, commit, 2FA_enabled, username, password and token. The repo field is required. All other fields are optional. repo specifies the Git repository where your training script is stored. If you don’t provide branch, the default value ‘master’ is used. If you don’t provide commit, the latest commit in the specified branch is used.

    Example

    The following config:

    >>> git_config = {'repo': 'https://github.com/aws/sagemaker-python-sdk.git',
    >>>               'branch': 'test-branch-git-config',
    >>>               'commit': '329bfcf884482002c05ff7f44f62599ebc9f445a'}
    

    results in cloning the repo specified in ‘repo’, then checking out the ‘master’ branch, and checking out the specified commit.

    2FA_enabled, username, password and token are used for authentication. For GitHub (or other Git) accounts, set 2FA_enabled to ‘True’ if two-factor authentication is enabled for the account, otherwise set it to ‘False’. If you do not provide a value for 2FA_enabled, a default value of ‘False’ is used. CodeCommit does not support two-factor authentication, so do not provide “2FA_enabled” with CodeCommit repositories.

    For GitHub and other Git repos, when SSH URLs are provided, it doesn’t matter whether 2FA is enabled or disabled. You should either have no passphrase for the SSH key pairs or have the ssh-agent configured so that you will not be prompted for the SSH passphrase when you run the ‘git clone’ command with SSH URLs. When HTTPS URLs are provided, if 2FA is disabled, then either token or username and password are be used for authentication if provided. Token is prioritized. If 2FA is enabled, only token is used for authentication if provided. If required authentication info is not provided, the SageMaker Python SDK attempts to use local credentials to authenticate. If that fails, an error message is thrown.

    For CodeCommit repos, 2FA is not supported, so 2FA_enabled should not be provided. There is no token in CodeCommit, so token should also not be provided. When repo is an SSH URL, the requirements are the same as GitHub repos. When repo is an HTTPS URL, username and password are used for authentication if they are provided. If they are not provided, the SageMaker Python SDK attempts to use either the CodeCommit credential helper or local credential storage for authentication.

register(content_types, response_types, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, image_uri=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status=None, description=None, drift_check_baselines=None, customer_metadata_properties=None, validation_specification=None, domain=None, task=None, sample_payload_url=None, framework=None, framework_version=None, nearest_model_name=None, data_input_configuration=None)

Creates a model package for creating SageMaker models or listing on Marketplace.

Parameters
  • content_types (list[str] or list[PipelineVariable]) – The supported MIME types for the input data.

  • response_types (list[str] or list[PipelineVariable]) – The supported MIME types for the output data.

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

  • transform_instances (list[str] or list[PipelineVariable]) – 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 or PipelineVariable) – 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 or PipelineVariable) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned (default: None).

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

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

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

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

  • framework (str or PipelineVariable) – Machine learning framework of the model package container image (default: None).

  • framework_version (str or PipelineVariable) – Framework version of the Model Package Container Image (default: None).

  • nearest_model_name (str or PipelineVariable) – Name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender (default: None).

  • data_input_configuration (str or PipelineVariable) – Input object for the model (default: None).

Returns

A sagemaker.model.ModelPackage instance or pipeline step arguments in case the Model instance is built with PipelineSession

create(instance_type=None, accelerator_type=None, serverless_inference_config=None, tags=None)

Create a SageMaker Model Entity

Parameters
  • instance_type (str) – The EC2 instance type that this Model will be used for, this is only used to determine if the image needs GPU support or not (default: None).

  • accelerator_type (str) – Type of Elastic Inference accelerator to attach to an endpoint for model loading and inference, for example, ‘ml.eia1.medium’. If not specified, no Elastic Inference accelerator will be attached to the endpoint (default: None).

  • serverless_inference_config (ServerlessInferenceConfig) – Specifies configuration related to serverless endpoint. Instance type is not provided in serverless inference. So this is used to find image URIs (default: None).

  • tags (list[dict[str, str] or list[dict[str, PipelineVariable]]) –

    The list of tags to add to the model (default: None). Example:

    tags = [{'Key': 'tagname', 'Value':'tagvalue'}]
    

    For more information about tags, see boto3 documentation

Returns

None or pipeline step arguments in case the Model instance is built with PipelineSession

prepare_container_def(instance_type=None, accelerator_type=None, serverless_inference_config=None)

Return a dict created by sagemaker.container_def().

It is used for deploying this model to a specified instance type.

Subclasses can override this to provide custom container definitions for deployment to a specific instance type. Called by deploy().

Parameters
  • instance_type (str) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’.

  • accelerator_type (str) – The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. For example, ‘ml.eia1.medium’.

  • serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig) – Specifies configuration related to serverless endpoint. Instance type is not provided in serverless inference. So this is used to find image URIs.

Returns

A container definition object usable with the CreateModel API.

Return type

dict

enable_network_isolation()

Whether to enable network isolation when creating this Model

Returns

If network isolation should be enabled or not.

Return type

bool

package_for_edge(output_path, model_name, model_version, role=None, job_name=None, resource_key=None, s3_kms_key=None, tags=None)

Package this Model with SageMaker Edge.

Creates a new EdgePackagingJob and wait for it to finish. model_data will now point to the packaged artifacts.

Parameters
  • output_path (str) – Specifies where to store the packaged model

  • role (str) – Execution role

  • model_name (str) – the name to attach to the model metadata

  • model_version (str) – the version to attach to the model metadata

  • job_name (str) – The name of the edge packaging job

  • resource_key (str) – the kms key to encrypt the disk with

  • s3_kms_key (str) – the kms key to encrypt the output with

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

Returns

A SageMaker Model object. See Model() for full details.

Return type

sagemaker.model.Model

compile(target_instance_family, input_shape, output_path, role, tags=None, job_name=None, compile_max_run=900, framework=None, framework_version=None, target_platform_os=None, target_platform_arch=None, target_platform_accelerator=None, compiler_options=None)

Compile this Model with SageMaker Neo.

Parameters
  • target_instance_family (str) – Identifies the device that you want to run your model after compilation, for example: ml_c5. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html. Alternatively, you can select an OS, Architecture and Accelerator using target_platform_os, target_platform_arch, and target_platform_accelerator.

  • input_shape (dict) – Specifies the name and shape of the expected inputs for your trained model in json dictionary form, for example: {‘data’: [1,3,1024,1024]}, or {‘var1’: [1,1,28,28], ‘var2’: [1,1,28,28]}

  • output_path (str) – Specifies where to store the compiled model

  • role (str) – Execution role

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

  • job_name (str) – The name of the compilation job

  • compile_max_run (int) – Timeout in seconds for compilation (default: 15 * 60). After this amount of time Amazon SageMaker Neo terminates the compilation job regardless of its current status.

  • framework (str) – The framework that is used to train the original model. Allowed values: ‘mxnet’, ‘tensorflow’, ‘keras’, ‘pytorch’, ‘onnx’, ‘xgboost’

  • framework_version (str) – The version of framework, for example: ‘1.5’ for PyTorch

  • target_platform_os (str) – Target Platform OS, for example: ‘LINUX’. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html. It can be used instead of target_instance_family by setting target_instance family to None.

  • target_platform_arch (str) – Target Platform Architecture, for example: ‘X86_64’. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html. It can be used instead of target_instance_family by setting target_instance family to None.

  • target_platform_accelerator (str, optional) – Target Platform Accelerator, for example: ‘NVIDIA’. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html. It can be used instead of target_instance_family by setting target_instance family to None.

  • compiler_options (dict, optional) – Additional parameters for compiler. Compiler Options are TargetPlatform / target_instance_family specific. See https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

Returns

A SageMaker Model object. See Model() for full details.

Return type

sagemaker.model.Model

deploy(initial_instance_count=None, instance_type=None, serializer=None, deserializer=None, accelerator_type=None, endpoint_name=None, tags=None, kms_key=None, wait=True, data_capture_config=None, async_inference_config=None, serverless_inference_config=None, volume_size=None, model_data_download_timeout=None, container_startup_health_check_timeout=None, **kwargs)

Deploy this Model to an Endpoint and optionally return a Predictor.

Create a SageMaker Model and EndpointConfig, and deploy an Endpoint from this Model. If self.predictor_cls is not None, this method returns a the result of invoking self.predictor_cls on the created endpoint name.

The name of the created model is accessible in the name field of this Model after deploy returns

The name of the created endpoint is accessible in the endpoint_name field of this Model after deploy returns.

Parameters
  • initial_instance_count (int) – The initial number of instances to run in the Endpoint created from this Model. If not using serverless inference, then it need to be a number larger or equals to 1 (default: None)

  • instance_type (str) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’, or ‘local’ for local mode. If not using serverless inference, then it is required to deploy a model. (default: None)

  • serializer (BaseSerializer) – A serializer object, used to encode data for an inference endpoint (default: None). If serializer is not None, then serializer will override the default serializer. The default serializer is set by the predictor_cls.

  • deserializer (BaseDeserializer) – A deserializer object, used to decode data from an inference endpoint (default: None). If deserializer is not None, then deserializer will override the default deserializer. The default deserializer is set by the predictor_cls.

  • accelerator_type (str) – Type of Elastic Inference accelerator to deploy this model for model loading and inference, for example, ‘ml.eia1.medium’. If not specified, no Elastic Inference accelerator will be attached to the endpoint. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

  • endpoint_name (str) – The name of the endpoint to create (default: None). If not specified, a unique endpoint name will be created.

  • tags (List[dict[str, str]]) – The list of tags to attach to this specific endpoint.

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

  • wait (bool) – Whether the call should wait until the deployment of this model completes (default: True).

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

  • async_inference_config (sagemaker.model_monitor.AsyncInferenceConfig) – Specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference. If empty config object passed through, will use default config to deploy async endpoint. Deploy a real-time endpoint if it’s None. (default: None)

  • serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig) – Specifies configuration related to serverless endpoint. Use this configuration when trying to create serverless endpoint and make serverless inference. If empty object passed through, will use pre-defined values in ServerlessInferenceConfig class to deploy serverless endpoint. Deploy an instance based endpoint if it’s None. (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

Raises

ValueError – If arguments combination check failed in these circumstances: - If no role is specified or - If serverless inference config is not specified and instance type and instance count are also not specified or - If a wrong type of object is provided as serverless inference config or async inference config

Returns

Invocation of

self.predictor_cls on the created endpoint name, if self.predictor_cls is not None. Otherwise, return None.

Return type

callable[string, sagemaker.session.Session] or None

transformer(instance_count, instance_type, strategy=None, assemble_with=None, output_path=None, output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None, max_payload=None, tags=None, volume_kms_key=None)

Return a Transformer that uses this Model.

Parameters
  • instance_count (int) – Number of EC2 instances to use.

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

  • strategy (str) – The strategy used to decide how to batch records in a single request (default: None). Valid values: ‘MultiRecord’ and ‘SingleRecord’.

  • assemble_with (str) – How the output is assembled (default: None). Valid values: ‘Line’ or ‘None’.

  • output_path (str) – S3 location for saving the transform result. If not specified, results are stored to a default bucket.

  • output_kms_key (str) – Optional. KMS key ID for encrypting the transform output (default: None).

  • accept (str) – The accept header passed by the client to the inference endpoint. If it is supported by the endpoint, it will be the format of the batch transform output.

  • env (dict) – Environment variables to be set for use during the transform job (default: None).

  • 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.

  • tags (list[dict]) – List of tags for labeling a transform job. If none specified, then the tags used for the training job are used for the transform job.

  • volume_kms_key (str) – Optional. KMS key ID for encrypting the volume attached to the ML compute instance (default: None).

delete_model()

Delete an Amazon SageMaker Model.

Raises

ValueError – if the model is not created yet.

class sagemaker.model.FrameworkModel(model_data, image_uri, role, entry_point, source_dir=None, predictor_cls=None, env=None, name=None, container_log_level=20, code_location=None, sagemaker_session=None, dependencies=None, git_config=None, **kwargs)

Bases: sagemaker.model.Model

A Model for working with an SageMaker Framework.

This class hosts user-defined code in S3 and sets code location and configuration in model environment variables.

Initialize a FrameworkModel.

Parameters
  • model_data (str or PipelineVariable) – The S3 location of a SageMaker model data .tar.gz file.

  • image_uri (str or PipelineVariable) – A Docker image URI.

  • role (str) – An IAM role name or ARN for SageMaker to access AWS resources on your behalf.

  • entry_point (str) –

    Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. If source_dir is specified, then entry_point must point to a file located at the root of source_dir. If ‘git_config’ is provided, ‘entry_point’ should be a relative location to the Python source file in the Git repo.

    Example

    With the following GitHub repo directory structure:

    >>> |----- README.md
    >>> |----- src
    >>>         |----- inference.py
    >>>         |----- test.py
    

    You can assign entry_point=’src/inference.py’.

  • source_dir (str) –

    Path (absolute, relative or an S3 URI) to a directory with any other training source code dependencies aside from the entry point file (default: None). If source_dir is an S3 URI, it must point to a tar.gz file. Structure within this directory are preserved when training on Amazon SageMaker. If ‘git_config’ is provided, ‘source_dir’ should be a relative location to a directory in the Git repo. If the directory points to S3, no code will be uploaded and the S3 location will be used instead.

    Example

    With the following GitHub repo directory structure:

    >>> |----- README.md
    >>> |----- src
    >>>         |----- inference.py
    >>>         |----- test.py
    

    You can assign entry_point=’inference.py’, source_dir=’src’.

  • predictor_cls (callable[string, sagemaker.session.Session]) – A function to call to create a predictor (default: None). If not None, deploy will return the result of invoking this function on the created endpoint name.

  • env (dict[str, str] or dict[str, PipelineVariable]) – Environment variables to run with image_uri when hosted in SageMaker (default: None).

  • name (str) – The model name. If None, a default model name will be selected on each deploy.

  • container_log_level (int or PipelineVariable) – Log level to use within the container (default: logging.INFO). Valid values are defined in the Python logging module.

  • code_location (str) – Name of the S3 bucket where custom code is uploaded (default: None). If not specified, default bucket created by sagemaker.session.Session is used.

  • sagemaker_session (sagemaker.session.Session) – A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

  • dependencies (list[str]) –

    A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container (default: []). The library folders will be copied to SageMaker in the same folder where the entrypoint is copied. If ‘git_config’ is provided, ‘dependencies’ should be a list of relative locations to directories with any additional libraries needed in the Git repo. If the `source_dir` points to S3, code will be uploaded and the S3 location will be used instead.

    Example

    The following call

    >>> Model(entry_point='inference.py',
    ...       dependencies=['my/libs/common', 'virtual-env'])
    

    results in the following inside the container:

    >>> $ ls
    
    >>> opt/ml/code
    >>>     |------ inference.py
    >>>     |------ common
    >>>     |------ virtual-env
    

    This is not supported with “local code” in Local Mode.

  • git_config (dict[str, str]) –

    Git configurations used for cloning files, including repo, branch, commit, 2FA_enabled, username, password and token. The repo field is required. All other fields are optional. repo specifies the Git repository where your training script is stored. If you don’t provide branch, the default value ‘master’ is used. If you don’t provide commit, the latest commit in the specified branch is used.

    Example

    The following config:

    >>> git_config = {'repo': 'https://github.com/aws/sagemaker-python-sdk.git',
    >>>               'branch': 'test-branch-git-config',
    >>>               'commit': '329bfcf884482002c05ff7f44f62599ebc9f445a'}
    

    results in cloning the repo specified in ‘repo’, then checkout the ‘master’ branch, and checkout the specified commit.

    2FA_enabled, username, password and token are used for authentication. For GitHub (or other Git) accounts, set 2FA_enabled to ‘True’ if two-factor authentication is enabled for the account, otherwise set it to ‘False’. If you do not provide a value for 2FA_enabled, a default value of ‘False’ is used. CodeCommit does not support two-factor authentication, so do not provide “2FA_enabled” with CodeCommit repositories.

    For GitHub and other Git repos, when SSH URLs are provided, it doesn’t matter whether 2FA is enabled or disabled; you should either have no passphrase for the SSH key pairs, or have the ssh-agent configured so that you will not be prompted for SSH passphrase when you do ‘git clone’ command with SSH URLs. When HTTPS URLs are provided: if 2FA is disabled, then either token or username+password will be used for authentication if provided (token prioritized); if 2FA is enabled, only token will be used for authentication if provided. If required authentication info is not provided, python SDK will try to use local credentials storage to authenticate. If that fails either, an error message will be thrown.

    For CodeCommit repos, 2FA is not supported, so ‘2FA_enabled’ should not be provided. There is no token in CodeCommit, so ‘token’ should not be provided too. When ‘repo’ is an SSH URL, the requirements are the same as GitHub-like repos. When ‘repo’ is an HTTPS URL, username+password will be used for authentication if they are provided; otherwise, python SDK will try to use either CodeCommit credential helper or local credential storage for authentication.

  • **kwargs – Keyword arguments passed to the superclass Model.

Tip

You can find additional parameters for initializing this class at Model.

class sagemaker.model.ModelPackage(role, model_data=None, algorithm_arn=None, model_package_arn=None, **kwargs)

Bases: sagemaker.model.Model

A SageMaker Model that can be deployed to an Endpoint.

Initialize a SageMaker ModelPackage.

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. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.

  • model_data (str) – The S3 location of a SageMaker model data .tar.gz file. Must be provided if algorithm_arn is provided.

  • algorithm_arn (str) – algorithm arn used to train the model, can be just the name if your account owns the algorithm. Must also provide model_data.

  • model_package_arn (str) – An existing SageMaker Model Package arn, can be just the name if your account owns the Model Package. model_data is not required.

  • **kwargs – Additional kwargs passed to the Model constructor.

enable_network_isolation()

Whether to enable network isolation when creating a model out of this ModelPackage

Returns

If network isolation should be enabled or not.

Return type

bool