class_docstr: JumpStartEstimator class.
  
  This class sets defaults based on the model ID and version.
docstr: Initializes a ``JumpStartEstimator``.
  
  This method sets model-specific defaults for the ``Estimator.__init__`` method.
  
  Only model ID is required to instantiate this class, however any field can be overriden.
  Any field set to ``None`` does not get passed to the parent class method.
  
  
  Args:
      model_id (Optional[str]): JumpStart model ID to use. See
          https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html
          for list of model IDs.
      model_version (Optional[str]): Version for JumpStart model to use (Default: None).
      tolerate_vulnerable_model (Optional[bool]): True if vulnerable versions of model
          specifications should be tolerated (exception not raised). If False, raises an
          exception if the script used by this version of the model has dependencies
          with known security vulnerabilities. (Default: None).
      tolerate_deprecated_model (Optional[bool]): True if deprecated models should be
          tolerated (exception not raised). False if these models should raise an exception.
          (Default: None).
      region (Optional[str]): The AWS region in which to launch the model. (Default: None).
      image_uri (Optional[Union[str, PipelineVariable]]): The container image to use for
          training. (Default: None).
      role (Optional[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. (Default: None).
      instance_count (Optional[Union[int, PipelineVariable]]): Number of Amazon EC2
          instances to usefor training. Required if instance_groups is not set.
          (Default: None).
      instance_type (Optional[Union[str, PipelineVariable]]): Type of EC2 instance to use
          for training, for example, ``'ml.c4.xlarge'``. Required if instance_groups is
          not set. (Default: None).
      keep_alive_period_in_seconds (Optional[int]): The duration of time in seconds
          to retain configured resources in a warm pool for subsequent
          training jobs. (Default: None).
      volume_size (Optional[int, PipelineVariable]): Size in GB of the storage volume to
          use for storing input and output data during training.
  
          Must be large enough to store training data if File mode is
          used, which is the default mode.
  
          When you use an ML instance with the EBS-only storage option
          such as ``ml.c5`` and ``ml.p2``,
          you must define the size of the EBS
          volume through the ``volume_size`` parameter in the estimator class.
  
          .. note::
  
              When you use an ML instance with `NVMe SSD volumes
              <https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html#nvme-ssd-volumes>`_
              such as ``ml.p4d``, ``ml.g4dn``, and ``ml.g5``,
              do not include this parameter in the estimator configuration.
              If you use one of those ML instance types,
              SageMaker doesn't provision Amazon EBS General Purpose SSD
              (gp2) storage nor take this parameter to adjust the NVMe instance storage.
              Available storage is fixed to the NVMe instance storage
              capacity. SageMaker configures storage paths for training
              datasets, checkpoints, model artifacts, and outputs to use the
              entire capacity of the instance storage.
  
              Note that if you include this parameter and specify a number that
              exceeds the size of the NVMe volume attached to the instance type,
              SageMaker returns an ``Invalid VolumeSizeInGB`` error.
  
          To look up instance types and their instance storage types
          and volumes, see `Amazon EC2 Instance Types
          <http://aws.amazon.com/ec2/instance-types/>`_.
  
          To find the default local paths defined by the SageMaker
          training platform, see `Amazon SageMaker Training Storage
          Folders for Training Datasets, Checkpoints, Model Artifacts,
          and Outputs
          <https://docs.aws.amazon.com/sagemaker/latest/dg/model-train-storage.html>`_.
          (Default: None).
      volume_kms_key (Optional[Union[str, PipelineVariable]]): KMS key ID for encrypting EBS
          volume attached to the training instance. (Default: None).
      max_run (Optional[Union[int, PipelineVariable]]): Timeout in seconds for training.
          After this amount of time Amazon SageMaker terminates
          the job regardless of its current status. (Default: None).
      input_mode (Optional[Union[str, PipelineVariable]]): The input mode that the
          algorithm supports. Valid modes:
  
          * 'File' - Amazon SageMaker copies the training dataset from the
            S3 location to a local directory.
          * 'Pipe' - Amazon SageMaker streams data directly from S3 to the
            container via a Unix-named pipe.
  
          This argument can be overriden on a per-channel basis using
          ``sagemaker.inputs.TrainingInput.input_mode``. (Default: None).
      output_path (Optional[Union[str, PipelineVariable]]): S3 location for saving
          the training result (model artifacts and output files). If not specified,
          results are stored to a default bucket. If the bucket with the specific name
          does not exist, the estimator creates the bucket during the
          :meth:`~sagemaker.estimator.EstimatorBase.fit` method execution.
          (Default: None).
      output_kms_key (Optional[Union[str, PipelineVariable]]): KMS key ID for encrypting the
          training output. (Default: None).
      base_job_name (Optional[str]): Prefix for training job name when the
          :meth:`~sagemaker.estimator.EstimatorBase.fit` method launches.
          If not specified, the estimator generates a default job name,
          based on the training image name and current timestamp. (Default: None).
      sagemaker_session (Optional[sagemaker.session.Session]): Session object which
          manages interactions with Amazon SageMaker APIs and any other
          AWS services needed. If not specified, the estimator creates one
          using the default AWS configuration chain. (Default: None).
      hyperparameters (Optional[Union[dict[str, str],dict[str, PipelineVariable]]]):
          Dictionary containing the hyperparameters to initialize this estimator with.
  
          .. caution::
              You must not include any security-sensitive information, such as
              account access IDs, secrets, and tokens, in the dictionary for configuring
              hyperparameters. SageMaker rejects the training job request and returns an
              validation error for detected credentials, if such user input is found.
  
          (Default: None).
      tags (Optional[Tags]):
          Tags for labeling a training job. For more, see
          https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
          (Default: None).
      subnets (Optional[Union[list[str], list[PipelineVariable]]]): List of subnet ids.
          If not specified training job will be created without VPC config. (Default: None).
      security_group_ids (Optional[Union[list[str], list[PipelineVariable]]]): List of
          security group ids. If not specified training job will be created without
          VPC config. (Default: None).
      model_uri (Optional[str]): URI where a pre-trained model is stored, either
          locally or in S3 (Default: None). If specified, the estimator
          will create a channel pointing to the model so the training job
          can download it. This model can be a 'model.tar.gz' from a
          previous training job, or other artifacts coming from a
          different source.
  
          In local mode, this should point to the path in which the model
          is located and not the file itself, as local Docker containers
          will try to mount the URI as a volume.
  
          More information:
          https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html#td-deserialization
  
          (Default: None).
      model_channel_name (Optional[Union[str, PipelineVariable]]): Name of the channel where
          'model_uri' will be downloaded. (Default: None).
      metric_definitions (Optional[list[dict[str, Union[str, PipelineVariable]]]]):
          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. (Default: None).
      encrypt_inter_container_traffic (Optional[Union[bool, PipelineVariable]]]): Specifies
          whether traffic between training containers is encrypted for the training job
          (Default: None).
      use_spot_instances (Optional[Union[bool, PipelineVariable]]): Specifies whether to
          use SageMaker Managed Spot instances for training. If enabled then the
          ``max_wait`` arg should also be set.
  
          More information:
          https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html
          (Default: None).
      max_wait (Optional[Union[int, PipelineVariable]]): Timeout in seconds waiting
          for spot training job. After this amount of time Amazon
          SageMaker will stop waiting for managed spot training job to
          complete. (Default: None).
      checkpoint_s3_uri (Optional[Union[str, PipelineVariable]]): The S3 URI in which
          to persist checkpoints that the algorithm persists (if any) during training.
          (Default: None).
      checkpoint_local_path (Optional[Union[str, PipelineVariable]]): 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).
      enable_network_isolation (Optional[Union[bool, PipelineVariable]]): Specifies
          whether container will run in network isolation mode. Network isolation mode
          restricts the container access to outside networks (such as the Internet).
          The container does not make any inbound or outbound network calls.
          Also known as Internet-free mode. (Default: None).
      rules (Optional[list[:class:`~sagemaker.debugger.RuleBase`]]): A list of
          :class:`~sagemaker.debugger.RuleBase` objects used to define
          SageMaker Debugger rules for real-time analysis
          (Default: None). For more information,
          see `Continuous analyses through rules
          <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#
          continuous-analyses-through-rules)>`_.
          (Default: None).
      debugger_hook_config (Optional[Union[DebuggerHookConfig, bool]]):
          Configuration for how debugging information is emitted with
          SageMaker Debugger. If not specified, a default one is created using
          the estimator's ``output_path``, unless the region does not
          support SageMaker Debugger. To disable SageMaker Debugger,
          set this parameter to ``False``. For more information, see
          `Capture real-time debugging data during model training in Amazon SageMaker
          <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#
          capture-real-time-debugging-data-during-model-training-in-amazon-sagemaker>`_.
          (Default: None).
      tensorboard_output_config (:class:`~sagemaker.debugger.TensorBoardOutputConfig`):
          Configuration for customizing debugging visualization using TensorBoard.
          For more information, see `Capture real time tensorboard data
          <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#
          capture-real-time-tensorboard-data-from-the-debugging-hook>`_.
          (Default: None).
      enable_sagemaker_metrics (Optional[Union[bool, PipelineVariable]]): enable
          SageMaker Metrics Time Series. For more information, see `AlgorithmSpecification
          API <https://docs.aws.amazon.com/sagemaker/latest/dg/
          API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-
          EnableSageMakerMetricsTimeSeries>`_.
          (Default: None).
      profiler_config (Optional[:class:`~sagemaker.debugger.ProfilerConfig`]):
          Configuration for how SageMaker Debugger collects
          monitoring and profiling information from your training job.
          If not specified, Debugger will be configured with
          a default configuration and will save system and framework metrics
          the estimator's default ``output_path`` in Amazon S3.
          Use :class:`~sagemaker.debugger.ProfilerConfig` to configure this parameter.
          To disable SageMaker Debugger monitoring and profiling, set the
          ``disable_profiler`` parameter to ``True``. (Default: None).
      disable_profiler (Optional[bool]): Specifies whether Debugger monitoring and profiling
          will be disabled. (Default: None).
      environment (Optional[Union[dict[str, str], dict[str, PipelineVariable]]]):
          Environment variables to be set for use during training job. (Default: None).
      max_retry_attempts (Optional[Union[int, PipelineVariable]]): The number of times
          to move a job to the STARTING status. You can specify between 1 and 30 attempts.
          If the value of attempts is greater than zero,
          the job is retried on InternalServerFailure
          the same number of attempts as the value.
          You can cap the total duration for your job by setting ``max_wait`` and ``max_run``.
          (Default: None).
      source_dir (Optional[Union[str, PipelineVariable]]): The absolute, relative, or
          S3 URI Path to a directory with any other training source code dependencies
          aside from the entry point file. 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.
          (Default: None).
  
          .. admonition:: Example
  
              With the following GitHub repo directory structure:
  
              >>> |----- README.md
              >>> |----- src
              >>>         |----- train.py
              >>>         |----- test.py
  
              if you need 'train.py'
              as the entry point and 'test.py' as the training source code, you can assign
              entry_point='train.py', source_dir='src'.
      git_config (Optional[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.
  
          ``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.
          (Default: None).
  
          .. admonition:: 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.
      container_log_level (Optional[Union[int, PipelineVariable]]): The log level to use
          within the container. Valid values are defined in the Python logging module.
          (Default: None).
      code_location (Optional[str]): The S3 prefix URI where custom code is
          uploaded (Default: None). You must not include a trailing slash because
          a string prepended with a "/" is appended to ``code_location``. The code
          file uploaded to S3 is 'code_location/job-name/source/sourcedir.tar.gz'.
          If not specified, the default ``code location`` is 's3://output_bucket/job-name/'.
          (Default: None).
      entry_point (Optional[Union[str, PipelineVariable]]): The absolute or relative path
          to the local Python source file that should be executed as the entry point to
          training. 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.
          (Default: None).
  
          .. admonition:: Example
              With the following GitHub repo directory structure:
  
              >>> |----- README.md
              >>> |----- src
              >>>         |----- train.py
              >>>         |----- test.py
  
              You can assign entry_point='src/train.py'.
      dependencies (Optional[list[str]]): A list of absolute or relative paths to directories
          with any additional libraries that should be exported
          to the container. 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. This is not supported with "local code"
          in Local Mode. (Default: None).
  
          .. admonition:: Example
              The following Estimator call:
  
              >>> Estimator(entry_point='train.py',
              ...           dependencies=['my/libs/common', 'virtual-env'])
  
              results in the following structure inside the container:
  
              >>> $ ls
  
              >>> opt/ml/code
              >>>     |------ train.py
              >>>     |------ common
              >>>     |------ virtual-env
      instance_groups (Optional[list[:class:`sagemaker.instance_group.InstanceGroup`]]):
          A list of ``InstanceGroup`` objects for launching a training job with a
          heterogeneous cluster. For example:
  
          .. code:: python
  
              instance_groups=[
                  sagemaker.InstanceGroup(
                      'instance_group_name_1', 'ml.p3dn.24xlarge', 64),
                  sagemaker.InstanceGroup(
                      'instance_group_name_2', 'ml.c5n.18xlarge', 64)]
  
          For instructions on how to use ``InstanceGroup`` objects
          to configure a heterogeneous cluster
          through the SageMaker generic and framework estimator classes, see
          `Train Using a Heterogeneous Cluster
          <https://docs.aws.amazon.com/sagemaker/latest/dg/train-heterogeneous-cluster.html>`_
          in the *Amazon SageMaker developer guide*. (Default: None).
      training_repository_access_mode (Optional[str]): Specifies how SageMaker accesses the
          Docker image that contains the training algorithm (Default: None).
          Set this to one of the following values:
          * 'Platform' - The training image is hosted in Amazon ECR.
          * 'Vpc' - The training image is hosted in a private Docker registry in your VPC.
          When it's default to None, its behavior will be same as 'Platform' - image is hosted
          in ECR. (Default: None).
      training_repository_credentials_provider_arn (Optional[str]): The Amazon Resource Name
          (ARN) of an AWS Lambda function that provides credentials to authenticate to the
          private Docker registry where your training image is hosted (Default: None).
          When it's set to None, SageMaker will not do authentication before pulling the image
          in the private Docker registry. (Default: None).
      enable_infra_check (Optional[Union[bool, PipelineVariable]]):
          Specifies whether it is running Sagemaker built-in infra check jobs.
      container_entry_point (Optional[List[str]]): The entrypoint script for a Docker
          container used to run a training job. This script takes precedence over
          the default train processing instructions.
      container_arguments (Optional[List[str]]): The arguments for a container used to run
          a training job.
      disable_output_compression (Optional[bool]): When set to true, Model is uploaded
          to Amazon S3 without compression after training finishes.
      enable_remote_debug (bool or PipelineVariable): Optional.
          Specifies whether RemoteDebug is enabled for the training job
  
  Raises:
      ValueError: If the model ID is not recognized by JumpStart.
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code: def __init__(
      self,
      model_id: Optional[str] = None,
      model_version: Optional[str] = None,
      tolerate_vulnerable_model: Optional[bool] = None,
      tolerate_deprecated_model: Optional[bool] = None,
      region: Optional[str] = None,
      image_uri: Optional[Union[str, PipelineVariable]] = None,
      role: Optional[str] = None,
      instance_count: Optional[Union[int, PipelineVariable]] = None,
      instance_type: Optional[Union[str, PipelineVariable]] = None,
      keep_alive_period_in_seconds: Optional[
          Union[int, PipelineVariable]
      ] = None,
      volume_size: Optional[Union[int, PipelineVariable]] = None,
      volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
      max_run: Optional[Union[int, PipelineVariable]] = None,
      input_mode: Optional[Union[str, PipelineVariable]] = None,
      output_path: Optional[Union[str, PipelineVariable]] = None,
      output_kms_key: Optional[Union[str, PipelineVariable]] = None,
      base_job_name: Optional[str] = None,
      sagemaker_session: Optional[session.Session] = None,
      hyperparameters: Optional[
          Dict[str, Union[str, PipelineVariable]]
      ] = None,
      tags: Optional[Tags] = None,
      subnets: Optional[List[Union[str, PipelineVariable]]] = None,
      security_group_ids: Optional[
          List[Union[str, PipelineVariable]]
      ] = None,
      model_uri: Optional[str] = None,
      model_channel_name: Optional[Union[str, PipelineVariable]] = None,
      metric_definitions: Optional[
          List[Dict[str, Union[str, PipelineVariable]]]
      ] = None,
      encrypt_inter_container_traffic: Union[
          bool, PipelineVariable
      ] = None,
      use_spot_instances: Optional[
          Union[bool, PipelineVariable]
      ] = None,
      max_wait: Optional[Union[int, PipelineVariable]] = None,
      checkpoint_s3_uri: Optional[Union[str, PipelineVariable]] = None,
      checkpoint_local_path: Optional[
          Union[str, PipelineVariable]
      ] = None,
      enable_network_isolation: Union[bool, PipelineVariable] = None,
      rules: Optional[List[RuleBase]] = None,
      debugger_hook_config: Optional[
          Union[DebuggerHookConfig, bool]
      ] = None,
      tensorboard_output_config: Optional[
          TensorBoardOutputConfig
      ] = None,
      enable_sagemaker_metrics: Optional[
          Union[bool, PipelineVariable]
      ] = None,
      profiler_config: Optional[ProfilerConfig] = None,
      disable_profiler: Optional[bool] = None,
      environment: Optional[
          Dict[str, Union[str, PipelineVariable]]
      ] = None,
      max_retry_attempts: Optional[Union[int, PipelineVariable]] = None,
      source_dir: Optional[Union[str, PipelineVariable]] = None,
      git_config: Optional[Dict[str, str]] = None,
      container_log_level: Optional[
          Union[int, PipelineVariable]
      ] = None,
      code_location: Optional[str] = None,
      entry_point: Optional[Union[str, PipelineVariable]] = None,
      dependencies: Optional[List[str]] = None,
      instance_groups: Optional[List[InstanceGroup]] = None,
      training_repository_access_mode: Optional[
          Union[str, PipelineVariable]
      ] = None,
      training_repository_credentials_provider_arn: Optional[
          Union[str, PipelineVariable]
      ] = None,
      enable_infra_check: Optional[
          Union[bool, PipelineVariable]
      ] = None,
      container_entry_point: Optional[List[str]] = None,
      container_arguments: Optional[List[str]] = None,
      disable_output_compression: Optional[bool] = None,
      enable_remote_debug: Optional[
          Union[bool, PipelineVariable]
      ] = None,
  ):
      """Initializes a ``JumpStartEstimator``.
  
      This method sets model-specific defaults for the ``Estimator.__init__`` method.
  
      Only model ID is required to instantiate this class, however any field can be overriden.
      Any field set to ``None`` does not get passed to the parent class method.
  
  
      Args:
          model_id (Optional[str]): JumpStart model ID to use. See
              https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html
              for list of model IDs.
          model_version (Optional[str]): Version for JumpStart model to use (Default: None).
          tolerate_vulnerable_model (Optional[bool]): True if vulnerable versions of model
              specifications should be tolerated (exception not raised). If False, raises an
              exception if the script used by this version of the model has dependencies
              with known security vulnerabilities. (Default: None).
          tolerate_deprecated_model (Optional[bool]): True if deprecated models should be
              tolerated (exception not raised). False if these models should raise an exception.
              (Default: None).
          region (Optional[str]): The AWS region in which to launch the model. (Default: None).
          image_uri (Optional[Union[str, PipelineVariable]]): The container image to use for
              training. (Default: None).
          role (Optional[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. (Default: None).
          instance_count (Optional[Union[int, PipelineVariable]]): Number of Amazon EC2
              instances to usefor training. Required if instance_groups is not set.
              (Default: None).
          instance_type (Optional[Union[str, PipelineVariable]]): Type of EC2 instance to use
              for training, for example, ``'ml.c4.xlarge'``. Required if instance_groups is
              not set. (Default: None).
          keep_alive_period_in_seconds (Optional[int]): The duration of time in seconds
              to retain configured resources in a warm pool for subsequent
              training jobs. (Default: None).
          volume_size (Optional[int, PipelineVariable]): Size in GB of the storage volume to
              use for storing input and output data during training.
  
              Must be large enough to store training data if File mode is
              used, which is the default mode.
  
              When you use an ML instance with the EBS-only storage option
              such as ``ml.c5`` and ``ml.p2``,
              you must define the size of the EBS
              volume through the ``volume_size`` parameter in the estimator class.
  
              .. note::
  
                  When you use an ML instance with `NVMe SSD volumes
                  <https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html#nvme-ssd-volumes>`_
                  such as ``ml.p4d``, ``ml.g4dn``, and ``ml.g5``,
                  do not include this parameter in the estimator configuration.
                  If you use one of those ML instance types,
                  SageMaker doesn't provision Amazon EBS General Purpose SSD
                  (gp2) storage nor take this parameter to adjust the NVMe instance storage.
                  Available storage is fixed to the NVMe instance storage
                  capacity. SageMaker configures storage paths for training
                  datasets, checkpoints, model artifacts, and outputs to use the
                  entire capacity of the instance storage.
  
                  Note that if you include this parameter and specify a number that
                  exceeds the size of the NVMe volume attached to the instance type,
                  SageMaker returns an ``Invalid VolumeSizeInGB`` error.
  
              To look up instance types and their instance storage types
              and volumes, see `Amazon EC2 Instance Types
              <http://aws.amazon.com/ec2/instance-types/>`_.
  
              To find the default local paths defined by the SageMaker
              training platform, see `Amazon SageMaker Training Storage
              Folders for Training Datasets, Checkpoints, Model Artifacts,
              and Outputs
              <https://docs.aws.amazon.com/sagemaker/latest/dg/model-train-storage.html>`_.
              (Default: None).
          volume_kms_key (Optional[Union[str, PipelineVariable]]): KMS key ID for encrypting EBS
              volume attached to the training instance. (Default: None).
          max_run (Optional[Union[int, PipelineVariable]]): Timeout in seconds for training.
              After this amount of time Amazon SageMaker terminates
              the job regardless of its current status. (Default: None).
          input_mode (Optional[Union[str, PipelineVariable]]): The input mode that the
              algorithm supports. Valid modes:
  
              * 'File' - Amazon SageMaker copies the training dataset from the
                S3 location to a local directory.
              * 'Pipe' - Amazon SageMaker streams data directly from S3 to the
                container via a Unix-named pipe.
  
              This argument can be overriden on a per-channel basis using
              ``sagemaker.inputs.TrainingInput.input_mode``. (Default: None).
          output_path (Optional[Union[str, PipelineVariable]]): S3 location for saving
              the training result (model artifacts and output files). If not specified,
              results are stored to a default bucket. If the bucket with the specific name
              does not exist, the estimator creates the bucket during the
              :meth:`~sagemaker.estimator.EstimatorBase.fit` method execution.
              (Default: None).
          output_kms_key (Optional[Union[str, PipelineVariable]]): KMS key ID for encrypting the
              training output. (Default: None).
          base_job_name (Optional[str]): Prefix for training job name when the
              :meth:`~sagemaker.estimator.EstimatorBase.fit` method launches.
              If not specified, the estimator generates a default job name,
              based on the training image name and current timestamp. (Default: None).
          sagemaker_session (Optional[sagemaker.session.Session]): Session object which
              manages interactions with Amazon SageMaker APIs and any other
              AWS services needed. If not specified, the estimator creates one
              using the default AWS configuration chain. (Default: None).
          hyperparameters (Optional[Union[dict[str, str],dict[str, PipelineVariable]]]):
              Dictionary containing the hyperparameters to initialize this estimator with.
  
              .. caution::
                  You must not include any security-sensitive information, such as
                  account access IDs, secrets, and tokens, in the dictionary for configuring
                  hyperparameters. SageMaker rejects the training job request and returns an
                  validation error for detected credentials, if such user input is found.
  
              (Default: None).
          tags (Optional[Tags]):
              Tags for labeling a training job. For more, see
              https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
              (Default: None).
          subnets (Optional[Union[list[str], list[PipelineVariable]]]): List of subnet ids.
              If not specified training job will be created without VPC config. (Default: None).
          security_group_ids (Optional[Union[list[str], list[PipelineVariable]]]): List of
              security group ids. If not specified training job will be created without
              VPC config. (Default: None).
          model_uri (Optional[str]): URI where a pre-trained model is stored, either
              locally or in S3 (Default: None). If specified, the estimator
              will create a channel pointing to the model so the training job
              can download it. This model can be a 'model.tar.gz' from a
              previous training job, or other artifacts coming from a
              different source.
  
              In local mode, this should point to the path in which the model
              is located and not the file itself, as local Docker containers
              will try to mount the URI as a volume.
  
              More information:
              https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html#td-deserialization
  
              (Default: None).
          model_channel_name (Optional[Union[str, PipelineVariable]]): Name of the channel where
              'model_uri' will be downloaded. (Default: None).
          metric_definitions (Optional[list[dict[str, Union[str, PipelineVariable]]]]):
              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. (Default: None).
          encrypt_inter_container_traffic (Optional[Union[bool, PipelineVariable]]]): Specifies
              whether traffic between training containers is encrypted for the training job
              (Default: None).
          use_spot_instances (Optional[Union[bool, PipelineVariable]]): Specifies whether to
              use SageMaker Managed Spot instances for training. If enabled then the
              ``max_wait`` arg should also be set.
  
              More information:
              https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html
              (Default: None).
          max_wait (Optional[Union[int, PipelineVariable]]): Timeout in seconds waiting
              for spot training job. After this amount of time Amazon
              SageMaker will stop waiting for managed spot training job to
              complete. (Default: None).
          checkpoint_s3_uri (Optional[Union[str, PipelineVariable]]): The S3 URI in which
              to persist checkpoints that the algorithm persists (if any) during training.
              (Default: None).
          checkpoint_local_path (Optional[Union[str, PipelineVariable]]): 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).
          enable_network_isolation (Optional[Union[bool, PipelineVariable]]): Specifies
              whether container will run in network isolation mode. Network isolation mode
              restricts the container access to outside networks (such as the Internet).
              The container does not make any inbound or outbound network calls.
              Also known as Internet-free mode. (Default: None).
          rules (Optional[list[:class:`~sagemaker.debugger.RuleBase`]]): A list of
              :class:`~sagemaker.debugger.RuleBase` objects used to define
              SageMaker Debugger rules for real-time analysis
              (Default: None). For more information,
              see `Continuous analyses through rules
              <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#
              continuous-analyses-through-rules)>`_.
              (Default: None).
          debugger_hook_config (Optional[Union[DebuggerHookConfig, bool]]):
              Configuration for how debugging information is emitted with
              SageMaker Debugger. If not specified, a default one is created using
              the estimator's ``output_path``, unless the region does not
              support SageMaker Debugger. To disable SageMaker Debugger,
              set this parameter to ``False``. For more information, see
              `Capture real-time debugging data during model training in Amazon SageMaker
              <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#
              capture-real-time-debugging-data-during-model-training-in-amazon-sagemaker>`_.
              (Default: None).
          tensorboard_output_config (:class:`~sagemaker.debugger.TensorBoardOutputConfig`):
              Configuration for customizing debugging visualization using TensorBoard.
              For more information, see `Capture real time tensorboard data
              <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#
              capture-real-time-tensorboard-data-from-the-debugging-hook>`_.
              (Default: None).
          enable_sagemaker_metrics (Optional[Union[bool, PipelineVariable]]): enable
              SageMaker Metrics Time Series. For more information, see `AlgorithmSpecification
              API <https://docs.aws.amazon.com/sagemaker/latest/dg/
              API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-
              EnableSageMakerMetricsTimeSeries>`_.
              (Default: None).
          profiler_config (Optional[:class:`~sagemaker.debugger.ProfilerConfig`]):
              Configuration for how SageMaker Debugger collects
              monitoring and profiling information from your training job.
              If not specified, Debugger will be configured with
              a default configuration and will save system and framework metrics
              the estimator's default ``output_path`` in Amazon S3.
              Use :class:`~sagemaker.debugger.ProfilerConfig` to configure this parameter.
              To disable SageMaker Debugger monitoring and profiling, set the
              ``disable_profiler`` parameter to ``True``. (Default: None).
          disable_profiler (Optional[bool]): Specifies whether Debugger monitoring and profiling
              will be disabled. (Default: None).
          environment (Optional[Union[dict[str, str], dict[str, PipelineVariable]]]):
              Environment variables to be set for use during training job. (Default: None).
          max_retry_attempts (Optional[Union[int, PipelineVariable]]): The number of times
              to move a job to the STARTING status. You can specify between 1 and 30 attempts.
              If the value of attempts is greater than zero,
              the job is retried on InternalServerFailure
              the same number of attempts as the value.
              You can cap the total duration for your job by setting ``max_wait`` and ``max_run``.
              (Default: None).
          source_dir (Optional[Union[str, PipelineVariable]]): The absolute, relative, or
              S3 URI Path to a directory with any other training source code dependencies
              aside from the entry point file. 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.
              (Default: None).
  
              .. admonition:: Example
  
                  With the following GitHub repo directory structure:
  
                  >>> |----- README.md
                  >>> |----- src
                  >>>         |----- train.py
                  >>>         |----- test.py
  
                  if you need 'train.py'
                  as the entry point and 'test.py' as the training source code, you can assign
                  entry_point='train.py', source_dir='src'.
          git_config (Optional[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.
  
              ``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.
              (Default: None).
  
              .. admonition:: 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.
          container_log_level (Optional[Union[int, PipelineVariable]]): The log level to use
              within the container. Valid values are defined in the Python logging module.
              (Default: None).
          code_location (Optional[str]): The S3 prefix URI where custom code is
              uploaded (Default: None). You must not include a trailing slash because
              a string prepended with a "/" is appended to ``code_location``. The code
              file uploaded to S3 is 'code_location/job-name/source/sourcedir.tar.gz'.
              If not specified, the default ``code location`` is 's3://output_bucket/job-name/'.
              (Default: None).
          entry_point (Optional[Union[str, PipelineVariable]]): The absolute or relative path
              to the local Python source file that should be executed as the entry point to
              training. 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.
              (Default: None).
  
              .. admonition:: Example
                  With the following GitHub repo directory structure:
  
                  >>> |----- README.md
                  >>> |----- src
                  >>>         |----- train.py
                  >>>         |----- test.py
  
                  You can assign entry_point='src/train.py'.
          dependencies (Optional[list[str]]): A list of absolute or relative paths to directories
              with any additional libraries that should be exported
              to the container. 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. This is not supported with "local code"
              in Local Mode. (Default: None).
  
              .. admonition:: Example
                  The following Estimator call:
  
                  >>> Estimator(entry_point='train.py',
                  ...           dependencies=['my/libs/common', 'virtual-env'])
  
                  results in the following structure inside the container:
  
                  >>> $ ls
  
                  >>> opt/ml/code
                  >>>     |------ train.py
                  >>>     |------ common
                  >>>     |------ virtual-env
          instance_groups (Optional[list[:class:`sagemaker.instance_group.InstanceGroup`]]):
              A list of ``InstanceGroup`` objects for launching a training job with a
              heterogeneous cluster. For example:
  
              .. code:: python
  
                  instance_groups=[
                      sagemaker.InstanceGroup(
                          'instance_group_name_1', 'ml.p3dn.24xlarge', 64),
                      sagemaker.InstanceGroup(
                          'instance_group_name_2', 'ml.c5n.18xlarge', 64)]
  
              For instructions on how to use ``InstanceGroup`` objects
              to configure a heterogeneous cluster
              through the SageMaker generic and framework estimator classes, see
              `Train Using a Heterogeneous Cluster
              <https://docs.aws.amazon.com/sagemaker/latest/dg/train-heterogeneous-cluster.html>`_
              in the *Amazon SageMaker developer guide*. (Default: None).
          training_repository_access_mode (Optional[str]): Specifies how SageMaker accesses the
              Docker image that contains the training algorithm (Default: None).
              Set this to one of the following values:
              * 'Platform' - The training image is hosted in Amazon ECR.
              * 'Vpc' - The training image is hosted in a private Docker registry in your VPC.
              When it's default to None, its behavior will be same as 'Platform' - image is hosted
              in ECR. (Default: None).
          training_repository_credentials_provider_arn (Optional[str]): The Amazon Resource Name
              (ARN) of an AWS Lambda function that provides credentials to authenticate to the
              private Docker registry where your training image is hosted (Default: None).
              When it's set to None, SageMaker will not do authentication before pulling the image
              in the private Docker registry. (Default: None).
          enable_infra_check (Optional[Union[bool, PipelineVariable]]):
              Specifies whether it is running Sagemaker built-in infra check jobs.
          container_entry_point (Optional[List[str]]): The entrypoint script for a Docker
              container used to run a training job. This script takes precedence over
              the default train processing instructions.
          container_arguments (Optional[List[str]]): The arguments for a container used to run
              a training job.
          disable_output_compression (Optional[bool]): When set to true, Model is uploaded
              to Amazon S3 without compression after training finishes.
          enable_remote_debug (bool or PipelineVariable): Optional.
              Specifies whether RemoteDebug is enabled for the training job
  
      Raises:
          ValueError: If the model ID is not recognized by JumpStart.
      """
  
      def _is_valid_model_id_hook():
          return is_valid_model_id(
              model_id=model_id,
              model_version=model_version,
              region=region,
              script=JumpStartScriptScope.TRAINING,
              sagemaker_session=sagemaker_session,
          )
  
      if not _is_valid_model_id_hook():
          JumpStartModelsAccessor.reset_cache()
          if not _is_valid_model_id_hook():
              raise ValueError(
                  INVALID_MODEL_ID_ERROR_MSG.format(model_id=model_id)
              )
  
      estimator_init_kwargs = get_init_kwargs(
          model_id=model_id,
          model_version=model_version,
          tolerate_vulnerable_model=tolerate_vulnerable_model,
          tolerate_deprecated_model=tolerate_deprecated_model,
          role=role,
          region=region,
          instance_count=instance_count,
          instance_type=instance_type,
          keep_alive_period_in_seconds=keep_alive_period_in_seconds,
          volume_size=volume_size,
          volume_kms_key=volume_kms_key,
          max_run=max_run,
          input_mode=input_mode,
          output_path=output_path,
          output_kms_key=output_kms_key,
          base_job_name=base_job_name,
          sagemaker_session=sagemaker_session,
          tags=format_tags(tags),
          subnets=subnets,
          security_group_ids=security_group_ids,
          model_uri=model_uri,
          model_channel_name=model_channel_name,
          metric_definitions=metric_definitions,
          encrypt_inter_container_traffic=encrypt_inter_container_traffic,
          use_spot_instances=use_spot_instances,
          max_wait=max_wait,
          checkpoint_s3_uri=checkpoint_s3_uri,
          checkpoint_local_path=checkpoint_local_path,
          rules=rules,
          debugger_hook_config=debugger_hook_config,
          tensorboard_output_config=tensorboard_output_config,
          enable_sagemaker_metrics=enable_sagemaker_metrics,
          enable_network_isolation=enable_network_isolation,
          profiler_config=profiler_config,
          disable_profiler=disable_profiler,
          environment=environment,
          max_retry_attempts=max_retry_attempts,
          source_dir=source_dir,
          git_config=git_config,
          hyperparameters=hyperparameters,
          container_log_level=container_log_level,
          code_location=code_location,
          entry_point=entry_point,
          dependencies=dependencies,
          instance_groups=instance_groups,
          training_repository_access_mode=training_repository_access_mode,
          training_repository_credentials_provider_arn=(
              training_repository_credentials_provider_arn
          ),
          image_uri=image_uri,
          container_entry_point=container_entry_point,
          container_arguments=container_arguments,
          disable_output_compression=disable_output_compression,
          enable_infra_check=enable_infra_check,
          enable_remote_debug=enable_remote_debug,
      )
  
      self.model_id = estimator_init_kwargs.model_id
      self.model_version = estimator_init_kwargs.model_version
      self.instance_type = estimator_init_kwargs.instance_type
      self.tolerate_deprecated_model = (
          estimator_init_kwargs.tolerate_deprecated_model
      )
      self.tolerate_vulnerable_model = (
          estimator_init_kwargs.tolerate_vulnerable_model
      )
      self.instance_count = estimator_init_kwargs.instance_count
      self.region = estimator_init_kwargs.region
      self.orig_predictor_cls = None
      self.role = estimator_init_kwargs.role
      self.sagemaker_session = estimator_init_kwargs.sagemaker_session
      self._enable_network_isolation = (
          estimator_init_kwargs.enable_network_isolation
      )
  
      super(JumpStartEstimator, self).__init__(
          **estimator_init_kwargs.to_kwargs_dict()
      )
  
imports: from __future__ import absolute_import
  from typing import Dict, List, Optional, Union
  from sagemaker import session
  from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
  from sagemaker.base_deserializers import BaseDeserializer
  from sagemaker.base_serializers import BaseSerializer
  from sagemaker.debugger.debugger import DebuggerHookConfig, RuleBase, TensorBoardOutputConfig
  from sagemaker.debugger.profiler_config import ProfilerConfig
  from sagemaker.estimator import Estimator
  from sagemaker.explainer.explainer_config import ExplainerConfig
  from sagemaker.inputs import FileSystemInput, TrainingInput
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.jumpstart.accessors import JumpStartModelsAccessor
  from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
  from sagemaker.jumpstart.enums import JumpStartScriptScope
  from sagemaker.jumpstart.exceptions import INVALID_MODEL_ID_ERROR_MSG
  from sagemaker.jumpstart.factory.estimator import get_deploy_kwargs, get_fit_kwargs, get_init_kwargs
  from sagemaker.jumpstart.factory.model import get_default_predictor
  from sagemaker.jumpstart.utils import is_valid_model_id, resolve_model_sagemaker_config_field
  from sagemaker.utils import stringify_object, format_tags, Tags
  from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
  from sagemaker.predictor import PredictorBase
  from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 1 to create a new JumpStartEstimator object from inherited base classes: Estimator with example: obj = JumpStartEstimator(model_id, model_version, tolerate_vulnerable_model, tolerate_deprecated_model, region, image_uri, role, instance_count, instance_type, keep_alive_period_in_seconds, volume_size, volume_kms_key, max_run, input_mode, output_path, output_kms_key, base_job_name, sagemaker_session, hyperparameters, tags, subnets, security_group_ids, model_uri, model_channel_name, metric_definitions, encrypt_inter_container_traffic, use_spot_instances, max_wait, checkpoint_s3_uri, checkpoint_local_path, enable_network_isolation, rules, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics, profiler_config, disable_profiler, environment, max_retry_attempts, source_dir, git_config, container_log_level, code_location, entry_point, dependencies, instance_groups, training_repository_access_mode, training_repository_credentials_provider_arn, enable_infra_check, container_entry_point, container_arguments, disable_output_compression, enable_remote_debug)