def __init__( self, model_data, image, role=None, predictor_cls=None, env=None, name=None, vpc_config=None, sagemaker_session=None, enable_network_isolation=False, model_kms_key=None, ): """Initialize an SageMaker ``Model``. Args: model_data (str): The S3 location of a SageMaker model data ``.tar.gz`` file. image (str): A Docker image URI. 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]): Environment variables to run with ``image`` 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]]): 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): 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 """ LOGGER.warning( fw_utils.parameter_v2_rename_warning("image", "image_uri")) self.model_data = model_data self.image = image self.role = role self.predictor_cls = predictor_cls self.env = env or {} self.name = name self.vpc_config = vpc_config self.sagemaker_session = sagemaker_session self._model_name = None self.endpoint_name = None self._is_compiled_model = False self._enable_network_isolation = enable_network_isolation self.model_kms_key = model_kms_key
def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=None, py_version=None, framework_version=None, model_dir=None, requirements_file="", image_name=None, script_mode=False, distributions=None, **kwargs): """Initialize a ``TensorFlow`` estimator. Args: training_steps (int): Perform this many steps of training. `None`, the default means train forever. evaluation_steps (int): Perform this many steps of evaluation. `None`, the default means that evaluation runs until input from eval_input_fn is exhausted (or another exception is raised). checkpoint_path (str): Identifies S3 location where checkpoint data during model training can be saved (default: None). For distributed model training, this parameter is required. py_version (str): Python version you want to use for executing your model training code (default: 'py2'). framework_version (str): TensorFlow version you want to use for executing your model training code. If not specified, this will default to 1.11. model_dir (str): S3 location where the checkpoint data and models can be exported to during training (default: None). It will be passed in the training script as one of the command line arguments. If not specified, one is provided based on your training configuration: * *distributed training with MPI* - ``/opt/ml/model`` * *single-machine training or distributed training without MPI* - \ ``s3://{output_path}/model`` * *Local Mode with local sources (file:// instead of s3://)* - \ ``/opt/ml/shared/model`` requirements_file (str): Path to a ``requirements.txt`` file (default: ''). The path should be within and relative to ``source_dir``. Details on the format can be found in the Pip User Guide: <https://pip.pypa.io/en/stable/reference/pip_install/#requirements-file-format> image_name (str): If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. It can be an ECR url or dockerhub image and tag. Examples: 123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0 custom-image:latest. script_mode (bool): If set to True will the estimator will use the Script Mode containers (default: False). This will be ignored if py_version is set to 'py3'. distributions (dict): A dictionary with information on how to run distributed training (default: None). Currently we support distributed training with parameter servers and MPI. To enable parameter server use the following setup: .. code:: python { 'parameter_server': { 'enabled': True } } To enable MPI: .. code:: python { 'mpi': { 'enabled': True } } **kwargs: Additional kwargs passed to the Framework constructor. .. tip:: You can find additional parameters for initializing this class at :class:`~sagemaker.estimator.Framework` and :class:`~sagemaker.estimator.EstimatorBase`. """ if framework_version is None: logger.warning( fw.empty_framework_version_warning(defaults.TF_VERSION, self.LATEST_VERSION)) self.framework_version = framework_version or defaults.TF_VERSION if not py_version: py_version = "py3" if self._only_python_3_supported() else "py2" if py_version == "py2": logger.warning( fw.python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)) if distributions is not None: logger.warning( fw.parameter_v2_rename_warning("distribution", distributions)) train_instance_type = kwargs.get("train_instance_type") fw.warn_if_parameter_server_with_multi_gpu( training_instance_type=train_instance_type, distributions=distributions) if "enable_sagemaker_metrics" not in kwargs: # enable sagemaker metrics for TF v1.15 or greater: if fw.is_version_equal_or_higher([1, 15], self.framework_version): kwargs["enable_sagemaker_metrics"] = True super(TensorFlow, self).__init__(image_name=image_name, **kwargs) self.checkpoint_path = checkpoint_path self.py_version = py_version self.training_steps = training_steps self.evaluation_steps = evaluation_steps self.model_dir = model_dir self.script_mode = script_mode self.distributions = distributions or {} self._validate_args( py_version=py_version, script_mode=script_mode, framework_version=self.framework_version, training_steps=training_steps, evaluation_steps=evaluation_steps, requirements_file=requirements_file, checkpoint_path=checkpoint_path, ) self._validate_requirements_file(requirements_file) self.requirements_file = requirements_file
def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_version="py2", framework_version=None, image_name=None, distributions=None, **kwargs): """This ``Estimator`` executes an MXNet script in a managed MXNet execution environment, within a SageMaker Training Job. The managed MXNet environment is an Amazon-built Docker container that executes functions defined in the supplied ``entry_point`` Python script. Training is started by calling :meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator. After training is complete, calling :meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted SageMaker endpoint and returns an :class:`~sagemaker.amazon.mxnet.model.MXNetPredictor` instance that can be used to perform inference against the hosted model. Technical documentation on preparing MXNet scripts for SageMaker training and using the MXNet Estimator is available on the project home-page: https://github.com/aws/sagemaker-python-sdk Args: entry_point (str): Path (absolute or relative) to the Python source file which 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``. 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. hyperparameters (dict): Hyperparameters that will be used for training (default: None). 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. py_version (str): Python version you want to use for executing your model training code (default: 'py2'). One of 'py2' or 'py3'. framework_version (str): MXNet version you want to use for executing your model training code. List of supported versions https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators. If not specified, this will default to 1.2.1. image_name (str): If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. It can be an ECR url or dockerhub image and tag. Examples: * ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0`` * ``custom-image:latest`` distributions (dict): A dictionary with information on how to run distributed training (default: None). To have parameter servers launched for training, set this value to be ``{'parameter_server': {'enabled': True}}``. **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework` constructor. .. tip:: You can find additional parameters for initializing this class at :class:`~sagemaker.estimator.Framework` and :class:`~sagemaker.estimator.EstimatorBase`. """ if framework_version is None: logger.warning( empty_framework_version_warning(defaults.MXNET_VERSION, self.LATEST_VERSION)) self.framework_version = framework_version or defaults.MXNET_VERSION if "enable_sagemaker_metrics" not in kwargs: # enable sagemaker metrics for MXNet v1.6 or greater: if is_version_equal_or_higher([1, 6], self.framework_version): kwargs["enable_sagemaker_metrics"] = True super(MXNet, self).__init__(entry_point, source_dir, hyperparameters, image_name=image_name, **kwargs) if py_version == "py2": logger.warning( python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)) if distributions is not None: logger.warning( parameter_v2_rename_warning("distributions", "distribution")) train_instance_type = kwargs.get("train_instance_type") warn_if_parameter_server_with_multi_gpu( training_instance_type=train_instance_type, distributions=distributions) self.py_version = py_version self._configure_distribution(distributions)