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train | TensorFlow.create_model | Create a SageMaker ``TensorFlowModel`` object that can be deployed to an ``Endpoint``.
Args:
role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used during
transform jobs. If not specified, the role from the Estimator will be used.
model_s... | src/sagemaker/tensorflow/estimator.py | def create_model(self, model_server_workers=None, role=None,
vpc_config_override=VPC_CONFIG_DEFAULT, endpoint_type=None):
"""Create a SageMaker ``TensorFlowModel`` object that can be deployed to an ``Endpoint``.
Args:
role (str): The ``ExecutionRoleArn`` IAM Role ARN fo... | def create_model(self, model_server_workers=None, role=None,
vpc_config_override=VPC_CONFIG_DEFAULT, endpoint_type=None):
"""Create a SageMaker ``TensorFlowModel`` object that can be deployed to an ``Endpoint``.
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train | TensorFlow.hyperparameters | Return hyperparameters used by your custom TensorFlow code during model training. | src/sagemaker/tensorflow/estimator.py | def hyperparameters(self):
"""Return hyperparameters used by your custom TensorFlow code during model training."""
hyperparameters = super(TensorFlow, self).hyperparameters()
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"""Return hyperparameters used by your custom TensorFlow code during model training."""
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train | from_dict | Extracts subnets and security group ids as lists from a VpcConfig dict
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do_sanitize (bool): whether to sanitize the VpcConfig dict before extracting values
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"""
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Args:
vpc_config (dict): a VpcConfig dict containing 'Subnets' and 'SecurityGroupIds'
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Extracts subnets and security group ids as lists from a VpcConfig dict
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train | sanitize | Checks that an instance of VpcConfig has the expected keys and values, removes unexpected keys,
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vpc_config (dict): a VpcConfig dict containing 'Subnets' and 'SecurityGroupIds'
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train | KMeans.hyperparameters | Return the SageMaker hyperparameters for training this KMeans Estimator | src/sagemaker/amazon/kmeans.py | def hyperparameters(self):
"""Return the SageMaker hyperparameters for training this KMeans Estimator"""
hp_dict = dict(force_dense='True') # KMeans requires this hp to fit on Record objects
hp_dict.update(super(KMeans, self).hyperparameters())
return hp_dict | def hyperparameters(self):
"""Return the SageMaker hyperparameters for training this KMeans Estimator"""
hp_dict = dict(force_dense='True') # KMeans requires this hp to fit on Record objects
hp_dict.update(super(KMeans, self).hyperparameters())
return hp_dict | [
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train | create_identical_dataset_and_algorithm_tuner | Creates a new tuner by copying the request fields from the provided parent to the new instance of
``HyperparameterTuner`` followed by addition of warm start configuration with the type as
"IdenticalDataAndAlgorithm" and ``parents`` as the union of provided list of ``additional_parents`` and the
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"""Creates a new tuner by copying the request fields from the provided parent to the new instance of
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train | create_transfer_learning_tuner | Creates a new ``HyperParameterTuner`` by copying the request fields from the provided parent to the new instance
of ``HyperparameterTuner`` followed by addition of warm start configuration with the type as "TransferLearning"
and ``parents`` as the union of provided list of ``additional_parents`` and the... | src/sagemaker/tuner.py | def create_transfer_learning_tuner(parent, additional_parents=None, estimator=None, sagemaker_session=None):
"""Creates a new ``HyperParameterTuner`` by copying the request fields from the provided parent to the new instance
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train | WarmStartConfig.from_job_desc | Creates an instance of ``WarmStartConfig`` class, from warm start configuration response from
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Args:
warm_start_config (dict): The expected format of the ``warm_start_config`` contains two first-class
fields:
* "type": Type of warm start tuner... | src/sagemaker/tuner.py | def from_job_desc(cls, warm_start_config):
"""Creates an instance of ``WarmStartConfig`` class, from warm start configuration response from
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Args:
warm_start_config (dict): The expected format of the ``warm_start_config`` contains two first-class
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train | WarmStartConfig.to_input_req | Converts the ``self`` instance to the desired input request format.
Returns:
dict: Containing the "WarmStartType" and "ParentHyperParameterTuningJobs" as the first class fields.
Examples:
>>> warm_start_config = WarmStartConfig(warm_start_type=WarmStartTypes.TransferLearning,pa... | src/sagemaker/tuner.py | def to_input_req(self):
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Returns:
dict: Containing the "WarmStartType" and "ParentHyperParameterTuningJobs" as the first class fields.
Examples:
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train | HyperparameterTuner.fit | Start a hyperparameter tuning job.
Args:
inputs: Information about the training data. Please refer to the ``fit()`` method of
the associated estimator, as this can take any of the following forms:
* (str) - The S3 location where training data is saved.
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Args:
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train | HyperparameterTuner.attach | Attach to an existing hyperparameter tuning job.
Create a HyperparameterTuner bound to an existing hyperparameter tuning job. After attaching, if there exists a
best training job (or any other completed training job), that can be deployed to create
an Amazon SageMaker Endpoint and return a ``Pr... | src/sagemaker/tuner.py | def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estimator_cls=None):
"""Attach to an existing hyperparameter tuning job.
Create a HyperparameterTuner bound to an existing hyperparameter tuning job. After attaching, if there exists a
best training job (or any other com... | def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estimator_cls=None):
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train | HyperparameterTuner.deploy | Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
``sagemaker.RealTimePredictor`` object.
For more information: http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html
Args:
initial_instance_count (int): Minimum number of... | src/sagemaker/tuner.py | def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
"""Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
``sagemaker.RealTimePredictor`` object.
For more information: http://docs.aws.amazon.com/... | def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
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train | HyperparameterTuner.best_training_job | Return name of the best training job for the latest hyperparameter tuning job.
Raises:
Exception: If there is no best training job available for the hyperparameter tuning job. | src/sagemaker/tuner.py | def best_training_job(self):
"""Return name of the best training job for the latest hyperparameter tuning job.
Raises:
Exception: If there is no best training job available for the hyperparameter tuning job.
"""
self._ensure_last_tuning_job()
tuning_job_describe_res... | def best_training_job(self):
"""Return name of the best training job for the latest hyperparameter tuning job.
Raises:
Exception: If there is no best training job available for the hyperparameter tuning job.
"""
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train | HyperparameterTuner.delete_endpoint | Delete an Amazon SageMaker endpoint.
If an endpoint name is not specified, this defaults to looking for an endpoint that
shares a name with the best training job for deletion.
Args:
endpoint_name (str): Name of the endpoint to delete | src/sagemaker/tuner.py | def delete_endpoint(self, endpoint_name=None):
"""Delete an Amazon SageMaker endpoint.
If an endpoint name is not specified, this defaults to looking for an endpoint that
shares a name with the best training job for deletion.
Args:
endpoint_name (str): Name of the endpoint ... | def delete_endpoint(self, endpoint_name=None):
"""Delete an Amazon SageMaker endpoint.
If an endpoint name is not specified, this defaults to looking for an endpoint that
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train | HyperparameterTuner.hyperparameter_ranges | Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
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train | HyperparameterTuner.transfer_learning_tuner | Creates a new ``HyperparameterTuner`` by copying the request fields from the provided parent to the new
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train | HyperparameterTuner.identical_dataset_and_algorithm_tuner | Creates a new ``HyperparameterTuner`` by copying the request fields from the provided parent to the new
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train | HyperparameterTuner._create_warm_start_tuner | Creates a new ``HyperparameterTuner`` with ``WarmStartConfig``, where type will be equal to
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train | _TuningJob.start_new | Create a new Amazon SageMaker hyperparameter tuning job from the HyperparameterTuner.
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tuner (sagemaker.tuner.HyperparameterTuner): HyperparameterTuner object created by the user.
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train | some | Return True iff there is an element, a, of arr such that a is not None | src/sagemaker/logs.py | def some(arr):
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client (boto3 client): The boto client for logs.
log_group (str): The name of the log group.
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train | RLEstimator.create_model | Create a SageMaker ``RLEstimatorModel`` object that can be deployed to an Endpoint.
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role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used
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train | RLEstimator.train_image | Return the Docker image to use for training.
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train | RLEstimator._prepare_init_params_from_job_description | Convert the job description to init params that can be handled by the class constructor
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job_details: the returned job details from a describe_training_job API call.
model_channel_name (str): Name of the channel where pre-trained model data will be
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job_details: the returned job details from a describe_training_job API call.
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train | RLEstimator.hyperparameters | Return hyperparameters used by your custom TensorFlow code during model training. | src/sagemaker/rl/estimator.py | def hyperparameters(self):
"""Return hyperparameters used by your custom TensorFlow code during model training."""
hyperparameters = super(RLEstimator, self).hyperparameters()
additional_hyperparameters = {SAGEMAKER_OUTPUT_LOCATION: self.output_path,
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hyperparameters = super(RLEstimator, self).hyperparameters()
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train | RLEstimator.default_metric_definitions | Provides default metric definitions based on provided toolkit.
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toolkit(sagemaker.rl.RLToolkit): RL Toolkit to be used for training.
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Args:
toolkit(sagemaker.rl.RLToolkit): RL Toolkit to be used for training.
Returns:
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toolkit(sagemaker.rl.RLToolkit): RL Toolkit to be used for training.
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train | prepare_framework | Prepare S3 operations (specify where to upload `source_dir`) and environment variables
related to framework.
Args:
estimator (sagemaker.estimator.Estimator): The framework estimator to get information from and update.
s3_operations (dict): The dict to specify s3 operations (upload `source_dir`)... | src/sagemaker/workflow/airflow.py | def prepare_framework(estimator, s3_operations):
"""Prepare S3 operations (specify where to upload `source_dir`) and environment variables
related to framework.
Args:
estimator (sagemaker.estimator.Estimator): The framework estimator to get information from and update.
s3_operations (dict):... | def prepare_framework(estimator, s3_operations):
"""Prepare S3 operations (specify where to upload `source_dir`) and environment variables
related to framework.
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train | prepare_amazon_algorithm_estimator | Set up amazon algorithm estimator, adding the required `feature_dim` hyperparameter from training data.
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""" Set up amazon algorithm estimator, adding the required `feature_dim` hyperparameter from training data.
Args:
estimator (sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase):
An estimator for a b... | def prepare_amazon_algorithm_estimator(estimator, inputs, mini_batch_size=None):
""" Set up amazon algorithm estimator, adding the required `feature_dim` hyperparameter from training data.
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train | training_base_config | Export Airflow base training config from an estimator
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estimator (sagemaker.estimator.EstimatorBase):
The estimator to export training config from. Can be a BYO estimator,
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inputs: Information about the training data. Pl... | src/sagemaker/workflow/airflow.py | def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
"""Export Airflow base training config from an estimator
Args:
estimator (sagemaker.estimator.EstimatorBase):
The estimator to export training config from. Can be a BYO estimator,
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estimator (sagemaker.estimator.EstimatorBase):
The estimator to export training config from. Can be a BYO estimator,
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train | training_config | Export Airflow training config from an estimator
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The estimator to export training config from. Can be a BYO estimator,
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inputs: Information about the training data. Please ... | src/sagemaker/workflow/airflow.py | def training_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
"""Export Airflow training config from an estimator
Args:
estimator (sagemaker.estimator.EstimatorBase):
The estimator to export training config from. Can be a BYO estimator,
Framework estimator or... | def training_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
"""Export Airflow training config from an estimator
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The estimator to export training config from. Can be a BYO estimator,
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train | tuning_config | Export Airflow tuning config from an estimator
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tuner (sagemaker.tuner.HyperparameterTuner): The tuner to export tuning config from.
inputs: Information about the training data. Please refer to the ``fit()`` method of
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"""Export Airflow tuning config from an estimator
Args:
tuner (sagemaker.tuner.HyperparameterTuner): The tuner to export tuning config from.
inputs: Information about the training data. Please refer to the ``fit()`` method of
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"""Export Airflow tuning config from an estimator
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tuner (sagemaker.tuner.HyperparameterTuner): The tuner to export tuning config from.
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train | update_submit_s3_uri | Updated the S3 URI of the framework source directory in given estimator.
Args:
estimator (sagemaker.estimator.Framework): The Framework estimator to update.
job_name (str): The new job name included in the submit S3 URI
Returns:
str: The updated S3 URI of framework source directory | src/sagemaker/workflow/airflow.py | def update_submit_s3_uri(estimator, job_name):
"""Updated the S3 URI of the framework source directory in given estimator.
Args:
estimator (sagemaker.estimator.Framework): The Framework estimator to update.
job_name (str): The new job name included in the submit S3 URI
Returns:
str... | def update_submit_s3_uri(estimator, job_name):
"""Updated the S3 URI of the framework source directory in given estimator.
Args:
estimator (sagemaker.estimator.Framework): The Framework estimator to update.
job_name (str): The new job name included in the submit S3 URI
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train | update_estimator_from_task | Update training job of the estimator from a task in the DAG
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estimator (sagemaker.estimator.EstimatorBase): The estimator to update
task_id (str): The task id of any airflow.contrib.operators.SageMakerTrainingOperator or
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Args:
estimator (sagemaker.estimator.EstimatorBase): The estimator to update
task_id (str): The task id of any airflow.contrib.operators.SageMakerTrainingOperator or
... | def update_estimator_from_task(estimator, task_id, task_type):
"""Update training job of the estimator from a task in the DAG
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estimator (sagemaker.estimator.EstimatorBase): The estimator to update
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train | prepare_framework_container_def | Prepare the framework model container information. Specify related S3 operations for Airflow to perform.
(Upload `source_dir`)
Args:
model (sagemaker.model.FrameworkModel): The framework model
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
... | src/sagemaker/workflow/airflow.py | def prepare_framework_container_def(model, instance_type, s3_operations):
"""Prepare the framework model container information. Specify related S3 operations for Airflow to perform.
(Upload `source_dir`)
Args:
model (sagemaker.model.FrameworkModel): The framework model
instance_type (str): ... | def prepare_framework_container_def(model, instance_type, s3_operations):
"""Prepare the framework model container information. Specify related S3 operations for Airflow to perform.
(Upload `source_dir`)
Args:
model (sagemaker.model.FrameworkModel): The framework model
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train | model_config | Export Airflow model config from a SageMaker model
Args:
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'
model (sagemaker.model.FrameworkModel): The SageMaker model to export Airflow config from
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"""Export Airflow model config from a SageMaker model
Args:
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'
model (sagemaker.model.FrameworkModel): The SageMaker model to export Airflo... | def model_config(instance_type, model, role=None, image=None):
"""Export Airflow model config from a SageMaker model
Args:
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'
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train | model_config_from_estimator | Export Airflow model config from a SageMaker estimator
Args:
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'
estimator (sagemaker.model.EstimatorBase): The SageMaker estimator to export Airflow config from.
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model_server_workers=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT):
"""Export Airflow model config from a SageMaker estimator
Args:
instance_type (st... | def model_config_from_estimator(instance_type, estimator, task_id, task_type, role=None, image=None, name=None,
model_server_workers=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT):
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train | transform_config | Export Airflow transform config from a SageMaker transformer
Args:
transformer (sagemaker.transformer.Transformer): The SageMaker transformer to export Airflow
config from.
data (str): Input data location in S3.
data_type (str): What the S3 location defines (default: 'S3Prefix')... | src/sagemaker/workflow/airflow.py | def transform_config(transformer, data, data_type='S3Prefix', content_type=None, compression_type=None,
split_type=None, job_name=None):
"""Export Airflow transform config from a SageMaker transformer
Args:
transformer (sagemaker.transformer.Transformer): The SageMaker transformer ... | def transform_config(transformer, data, data_type='S3Prefix', content_type=None, compression_type=None,
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Args:
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train | transform_config_from_estimator | Export Airflow transform config from a SageMaker estimator
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estimator (sagemaker.model.EstimatorBase): The SageMaker estimator to export Airflow config from.
It has to be an estimator associated with a training job.
task_id (str): The task id of any airflow.contrib.operators.SageMa... | src/sagemaker/workflow/airflow.py | def transform_config_from_estimator(estimator, task_id, task_type, instance_count, instance_type, data,
data_type='S3Prefix', content_type=None, compression_type=None, split_type=None,
job_name=None, model_name=None, strategy=None, assemble_with=No... | def transform_config_from_estimator(estimator, task_id, task_type, instance_count, instance_type, data,
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train | deploy_config | Export Airflow deploy config from a SageMaker model
Args:
model (sagemaker.model.Model): The SageMaker model to export the Airflow config from.
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
initial_instance_count (int): The initial number o... | src/sagemaker/workflow/airflow.py | def deploy_config(model, initial_instance_count, instance_type, endpoint_name=None, tags=None):
"""Export Airflow deploy config from a SageMaker model
Args:
model (sagemaker.model.Model): The SageMaker model to export the Airflow config from.
instance_type (str): The EC2 instance type to deploy... | def deploy_config(model, initial_instance_count, instance_type, endpoint_name=None, tags=None):
"""Export Airflow deploy config from a SageMaker model
Args:
model (sagemaker.model.Model): The SageMaker model to export the Airflow config from.
instance_type (str): The EC2 instance type to deploy... | [
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train | deploy_config_from_estimator | Export Airflow deploy config from a SageMaker estimator
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estimator (sagemaker.model.EstimatorBase): The SageMaker estimator to export Airflow config from.
It has to be an estimator associated with a training job.
task_id (str): The task id of any airflow.contrib.operators.SageMaker... | src/sagemaker/workflow/airflow.py | def deploy_config_from_estimator(estimator, task_id, task_type, initial_instance_count, instance_type,
model_name=None, endpoint_name=None, tags=None, **kwargs):
"""Export Airflow deploy config from a SageMaker estimator
Args:
estimator (sagemaker.model.EstimatorBase): ... | def deploy_config_from_estimator(estimator, task_id, task_type, initial_instance_count, instance_type,
model_name=None, endpoint_name=None, tags=None, **kwargs):
"""Export Airflow deploy config from a SageMaker estimator
Args:
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train | AlgorithmEstimator.create_model | Create a model to deploy.
The serializer, deserializer, content_type, and accept arguments are only used to define a default
RealTimePredictor. They are ignored if an explicit predictor class is passed in. Other arguments
are passed through to the Model class.
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"""Create a model to deploy.
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content_type=None,
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vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
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train | AlgorithmEstimator.transformer | Return a ``Transformer`` that uses a SageMaker Model based on the training job. It reuses the
SageMaker Session and base job name used by the Estimator.
Args:
instance_count (int): Number of EC2 instances to use.
instance_type (str): Type of EC2 instance to use, for example, 'ml... | src/sagemaker/algorithm.py | def transformer(self, 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, role=None, volume_kms_key=None):
"""Return a ``Transformer`` ... | def transformer(self, instance_count, instance_type, strategy=None, assemble_with=None, output_path=None,
output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None,
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train | EstimatorBase._prepare_for_training | Set any values in the estimator that need to be set before training.
Args:
* job_name (str): Name of the training job to be created. If not specified, one is generated,
using the base name given to the constructor if applicable. | src/sagemaker/estimator.py | def _prepare_for_training(self, job_name=None):
"""Set any values in the estimator that need to be set before training.
Args:
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train | EstimatorBase.fit | Train a model using the input training dataset.
The API calls the Amazon SageMaker CreateTrainingJob API to start model training.
The API uses configuration you provided to create the estimator and the
specified input training data to send the CreatingTrainingJob request to Amazon SageMaker.
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"""Train a model using the input training dataset.
The API calls the Amazon SageMaker CreateTrainingJob API to start model training.
The API uses configuration you provided to create the estimator and the
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train | EstimatorBase.compile_model | Compile a Neo model using the input model.
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target_instance_family (str): Identifies the device that you want to run your model after compilation, for
example: ml_c5. Allowed strings are: ml_c5, ml_m5, ml_c4, ml_m4, jetsontx1, jetsontx2, ml_p2, ml_p3,
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train | EstimatorBase.attach | Attach to an existing training job.
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``_prepare_init_params_from_job_description()`` as this method delegates the actual conversion of a training
job description to the arguments that the class const... | src/sagemaker/estimator.py | def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='model'):
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Create an Estimator bound to an existing training job, each subclass is responsible to implement
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train | EstimatorBase.deploy | Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
More information:
http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html
Args:
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"""Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
More information... | def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
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train | EstimatorBase.model_data | str: The model location in S3. Only set if Estimator has been ``fit()``. | src/sagemaker/estimator.py | def model_data(self):
"""str: The model location in S3. Only set if Estimator has been ``fit()``."""
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model_uri = self.sagemaker_session.sagemaker_client.describe_training_job(
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train | EstimatorBase._prepare_init_params_from_job_description | Convert the job description to init params that can be handled by the class constructor
Args:
job_details: the returned job details from a describe_training_job API call.
model_channel_name (str): Name of the channel where pre-trained model data will be downloaded.
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job_details: the returned job details from a describe_training_job API call.
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train | EstimatorBase.delete_endpoint | Delete an Amazon SageMaker ``Endpoint``.
Raises:
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"""Delete an Amazon SageMaker ``Endpoint``.
Raises:
ValueError: If the endpoint does not exist.
"""
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train | EstimatorBase.transformer | Return a ``Transformer`` that uses a SageMaker Model based on the training job. It reuses the
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instance_count (int): Number of EC2 instances to use.
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train | EstimatorBase.training_job_analytics | Return a ``TrainingJobAnalytics`` object for the current training job. | src/sagemaker/estimator.py | def training_job_analytics(self):
"""Return a ``TrainingJobAnalytics`` object for the current training job.
"""
if self._current_job_name is None:
raise ValueError('Estimator is not associated with a TrainingJob')
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train | EstimatorBase.get_vpc_config | Returns VpcConfig dict either from this Estimator's subnets and security groups,
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or else validate and return an optional override value.
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train | _TrainingJob.start_new | Create a new Amazon SageMaker training job from the estimator.
Args:
estimator (sagemaker.estimator.EstimatorBase): Estimator object created by the user.
inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`.
Returns:
sagemaker.es... | src/sagemaker/estimator.py | def start_new(cls, estimator, inputs):
"""Create a new Amazon SageMaker training job from the estimator.
Args:
estimator (sagemaker.estimator.EstimatorBase): Estimator object created by the user.
inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBas... | def start_new(cls, estimator, inputs):
"""Create a new Amazon SageMaker training job from the estimator.
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train | Estimator.create_model | Create a model to deploy.
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role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used during
transform jobs. If not specified, the role from the Estimator will be used.
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train | Estimator._prepare_init_params_from_job_description | Convert the job description to init params that can be handled by the class constructor
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job_details: the returned job details from a describe_training_job API call.
model_channel_name (str): Name of the channel where pre-trained model data will be downloaded
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job_details: the returned job details from a describe_training_job API call.
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train | Framework._prepare_for_training | Set hyperparameters needed for training. This method will also validate ``source_dir``.
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train | Framework._stage_user_code_in_s3 | Upload the user training script to s3 and return the location.
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Returns: s3 uri
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train | Framework._model_source_dir | Get the appropriate value to pass as source_dir to model constructor on deploying
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str: Either a local or an S3 path pointing to the source_dir to be used for code by the model to be deployed | src/sagemaker/estimator.py | def _model_source_dir(self):
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train | Framework._prepare_init_params_from_job_description | Convert the job description to init params that can be handled by the class constructor
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job_details: the returned job details from a describe_training_job API call.
model_channel_name (str): Name of the channel where pre-trained model data will be downloaded
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job_details: the returned job details from a describe_training_job API call.
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train | Framework.train_image | Return the Docker image to use for training.
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train | Framework.attach | Attach to an existing training job.
Create an Estimator bound to an existing training job, each subclass is responsible to implement
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job description to the arguments that the class const... | src/sagemaker/estimator.py | def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='model'):
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train | Framework.transformer | Return a ``Transformer`` that uses a SageMaker Model based on the training job. It reuses the
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instance_count (int): Number of EC2 instances to use.
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train | Hyperparameter.serialize_all | Return all non-None ``hyperparameter`` values on ``obj`` as a ``dict[str,str].`` | src/sagemaker/amazon/hyperparameter.py | def serialize_all(obj):
"""Return all non-None ``hyperparameter`` values on ``obj`` as a ``dict[str,str].``"""
if '_hyperparameters' not in dir(obj):
return {}
return {k: str(v) for k, v in obj._hyperparameters.items() if v is not None} | def serialize_all(obj):
"""Return all non-None ``hyperparameter`` values on ``obj`` as a ``dict[str,str].``"""
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train | _LocalTransformJob.start | Start the Local Transform Job
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input_data (dict): Describes the dataset to be transformed and the location where it is stored.
output_data (dict): Identifies the location where to save the results from the transform job
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input_data (dict): Describes the dataset to be transformed and the location where it is stored.
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train | _LocalTransformJob.describe | Describe this _LocalTransformJob
The response is a JSON-like dictionary that follows the response of the
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Returns:
dict: description of this _LocalTransformJob | src/sagemaker/local/entities.py | def describe(self):
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The response is a JSON-like dictionary that follows the response of the
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dict: description of this _LocalTransformJob
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train | _LocalTransformJob._get_container_environment | Get all the Environment variables that will be passed to the container
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variables, such as SingleRecord vs SINGLE_RECORD. This method also handles this conversion.
Args:
**kwargs: existin... | src/sagemaker/local/entities.py | def _get_container_environment(self, **kwargs):
"""Get all the Environment variables that will be passed to the container
Certain input fields such as BatchStrategy have different values for the API vs the Environment
variables, such as SingleRecord vs SINGLE_RECORD. This method also handles th... | def _get_container_environment(self, **kwargs):
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train | ParameterRange.as_tuning_range | Represent the parameter range as a dicionary suitable for a request to
create an Amazon SageMaker hyperparameter tuning job.
Args:
name (str): The name of the hyperparameter.
Returns:
dict[str, str]: A dictionary that contains the name and values of the hyperparameter. | src/sagemaker/parameter.py | def as_tuning_range(self, name):
"""Represent the parameter range as a dicionary suitable for a request to
create an Amazon SageMaker hyperparameter tuning job.
Args:
name (str): The name of the hyperparameter.
Returns:
dict[str, str]: A dictionary that contains... | def as_tuning_range(self, name):
"""Represent the parameter range as a dicionary suitable for a request to
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Args:
name (str): The name of the hyperparameter.
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train | CategoricalParameter.as_json_range | Represent the parameter range as a dictionary suitable for a request to
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The deep learning framework images require that hyperparameters be serialized as JSON.
Args:
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"""Represent the parameter range as a dictionary suitable for a request to
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train | container_def | Create a definition for executing a container as part of a SageMaker model.
Args:
image (str): Docker image to run for this container.
model_data_url (str): S3 URI of data required by this container,
e.g. SageMaker training job model artifacts (default: None).
env (dict[str, str... | src/sagemaker/session.py | def container_def(image, model_data_url=None, env=None):
"""Create a definition for executing a container as part of a SageMaker model.
Args:
image (str): Docker image to run for this container.
model_data_url (str): S3 URI of data required by this container,
e.g. SageMaker training... | def container_def(image, model_data_url=None, env=None):
"""Create a definition for executing a container as part of a SageMaker model.
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train | pipeline_container_def | Create a definition for executing a pipeline of containers as part of a SageMaker model.
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models (list[sagemaker.Model]): this will be a list of ``sagemaker.Model`` objects in the order the inference
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train | production_variant | Create a production variant description suitable for use in a ``ProductionVariant`` list as part of a
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Args:
model_name (str): The name of the SageMaker model this production variant references.
instance_type (str): The EC2 instance type for this production vari... | src/sagemaker/session.py | def production_variant(model_name, instance_type, initial_instance_count=1, variant_name='AllTraffic',
initial_weight=1, accelerator_type=None):
"""Create a production variant description suitable for use in a ``ProductionVariant`` list as part of a
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... | def production_variant(model_name, instance_type, initial_instance_count=1, variant_name='AllTraffic',
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"""Create a production variant description suitable for use in a ``ProductionVariant`` list as part of a
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train | get_execution_role | Return the role ARN whose credentials are used to call the API.
Throws an exception if
Args:
sagemaker_session(Session): Current sagemaker session
Returns:
(str): The role ARN | src/sagemaker/session.py | def get_execution_role(sagemaker_session=None):
"""Return the role ARN whose credentials are used to call the API.
Throws an exception if
Args:
sagemaker_session(Session): Current sagemaker session
Returns:
(str): The role ARN
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"""Return the role ARN whose credentials are used to call the API.
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train | Session._initialize | Initialize this SageMaker Session.
Creates or uses a boto_session, sagemaker_client and sagemaker_runtime_client.
Sets the region_name. | src/sagemaker/session.py | def _initialize(self, boto_session, sagemaker_client, sagemaker_runtime_client):
"""Initialize this SageMaker Session.
Creates or uses a boto_session, sagemaker_client and sagemaker_runtime_client.
Sets the region_name.
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self.boto_session = boto_session or boto3.Session()
... | def _initialize(self, boto_session, sagemaker_client, sagemaker_runtime_client):
"""Initialize this SageMaker Session.
Creates or uses a boto_session, sagemaker_client and sagemaker_runtime_client.
Sets the region_name.
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self.boto_session = boto_session or boto3.Session()
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train | Session.upload_data | Upload local file or directory to S3.
If a single file is specified for upload, the resulting S3 object key is ``{key_prefix}/{filename}``
(filename does not include the local path, if any specified).
If a directory is specified for upload, the API uploads all content, recursively,
pre... | src/sagemaker/session.py | def upload_data(self, path, bucket=None, key_prefix='data'):
"""Upload local file or directory to S3.
If a single file is specified for upload, the resulting S3 object key is ``{key_prefix}/{filename}``
(filename does not include the local path, if any specified).
If a directory is spe... | def upload_data(self, path, bucket=None, key_prefix='data'):
"""Upload local file or directory to S3.
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train | Session.default_bucket | Return the name of the default bucket to use in relevant Amazon SageMaker interactions.
Returns:
str: The name of the default bucket, which is of the form: ``sagemaker-{region}-{AWS account ID}``. | src/sagemaker/session.py | def default_bucket(self):
"""Return the name of the default bucket to use in relevant Amazon SageMaker interactions.
Returns:
str: The name of the default bucket, which is of the form: ``sagemaker-{region}-{AWS account ID}``.
"""
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"""Return the name of the default bucket to use in relevant Amazon SageMaker interactions.
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train | Session.train | Create an Amazon SageMaker training job.
Args:
input_mode (str): The input mode that the algorithm supports. Valid modes:
* 'File' - Amazon SageMaker copies the training dataset from the S3 location to
a directory in the Docker container.
* 'Pipe... | src/sagemaker/session.py | def train(self, input_mode, input_config, role, job_name, output_config, # noqa: C901
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enable_network_isolation=False, image=None, algorithm_arn=None,
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train | Session.compile_model | Create an Amazon SageMaker Neo compilation job.
Args:
input_model_config (dict): the trained model and the Amazon S3 location where it is stored.
output_model_config (dict): Identifies the Amazon S3 location where you want Amazon SageMaker Neo to save
the results of comp... | src/sagemaker/session.py | def compile_model(self, input_model_config, output_model_config, role,
job_name, stop_condition, tags):
"""Create an Amazon SageMaker Neo compilation job.
Args:
input_model_config (dict): the trained model and the Amazon S3 location where it is stored.
outp... | def compile_model(self, input_model_config, output_model_config, role,
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"""Create an Amazon SageMaker Neo compilation job.
Args:
input_model_config (dict): the trained model and the Amazon S3 location where it is stored.
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train | Session.tune | Create an Amazon SageMaker hyperparameter tuning job
Args:
job_name (str): Name of the tuning job being created.
strategy (str): Strategy to be used for hyperparameter estimations.
objective_type (str): The type of the objective metric for evaluating training jobs. This valu... | src/sagemaker/session.py | def tune(self, job_name, strategy, objective_type, objective_metric_name,
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train | Session.stop_tuning_job | Stop the Amazon SageMaker hyperparameter tuning job with the specified name.
Args:
name (str): Name of the Amazon SageMaker hyperparameter tuning job.
Raises:
ClientError: If an error occurs while trying to stop the hyperparameter tuning job. | src/sagemaker/session.py | def stop_tuning_job(self, name):
"""Stop the Amazon SageMaker hyperparameter tuning job with the specified name.
Args:
name (str): Name of the Amazon SageMaker hyperparameter tuning job.
Raises:
ClientError: If an error occurs while trying to stop the hyperparameter tun... | def stop_tuning_job(self, name):
"""Stop the Amazon SageMaker hyperparameter tuning job with the specified name.
Args:
name (str): Name of the Amazon SageMaker hyperparameter tuning job.
Raises:
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train | Session.transform | Create an Amazon SageMaker transform job.
Args:
job_name (str): Name of the transform job being created.
model_name (str): Name of the SageMaker model being used for the transform job.
strategy (str): The strategy used to decide how to batch records in a single request.
... | src/sagemaker/session.py | def transform(self, job_name, model_name, strategy, max_concurrent_transforms, max_payload, env,
input_config, output_config, resource_config, tags):
"""Create an Amazon SageMaker transform job.
Args:
job_name (str): Name of the transform job being created.
mod... | def transform(self, job_name, model_name, strategy, max_concurrent_transforms, max_payload, env,
input_config, output_config, resource_config, tags):
"""Create an Amazon SageMaker transform job.
Args:
job_name (str): Name of the transform job being created.
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train | Session.create_model | Create an Amazon SageMaker ``Model``.
Specify the S3 location of the model artifacts and Docker image containing
the inference code. Amazon SageMaker uses this information to deploy the
model in Amazon SageMaker. This method can also be used to create a Model for an Inference Pipeline
if... | src/sagemaker/session.py | def create_model(self, name, role, container_defs, vpc_config=None,
enable_network_isolation=False, primary_container=None,
tags=None):
"""Create an Amazon SageMaker ``Model``.
Specify the S3 location of the model artifacts and Docker image containing
th... | def create_model(self, name, role, container_defs, vpc_config=None,
enable_network_isolation=False, primary_container=None,
tags=None):
"""Create an Amazon SageMaker ``Model``.
Specify the S3 location of the model artifacts and Docker image containing
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train | Session.create_model_from_job | Create an Amazon SageMaker ``Model`` from a SageMaker Training Job.
Args:
training_job_name (str): The Amazon SageMaker Training Job name.
name (str): The name of the SageMaker ``Model`` to create (default: None).
If not specified, the training job name is used.
... | src/sagemaker/session.py | def create_model_from_job(self, training_job_name, name=None, role=None, primary_container_image=None,
model_data_url=None, env=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT):
"""Create an Amazon SageMaker ``Model`` from a SageMaker Training Job.
Args:
... | def create_model_from_job(self, training_job_name, name=None, role=None, primary_container_image=None,
model_data_url=None, env=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT):
"""Create an Amazon SageMaker ``Model`` from a SageMaker Training Job.
Args:
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train | Session.create_model_package_from_algorithm | Create a SageMaker Model Package from the results of training with an Algorithm Package
Args:
name (str): ModelPackage name
description (str): Model Package description
algorithm_arn (str): arn or name of the algorithm used for training.
model_data (str): s3 URI ... | src/sagemaker/session.py | def create_model_package_from_algorithm(self, name, description, algorithm_arn, model_data):
"""Create a SageMaker Model Package from the results of training with an Algorithm Package
Args:
name (str): ModelPackage name
description (str): Model Package description
al... | def create_model_package_from_algorithm(self, name, description, algorithm_arn, model_data):
"""Create a SageMaker Model Package from the results of training with an Algorithm Package
Args:
name (str): ModelPackage name
description (str): Model Package description
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train | Session.wait_for_model_package | Wait for an Amazon SageMaker endpoint deployment to complete.
Args:
endpoint (str): Name of the ``Endpoint`` to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
dict: Return value from the ``DescribeEndpoint`` API. | src/sagemaker/session.py | def wait_for_model_package(self, model_package_name, poll=5):
"""Wait for an Amazon SageMaker endpoint deployment to complete.
Args:
endpoint (str): Name of the ``Endpoint`` to wait for.
poll (int): Polling interval in seconds (default: 5).
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dict: Re... | def wait_for_model_package(self, model_package_name, poll=5):
"""Wait for an Amazon SageMaker endpoint deployment to complete.
Args:
endpoint (str): Name of the ``Endpoint`` to wait for.
poll (int): Polling interval in seconds (default: 5).
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train | Session.create_endpoint_config | Create an Amazon SageMaker endpoint configuration.
The endpoint configuration identifies the Amazon SageMaker model (created using the
``CreateModel`` API) and the hardware configuration on which to deploy the model. Provide this
endpoint configuration to the ``CreateEndpoint`` API, which then ... | src/sagemaker/session.py | def create_endpoint_config(self, name, model_name, initial_instance_count, instance_type,
accelerator_type=None, tags=None, kms_key=None):
"""Create an Amazon SageMaker endpoint configuration.
The endpoint configuration identifies the Amazon SageMaker model (created using... | def create_endpoint_config(self, name, model_name, initial_instance_count, instance_type,
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train | Session.create_endpoint | Create an Amazon SageMaker ``Endpoint`` according to the endpoint configuration specified in the request.
Once the ``Endpoint`` is created, client applications can send requests to obtain inferences.
The endpoint configuration is created using the ``CreateEndpointConfig`` API.
Args:
... | src/sagemaker/session.py | def create_endpoint(self, endpoint_name, config_name, tags=None, wait=True):
"""Create an Amazon SageMaker ``Endpoint`` according to the endpoint configuration specified in the request.
Once the ``Endpoint`` is created, client applications can send requests to obtain inferences.
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train | Session.update_endpoint | Update an Amazon SageMaker ``Endpoint`` according to the endpoint configuration specified in the request
Raise an error if endpoint with endpoint_name does not exist.
Args:
endpoint_name (str): Name of the Amazon SageMaker ``Endpoint`` to update.
endpoint_config_name (str): Nam... | src/sagemaker/session.py | def update_endpoint(self, endpoint_name, endpoint_config_name):
""" Update an Amazon SageMaker ``Endpoint`` according to the endpoint configuration specified in the request
Raise an error if endpoint with endpoint_name does not exist.
Args:
endpoint_name (str): Name of the Amazon S... | def update_endpoint(self, endpoint_name, endpoint_config_name):
""" Update an Amazon SageMaker ``Endpoint`` according to the endpoint configuration specified in the request
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train | Session.delete_endpoint | Delete an Amazon SageMaker ``Endpoint``.
Args:
endpoint_name (str): Name of the Amazon SageMaker ``Endpoint`` to delete. | src/sagemaker/session.py | def delete_endpoint(self, endpoint_name):
"""Delete an Amazon SageMaker ``Endpoint``.
Args:
endpoint_name (str): Name of the Amazon SageMaker ``Endpoint`` to delete.
"""
LOGGER.info('Deleting endpoint with name: {}'.format(endpoint_name))
self.sagemaker_client.delete... | def delete_endpoint(self, endpoint_name):
"""Delete an Amazon SageMaker ``Endpoint``.
Args:
endpoint_name (str): Name of the Amazon SageMaker ``Endpoint`` to delete.
"""
LOGGER.info('Deleting endpoint with name: {}'.format(endpoint_name))
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train | Session.delete_endpoint_config | Delete an Amazon SageMaker endpoint configuration.
Args:
endpoint_config_name (str): Name of the Amazon SageMaker endpoint configuration to delete. | src/sagemaker/session.py | def delete_endpoint_config(self, endpoint_config_name):
"""Delete an Amazon SageMaker endpoint configuration.
Args:
endpoint_config_name (str): Name of the Amazon SageMaker endpoint configuration to delete.
"""
LOGGER.info('Deleting endpoint configuration with name: {}'.form... | def delete_endpoint_config(self, endpoint_config_name):
"""Delete an Amazon SageMaker endpoint configuration.
Args:
endpoint_config_name (str): Name of the Amazon SageMaker endpoint configuration to delete.
"""
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train | Session.delete_model | Delete an Amazon SageMaker Model.
Args:
model_name (str): Name of the Amazon SageMaker model to delete. | src/sagemaker/session.py | def delete_model(self, model_name):
"""Delete an Amazon SageMaker Model.
Args:
model_name (str): Name of the Amazon SageMaker model to delete.
"""
LOGGER.info('Deleting model with name: {}'.format(model_name))
self.sagemaker_client.delete_model(ModelName=model_name) | def delete_model(self, model_name):
"""Delete an Amazon SageMaker Model.
Args:
model_name (str): Name of the Amazon SageMaker model to delete.
"""
LOGGER.info('Deleting model with name: {}'.format(model_name))
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train | Session.wait_for_job | Wait for an Amazon SageMaker training job to complete.
Args:
job (str): Name of the training job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value from the ``DescribeTrainingJob`` API.
Raises:
Value... | src/sagemaker/session.py | def wait_for_job(self, job, poll=5):
"""Wait for an Amazon SageMaker training job to complete.
Args:
job (str): Name of the training job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value from the ``DescribeTrain... | def wait_for_job(self, job, poll=5):
"""Wait for an Amazon SageMaker training job to complete.
Args:
job (str): Name of the training job to wait for.
poll (int): Polling interval in seconds (default: 5).
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train | Session.wait_for_compilation_job | Wait for an Amazon SageMaker Neo compilation job to complete.
Args:
job (str): Name of the compilation job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value from the ``DescribeCompilationJob`` API.
Raises:
... | src/sagemaker/session.py | def wait_for_compilation_job(self, job, poll=5):
"""Wait for an Amazon SageMaker Neo compilation job to complete.
Args:
job (str): Name of the compilation job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value fr... | def wait_for_compilation_job(self, job, poll=5):
"""Wait for an Amazon SageMaker Neo compilation job to complete.
Args:
job (str): Name of the compilation job to wait for.
poll (int): Polling interval in seconds (default: 5).
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train | Session.wait_for_tuning_job | Wait for an Amazon SageMaker hyperparameter tuning job to complete.
Args:
job (str): Name of the tuning job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value from the ``DescribeHyperParameterTuningJob`` API.
Ra... | src/sagemaker/session.py | def wait_for_tuning_job(self, job, poll=5):
"""Wait for an Amazon SageMaker hyperparameter tuning job to complete.
Args:
job (str): Name of the tuning job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value from t... | def wait_for_tuning_job(self, job, poll=5):
"""Wait for an Amazon SageMaker hyperparameter tuning job to complete.
Args:
job (str): Name of the tuning job to wait for.
poll (int): Polling interval in seconds (default: 5).
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train | Session.wait_for_transform_job | Wait for an Amazon SageMaker transform job to complete.
Args:
job (str): Name of the transform job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value from the ``DescribeTransformJob`` API.
Raises:
Va... | src/sagemaker/session.py | def wait_for_transform_job(self, job, poll=5):
"""Wait for an Amazon SageMaker transform job to complete.
Args:
job (str): Name of the transform job to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
(dict): Return value from the ``D... | def wait_for_transform_job(self, job, poll=5):
"""Wait for an Amazon SageMaker transform job to complete.
Args:
job (str): Name of the transform job to wait for.
poll (int): Polling interval in seconds (default: 5).
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train | Session._check_job_status | Check to see if the job completed successfully and, if not, construct and
raise a ValueError.
Args:
job (str): The name of the job to check.
desc (dict[str, str]): The result of ``describe_training_job()``.
status_key_name (str): Status key name to check for.
... | src/sagemaker/session.py | def _check_job_status(self, job, desc, status_key_name):
"""Check to see if the job completed successfully and, if not, construct and
raise a ValueError.
Args:
job (str): The name of the job to check.
desc (dict[str, str]): The result of ``describe_training_job()``.
... | def _check_job_status(self, job, desc, status_key_name):
"""Check to see if the job completed successfully and, if not, construct and
raise a ValueError.
Args:
job (str): The name of the job to check.
desc (dict[str, str]): The result of ``describe_training_job()``.
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train | Session.wait_for_endpoint | Wait for an Amazon SageMaker endpoint deployment to complete.
Args:
endpoint (str): Name of the ``Endpoint`` to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
dict: Return value from the ``DescribeEndpoint`` API. | src/sagemaker/session.py | def wait_for_endpoint(self, endpoint, poll=5):
"""Wait for an Amazon SageMaker endpoint deployment to complete.
Args:
endpoint (str): Name of the ``Endpoint`` to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
dict: Return value from... | def wait_for_endpoint(self, endpoint, poll=5):
"""Wait for an Amazon SageMaker endpoint deployment to complete.
Args:
endpoint (str): Name of the ``Endpoint`` to wait for.
poll (int): Polling interval in seconds (default: 5).
Returns:
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train | Session.endpoint_from_job | Create an ``Endpoint`` using the results of a successful training job.
Specify the job name, Docker image containing the inference code, and hardware configuration to deploy
the model. Internally the API, creates an Amazon SageMaker model (that describes the model artifacts and
the Docker image... | src/sagemaker/session.py | def endpoint_from_job(self, job_name, initial_instance_count, instance_type,
deployment_image=None, name=None, role=None, wait=True,
model_environment_vars=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
accelerator_type=None):
... | def endpoint_from_job(self, job_name, initial_instance_count, instance_type,
deployment_image=None, name=None, role=None, wait=True,
model_environment_vars=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
accelerator_type=None):
... | [
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] | aws/sagemaker-python-sdk | python | https://github.com/aws/sagemaker-python-sdk/blob/a9e724c7d3f5572b68c3903548c792a59d99799a/src/sagemaker/session.py#L937-L985 | [
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train | Session.endpoint_from_model_data | Create and deploy to an ``Endpoint`` using existing model data stored in S3.
Args:
model_s3_location (str): S3 URI of the model artifacts to use for the endpoint.
deployment_image (str): The Docker image which defines the runtime code to be used as
the entry point for ac... | src/sagemaker/session.py | def endpoint_from_model_data(self, model_s3_location, deployment_image, initial_instance_count, instance_type,
name=None, role=None, wait=True, model_environment_vars=None, model_vpc_config=None,
accelerator_type=None):
"""Create and deploy to an... | def endpoint_from_model_data(self, model_s3_location, deployment_image, initial_instance_count, instance_type,
name=None, role=None, wait=True, model_environment_vars=None, model_vpc_config=None,
accelerator_type=None):
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] | aws/sagemaker-python-sdk | python | https://github.com/aws/sagemaker-python-sdk/blob/a9e724c7d3f5572b68c3903548c792a59d99799a/src/sagemaker/session.py#L987-L1042 | [
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