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aws/sagemaker-python-sdk | src/sagemaker/rl/estimator.py | RLEstimator._prepare_init_params_from_job_description | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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_n... | python | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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.
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aws/sagemaker-python-sdk | src/sagemaker/rl/estimator.py | RLEstimator.hyperparameters | 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,
# TODO... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/rl/estimator.py | RLEstimator.default_metric_definitions | def default_metric_definitions(cls, toolkit):
"""Provides default metric definitions based on provided toolkit.
Args:
toolkit(sagemaker.rl.RLToolkit): RL Toolkit to be used for training.
Returns:
list: metric definitions
"""
if toolkit is RLToolkit.COACH... | python | def default_metric_definitions(cls, toolkit):
"""Provides default metric definitions based on provided toolkit.
Args:
toolkit(sagemaker.rl.RLToolkit): RL Toolkit to be used for training.
Returns:
list: metric definitions
"""
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | prepare_framework | 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):... | python | 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.
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | prepare_amazon_algorithm_estimator | 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.
Args:
estimator (sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase):
An estimator for a b... | python | 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.
Args:
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | training_base_config | 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,
Framework es... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | training_config | 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... | python | 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,
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | tuning_config | def tuning_config(tuner, inputs, job_name=None):
"""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
the ... | python | def tuning_config(tuner, inputs, job_name=None):
"""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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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | update_submit_s3_uri | 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... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | update_estimator_from_task | def update_estimator_from_task(estimator, task_id, task_type):
"""Update training job of the estimator from a task in the DAG
Args:
estimator (sagemaker.estimator.EstimatorBase): The estimator to update
task_id (str): The task id of any airflow.contrib.operators.SageMakerTrainingOperator or
... | python | def update_estimator_from_task(estimator, task_id, task_type):
"""Update training job of the estimator from a task in the DAG
Args:
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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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | prepare_framework_container_def | 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): ... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | model_config | 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'
model (sagemaker.model.FrameworkModel): The SageMaker model to export Airflo... | python | def model_config(instance_type, model, role=None, image=None):
"""Export Airflow model config from a SageMaker model
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instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | model_config_from_estimator | 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):
"""Export Airflow model config from a SageMaker estimator
Args:
instance_type (st... | python | 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):
"""Export Airflow model config from a SageMaker estimator
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | transform_config | 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 ... | python | def transform_config(transformer, data, data_type='S3Prefix', content_type=None, compression_type=None,
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"""Export Airflow transform config from a SageMaker transformer
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | transform_config_from_estimator | def transform_config_from_estimator(estimator, task_id, task_type, instance_count, instance_type, data,
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | deploy_config | 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... | python | 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.
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aws/sagemaker-python-sdk | src/sagemaker/workflow/airflow.py | deploy_config_from_estimator | 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): ... | python | 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
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aws/sagemaker-python-sdk | src/sagemaker/algorithm.py | AlgorithmEstimator.create_model | def create_model(
self,
role=None,
predictor_cls=None,
serializer=None,
deserializer=None,
content_type=None,
accept=None,
vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
**kwargs
):
"""Create a model to deploy.
The serialize... | python | def create_model(
self,
role=None,
predictor_cls=None,
serializer=None,
deserializer=None,
content_type=None,
accept=None,
vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
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"""Create a model to deploy.
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aws/sagemaker-python-sdk | src/sagemaker/algorithm.py | AlgorithmEstimator.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,
max_payload=None, tags=None, role=None, volume_kms_key=None):
"""Return a ``Transformer`` ... | python | 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):
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase._prepare_for_training | def _prepare_for_training(self, job_name=None):
"""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 applica... | python | def _prepare_for_training(self, job_name=None):
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.fit | def fit(self, inputs=None, wait=True, logs=True, job_name=None):
"""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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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.compile_model | def compile_model(self, target_instance_family, input_shape, output_path, framework=None, framework_version=None,
compile_max_run=5 * 60, tags=None, **kwargs):
"""Compile a Neo model using the input model.
Args:
target_instance_family (str): Identifies the device that ... | python | def compile_model(self, target_instance_family, input_shape, output_path, framework=None, framework_version=None,
compile_max_run=5 * 60, tags=None, **kwargs):
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.attach | def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='model'):
"""Attach to an existing training job.
Create an Estimator bound to an existing training job, each subclass is responsible to implement
``_prepare_init_params_from_job_description()`` as this method delegate... | python | def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='model'):
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.deploy | def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
use_compiled_model=False, update_endpoint=False, **kwargs):
"""Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
More information... | python | def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
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"""Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.model_data | def model_data(self):
"""str: The model location in S3. Only set if Estimator has been ``fit()``."""
if self.latest_training_job is not None:
model_uri = self.sagemaker_session.sagemaker_client.describe_training_job(
TrainingJobName=self.latest_training_job.name)['ModelArtifa... | python | def model_data(self):
"""str: The model location in S3. Only set if Estimator has been ``fit()``."""
if self.latest_training_job is not None:
model_uri = self.sagemaker_session.sagemaker_client.describe_training_job(
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase._prepare_init_params_from_job_description | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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_n... | python | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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.
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.delete_endpoint | def delete_endpoint(self):
"""Delete an Amazon SageMaker ``Endpoint``.
Raises:
ValueError: If the endpoint does not exist.
"""
self._ensure_latest_training_job(error_message='Endpoint was not created yet')
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"""Delete an Amazon SageMaker ``Endpoint``.
Raises:
ValueError: If the endpoint does not exist.
"""
self._ensure_latest_training_job(error_message='Endpoint was not created yet')
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.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,
max_payload=None, tags=None, role=None, volume_kms_key=None):
"""Return a ``Transformer`` ... | python | 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):
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.training_job_analytics | 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')
return TrainingJobAnalytics(self._current_job_name, sagemaker_... | python | 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')
return TrainingJobAnalytics(self._current_job_name, sagemaker_... | [
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | EstimatorBase.get_vpc_config | def get_vpc_config(self, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT):
"""
Returns VpcConfig dict either from this Estimator's subnets and security groups,
or else validate and return an optional override value.
"""
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... | python | def get_vpc_config(self, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT):
"""
Returns VpcConfig dict either from this Estimator's subnets and security groups,
or else validate and return an optional override value.
"""
if vpc_config_override is vpc_utils.VPC_CONFIG_DEFAULT:
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | _TrainingJob.start_new | 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... | python | def start_new(cls, estimator, inputs):
"""Create a new Amazon SageMaker training job from the estimator.
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estimator (sagemaker.estimator.EstimatorBase): Estimator object created by the user.
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Estimator.create_model | def create_model(self, role=None, image=None, predictor_cls=None, serializer=None, deserializer=None,
content_type=None, accept=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT, **kwargs):
"""
Create a model to deploy.
Args:
role (str): The ``ExecutionRole... | python | def create_model(self, role=None, image=None, predictor_cls=None, serializer=None, deserializer=None,
content_type=None, accept=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT, **kwargs):
"""
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Estimator._prepare_init_params_from_job_description | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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_n... | python | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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.
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Framework._prepare_for_training | def _prepare_for_training(self, job_name=None):
"""Set hyperparameters needed for training. This method will also validate ``source_dir``.
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 con... | python | def _prepare_for_training(self, job_name=None):
"""Set hyperparameters needed for training. This method will also validate ``source_dir``.
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* job_name (str): Name of the training job to be created. If not specified, one is generated,
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Framework._stage_user_code_in_s3 | def _stage_user_code_in_s3(self):
"""Upload the user training script to s3 and return the location.
Returns: s3 uri
"""
local_mode = self.output_path.startswith('file://')
if self.code_location is None and local_mode:
code_bucket = self.sagemaker_session.default_bu... | python | def _stage_user_code_in_s3(self):
"""Upload the user training script to s3 and return the location.
Returns: s3 uri
"""
local_mode = self.output_path.startswith('file://')
if self.code_location is None and local_mode:
code_bucket = self.sagemaker_session.default_bu... | [
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Framework._model_source_dir | def _model_source_dir(self):
"""Get the appropriate value to pass as source_dir to model constructor on deploying
Returns:
str: Either a local or an S3 path pointing to the source_dir to be used for code by the model to be deployed
"""
return self.source_dir if self.sagemake... | python | def _model_source_dir(self):
"""Get the appropriate value to pass as source_dir to model constructor on deploying
Returns:
str: Either a local or an S3 path pointing to the source_dir to be used for code by the model to be deployed
"""
return self.source_dir if self.sagemake... | [
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Framework._prepare_init_params_from_job_description | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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_n... | python | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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.
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Framework.train_image | def train_image(self):
"""Return the Docker image to use for training.
The :meth:`~sagemaker.estimator.EstimatorBase.fit` method, which does the model training,
calls this method to find the image to use for model training.
Returns:
str: The URI of the Docker image.
... | python | def train_image(self):
"""Return the Docker image to use for training.
The :meth:`~sagemaker.estimator.EstimatorBase.fit` method, which does the model training,
calls this method to find the image to use for model training.
Returns:
str: The URI of the Docker image.
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Framework.attach | def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='model'):
"""Attach to an existing training job.
Create an Estimator bound to an existing training job, each subclass is responsible to implement
``_prepare_init_params_from_job_description()`` as this method delegate... | python | def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='model'):
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | Framework.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,
max_payload=None, tags=None, role=None, model_server_workers=None, volume_kms_key=None):
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output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None,
max_payload=None, tags=None, role=None, model_server_workers=None, volume_kms_key=None):
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aws/sagemaker-python-sdk | src/sagemaker/amazon/hyperparameter.py | Hyperparameter.serialize_all | def serialize_all(obj):
"""Return all non-None ``hyperparameter`` values on ``obj`` as a ``dict[str,str].``"""
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return {}
return {k: str(v) for k, v in obj._hyperparameters.items() if v is not None} | python | def serialize_all(obj):
"""Return all non-None ``hyperparameter`` values on ``obj`` as a ``dict[str,str].``"""
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aws/sagemaker-python-sdk | src/sagemaker/local/entities.py | _LocalTransformJob.start | def start(self, input_data, output_data, transform_resources, **kwargs):
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input_data (dict): Describes the dataset to be transformed and the location where it is stored.
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aws/sagemaker-python-sdk | src/sagemaker/local/entities.py | _LocalTransformJob.describe | def describe(self):
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Returns:
dict: description of this _LocalTransformJob
"""
response = {
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"""Describe this _LocalTransformJob
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dict: description of this _LocalTransformJob
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aws/sagemaker-python-sdk | src/sagemaker/local/entities.py | _LocalTransformJob._get_container_environment | def _get_container_environment(self, **kwargs):
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aws/sagemaker-python-sdk | src/sagemaker/parameter.py | ParameterRange.as_tuning_range | 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... | python | def as_tuning_range(self, name):
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name (str): The name of the hyperparameter.
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"""Represent the parameter range as a dictionary suitable for a request to
create an Amazon SageMaker hyperparameter tuning job using one of the deep learning frameworks.
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... | python | def as_json_range(self, name):
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aws/sagemaker-python-sdk | src/sagemaker/session.py | container_def | 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... | python | 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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image (str): Docker image to run for this container.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | pipeline_container_def | def pipeline_container_def(models, instance_type=None):
"""
Create a definition for executing a pipeline of containers as part of a SageMaker model.
Args:
models (list[sagemaker.Model]): this will be a list of ``sagemaker.Model`` objects in the order the inference
should be invoked.
... | python | def pipeline_container_def(models, instance_type=None):
"""
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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aws/sagemaker-python-sdk | src/sagemaker/session.py | production_variant | 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
``CreateEndpointConfig`` request.
... | python | 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
``CreateEndpointConfig`` request.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | get_execution_role | 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
"""
if not sagemaker_session:
sagemaker_sessi... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session._initialize | 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.
"""
self.boto_session = boto_session or boto3.Session()
... | python | 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.
"""
self.boto_session = boto_session or boto3.Session()
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.upload_data | def upload_data(self, path, bucket=None, key_prefix='data'):
"""Upload local file or directory to S3.
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"""Upload local file or directory to S3.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.default_bucket | 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}``.
"""
if self._default_bucket:
return s... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.train | def train(self, input_mode, input_config, role, job_name, output_config, # noqa: C901
resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions,
enable_network_isolation=False, image=None, algorithm_arn=None,
encrypt_inter_container_traffic=False):... | python | def train(self, input_mode, input_config, role, job_name, output_config, # noqa: C901
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.compile_model | 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... | python | 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.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.tune | def tune(self, job_name, strategy, objective_type, objective_metric_name,
max_jobs, max_parallel_jobs, parameter_ranges,
static_hyperparameters, input_mode, metric_definitions,
role, input_config, output_config, resource_config, stop_condition, tags,
warm_start_config... | python | def tune(self, job_name, strategy, objective_type, objective_metric_name,
max_jobs, max_parallel_jobs, parameter_ranges,
static_hyperparameters, input_mode, metric_definitions,
role, input_config, output_config, resource_config, stop_condition, tags,
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.stop_tuning_job | 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... | python | def stop_tuning_job(self, name):
"""Stop the Amazon SageMaker hyperparameter tuning job with the specified name.
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name (str): Name of the Amazon SageMaker hyperparameter tuning job.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.transform | 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... | python | def transform(self, job_name, model_name, strategy, max_concurrent_transforms, max_payload, env,
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"""Create an Amazon SageMaker transform job.
Args:
job_name (str): Name of the transform job being created.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.create_model | def create_model(self, name, role, container_defs, vpc_config=None,
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tags=None):
"""Create an Amazon SageMaker ``Model``.
Specify the S3 location of the model artifacts and Docker image containing
th... | python | def create_model(self, name, role, container_defs, vpc_config=None,
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tags=None):
"""Create an Amazon SageMaker ``Model``.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.create_model_from_job | 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:
... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.create_model_package_from_algorithm | 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... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.wait_for_model_package | 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... | python | def wait_for_model_package(self, model_package_name, poll=5):
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endpoint (str): Name of the ``Endpoint`` to wait for.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.create_endpoint_config | def create_endpoint_config(self, name, model_name, initial_instance_count, instance_type,
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"""Create an Amazon SageMaker endpoint configuration.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.create_endpoint | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.update_endpoint | 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... | python | def update_endpoint(self, endpoint_name, endpoint_config_name):
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.delete_endpoint | 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... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.delete_endpoint_config | 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... | python | 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... | [
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.delete_model | 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) | python | 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) | [
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.wait_for_job | 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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(dict): Return value from the ``DescribeTrain... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.wait_for_compilation_job | 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... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.wait_for_tuning_job | 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... | python | def wait_for_tuning_job(self, job, poll=5):
"""Wait for an Amazon SageMaker hyperparameter tuning job to complete.
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job (str): Name of the tuning job to wait for.
poll (int): Polling interval in seconds (default: 5).
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.wait_for_transform_job | 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... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session._check_job_status | def _check_job_status(self, job, desc, status_key_name):
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raise a ValueError.
Args:
job (str): The name of the job to check.
desc (dict[str, str]): The result of ``describe_training_job()``.
... | python | 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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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.wait_for_endpoint | 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... | python | 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).
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.endpoint_from_job | 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):
... | python | 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,
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.endpoint_from_model_data | 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... | python | 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,
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.endpoint_from_production_variants | def endpoint_from_production_variants(self, name, production_variants, tags=None, kms_key=None, wait=True):
"""Create an SageMaker ``Endpoint`` from a list of production variants.
Args:
name (str): The name of the ``Endpoint`` to create.
production_variants (list[dict[str, str]]... | python | def endpoint_from_production_variants(self, name, production_variants, tags=None, kms_key=None, wait=True):
"""Create an SageMaker ``Endpoint`` from a list of production variants.
Args:
name (str): The name of the ``Endpoint`` to create.
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.expand_role | def expand_role(self, role):
"""Expand an IAM role name into an ARN.
If the role is already in the form of an ARN, then the role is simply returned. Otherwise we retrieve the full
ARN and return it.
Args:
role (str): An AWS IAM role (either name or full ARN).
Retur... | python | def expand_role(self, role):
"""Expand an IAM role name into an ARN.
If the role is already in the form of an ARN, then the role is simply returned. Otherwise we retrieve the full
ARN and return it.
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role (str): An AWS IAM role (either name or full ARN).
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.get_caller_identity_arn | def get_caller_identity_arn(self):
"""Returns the ARN user or role whose credentials are used to call the API.
Returns:
(str): The ARN user or role
"""
assumed_role = self.boto_session.client('sts').get_caller_identity()['Arn']
if 'AmazonSageMaker-ExecutionRole' in a... | python | def get_caller_identity_arn(self):
"""Returns the ARN user or role whose credentials are used to call the API.
Returns:
(str): The ARN user or role
"""
assumed_role = self.boto_session.client('sts').get_caller_identity()['Arn']
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aws/sagemaker-python-sdk | src/sagemaker/session.py | Session.logs_for_job | def logs_for_job(self, job_name, wait=False, poll=10): # noqa: C901 - suppress complexity warning for this method
"""Display the logs for a given training job, optionally tailing them until the
job is complete. If the output is a tty or a Jupyter cell, it will be color-coded
based on which inst... | python | def logs_for_job(self, job_name, wait=False, poll=10): # noqa: C901 - suppress complexity warning for this method
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aws/sagemaker-python-sdk | src/sagemaker/fw_registry.py | registry | def registry(region_name, framework=None):
"""
Return docker registry for the given AWS region for the given framework.
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"""
try:
account_id = image_registry_map[region_name][framework]
return get_ecr_image_uri_prefix(account_id,... | python | def registry(region_name, framework=None):
"""
Return docker registry for the given AWS region for the given framework.
This is only used for SparkML and Scikit-learn for now.
"""
try:
account_id = image_registry_map[region_name][framework]
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aws/sagemaker-python-sdk | src/sagemaker/amazon/knn.py | KNN.create_model | def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
"""Return a :class:`~sagemaker.amazon.KNNModel` referencing the latest
s3 model data produced by this Estimator.
Args:
vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the model.
... | python | def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
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aws/sagemaker-python-sdk | src/sagemaker/local/utils.py | copy_directory_structure | def copy_directory_structure(destination_directory, relative_path):
"""Create all the intermediate directories required for relative_path to exist within destination_directory.
This assumes that relative_path is a directory located within root_dir.
Examples:
destination_directory: /tmp/destination
... | python | def copy_directory_structure(destination_directory, relative_path):
"""Create all the intermediate directories required for relative_path to exist within destination_directory.
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destination_directory: /tmp/destination
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aws/sagemaker-python-sdk | src/sagemaker/local/utils.py | move_to_destination | def move_to_destination(source, destination, job_name, sagemaker_session):
"""move source to destination. Can handle uploading to S3
Args:
source (str): root directory to move
destination (str): file:// or s3:// URI that source will be moved to.
job_name (str): SageMaker job name.
... | python | def move_to_destination(source, destination, job_name, sagemaker_session):
"""move source to destination. Can handle uploading to S3
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source (str): root directory to move
destination (str): file:// or s3:// URI that source will be moved to.
job_name (str): SageMaker job name.
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aws/sagemaker-python-sdk | src/sagemaker/local/utils.py | recursive_copy | def recursive_copy(source, destination):
"""A wrapper around distutils.dir_util.copy_tree but won't throw any exception when the source
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Args:
source (str): source path
destination (str): destination path
"""
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copy_tree(sourc... | python | def recursive_copy(source, destination):
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source (str): source path
destination (str): destination path
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aws/sagemaker-python-sdk | src/sagemaker/mxnet/estimator.py | MXNet._prepare_init_params_from_job_description | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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_n... | python | def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""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.
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aws/sagemaker-python-sdk | src/sagemaker/transformer.py | Transformer.transform | def transform(self, data, data_type='S3Prefix', content_type=None, compression_type=None, split_type=None,
job_name=None):
"""Start a new transform job.
Args:
data (str): Input data location in S3.
data_type (str): What the S3 location defines (default: 'S3Pref... | python | def transform(self, data, data_type='S3Prefix', content_type=None, compression_type=None, split_type=None,
job_name=None):
"""Start a new transform job.
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data (str): Input data location in S3.
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aws/sagemaker-python-sdk | src/sagemaker/transformer.py | Transformer.attach | def attach(cls, transform_job_name, sagemaker_session=None):
"""Attach an existing transform job to a new Transformer instance
Args:
transform_job_name (str): Name for the transform job to be attached.
sagemaker_session (sagemaker.session.Session): Session object which manages i... | python | def attach(cls, transform_job_name, sagemaker_session=None):
"""Attach an existing transform job to a new Transformer instance
Args:
transform_job_name (str): Name for the transform job to be attached.
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aws/sagemaker-python-sdk | src/sagemaker/transformer.py | Transformer._prepare_init_params_from_job_description | def _prepare_init_params_from_job_description(cls, job_details):
"""Convert the transform job description to init params that can be handled by the class constructor
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job_details (dict): the returned job details from a describe_transform_job API call.
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dict... | python | def _prepare_init_params_from_job_description(cls, job_details):
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job_details (dict): the returned job details from a describe_transform_job API call.
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | piece_size | def piece_size(model_file=None, model_proto=None, name=None):
"""Returns the piece size (vocabulary size).
Args:
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
Either `model_file` or `model_proto` must be set.
name: The name argume... | python | def piece_size(model_file=None, model_proto=None, name=None):
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model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | piece_to_id | def piece_to_id(input, model_file=None, model_proto=None, name=None):
"""Converts piece into vocabulary id.
Args:
input: An arbitrary tensor of string.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
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input: An arbitrary tensor of string.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | id_to_piece | def id_to_piece(input, model_file=None, model_proto=None, name=None):
"""Converts vocabulary id into piece.
Args:
input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
Either `model_file` or `model_pro... | python | def id_to_piece(input, model_file=None, model_proto=None, name=None):
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input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | is_unknown | def is_unknown(input, model_file=None, model_proto=None, name=None):
"""Returns true if input id is unknown piece.
Args:
input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
Either `model_file` or `mo... | python | def is_unknown(input, model_file=None, model_proto=None, name=None):
"""Returns true if input id is unknown piece.
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input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | is_control | def is_control(input, model_file=None, model_proto=None, name=None):
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Args:
input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
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input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | is_unused | def is_unused(input, model_file=None, model_proto=None, name=None):
"""Returns true if input id is unused piece.
Args:
input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
Either `model_file` or `mode... | python | def is_unused(input, model_file=None, model_proto=None, name=None):
"""Returns true if input id is unused piece.
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input: An arbitrary tensor of int32.
model_file: The sentencepiece model file path.
model_proto: The sentencepiece model serialized proto.
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] | ffa2c8218f7afbb06d0c1bb87c82efb6867db41a | https://github.com/google/sentencepiece/blob/ffa2c8218f7afbb06d0c1bb87c82efb6867db41a/tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py#L132-L147 | train | Returns true if input id is unused piece. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | encode_dense | def encode_dense(input_sentences, nbest_size=0, alpha=1.0,
model_file=None, model_proto=None,
reverse=False, add_bos=False, add_eos=False,
out_type=tf.int32, name=None):
"""Encodes sentences into pieces in dense tensor format.
Args:
input_sentences: A 1D strin... | python | def encode_dense(input_sentences, nbest_size=0, alpha=1.0,
model_file=None, model_proto=None,
reverse=False, add_bos=False, add_eos=False,
out_type=tf.int32, name=None):
"""Encodes sentences into pieces in dense tensor format.
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input_sentences: A 1D strin... | [
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | encode_sparse | def encode_sparse(input_sentences, nbest_size=0, alpha=1.0,
model_file=None, model_proto=None,
reverse=False, add_bos=False, add_eos=False,
out_type=tf.int32, name=None):
"""Encodes sentences into pieces in sparse tensor format.
Args:
input_sentences: A 1D ... | python | def encode_sparse(input_sentences, nbest_size=0, alpha=1.0,
model_file=None, model_proto=None,
reverse=False, add_bos=False, add_eos=False,
out_type=tf.int32, name=None):
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google/sentencepiece | tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py | decode | def decode(pieces, sequence_length, model_file=None, model_proto=None,
reverse=False, name=None):
"""Decode pieces into postprocessed text.
Args:
pieces: A 2D int32 or string tensor [batch_size x max_length] of
encoded sequences.
sequence_length: A 1D int32 tensor [batch_size] repres... | python | def decode(pieces, sequence_length, model_file=None, model_proto=None,
reverse=False, name=None):
"""Decode pieces into postprocessed text.
Args:
pieces: A 2D int32 or string tensor [batch_size x max_length] of
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] | ffa2c8218f7afbb06d0c1bb87c82efb6867db41a | https://github.com/google/sentencepiece/blob/ffa2c8218f7afbb06d0c1bb87c82efb6867db41a/tensorflow/tf_sentencepiece/sentencepiece_processor_ops.py#L230-L251 | train | Decode pieces into postprocessed text. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
eternnoir/pyTelegramBotAPI | telebot/util.py | split_string | def split_string(text, chars_per_string):
"""
Splits one string into multiple strings, with a maximum amount of `chars_per_string` characters per string.
This is very useful for splitting one giant message into multiples.
:param text: The text to split
:param chars_per_string: The number of charact... | python | def split_string(text, chars_per_string):
"""
Splits one string into multiple strings, with a maximum amount of `chars_per_string` characters per string.
This is very useful for splitting one giant message into multiples.
:param text: The text to split
:param chars_per_string: The number of charact... | [
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This is very useful for splitting one giant message into multiples.
:param text: The text to split
:param chars_per_string: The number of characters per line the text is split into.
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eternnoir/pyTelegramBotAPI | telebot/util.py | extract_arguments | def extract_arguments(text):
"""
Returns the argument after the command.
Examples:
extract_arguments("/get name"): 'name'
extract_arguments("/get"): ''
extract_arguments("/get@botName name"): 'name'
:param text: String to extract the arguments from a command
:return: the argume... | python | def extract_arguments(text):
"""
Returns the argument after the command.
Examples:
extract_arguments("/get name"): 'name'
extract_arguments("/get"): ''
extract_arguments("/get@botName name"): 'name'
:param text: String to extract the arguments from a command
:return: the argume... | [
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] | 47b53b88123097f1b9562a6cd5d4e080b86185d1 | https://github.com/eternnoir/pyTelegramBotAPI/blob/47b53b88123097f1b9562a6cd5d4e080b86185d1/telebot/util.py#L235-L249 | train | Extracts the arguments after the command. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
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