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.EstimatorBase.fit`.
Returns:
sagemaker.estimator._TrainingJob: Constructed object that captures all information about the started
training job.
"""
local_mode = estimator.sagemaker_session.local_mode
# Allow file:// input only in local mode
if isinstance(inputs, str) and inputs.startswith('file://'):
if not local_mode:
raise ValueError('File URIs are supported in local mode only. Please use a S3 URI instead.')
config = _Job._load_config(inputs, estimator)
if estimator.hyperparameters() is not None:
hyperparameters = {str(k): str(v) for (k, v) in estimator.hyperparameters().items()}
estimator.sagemaker_session.train(image=estimator.train_image(), input_mode=estimator.input_mode,
input_config=config['input_config'], role=config['role'],
job_name=estimator._current_job_name, output_config=config['output_config'],
resource_config=config['resource_config'], hyperparameters=hyperparameters,
stop_condition=config['stop_condition'], tags=estimator.tags)
return cls(estimator.sagemaker_session, estimator._current_job_name)
def start_new(cls, tuner, inputs):
"""Create a new Amazon SageMaker hyperparameter tuning job from the HyperparameterTuner.
Args:
tuner (sagemaker.tuner.HyperparameterTuner): HyperparameterTuner object created by the user.
inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`.
Returns:
sagemaker.tuner._TuningJob: Constructed object that captures all information about the started job.
"""
config = _Job._load_config(inputs, tuner.estimator)
tuner.estimator.sagemaker_session.tune(
job_name=tuner._current_job_name,
strategy=tuner.strategy,
objective_type=tuner.objective_type,
objective_metric_name=tuner.objective_metric_name,
max_jobs=tuner.max_jobs,
max_parallel_jobs=tuner.max_parallel_jobs,
parameter_ranges=tuner.hyperparameter_ranges(),
static_hyperparameters=tuner.static_hyperparameters,
image=tuner.estimator.train_image(),
input_mode=tuner.estimator.input_mode,
metric_definitions=tuner.metric_definitions,
role=(config['role']),
input_config=(config['input_config']),
output_config=(config['output_config']),
resource_config=(config['resource_config']),
stop_condition=(config['stop_condition']),
tags=tuner.tags)
return cls(tuner.sagemaker_session, tuner._current_job_name)
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.EstimatorBase.fit`.
Returns:
sagemaker.estimator._TrainingJob: Constructed object that captures all information about the started
training job.
"""
local_mode = estimator.sagemaker_session.local_mode
# Allow file:// input only in local mode
if isinstance(inputs, str) and inputs.startswith('file://'):
if not local_mode:
raise ValueError('File URIs are supported in local mode only. Please use a S3 URI instead.')
config = _Job._load_config(inputs, estimator)
if estimator.hyperparameters() is not None:
hyperparameters = {str(k): str(v) for (k, v) in estimator.hyperparameters().items()}
estimator.sagemaker_session.train(image=estimator.train_image(), input_mode=estimator.input_mode,
input_config=config['input_config'], role=config['role'],
job_name=estimator._current_job_name, output_config=config['output_config'],
resource_config=config['resource_config'], hyperparameters=hyperparameters,
stop_condition=config['stop_condition'], tags=estimator.tags)
return cls(estimator.sagemaker_session, estimator._current_job_name)
def start_new(cls, tuner, inputs):
"""Create a new Amazon SageMaker hyperparameter tuning job from the HyperparameterTuner.
Args:
tuner (sagemaker.tuner.HyperparameterTuner): HyperparameterTuner object created by the user.
inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`.
Returns:
sagemaker.tuner._TuningJob: Constructed object that captures all information about the started job.
"""
config = _Job._load_config(inputs, tuner.estimator)
tuner.estimator.sagemaker_session.tune(job_name=tuner._current_job_name, strategy=tuner.strategy,
objective_type=tuner.objective_type,
objective_metric_name=tuner.objective_metric_name,
max_jobs=tuner.max_jobs, max_parallel_jobs=tuner.max_parallel_jobs,
parameter_ranges=tuner.hyperparameter_ranges(),
static_hyperparameters=tuner.static_hyperparameters,
image=tuner.estimator.train_image(),
input_mode=tuner.estimator.input_mode,
metric_definitions=tuner.metric_definitions,
role=(config['role']), input_config=(config['input_config']),
output_config=(config['output_config']),
resource_config=(config['resource_config']),
stop_condition=(config['stop_condition']), tags=tuner.tags)
return cls(tuner.sagemaker_session, tuner._current_job_name)
def start_new(cls, tuner, inputs):
"""Create a new Amazon SageMaker hyperparameter tuning job from the
HyperparameterTuner.
Args:
tuner (sagemaker.tuner.HyperparameterTuner): HyperparameterTuner
object created by the user.
inputs (str): Parameters used when called
:meth:`~sagemaker.estimator.EstimatorBase.fit`.
Returns:
sagemaker.tuner._TuningJob: Constructed object that captures all
information about the started job.
"""
config = _Job._load_config(inputs, tuner.estimator)
warm_start_config_req = None
if tuner.warm_start_config:
warm_start_config_req = tuner.warm_start_config.to_input_req()
tuner_args = config.copy()
tuner_args["job_name"] = tuner._current_job_name
tuner_args["strategy"] = tuner.strategy
tuner_args["objective_type"] = tuner.objective_type
tuner_args["objective_metric_name"] = tuner.objective_metric_name
tuner_args["max_jobs"] = tuner.max_jobs
tuner_args["max_parallel_jobs"] = tuner.max_parallel_jobs
tuner_args["parameter_ranges"] = tuner.hyperparameter_ranges()
tuner_args["static_hyperparameters"] = tuner.static_hyperparameters
tuner_args["input_mode"] = tuner.estimator.input_mode
tuner_args["metric_definitions"] = tuner.metric_definitions
tuner_args["tags"] = tuner.tags
tuner_args["warm_start_config"] = warm_start_config_req
tuner_args["early_stopping_type"] = tuner.early_stopping_type
if isinstance(inputs, s3_input):
if "InputMode" in inputs.config:
logging.debug(
"Selecting s3_input's input_mode (%s) for TrainingInputMode.",
inputs.config["InputMode"],
)
tuner_args["input_mode"] = inputs.config["InputMode"]
if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
tuner_args["algorithm_arn"] = tuner.estimator.algorithm_arn
else:
tuner_args["image"] = tuner.estimator.train_image()
tuner_args[
"enable_network_isolation"] = tuner.estimator.enable_network_isolation(
)
tuner_args[
"encrypt_inter_container_traffic"] = tuner.estimator.encrypt_inter_container_traffic
tuner.estimator.sagemaker_session.tune(**tuner_args)
return cls(tuner.sagemaker_session, tuner._current_job_name)
def test_load_config(estimator):
inputs = s3_input(BUCKET_NAME)
config = _Job._load_config(inputs, estimator)
assert config['input_config'][0]['DataSource']['S3DataSource']['S3Uri'] == BUCKET_NAME
assert config['role'] == ROLE
assert config['output_config']['S3OutputPath'] == S3_OUTPUT_PATH
assert 'KmsKeyId' not in config['output_config']
assert config['resource_config']['InstanceCount'] == INSTANCE_COUNT
assert config['resource_config']['InstanceType'] == INSTANCE_TYPE
assert config['resource_config']['VolumeSizeInGB'] == VOLUME_SIZE
assert config['stop_condition']['MaxRuntimeInSeconds'] == MAX_RUNTIME
def test_load_config(estimator):
inputs = s3_input(BUCKET_NAME)
config = _Job._load_config(inputs, estimator)
assert config["input_config"][0]["DataSource"]["S3DataSource"]["S3Uri"] == BUCKET_NAME
assert config["role"] == ROLE
assert config["output_config"]["S3OutputPath"] == S3_OUTPUT_PATH
assert "KmsKeyId" not in config["output_config"]
assert config["resource_config"]["InstanceCount"] == INSTANCE_COUNT
assert config["resource_config"]["InstanceType"] == INSTANCE_TYPE
assert config["resource_config"]["VolumeSizeInGB"] == VOLUME_SIZE
assert config["stop_condition"]["MaxRuntimeInSeconds"] == MAX_RUNTIME
def test_load_config_with_code_channel_no_code_uri(framework):
inputs = s3_input(BUCKET_NAME)
framework.model_uri = MODEL_URI
framework.model_channel_name = MODEL_CHANNEL_NAME
framework._enable_network_isolation = True
config = _Job._load_config(inputs, framework)
assert len(config["input_config"]) == 2
assert config["input_config"][0]["DataSource"]["S3DataSource"]["S3Uri"] == BUCKET_NAME
assert config["role"] == ROLE
assert config["output_config"]["S3OutputPath"] == S3_OUTPUT_PATH
assert "KmsKeyId" not in config["output_config"]
assert config["resource_config"]["InstanceCount"] == INSTANCE_COUNT
assert config["resource_config"]["InstanceType"] == INSTANCE_TYPE
def test_load_config_with_model_channel_no_inputs(estimator):
estimator.model_uri = MODEL_URI
estimator.model_channel_name = MODEL_CHANNEL_NAME
config = _Job._load_config(inputs=None, estimator=estimator)
assert config["input_config"][0]["DataSource"]["S3DataSource"]["S3Uri"] == MODEL_URI
assert config["input_config"][0]["ChannelName"] == MODEL_CHANNEL_NAME
assert config["role"] == ROLE
assert config["output_config"]["S3OutputPath"] == S3_OUTPUT_PATH
assert "KmsKeyId" not in config["output_config"]
assert config["resource_config"]["InstanceCount"] == INSTANCE_COUNT
assert config["resource_config"]["InstanceType"] == INSTANCE_TYPE
assert config["resource_config"]["VolumeSizeInGB"] == VOLUME_SIZE
assert config["stop_condition"]["MaxRuntimeInSeconds"] == MAX_RUNTIME
def test_load_config_with_model_channel_no_inputs(estimator):
estimator.model_uri = MODEL_URI
estimator.model_channel_name = CHANNEL_NAME
config = _Job._load_config(inputs=None, estimator=estimator)
assert config['input_config'][0]['DataSource']['S3DataSource']['S3Uri'] == MODEL_URI
assert config['input_config'][0]['ChannelName'] == CHANNEL_NAME
assert config['role'] == ROLE
assert config['output_config']['S3OutputPath'] == S3_OUTPUT_PATH
assert 'KmsKeyId' not in config['output_config']
assert config['resource_config']['InstanceCount'] == INSTANCE_COUNT
assert config['resource_config']['InstanceType'] == INSTANCE_TYPE
assert config['resource_config']['VolumeSizeInGB'] == VOLUME_SIZE
assert config['stop_condition']['MaxRuntimeInSeconds'] == MAX_RUNTIME
def start_new(cls, tuner, inputs):
"""Create a new Amazon SageMaker hyperparameter tuning job from the HyperparameterTuner.
Args:
tuner (sagemaker.tuner.HyperparameterTuner): HyperparameterTuner object created by the user.
inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`.
Returns:
sagemaker.tuner._TuningJob: Constructed object that captures all information about the started job.
"""
config = _Job._load_config(inputs, tuner.estimator)
warm_start_config_req = None
if tuner.warm_start_config:
warm_start_config_req = tuner.warm_start_config.to_input_req()
tuner_args = config.copy()
tuner_args['job_name'] = tuner._current_job_name
tuner_args['strategy'] = tuner.strategy
tuner_args['objective_type'] = tuner.objective_type
tuner_args['objective_metric_name'] = tuner.objective_metric_name
tuner_args['max_jobs'] = tuner.max_jobs
tuner_args['max_parallel_jobs'] = tuner.max_parallel_jobs
tuner_args['parameter_ranges'] = tuner.hyperparameter_ranges()
tuner_args['static_hyperparameters'] = tuner.static_hyperparameters
tuner_args['input_mode'] = tuner.estimator.input_mode
tuner_args['metric_definitions'] = tuner.metric_definitions
tuner_args['tags'] = tuner.tags
tuner_args['warm_start_config'] = warm_start_config_req
tuner_args['early_stopping_type'] = tuner.early_stopping_type
if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
else:
tuner_args['image'] = tuner.estimator.train_image()
tuner_args[
'enable_network_isolation'] = tuner.estimator.enable_network_isolation(
)
tuner_args['encrypt_inter_container_traffic'] = \
tuner.estimator.encrypt_inter_container_traffic
tuner.estimator.sagemaker_session.tune(**tuner_args)
return cls(tuner.sagemaker_session, tuner._current_job_name)