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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""Placeholder docstring"""
from __future__ import absolute_import
from abc import abstractmethod
from six import string_types
from sagemaker.inputs import FileSystemInput, TrainingInput
from sagemaker.local import file_input
from sagemaker.workflow import is_pipeline_variable
class _Job(object):
"""Handle creating, starting and waiting for Amazon SageMaker jobs to finish.
This class shouldn't be directly instantiated.
Subclasses must define a way to create, start and wait for an Amazon
SageMaker job.
"""
def __init__(self, sagemaker_session, job_name):
"""Placeholder docstring"""
self.sagemaker_session = sagemaker_session
self.job_name = job_name
@abstractmethod
def start_new(self, estimator, inputs):
"""Create a new Amazon SageMaker 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.job: Constructed object that captures all information
about the started job.
"""
@abstractmethod
def wait(self):
"""Wait for the Amazon SageMaker job to finish."""
@abstractmethod
def describe(self):
"""Describe the job."""
@abstractmethod
def stop(self):
"""Stop the job."""
@staticmethod
def _load_config(inputs, estimator, expand_role=True, validate_uri=True):
"""Placeholder docstring"""
input_config = _Job._format_inputs_to_input_config(inputs, validate_uri)
role = (
estimator.sagemaker_session.expand_role(estimator.role)
if (expand_role and not is_pipeline_variable(estimator.role))
else estimator.role
)
output_config = _Job._prepare_output_config(estimator.output_path, estimator.output_kms_key)
resource_config = _Job._prepare_resource_config(
estimator.instance_count,
estimator.instance_type,
estimator.instance_groups,
estimator.volume_size,
estimator.volume_kms_key,
estimator.keep_alive_period_in_seconds,
)
stop_condition = _Job._prepare_stop_condition(estimator.max_run, estimator.max_wait)
vpc_config = estimator.get_vpc_config()
model_channel = _Job._prepare_channel(
input_config,
estimator.model_uri,
estimator.model_channel_name,
validate_uri,
content_type="application/x-sagemaker-model",
input_mode="File",
)
if model_channel:
input_config = [] if input_config is None else input_config
input_config.append(model_channel)
if estimator.enable_network_isolation():
code_channel = _Job._prepare_channel(
input_config, estimator.code_uri, estimator.code_channel_name, validate_uri
)
if code_channel:
input_config = [] if input_config is None else input_config
input_config.append(code_channel)
return {
"input_config": input_config,
"role": role,
"output_config": output_config,
"resource_config": resource_config,
"stop_condition": stop_condition,
"vpc_config": vpc_config,
}
@staticmethod
def _format_inputs_to_input_config(inputs, validate_uri=True):
"""Placeholder docstring"""
if inputs is None:
return None
# Deferred import due to circular dependency
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import FileSystemRecordSet
if isinstance(inputs, (RecordSet, FileSystemRecordSet)):
inputs = inputs.data_channel()
input_dict = {}
if isinstance(inputs, string_types):
input_dict["training"] = _Job._format_string_uri_input(inputs, validate_uri)
elif isinstance(inputs, TrainingInput):
input_dict["training"] = inputs
elif isinstance(inputs, file_input):
input_dict["training"] = inputs
elif isinstance(inputs, dict):
for k, v in inputs.items():
input_dict[k] = _Job._format_string_uri_input(v, validate_uri)
elif isinstance(inputs, list):
input_dict = _Job._format_record_set_list_input(inputs)
elif isinstance(inputs, FileSystemInput):
input_dict["training"] = inputs
else:
msg = (
"Cannot format input {}. Expecting one of str, dict, TrainingInput or "
"FileSystemInput"
)
raise ValueError(msg.format(inputs))
channels = [
_Job._convert_input_to_channel(name, input) for name, input in input_dict.items()
]
return channels
@staticmethod
def _convert_input_to_channel(channel_name, channel_s3_input):
"""Placeholder docstring"""
channel_config = channel_s3_input.config.copy()
channel_config["ChannelName"] = channel_name
return channel_config
@staticmethod
def _format_string_uri_input(
uri_input,
validate_uri=True,
content_type=None,
input_mode=None,
compression=None,
target_attribute_name=None,
):
"""Placeholder docstring"""
s3_input_result = TrainingInput(
uri_input,
content_type=content_type,
input_mode=input_mode,
compression=compression,
target_attribute_name=target_attribute_name,
)
if isinstance(uri_input, str) and validate_uri and uri_input.startswith("s3://"):
return s3_input_result
if isinstance(uri_input, str) and validate_uri and uri_input.startswith("file://"):
return file_input(uri_input)
if isinstance(uri_input, str) and validate_uri:
raise ValueError(
'URI input {} must be a valid S3 or FILE URI: must start with "s3://" or '
'"file://"'.format(uri_input)
)
if isinstance(uri_input, str):
return s3_input_result
if isinstance(uri_input, (TrainingInput, file_input, FileSystemInput)):
return uri_input
if is_pipeline_variable(uri_input):
return s3_input_result
raise ValueError(
"Cannot format input {}. Expecting one of str, TrainingInput, file_input or "
"FileSystemInput".format(uri_input)
)
@staticmethod
def _prepare_channel(
input_config,
channel_uri=None,
channel_name=None,
validate_uri=True,
content_type=None,
input_mode=None,
):
"""Placeholder docstring"""
if not channel_uri:
return None
if not channel_name:
raise ValueError(
"Expected a channel name if a channel URI {} is specified".format(channel_uri)
)
if input_config:
for existing_channel in input_config:
if existing_channel["ChannelName"] == channel_name:
raise ValueError("Duplicate channel {} not allowed.".format(channel_name))
channel_input = _Job._format_string_uri_input(
channel_uri, validate_uri, content_type, input_mode
)
channel = _Job._convert_input_to_channel(channel_name, channel_input)
return channel
@staticmethod
def _format_model_uri_input(model_uri, validate_uri=True):
"""Placeholder docstring"""
if isinstance(model_uri, string_types) and validate_uri and model_uri.startswith("s3://"):
return TrainingInput(
model_uri,
input_mode="File",
distribution="FullyReplicated",
content_type="application/x-sagemaker-model",
)
if isinstance(model_uri, string_types) and validate_uri and model_uri.startswith("file://"):
return file_input(model_uri)
if isinstance(model_uri, string_types) and validate_uri:
raise ValueError(
'Model URI must be a valid S3 or FILE URI: must start with "s3://" or ' '"file://'
)
if isinstance(model_uri, string_types):
return TrainingInput(
model_uri,
input_mode="File",
distribution="FullyReplicated",
content_type="application/x-sagemaker-model",
)
raise ValueError("Cannot format model URI {}. Expecting str".format(model_uri))
@staticmethod
def _format_record_set_list_input(inputs):
"""Placeholder docstring"""
# Deferred import due to circular dependency
from sagemaker.amazon.amazon_estimator import FileSystemRecordSet, RecordSet
input_dict = {}
for record in inputs:
if not isinstance(record, (RecordSet, FileSystemRecordSet)):
raise ValueError("List compatible only with RecordSets or FileSystemRecordSets.")
if record.channel in input_dict:
raise ValueError("Duplicate channels not allowed.")
if isinstance(record, RecordSet):
input_dict[record.channel] = record.records_s3_input()
if isinstance(record, FileSystemRecordSet):
input_dict[record.channel] = record.file_system_input
return input_dict
@staticmethod
def _prepare_output_config(s3_path, kms_key_id):
"""Placeholder docstring"""
config = {"S3OutputPath": s3_path}
if kms_key_id is not None:
config["KmsKeyId"] = kms_key_id
return config
@staticmethod
def _prepare_resource_config(
instance_count,
instance_type,
instance_groups,
volume_size,
volume_kms_key,
keep_alive_period_in_seconds,
):
"""Placeholder docstring"""
resource_config = {
"VolumeSizeInGB": volume_size,
}
if volume_kms_key is not None:
resource_config["VolumeKmsKeyId"] = volume_kms_key
if keep_alive_period_in_seconds is not None:
resource_config["KeepAlivePeriodInSeconds"] = keep_alive_period_in_seconds
if instance_groups is not None:
if instance_count is not None or instance_type is not None:
raise ValueError(
"instance_count and instance_type cannot be set when instance_groups is set"
)
resource_config["InstanceGroups"] = [
group._to_request_dict() for group in instance_groups
]
else:
if instance_count is None or instance_type is None:
raise ValueError(
"instance_count and instance_type must be set if instance_groups is not set"
)
resource_config["InstanceCount"] = instance_count
resource_config["InstanceType"] = instance_type
return resource_config
@staticmethod
def _prepare_stop_condition(max_run, max_wait):
"""Placeholder docstring"""
if max_wait:
return {"MaxRuntimeInSeconds": max_run, "MaxWaitTimeInSeconds": max_wait}
return {"MaxRuntimeInSeconds": max_run}
@property
def name(self):
"""Placeholder docstring"""
return self.job_name
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