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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.
"""A class for AutoML Job's Candidate."""
from __future__ import absolute_import
from six import string_types
from sagemaker import Session
from sagemaker.job import _Job
from sagemaker.utils import name_from_base
class CandidateEstimator(object):
"""A class for SageMaker AutoML Job Candidate"""
def __init__(self, candidate, sagemaker_session=None):
"""Constructor of CandidateEstimator.
Args:
candidate (dict): a dictionary of candidate returned by AutoML.list_candidates()
or AutoML.best_candidate().
sagemaker_session (sagemaker.session.Session): A SageMaker Session
object, used for SageMaker interactions (default: None). If not
specified, one is created using the default AWS configuration
chain.
"""
self.name = candidate["CandidateName"]
self.containers = candidate["InferenceContainers"]
self.steps = self._process_steps(candidate["CandidateSteps"])
self.sagemaker_session = sagemaker_session or Session()
def get_steps(self):
"""Get the step job of a candidate so that users can construct estimators/transformers
Returns:
list: a list of dictionaries that provide information about each step job's name,
type, inputs and description
"""
candidate_steps = []
for step in self.steps:
step_type = step["type"]
step_name = step["name"]
if step_type == "TrainingJob":
training_job = self.sagemaker_session.sagemaker_client.describe_training_job(
TrainingJobName=step_name
)
inputs = training_job["InputDataConfig"]
candidate_step = CandidateStep(step_name, inputs, step_type, training_job)
candidate_steps.append(candidate_step)
elif step_type == "TransformJob":
transform_job = self.sagemaker_session.sagemaker_client.describe_transform_job(
TransformJobName=step_name
)
inputs = transform_job["TransformInput"]
candidate_step = CandidateStep(step_name, inputs, step_type, transform_job)
candidate_steps.append(candidate_step)
return candidate_steps
def fit(
self,
inputs,
candidate_name=None,
volume_kms_key=None,
encrypt_inter_container_traffic=False,
vpc_config=None,
wait=True,
logs=True,
):
"""Rerun a candidate's step jobs with new input datasets or security config.
Args:
inputs (str or list[str]): Local path or S3 Uri where the training data is stored. If a
local path is provided, the dataset will be uploaded to an S3 location.
candidate_name (str): name of the candidate to be rerun, if None, candidate's original
name will be used.
volume_kms_key (str): The KMS key id to encrypt data on the storage volume attached to
the ML compute instance(s).
encrypt_inter_container_traffic (bool): To encrypt all communications between ML compute
instances in distributed training. Default: False.
vpc_config (dict): Specifies a VPC that jobs and hosted models have access to.
Control access to and from training and model containers by configuring the VPC
wait (bool): Whether the call should wait until all jobs completes (default: True).
logs (bool): Whether to show the logs produced by the job.
Only meaningful when wait is True (default: True).
"""
if logs and not wait:
raise ValueError(
"""Logs can only be shown if wait is set to True.
Please either set wait to True or set logs to False."""
)
self.name = candidate_name or self.name
running_jobs = {}
# convert inputs to TrainingInput format
if isinstance(inputs, string_types):
if not inputs.startswith("s3://"):
inputs = self.sagemaker_session.upload_data(inputs, key_prefix="auto-ml-input-data")
for step in self.steps:
step_type = step["type"]
step_name = step["name"]
if step_type == "TrainingJob":
# prepare inputs
input_dict = {}
if isinstance(inputs, string_types):
input_dict["train"] = _Job._format_string_uri_input(inputs)
else:
msg = "Cannot format input {}. Expecting a string."
raise ValueError(msg.format(inputs))
channels = [
_Job._convert_input_to_channel(name, input)
for name, input in input_dict.items()
]
desc = self.sagemaker_session.sagemaker_client.describe_training_job(
TrainingJobName=step_name
)
base_name = "sagemaker-automl-training-rerun"
step_name = name_from_base(base_name)
step["name"] = step_name
train_args = self._get_train_args(
desc,
channels,
step_name,
volume_kms_key,
encrypt_inter_container_traffic,
vpc_config,
)
self.sagemaker_session.train(**train_args)
running_jobs[step_name] = True
elif step_type == "TransformJob":
# prepare inputs
if not isinstance(inputs, string_types) or not inputs.startswith("s3://"):
msg = "Cannot format input {}. Expecting a string starts with file:// or s3://"
raise ValueError(msg.format(inputs))
desc = self.sagemaker_session.sagemaker_client.describe_transform_job(
TransformJobName=step_name
)
base_name = "sagemaker-automl-transform-rerun"
step_name = name_from_base(base_name)
step["name"] = step_name
transform_args = self._get_transform_args(desc, inputs, step_name, volume_kms_key)
self.sagemaker_session.transform(**transform_args)
running_jobs[step_name] = True
if wait:
while True:
for step in self.steps:
status = None
step_type = step["type"]
step_name = step["name"]
if step_type == "TrainingJob":
status = self.sagemaker_session.sagemaker_client.describe_training_job(
TrainingJobName=step_name
)["TrainingJobStatus"]
elif step_type == "TransformJob":
status = self.sagemaker_session.sagemaker_client.describe_transform_job(
TransformJobName=step_name
)["TransformJobStatus"]
if status in ("Completed", "Failed", "Stopped"):
running_jobs[step_name] = False
if self._check_all_job_finished(running_jobs):
break
def _check_all_job_finished(self, running_jobs):
"""Check if all step jobs are finished.
Args:
running_jobs (dict): a dictionary that keeps track of the status
of each step job.
Returns (bool): True if all step jobs are finished. False if one or
more step jobs are still running.
"""
for _, v in running_jobs.items():
if v:
return False
return True
def _get_train_args(
self, desc, inputs, name, volume_kms_key, encrypt_inter_container_traffic, vpc_config
):
"""Format training args to pass in sagemaker_session.train.
Args:
desc (dict): the response from DescribeTrainingJob API.
inputs (list): a list of input data channels.
name (str): the name of the step job.
volume_kms_key (str): The KMS key id to encrypt data on the storage volume attached to
the ML compute instance(s).
encrypt_inter_container_traffic (bool): To encrypt all communications between ML compute
instances in distributed training.
vpc_config (dict): Specifies a VPC that jobs and hosted models have access to.
Control access to and from training and model containers by configuring the VPC
Returns (dcit): a dictionary that can be used as args of
sagemaker_session.train method.
"""
train_args = {
"input_config": inputs,
"job_name": name,
"input_mode": desc["AlgorithmSpecification"]["TrainingInputMode"],
"role": desc["RoleArn"],
"output_config": desc["OutputDataConfig"],
"resource_config": desc["ResourceConfig"],
"image_uri": desc["AlgorithmSpecification"]["TrainingImage"],
"enable_network_isolation": desc["EnableNetworkIsolation"],
"encrypt_inter_container_traffic": encrypt_inter_container_traffic,
"use_spot_instances": desc["EnableManagedSpotTraining"],
"hyperparameters": {},
"stop_condition": {},
"metric_definitions": None,
"checkpoint_s3_uri": None,
"checkpoint_local_path": None,
"tags": [],
"vpc_config": None,
}
if volume_kms_key is not None:
train_args["resource_config"]["VolumeKmsKeyId"] = volume_kms_key
if "VpcConfig" in desc:
train_args["vpc_config"] = desc["VpcConfig"]
elif vpc_config is not None:
train_args["vpc_config"] = vpc_config
if "Hyperparameters" in desc:
train_args["hyperparameters"] = desc["Hyperparameters"]
if "CheckpointConfig" in desc:
train_args["checkpoint_s3_uri"] = desc["CheckpointConfig"]["S3Uri"]
train_args["checkpoint_local_path"] = desc["CheckpointConfig"]["LocalPath"]
if "StoppingCondition" in desc:
train_args["stop_condition"] = desc["StoppingCondition"]
return train_args
def _get_transform_args(self, desc, inputs, name, volume_kms_key):
"""Format training args to pass in sagemaker_session.train.
Args:
desc (dict): the response from DescribeTrainingJob API.
inputs (str): an S3 uri where new input dataset is stored.
name (str): the name of the step job.
volume_kms_key (str): The KMS key id to encrypt data on the storage volume attached to
the ML compute instance(s).
Returns (dcit): a dictionary that can be used as args of
sagemaker_session.transform method.
"""
transform_args = {}
transform_args["job_name"] = name
transform_args["model_name"] = desc["ModelName"]
transform_args["output_config"] = desc["TransformOutput"]
transform_args["resource_config"] = desc["TransformResources"]
transform_args["data_processing"] = desc["DataProcessing"]
transform_args["tags"] = []
transform_args["strategy"] = None
transform_args["max_concurrent_transforms"] = None
transform_args["max_payload"] = None
transform_args["env"] = None
transform_args["experiment_config"] = None
input_config = desc["TransformInput"]
input_config["DataSource"]["S3DataSource"]["S3Uri"] = inputs
transform_args["input_config"] = input_config
if volume_kms_key is not None:
transform_args["resource_config"]["VolumeKmsKeyId"] = volume_kms_key
if "BatchStrategy" in desc:
transform_args["strategy"] = desc["BatchStrategy"]
if "MaxConcurrentTransforms" in desc:
transform_args["max_concurrent_transforms"] = desc["MaxConcurrentTransforms"]
if "MaxPayloadInMB" in desc:
transform_args["max_payload"] = desc["MaxPayloadInMB"]
if "Environment" in desc:
transform_args["env"] = desc["Environment"]
return transform_args
def _process_steps(self, steps):
"""Extract candidate's step jobs name and type.
Args:
steps (list): a list of a candidate's step jobs.
Returns (list): a list of extracted information about step jobs'
name and type.
"""
processed_steps = []
for step in steps:
step_name = step["CandidateStepName"]
step_type = step["CandidateStepType"].split("::")[2]
processed_steps.append({"name": step_name, "type": step_type})
return processed_steps
class CandidateStep(object):
"""A class that maintains an AutoML Candidate step's name, inputs, type, and description."""
def __init__(self, name, inputs, step_type, description):
self._name = name
self._inputs = inputs
self._type = step_type
self._description = description
@property
def name(self):
"""Name of the candidate step -> (str)"""
return self._name
@property
def inputs(self):
"""Inputs of the candidate step -> (dict)"""
return self._inputs
@property
def type(self):
"""Type of the candidate step, Training or Transform -> (str)"""
return self._type
@property
def description(self):
"""Description of candidate step job -> (dict)"""
return self._description
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