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session.py
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# 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, print_function
import json
import logging
import os
import re
import sys
import time
import typing
import warnings
from typing import List, Dict, Any, Sequence
import boto3
import botocore
import botocore.config
from botocore.exceptions import ClientError
import six
import sagemaker.logs
from sagemaker import vpc_utils
from sagemaker._studio import _append_project_tags
from sagemaker.deprecations import deprecated_class
from sagemaker.inputs import ShuffleConfig, TrainingInput, BatchDataCaptureConfig
from sagemaker.user_agent import prepend_user_agent
from sagemaker.utils import (
name_from_image,
secondary_training_status_changed,
secondary_training_status_message,
sts_regional_endpoint,
retries,
)
from sagemaker import exceptions
from sagemaker.session_settings import SessionSettings
LOGGER = logging.getLogger("sagemaker")
NOTEBOOK_METADATA_FILE = "/opt/ml/metadata/resource-metadata.json"
_STATUS_CODE_TABLE = {
"COMPLETED": "Completed",
"INPROGRESS": "InProgress",
"FAILED": "Failed",
"STOPPED": "Stopped",
"STOPPING": "Stopping",
"STARTING": "Starting",
}
class LogState(object):
"""Placeholder docstring"""
STARTING = 1
WAIT_IN_PROGRESS = 2
TAILING = 3
JOB_COMPLETE = 4
COMPLETE = 5
class Session(object): # pylint: disable=too-many-public-methods
"""Manage interactions with the Amazon SageMaker APIs and any other AWS services needed.
This class provides convenient methods for manipulating entities and resources that Amazon
SageMaker uses, such as training jobs, endpoints, and input datasets in S3.
AWS service calls are delegated to an underlying Boto3 session, which by default
is initialized using the AWS configuration chain. When you make an Amazon SageMaker API call
that accesses an S3 bucket location and one is not specified, the ``Session`` creates a default
bucket based on a naming convention which includes the current AWS account ID.
"""
def __init__(
self,
boto_session=None,
sagemaker_client=None,
sagemaker_runtime_client=None,
sagemaker_featurestore_runtime_client=None,
default_bucket=None,
settings=SessionSettings(),
):
"""Initialize a SageMaker ``Session``.
Args:
boto_session (boto3.session.Session): The underlying Boto3 session which AWS service
calls are delegated to (default: None). If not provided, one is created with
default AWS configuration chain.
sagemaker_client (boto3.SageMaker.Client): Client which makes Amazon SageMaker service
calls other than ``InvokeEndpoint`` (default: None). Estimators created using this
``Session`` use this client. If not provided, one will be created using this
instance's ``boto_session``.
sagemaker_runtime_client (boto3.SageMakerRuntime.Client): Client which makes
``InvokeEndpoint`` calls to Amazon SageMaker (default: None). Predictors created
using this ``Session`` use this client. If not provided, one will be created using
this instance's ``boto_session``.
sagemaker_featurestore_runtime_client (boto3.SageMakerFeatureStoreRuntime.Client):
Client which makes SageMaker FeatureStore record related calls to Amazon SageMaker
(default: None). If not provided, one will be created using
this instance's ``boto_session``.
default_bucket (str): The default Amazon S3 bucket to be used by this session.
This will be created the next time an Amazon S3 bucket is needed (by calling
:func:`default_bucket`).
If not provided, a default bucket will be created based on the following format:
"sagemaker-{region}-{aws-account-id}".
Example: "sagemaker-my-custom-bucket".
settings (sagemaker.session_settings.SessionSettings): Optional. Set of optional
parameters to apply to the session.
"""
self._default_bucket = None
self._default_bucket_name_override = default_bucket
self.s3_resource = None
self.s3_client = None
self.config = None
self.lambda_client = None
self.settings = settings
self._initialize(
boto_session=boto_session,
sagemaker_client=sagemaker_client,
sagemaker_runtime_client=sagemaker_runtime_client,
sagemaker_featurestore_runtime_client=sagemaker_featurestore_runtime_client,
)
def _initialize(
self,
boto_session,
sagemaker_client,
sagemaker_runtime_client,
sagemaker_featurestore_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.DEFAULT_SESSION or boto3.Session()
self._region_name = self.boto_session.region_name
if self._region_name is None:
raise ValueError(
"Must setup local AWS configuration with a region supported by SageMaker."
)
self.sagemaker_client = sagemaker_client or self.boto_session.client("sagemaker")
prepend_user_agent(self.sagemaker_client)
if sagemaker_runtime_client is not None:
self.sagemaker_runtime_client = sagemaker_runtime_client
else:
config = botocore.config.Config(read_timeout=80)
self.sagemaker_runtime_client = self.boto_session.client(
"runtime.sagemaker", config=config
)
prepend_user_agent(self.sagemaker_runtime_client)
if sagemaker_featurestore_runtime_client:
self.sagemaker_featurestore_runtime_client = sagemaker_featurestore_runtime_client
else:
self.sagemaker_featurestore_runtime_client = self.boto_session.client(
"sagemaker-featurestore-runtime"
)
self.local_mode = False
@property
def boto_region_name(self):
"""Placeholder docstring"""
return self._region_name
def upload_data(self, path, bucket=None, key_prefix="data", extra_args=None):
"""Upload local file or directory to S3.
If a single file is specified for upload, the resulting S3 object key is
``{key_prefix}/{filename}`` (filename does not include the local path, if any specified).
If a directory is specified for upload, the API uploads all content, recursively,
preserving relative structure of subdirectories. The resulting object key names are:
``{key_prefix}/{relative_subdirectory_path}/filename``.
Args:
path (str): Path (absolute or relative) of local file or directory to upload.
bucket (str): Name of the S3 Bucket to upload to (default: None). If not specified, the
default bucket of the ``Session`` is used (if default bucket does not exist, the
``Session`` creates it).
key_prefix (str): Optional S3 object key name prefix (default: 'data'). S3 uses the
prefix to create a directory structure for the bucket content that it display in
the S3 console.
extra_args (dict): Optional extra arguments that may be passed to the upload operation.
Similar to ExtraArgs parameter in S3 upload_file function. Please refer to the
ExtraArgs parameter documentation here:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-uploading-files.html#the-extraargs-parameter
Returns:
str: The S3 URI of the uploaded file(s). If a file is specified in the path argument,
the URI format is: ``s3://{bucket name}/{key_prefix}/{original_file_name}``.
If a directory is specified in the path argument, the URI format is
``s3://{bucket name}/{key_prefix}``.
"""
# Generate a tuple for each file that we want to upload of the form (local_path, s3_key).
files = []
key_suffix = None
if os.path.isdir(path):
for dirpath, _, filenames in os.walk(path):
for name in filenames:
local_path = os.path.join(dirpath, name)
s3_relative_prefix = (
"" if path == dirpath else os.path.relpath(dirpath, start=path) + "/"
)
s3_key = "{}/{}{}".format(key_prefix, s3_relative_prefix, name)
files.append((local_path, s3_key))
else:
_, name = os.path.split(path)
s3_key = "{}/{}".format(key_prefix, name)
files.append((path, s3_key))
key_suffix = name
bucket = bucket or self.default_bucket()
if self.s3_resource is None:
s3 = self.boto_session.resource("s3", region_name=self.boto_region_name)
else:
s3 = self.s3_resource
for local_path, s3_key in files:
s3.Object(bucket, s3_key).upload_file(local_path, ExtraArgs=extra_args)
s3_uri = "s3://{}/{}".format(bucket, key_prefix)
# If a specific file was used as input (instead of a directory), we return the full S3 key
# of the uploaded object. This prevents unintentionally using other files under the same
# prefix during training.
if key_suffix:
s3_uri = "{}/{}".format(s3_uri, key_suffix)
return s3_uri
def upload_string_as_file_body(self, body, bucket, key, kms_key=None):
"""Upload a string as a file body.
Args:
body (str): String representing the body of the file.
bucket (str): Name of the S3 Bucket to upload to (default: None). If not specified, the
default bucket of the ``Session`` is used (if default bucket does not exist, the
``Session`` creates it).
key (str): S3 object key. This is the s3 path to the file.
kms_key (str): The KMS key to use for encrypting the file.
Returns:
str: The S3 URI of the uploaded file.
The URI format is: ``s3://{bucket name}/{key}``.
"""
if self.s3_resource is None:
s3 = self.boto_session.resource("s3", region_name=self.boto_region_name)
else:
s3 = self.s3_resource
s3_object = s3.Object(bucket_name=bucket, key=key)
if kms_key is not None:
s3_object.put(Body=body, SSEKMSKeyId=kms_key, ServerSideEncryption="aws:kms")
else:
s3_object.put(Body=body)
s3_uri = "s3://{}/{}".format(bucket, key)
return s3_uri
def download_data(self, path, bucket, key_prefix="", extra_args=None):
"""Download file or directory from S3.
Args:
path (str): Local path where the file or directory should be downloaded to.
bucket (str): Name of the S3 Bucket to download from.
key_prefix (str): Optional S3 object key name prefix.
extra_args (dict): Optional extra arguments that may be passed to the
download operation. Please refer to the ExtraArgs parameter in the boto3
documentation here:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-example-download-file.html
"""
# Initialize the S3 client.
if self.s3_client is None:
s3 = self.boto_session.client("s3", region_name=self.boto_region_name)
else:
s3 = self.s3_client
# Initialize the variables used to loop through the contents of the S3 bucket.
keys = []
next_token = ""
base_parameters = {"Bucket": bucket, "Prefix": key_prefix}
# Loop through the contents of the bucket, 1,000 objects at a time. Gathering all keys into
# a "keys" list.
while next_token is not None:
request_parameters = base_parameters.copy()
if next_token != "":
request_parameters.update({"ContinuationToken": next_token})
response = s3.list_objects_v2(**request_parameters)
contents = response.get("Contents")
# For each object, save its key or directory.
for s3_object in contents:
key = s3_object.get("Key")
keys.append(key)
next_token = response.get("NextContinuationToken")
# For each object key, create the directory on the local machine if needed, and then
# download the file.
for key in keys:
tail_s3_uri_path = os.path.basename(key_prefix)
if not os.path.splitext(key_prefix)[1]:
tail_s3_uri_path = os.path.relpath(key, key_prefix)
destination_path = os.path.join(path, tail_s3_uri_path)
if not os.path.exists(os.path.dirname(destination_path)):
os.makedirs(os.path.dirname(destination_path))
s3.download_file(
Bucket=bucket, Key=key, Filename=destination_path, ExtraArgs=extra_args
)
def read_s3_file(self, bucket, key_prefix):
"""Read a single file from S3.
Args:
bucket (str): Name of the S3 Bucket to download from.
key_prefix (str): S3 object key name prefix.
Returns:
str: The body of the s3 file as a string.
"""
if self.s3_client is None:
s3 = self.boto_session.client("s3", region_name=self.boto_region_name)
else:
s3 = self.s3_client
# Explicitly passing a None kms_key to boto3 throws a validation error.
s3_object = s3.get_object(Bucket=bucket, Key=key_prefix)
return s3_object["Body"].read().decode("utf-8")
def list_s3_files(self, bucket, key_prefix):
"""Lists the S3 files given an S3 bucket and key.
Args:
bucket (str): Name of the S3 Bucket to download from.
key_prefix (str): S3 object key name prefix.
Returns:
[str]: The list of files at the S3 path.
"""
if self.s3_resource is None:
s3 = self.boto_session.resource("s3", region_name=self.boto_region_name)
else:
s3 = self.s3_resource
s3_bucket = s3.Bucket(name=bucket)
s3_objects = s3_bucket.objects.filter(Prefix=key_prefix).all()
return [s3_object.key for s3_object in s3_objects]
def default_bucket(self):
"""Return the name of the default bucket to use in relevant Amazon SageMaker interactions.
This function will create the s3 bucket if it does not exist.
Returns:
str: The name of the default bucket, which is of the form:
``sagemaker-{region}-{AWS account ID}``.
"""
if self._default_bucket:
return self._default_bucket
region = self.boto_session.region_name
default_bucket = self._default_bucket_name_override
if not default_bucket:
account = self.boto_session.client(
"sts", region_name=region, endpoint_url=sts_regional_endpoint(region)
).get_caller_identity()["Account"]
default_bucket = "sagemaker-{}-{}".format(region, account)
self._create_s3_bucket_if_it_does_not_exist(bucket_name=default_bucket, region=region)
self._default_bucket = default_bucket
return self._default_bucket
def _create_s3_bucket_if_it_does_not_exist(self, bucket_name, region):
"""Creates an S3 Bucket if it does not exist.
Also swallows a few common exceptions that indicate that the bucket already exists or
that it is being created.
Args:
bucket_name (str): Name of the S3 bucket to be created.
region (str): The region in which to create the bucket.
Raises:
botocore.exceptions.ClientError: If S3 throws an unexpected exception during bucket
creation.
If the exception is due to the bucket already existing or
already being created, no exception is raised.
"""
if self.s3_resource is None:
s3 = self.boto_session.resource("s3", region_name=region)
else:
s3 = self.s3_resource
bucket = s3.Bucket(name=bucket_name)
if bucket.creation_date is None:
try:
# trying head bucket call
s3.meta.client.head_bucket(Bucket=bucket.name)
except ClientError as e:
# bucket does not exist or forbidden to access
error_code = e.response["Error"]["Code"]
message = e.response["Error"]["Message"]
if error_code == "404" and message == "Not Found":
# bucket does not exist, create one
try:
if region == "us-east-1":
# 'us-east-1' cannot be specified because it is the default region:
# https://github.com/boto/boto3/issues/125
s3.create_bucket(Bucket=bucket_name)
else:
s3.create_bucket(
Bucket=bucket_name,
CreateBucketConfiguration={"LocationConstraint": region},
)
LOGGER.info("Created S3 bucket: %s", bucket_name)
except ClientError as e:
error_code = e.response["Error"]["Code"]
message = e.response["Error"]["Message"]
if (
error_code == "OperationAborted"
and "conflicting conditional operation" in message
):
# If this bucket is already being concurrently created,
# we don't need to create it again.
pass
else:
raise
elif error_code == "403" and message == "Forbidden":
LOGGER.error(
"Bucket %s exists, but access is forbidden. Please try again after "
"adding appropriate access.",
bucket.name,
)
raise
else:
raise
def train( # noqa: C901
self,
input_mode,
input_config,
role,
job_name,
output_config,
resource_config,
vpc_config,
hyperparameters,
stop_condition,
tags,
metric_definitions,
enable_network_isolation=False,
image_uri=None,
algorithm_arn=None,
encrypt_inter_container_traffic=False,
use_spot_instances=False,
checkpoint_s3_uri=None,
checkpoint_local_path=None,
experiment_config=None,
debugger_rule_configs=None,
debugger_hook_config=None,
tensorboard_output_config=None,
enable_sagemaker_metrics=None,
profiler_rule_configs=None,
profiler_config=None,
environment=None,
retry_strategy=None,
):
"""Create an Amazon SageMaker training job.
Args:
input_mode (str): The input mode that the algorithm supports. Valid modes:
* 'File' - Amazon SageMaker copies the training dataset from the S3 location to
a directory in the Docker container.
* 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a
Unix-named pipe.
* 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
downloading the entire dataset before training begins.
input_config (list): A list of Channel objects. Each channel is a named input source.
Please refer to the format details described:
https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training
jobs and APIs that create Amazon SageMaker endpoints use this role to access
training data and model artifacts. You must grant sufficient permissions to this
role.
job_name (str): Name of the training job being created.
output_config (dict): The S3 URI where you want to store the training results and
optional KMS key ID.
resource_config (dict): Contains values for ResourceConfig:
* instance_count (int): Number of EC2 instances to use for training.
The key in resource_config is 'InstanceCount'.
* instance_type (str): Type of EC2 instance to use for training, for example,
'ml.c4.xlarge'. The key in resource_config is 'InstanceType'.
vpc_config (dict): Contains values for VpcConfig:
* subnets (list[str]): List of subnet ids.
The key in vpc_config is 'Subnets'.
* security_group_ids (list[str]): List of security group ids.
The key in vpc_config is 'SecurityGroupIds'.
hyperparameters (dict): Hyperparameters for model training. The hyperparameters are
made accessible as a dict[str, str] to the training code on SageMaker. For
convenience, this accepts other types for keys and values, but ``str()`` will be
called to convert them before training.
stop_condition (dict): Defines when training shall finish. Contains entries that can
be understood by the service like ``MaxRuntimeInSeconds``.
tags (list[dict]): List of tags for labeling a training job. For more, see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
metric_definitions (list[dict]): A list of dictionaries that defines the metric(s)
used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for
the name of the metric, and 'Regex' for the regular expression used to extract the
metric from the logs.
enable_network_isolation (bool): Whether to request for the training job to run with
network isolation or not.
image_uri (str): Docker image containing training code.
algorithm_arn (str): Algorithm Arn from Marketplace.
encrypt_inter_container_traffic (bool): Specifies whether traffic between training
containers is encrypted for the training job (default: ``False``).
use_spot_instances (bool): whether to use spot instances for training.
checkpoint_s3_uri (str): The S3 URI in which to persist checkpoints
that the algorithm persists (if any) during training. (default:
``None``).
checkpoint_local_path (str): The local path that the algorithm
writes its checkpoints to. SageMaker will persist all files
under this path to `checkpoint_s3_uri` continually during
training. On job startup the reverse happens - data from the
s3 location is downloaded to this path before the algorithm is
started. If the path is unset then SageMaker assumes the
checkpoints will be provided under `/opt/ml/checkpoints/`.
(default: ``None``).
experiment_config (dict[str, str]): Experiment management configuration.
Optionally, the dict can contain three keys:
'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.
The behavior of setting these keys is as follows:
* If `ExperimentName` is supplied but `TrialName` is not a Trial will be
automatically created and the job's Trial Component associated with the Trial.
* If `TrialName` is supplied and the Trial already exists the job's Trial Component
will be associated with the Trial.
* If both `ExperimentName` and `TrialName` are not supplied the trial component
will be unassociated.
* `TrialComponentDisplayName` is used for display in Studio.
enable_sagemaker_metrics (bool): enable SageMaker Metrics Time
Series. For more information see:
https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries
(default: ``None``).
profiler_rule_configs (list[dict]): A list of profiler rule
configurations.src/sagemaker/lineage/artifact.py:285
profiler_config (dict): Configuration for how profiling information is emitted
with SageMaker Profiler. (default: ``None``).
environment (dict[str, str]) : Environment variables to be set for
use during training job (default: ``None``)
retry_strategy(dict): Defines RetryStrategy for InternalServerFailures.
* max_retry_attsmpts (int): Number of times a job should be retried.
The key in RetryStrategy is 'MaxRetryAttempts'.
Returns:
str: ARN of the training job, if it is created.
"""
tags = _append_project_tags(tags)
train_request = self._get_train_request(
input_mode=input_mode,
input_config=input_config,
role=role,
job_name=job_name,
output_config=output_config,
resource_config=resource_config,
vpc_config=vpc_config,
hyperparameters=hyperparameters,
stop_condition=stop_condition,
tags=tags,
metric_definitions=metric_definitions,
enable_network_isolation=enable_network_isolation,
image_uri=image_uri,
algorithm_arn=algorithm_arn,
encrypt_inter_container_traffic=encrypt_inter_container_traffic,
use_spot_instances=use_spot_instances,
checkpoint_s3_uri=checkpoint_s3_uri,
checkpoint_local_path=checkpoint_local_path,
experiment_config=experiment_config,
debugger_rule_configs=debugger_rule_configs,
debugger_hook_config=debugger_hook_config,
tensorboard_output_config=tensorboard_output_config,
enable_sagemaker_metrics=enable_sagemaker_metrics,
profiler_rule_configs=profiler_rule_configs,
profiler_config=profiler_config,
environment=environment,
retry_strategy=retry_strategy,
)
def submit(request):
LOGGER.info("Creating training-job with name: %s", job_name)
LOGGER.debug("train request: %s", json.dumps(request, indent=4))
self.sagemaker_client.create_training_job(**request)
self._intercept_create_request(train_request, submit, self.train.__name__)
def _get_train_request( # noqa: C901
self,
input_mode,
input_config,
role,
job_name,
output_config,
resource_config,
vpc_config,
hyperparameters,
stop_condition,
tags,
metric_definitions,
enable_network_isolation=False,
image_uri=None,
algorithm_arn=None,
encrypt_inter_container_traffic=False,
use_spot_instances=False,
checkpoint_s3_uri=None,
checkpoint_local_path=None,
experiment_config=None,
debugger_rule_configs=None,
debugger_hook_config=None,
tensorboard_output_config=None,
enable_sagemaker_metrics=None,
profiler_rule_configs=None,
profiler_config=None,
environment=None,
retry_strategy=None,
):
"""Constructs a request compatible for creating an Amazon SageMaker training job.
Args:
input_mode (str): The input mode that the algorithm supports. Valid modes:
* 'File' - Amazon SageMaker copies the training dataset from the S3 location to
a directory in the Docker container.
* 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a
Unix-named pipe.
* 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
downloading the entire dataset before training begins.
input_config (list): A list of Channel objects. Each channel is a named input source.
Please refer to the format details described:
https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training
jobs and APIs that create Amazon SageMaker endpoints use this role to access
training data and model artifacts. You must grant sufficient permissions to this
role.
job_name (str): Name of the training job being created.
output_config (dict): The S3 URI where you want to store the training results and
optional KMS key ID.
resource_config (dict): Contains values for ResourceConfig:
* instance_count (int): Number of EC2 instances to use for training.
The key in resource_config is 'InstanceCount'.
* instance_type (str): Type of EC2 instance to use for training, for example,
'ml.c4.xlarge'. The key in resource_config is 'InstanceType'.
vpc_config (dict): Contains values for VpcConfig:
* subnets (list[str]): List of subnet ids.
The key in vpc_config is 'Subnets'.
* security_group_ids (list[str]): List of security group ids.
The key in vpc_config is 'SecurityGroupIds'.
hyperparameters (dict): Hyperparameters for model training. The hyperparameters are
made accessible as a dict[str, str] to the training code on SageMaker. For
convenience, this accepts other types for keys and values, but ``str()`` will be
called to convert them before training.
stop_condition (dict): Defines when training shall finish. Contains entries that can
be understood by the service like ``MaxRuntimeInSeconds``.
tags (list[dict]): List of tags for labeling a training job. For more, see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
metric_definitions (list[dict]): A list of dictionaries that defines the metric(s)
used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for
the name of the metric, and 'Regex' for the regular expression used to extract the
metric from the logs.
enable_network_isolation (bool): Whether to request for the training job to run with
network isolation or not.
image_uri (str): Docker image containing training code.
algorithm_arn (str): Algorithm Arn from Marketplace.
encrypt_inter_container_traffic (bool): Specifies whether traffic between training
containers is encrypted for the training job (default: ``False``).
use_spot_instances (bool): whether to use spot instances for training.
checkpoint_s3_uri (str): The S3 URI in which to persist checkpoints
that the algorithm persists (if any) during training. (default:
``None``).
checkpoint_local_path (str): The local path that the algorithm
writes its checkpoints to. SageMaker will persist all files
under this path to `checkpoint_s3_uri` continually during
training. On job startup the reverse happens - data from the
s3 location is downloaded to this path before the algorithm is
started. If the path is unset then SageMaker assumes the
checkpoints will be provided under `/opt/ml/checkpoints/`.
(default: ``None``).
experiment_config (dict[str, str]): Experiment management configuration.
Optionally, the dict can contain three keys:
'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.
The behavior of setting these keys is as follows:
* If `ExperimentName` is supplied but `TrialName` is not a Trial will be
automatically created and the job's Trial Component associated with the Trial.
* If `TrialName` is supplied and the Trial already exists the job's Trial Component
will be associated with the Trial.
* If both `ExperimentName` and `TrialName` are not supplied the trial component
will be unassociated.
* `TrialComponentDisplayName` is used for display in Studio.
enable_sagemaker_metrics (bool): enable SageMaker Metrics Time
Series. For more information see:
https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries
(default: ``None``).
profiler_rule_configs (list[dict]): A list of profiler rule configurations.
profiler_config(dict): Configuration for how profiling information is emitted with
SageMaker Profiler. (default: ``None``).
environment (dict[str, str]) : Environment variables to be set for
use during training job (default: ``None``)
retry_strategy(dict): Defines RetryStrategy for InternalServerFailures.
* max_retry_attsmpts (int): Number of times a job should be retried.
The key in RetryStrategy is 'MaxRetryAttempts'.
Returns:
Dict: a training request dict
"""
train_request = {
"AlgorithmSpecification": {"TrainingInputMode": input_mode},
"OutputDataConfig": output_config,
"TrainingJobName": job_name,
"StoppingCondition": stop_condition,
"ResourceConfig": resource_config,
"RoleArn": role,
}
if image_uri and algorithm_arn:
raise ValueError(
"image_uri and algorithm_arn are mutually exclusive."
"Both were provided: image_uri: %s algorithm_arn: %s" % (image_uri, algorithm_arn)
)
if image_uri is None and algorithm_arn is None:
raise ValueError("either image_uri or algorithm_arn is required. None was provided.")
if image_uri is not None:
train_request["AlgorithmSpecification"]["TrainingImage"] = image_uri
if algorithm_arn is not None:
train_request["AlgorithmSpecification"]["AlgorithmName"] = algorithm_arn
if input_config is not None:
train_request["InputDataConfig"] = input_config
if metric_definitions is not None:
train_request["AlgorithmSpecification"]["MetricDefinitions"] = metric_definitions
if enable_sagemaker_metrics is not None:
train_request["AlgorithmSpecification"][
"EnableSageMakerMetricsTimeSeries"
] = enable_sagemaker_metrics
if hyperparameters and len(hyperparameters) > 0:
train_request["HyperParameters"] = hyperparameters
if environment is not None:
train_request["Environment"] = environment
if tags is not None:
train_request["Tags"] = tags
if vpc_config is not None:
train_request["VpcConfig"] = vpc_config
if experiment_config and len(experiment_config) > 0:
train_request["ExperimentConfig"] = experiment_config
if enable_network_isolation:
train_request["EnableNetworkIsolation"] = enable_network_isolation
if encrypt_inter_container_traffic:
train_request["EnableInterContainerTrafficEncryption"] = encrypt_inter_container_traffic
if use_spot_instances:
# estimator.use_spot_instances may be a Pipeline ParameterBoolean object
# which is parsed during the Pipeline execution runtime
train_request["EnableManagedSpotTraining"] = use_spot_instances
if checkpoint_s3_uri:
checkpoint_config = {"S3Uri": checkpoint_s3_uri}
if checkpoint_local_path:
checkpoint_config["LocalPath"] = checkpoint_local_path
train_request["CheckpointConfig"] = checkpoint_config
if debugger_rule_configs is not None:
train_request["DebugRuleConfigurations"] = debugger_rule_configs
if debugger_hook_config is not None:
train_request["DebugHookConfig"] = debugger_hook_config
if tensorboard_output_config is not None:
train_request["TensorBoardOutputConfig"] = tensorboard_output_config
if profiler_rule_configs is not None:
train_request["ProfilerRuleConfigurations"] = profiler_rule_configs
if profiler_config is not None:
train_request["ProfilerConfig"] = profiler_config
if retry_strategy is not None:
train_request["RetryStrategy"] = retry_strategy
return train_request
def update_training_job(
self,
job_name,
profiler_rule_configs=None,
profiler_config=None,
resource_config=None,
):
"""Calls the UpdateTrainingJob API for the given job name and returns the response.
Args:
job_name (str): Name of the training job being updated.
profiler_rule_configs (list): List of profiler rule configurations. (default: ``None``).
profiler_config(dict): Configuration for how profiling information is emitted with
SageMaker Profiler. (default: ``None``).
resource_config (dict): Configuration of the resources for the training job. You can
update the keep-alive period if the warm pool status is `Available`. No other fields
can be updated. (default: ``None``).
"""
update_training_job_request = self._get_update_training_job_request(
job_name=job_name,
profiler_rule_configs=profiler_rule_configs,
profiler_config=profiler_config,
resource_config=resource_config,
)
LOGGER.info("Updating training job with name %s", job_name)
LOGGER.debug("Update request: %s", json.dumps(update_training_job_request, indent=4))
self.sagemaker_client.update_training_job(**update_training_job_request)
def _get_update_training_job_request(
self,
job_name,
profiler_rule_configs=None,
profiler_config=None,
resource_config=None,
):
"""Constructs a request compatible for updating an Amazon SageMaker training job.
Args:
job_name (str): Name of the training job being updated.
profiler_rule_configs (list): List of profiler rule configurations. (default: ``None``).
profiler_config(dict): Configuration for how profiling information is emitted with
SageMaker Profiler. (default: ``None``).
resource_config (dict): Configuration of the resources for the training job. You can
update the keep-alive period if the warm pool status is `Available`. No other fields
can be updated. (default: ``None``).
Returns:
Dict: an update training request dict
"""
update_training_job_request = {
"TrainingJobName": job_name,
}
if profiler_rule_configs is not None:
update_training_job_request["ProfilerRuleConfigurations"] = profiler_rule_configs
if profiler_config is not None:
update_training_job_request["ProfilerConfig"] = profiler_config
if resource_config is not None:
update_training_job_request["ResourceConfig"] = resource_config
return update_training_job_request
def process(
self,
inputs,
output_config,
job_name,
resources,
stopping_condition,
app_specification,
environment,
network_config,
role_arn,
tags,
experiment_config=None,
):
"""Create an Amazon SageMaker processing job.
Args:
inputs ([dict]): List of up to 10 ProcessingInput dictionaries.
output_config (dict): A config dictionary, which contains a list of up
to 10 ProcessingOutput dictionaries, as well as an optional KMS key ID.
job_name (str): The name of the processing job. The name must be unique
within an AWS Region in an AWS account. Names should have minimum
length of 1 and maximum length of 63 characters.
resources (dict): Encapsulates the resources, including ML instances
and storage, to use for the processing job.
stopping_condition (dict[str,int]): Specifies a limit to how long
the processing job can run, in seconds.
app_specification (dict[str,str]): Configures the processing job to
run the given image. Details are in the processing container
specification.
environment (dict): Environment variables to start the processing
container with.
network_config (dict): Specifies networking options, such as network
traffic encryption between processing containers, whether to allow
inbound and outbound network calls to and from processing containers,
and VPC subnets and security groups to use for VPC-enabled processing
jobs.
role_arn (str): The Amazon Resource Name (ARN) of an IAM role that
Amazon SageMaker can assume to perform tasks on your behalf.
tags ([dict[str,str]]): A list of dictionaries containing key-value
pairs.
experiment_config (dict[str, str]): Experiment management configuration.
Optionally, the dict can contain three keys:
'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.
The behavior of setting these keys is as follows:
* If `ExperimentName` is supplied but `TrialName` is not a Trial will be
automatically created and the job's Trial Component associated with the Trial.
* If `TrialName` is supplied and the Trial already exists the job's Trial Component
will be associated with the Trial.
* If both `ExperimentName` and `TrialName` are not supplied the trial component
will be unassociated.
* `TrialComponentDisplayName` is used for display in Studio.
"""
tags = _append_project_tags(tags)
process_request = self._get_process_request(
inputs=inputs,
output_config=output_config,
job_name=job_name,
resources=resources,
stopping_condition=stopping_condition,
app_specification=app_specification,
environment=environment,
network_config=network_config,
role_arn=role_arn,
tags=tags,
experiment_config=experiment_config,
)
def submit(request):
LOGGER.info("Creating processing-job with name %s", job_name)
LOGGER.debug("process request: %s", json.dumps(request, indent=4))
self.sagemaker_client.create_processing_job(**request)
self._intercept_create_request(process_request, submit, self.process.__name__)
def _get_process_request(
self,
inputs,
output_config,
job_name,
resources,
stopping_condition,
app_specification,
environment,
network_config,
role_arn,
tags,
experiment_config=None,
):
"""Constructs a request compatible for an Amazon SageMaker processing job.
Args:
inputs ([dict]): List of up to 10 ProcessingInput dictionaries.
output_config (dict): A config dictionary, which contains a list of up
to 10 ProcessingOutput dictionaries, as well as an optional KMS key ID.
job_name (str): The name of the processing job. The name must be unique
within an AWS Region in an AWS account. Names should have minimum
length of 1 and maximum length of 63 characters.
resources (dict): Encapsulates the resources, including ML instances
and storage, to use for the processing job.
stopping_condition (dict[str,int]): Specifies a limit to how long
the processing job can run, in seconds.
app_specification (dict[str,str]): Configures the processing job to
run the given image. Details are in the processing container
specification.
environment (dict): Environment variables to start the processing
container with.
network_config (dict): Specifies networking options, such as network
traffic encryption between processing containers, whether to allow
inbound and outbound network calls to and from processing containers,
and VPC subnets and security groups to use for VPC-enabled processing
jobs.
role_arn (str): The Amazon Resource Name (ARN) of an IAM role that
Amazon SageMaker can assume to perform tasks on your behalf.
tags ([dict[str,str]]): A list of dictionaries containing key-value
pairs.
experiment_config (dict[str, str]): Experiment management configuration.
Optionally, the dict can contain three keys: