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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
import logging
from typing import Union, Optional, Dict
from sagemaker import image_uris
from sagemaker.deprecations import renamed_kwargs
from sagemaker.estimator import Framework
from sagemaker.fw_utils import (
framework_name_from_image,
framework_version_from_tag,
validate_version_or_image_args,
)
from sagemaker.sklearn import defaults
from sagemaker.sklearn.model import SKLearnModel
from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
from sagemaker.workflow.entities import PipelineVariable
from sagemaker.workflow import is_pipeline_variable
logger = logging.getLogger("sagemaker")
class SKLearn(Framework):
"""Handle end-to-end training and deployment of custom Scikit-learn code."""
_framework_name = defaults.SKLEARN_NAME
def __init__(
self,
entry_point: Union[str, PipelineVariable],
framework_version: Optional[str] = None,
py_version: str = "py3",
source_dir: Optional[Union[str, PipelineVariable]] = None,
hyperparameters: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
image_uri: Optional[Union[str, PipelineVariable]] = None,
image_uri_region: Optional[str] = None,
**kwargs
):
"""Creates a SKLearn Estimator for Scikit-learn environment.
It will execute an Scikit-learn script within a SageMaker Training Job. The managed
Scikit-learn environment is an Amazon-built Docker container that executes functions
defined in the supplied ``entry_point`` Python script.
Training is started by calling
:meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator.
After training is complete, calling
:meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted
SageMaker endpoint and returns an
:class:`~sagemaker.amazon.sklearn.model.SKLearnPredictor` instance that
can be used to perform inference against the hosted model.
Technical documentation on preparing Scikit-learn scripts for
SageMaker training and using the Scikit-learn Estimator is available on
the project home-page: https://github.com/aws/sagemaker-python-sdk
Args:
entry_point (str or PipelineVariable): Path (absolute or relative) to the Python source
file which should be executed as the entry point to training.
If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
framework_version (str): Scikit-learn version you want to use for
executing your model training code. Defaults to ``None``. Required
unless ``image_uri`` is provided. List of supported versions:
https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators
py_version (str): Python version you want to use for executing your
model training code (default: 'py3'). Currently, 'py3' is the only
supported version. If ``None`` is passed in, ``image_uri`` must be
provided.
source_dir (str or PipelineVariable): Path (absolute, relative or an S3 URI) to
a directory with any other training source code dependencies aside from the entry
point file (default: None). If ``source_dir`` is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters (dict[str, str] or dict[str, PipelineVariable]): Hyperparameters
that will be used for training (default: None). 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.
image_uri (str or PipelineVariable)): If specified, the estimator will use this image
for training and hosting, instead of selecting the appropriate
SageMaker official image based on framework_version and
py_version. It can be an ECR url or dockerhub image and tag.
Examples:
123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
custom-image:latest.
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
image_uri_region (str): If ``image_uri`` argument is None, the image uri
associated with this object will be in this region.
Default: region associated with SageMaker session.
**kwargs: Additional kwargs passed to the
:class:`~sagemaker.estimator.Framework` constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
instance_type = renamed_kwargs(
"train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs
)
instance_count = renamed_kwargs(
"train_instance_count", "instance_count", kwargs.get("instance_count"), kwargs
)
validate_version_or_image_args(framework_version, py_version, image_uri)
if py_version and py_version != "py3":
raise AttributeError(
"Scikit-learn image only supports Python 3. Please use 'py3' for py_version."
)
self.framework_version = framework_version
self.py_version = py_version
# SciKit-Learn does not support distributed training or training on GPU instance types.
# Fail fast.
_validate_not_gpu_instance_type(instance_type)
if instance_count:
instance_cnt_err_msg = (
"Scikit-Learn does not support distributed training. Please remove the "
"'instance_count' argument or set 'instance_count=1' when initializing SKLearn."
)
if is_pipeline_variable(instance_count):
raise TypeError(
"Invalid type of instance_count (PipelineVariable - {}). ".format(
type(instance_count)
)
+ instance_cnt_err_msg
)
if instance_count != 1:
raise AttributeError(instance_cnt_err_msg)
super(SKLearn, self).__init__(
entry_point,
source_dir,
hyperparameters,
image_uri=image_uri,
**dict(kwargs, instance_count=1)
)
if image_uri is None:
self.image_uri = image_uris.retrieve(
SKLearn._framework_name,
image_uri_region or self.sagemaker_session.boto_region_name,
version=self.framework_version,
py_version=self.py_version,
instance_type=instance_type,
)
def create_model(
self,
model_server_workers=None,
role=None,
vpc_config_override=VPC_CONFIG_DEFAULT,
entry_point=None,
source_dir=None,
dependencies=None,
**kwargs
):
"""Create a SageMaker ``SKLearnModel`` object that can be deployed to an ``Endpoint``.
Args:
model_server_workers (int): Optional. The number of worker processes
used by the inference server. If None, server will use one
worker per vCPU.
role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
which is also used during transform jobs. If not specified, the
role from the Estimator will be used.
vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
the model. Default: use subnets and security groups from this Estimator.
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
entry_point (str): Path (absolute or relative) to the local Python source file which
should be executed as the entry point to training. If ``source_dir`` is specified,
then ``entry_point`` must point to a file located at the root of ``source_dir``.
If not specified, the training entry point is used.
source_dir (str): Path (absolute or relative) to a directory with any other serving
source code dependencies aside from the entry point file.
If not specified, the model source directory from training is used.
dependencies (list[str]): A list of paths to directories (absolute or relative) with
any additional libraries that will be exported to the container.
If not specified, the dependencies from training are used.
This is not supported with "local code" in Local Mode.
**kwargs: Additional kwargs passed to the :class:`~sagemaker.sklearn.model.SKLearnModel`
constructor.
Returns:
sagemaker.sklearn.model.SKLearnModel: A SageMaker ``SKLearnModel``
object. See :func:`~sagemaker.sklearn.model.SKLearnModel` for full details.
"""
role = role or self.role
kwargs["name"] = self._get_or_create_name(kwargs.get("name"))
if "image_uri" not in kwargs:
kwargs["image_uri"] = self.image_uri
if "enable_network_isolation" not in kwargs:
kwargs["enable_network_isolation"] = self.enable_network_isolation()
return SKLearnModel(
self.model_data,
role,
entry_point or self._model_entry_point(),
source_dir=(source_dir or self._model_source_dir()),
container_log_level=self.container_log_level,
code_location=self.code_location,
py_version=self.py_version,
framework_version=self.framework_version,
model_server_workers=model_server_workers,
sagemaker_session=self.sagemaker_session,
vpc_config=self.get_vpc_config(vpc_config_override),
dependencies=(dependencies or self.dependencies),
**kwargs
)
@classmethod
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_name (str): Name of the channel where pre-trained
model data will be downloaded (default: None).
Returns:
dictionary: The transformed init_params
"""
init_params = super(SKLearn, cls)._prepare_init_params_from_job_description(
job_details, model_channel_name
)
image_uri = init_params.pop("image_uri")
framework, py_version, tag, _ = framework_name_from_image(image_uri)
if tag is None:
framework_version = None
else:
framework_version = framework_version_from_tag(tag)
init_params["framework_version"] = framework_version
init_params["py_version"] = py_version
if not framework:
# If we were unable to parse the framework name from the image it is not one of our
# officially supported images, in this case just add the image to the init params.
init_params["image_uri"] = image_uri
return init_params
if framework and framework != "scikit-learn":
raise ValueError(
"Training job: {} didn't use image for requested framework".format(
job_details["TrainingJobName"]
)
)
return init_params
def _validate_not_gpu_instance_type(training_instance_type):
"""Placeholder docstring."""
gpu_instance_types = [
"ml.p2.xlarge",
"ml.p2.8xlarge",
"ml.p2.16xlarge",
"ml.p3.xlarge",
"ml.p3.8xlarge",
"ml.p3.16xlarge",
]
if is_pipeline_variable(training_instance_type):
warn_msg = (
"instance_type is a PipelineVariable (%s). "
"Its interpreted value in execution time should not be of GPU types "
"since GPU training is not supported for Scikit-Learn."
)
logger.warning(warn_msg, type(training_instance_type))
return
if training_instance_type in gpu_instance_types:
raise ValueError(
"GPU training is not supported for Scikit-Learn. "
"Please pick a different instance type from here: "
"https://aws.amazon.com/ec2/instance-types/"
)
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