testbed/aws__sagemaker-python-sdk/src/sagemaker/inputs.py

# 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.
"""Amazon SageMaker channel configurations for S3 data sources and file system data sources"""
from __future__ import absolute_import, print_function

from typing import Union, Optional, List
import attr

from sagemaker.workflow.entities import PipelineVariable

FILE_SYSTEM_TYPES = ["FSxLustre", "EFS"]
FILE_SYSTEM_ACCESS_MODES = ["ro", "rw"]


class TrainingInput(object):
    """Amazon SageMaker channel configurations for S3 data sources.

    Attributes:
        config (dict[str, dict]): A SageMaker ``DataSource`` referencing
            a SageMaker ``S3DataSource``.
    """

    def __init__(
        self,
        s3_data: Union[str, PipelineVariable],
        distribution: Optional[Union[str, PipelineVariable]] = None,
        compression: Optional[Union[str, PipelineVariable]] = None,
        content_type: Optional[Union[str, PipelineVariable]] = None,
        record_wrapping: Optional[Union[str, PipelineVariable]] = None,
        s3_data_type: Union[str, PipelineVariable] = "S3Prefix",
        instance_groups: Optional[List[Union[str, PipelineVariable]]] = None,
        input_mode: Optional[Union[str, PipelineVariable]] = None,
        attribute_names: Optional[List[Union[str, PipelineVariable]]] = None,
        target_attribute_name: Optional[Union[str, PipelineVariable]] = None,
        shuffle_config: Optional["ShuffleConfig"] = None,
    ):
        r"""Create a definition for input data used by an SageMaker training job.

        See AWS documentation on the ``CreateTrainingJob`` API for more details
        on the parameters.

        Args:
            s3_data (str or PipelineVariable): Defines the location of S3 data to train on.
            distribution (str or PipelineVariable): Valid values: ``'FullyReplicated'``,
                ``'ShardedByS3Key'`` (default: ``'FullyReplicated'``).
            compression (str or PipelineVariable): Valid values: ``'Gzip'``, ``None``
                (default: None). This is used only in Pipe input mode.
            content_type (str or PipelineVariable): MIME type of the input data
                (default: None).
            record_wrapping (str or PipelineVariable): Valid values: 'RecordIO'
                (default: None).
            s3_data_type (str or PipelineVariable): Valid values: ``'S3Prefix'``,
                ``'ManifestFile'``, ``'AugmentedManifestFile'``.
                If ``'S3Prefix'``, ``s3_data`` defines a prefix of s3 objects to train on.
                All objects with s3 keys beginning with ``s3_data`` will be used to train.
                If ``'ManifestFile'`` or ``'AugmentedManifestFile'``,
                then ``s3_data`` defines a
                single S3 manifest file or augmented manifest file respectively,
                listing the S3 data to train on. Both the ManifestFile and
                AugmentedManifestFile formats are described at `S3DataSource
                <https://docs.aws.amazon.com/sagemaker/latest/dg/API_S3DataSource.html>`_
                in the `Amazon SageMaker API reference`.
            instance_groups (list[str] or list[PipelineVariable]): Optional. A list of
                instance group names in string format that you specified while configuring
                a heterogeneous cluster using the :class:`sagemaker.instance_group.InstanceGroup`.
                S3 data will be sent to all instance groups in the specified list.
                For instructions on how to use InstanceGroup objects
                to configure a heterogeneous cluster
                through the SageMaker generic and framework estimator classes, see
                `Train Using a Heterogeneous Cluster
                <https://docs.aws.amazon.com/sagemaker/latest/dg/train-heterogeneous-cluster.html>`_
                in the *Amazon SageMaker developer guide*.
                (default: None)
            input_mode (str or PipelineVariable): Optional override for this channel's input mode
                (default: None). By default, channels will use the input mode defined on
                ``sagemaker.estimator.EstimatorBase.input_mode``, but they will ignore
                that setting if this parameter is set.

                    * None - Amazon SageMaker will use the input mode specified in the ``Estimator``
                    * 'File' - Amazon SageMaker copies the training dataset from the S3 location to
                        a local directory.
                    * '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.

            attribute_names (list[str] or list[PipelineVariable]): A list of one or more attribute
                names to use that are found in a specified AugmentedManifestFile.
            target_attribute_name (str or PipelineVariable): The name of the attribute will be
                predicted (classified) in a SageMaker AutoML job. It is required if the input is
                for SageMaker AutoML job.
            shuffle_config (sagemaker.inputs.ShuffleConfig): If specified this configuration enables
                shuffling on this channel. See the SageMaker API documentation for more info:
                https://docs.aws.amazon.com/sagemaker/latest/dg/API_ShuffleConfig.html
        """
        self.config = {
            "DataSource": {"S3DataSource": {"S3DataType": s3_data_type, "S3Uri": s3_data}}
        }

        if not (target_attribute_name or distribution):
            distribution = "FullyReplicated"

        if distribution is not None:
            self.config["DataSource"]["S3DataSource"]["S3DataDistributionType"] = distribution

        if compression is not None:
            self.config["CompressionType"] = compression
        if content_type is not None:
            self.config["ContentType"] = content_type
        if record_wrapping is not None:
            self.config["RecordWrapperType"] = record_wrapping
        if instance_groups is not None:
            self.config["DataSource"]["S3DataSource"]["InstanceGroupNames"] = instance_groups
        if input_mode is not None:
            self.config["InputMode"] = input_mode
        if attribute_names is not None:
            self.config["DataSource"]["S3DataSource"]["AttributeNames"] = attribute_names
        if target_attribute_name is not None:
            self.config["TargetAttributeName"] = target_attribute_name
        if shuffle_config is not None:
            self.config["ShuffleConfig"] = {"Seed": shuffle_config.seed}


class ShuffleConfig(object):
    """For configuring channel shuffling using a seed.

    For more detail, see the AWS documentation:
    https://docs.aws.amazon.com/sagemaker/latest/dg/API_ShuffleConfig.html
    """

    def __init__(self, seed):
        """Create a ShuffleConfig.

        Args:
            seed (long): the long value used to seed the shuffled sequence.
        """
        self.seed = seed


@attr.s
class CreateModelInput(object):
    """A class containing parameters which can be used to create a SageMaker Model

    Parameters:
        instance_type (str): type or EC2 instance will be used for model deployment.
        accelerator_type (str): elastic inference accelerator type.
    """

    instance_type: str = attr.ib(default=None)
    accelerator_type: str = attr.ib(default=None)


@attr.s
class TransformInput(object):
    """Create a class containing all the parameters.

    It can be used when calling ``sagemaker.transformer.Transformer.transform()``
    """

    data: str = attr.ib()
    data_type: str = attr.ib(default="S3Prefix")
    content_type: str = attr.ib(default=None)
    compression_type: str = attr.ib(default=None)
    split_type: str = attr.ib(default=None)
    input_filter: str = attr.ib(default=None)
    output_filter: str = attr.ib(default=None)
    join_source: str = attr.ib(default=None)
    model_client_config: dict = attr.ib(default=None)
    batch_data_capture_config: dict = attr.ib(default=None)


class FileSystemInput(object):
    """Amazon SageMaker channel configurations for file system data sources.

    Attributes:
        config (dict[str, dict]): A Sagemaker File System ``DataSource``.
    """

    def __init__(
        self,
        file_system_id,
        file_system_type,
        directory_path,
        file_system_access_mode="ro",
        content_type=None,
    ):
        """Create a new file system input used by an SageMaker training job.

        Args:
            file_system_id (str): An Amazon file system ID starting with 'fs-'.
            file_system_type (str): The type of file system used for the input.
                Valid values: 'EFS', 'FSxLustre'.
            directory_path (str): Absolute or normalized path to the root directory (mount point) in
                the file system.
                Reference: https://docs.aws.amazon.com/efs/latest/ug/mounting-fs.html and
                https://docs.aws.amazon.com/fsx/latest/LustreGuide/mount-fs-auto-mount-onreboot.html
            file_system_access_mode (str): Permissions for read and write.
                Valid values: 'ro' or 'rw'. Defaults to 'ro'.
        """

        if file_system_type not in FILE_SYSTEM_TYPES:
            raise ValueError(
                "Unrecognized file system type: %s. Valid values: %s."
                % (file_system_type, ", ".join(FILE_SYSTEM_TYPES))
            )

        if file_system_access_mode not in FILE_SYSTEM_ACCESS_MODES:
            raise ValueError(
                "Unrecognized file system access mode: %s. Valid values: %s."
                % (file_system_access_mode, ", ".join(FILE_SYSTEM_ACCESS_MODES))
            )

        self.config = {
            "DataSource": {
                "FileSystemDataSource": {
                    "FileSystemId": file_system_id,
                    "FileSystemType": file_system_type,
                    "DirectoryPath": directory_path,
                    "FileSystemAccessMode": file_system_access_mode,
                }
            }
        }

        if content_type:
            self.config["ContentType"] = content_type


class BatchDataCaptureConfig(object):
    """Configuration object passed in when create a batch transform job.

    Specifies configuration related to batch transform job data capture for use with
    Amazon SageMaker Model Monitoring
    """

    def __init__(
        self,
        destination_s3_uri: str,
        kms_key_id: str = None,
        generate_inference_id: bool = None,
    ):
        """Create new BatchDataCaptureConfig

        Args:
            destination_s3_uri (str): S3 Location to store the captured data
            kms_key_id (str): The KMS key to use when writing to S3.
                KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key,
                or alias of a KMS key. The KmsKeyId is applied to all outputs.
                (default: None)
            generate_inference_id (bool): Flag to generate an inference id
                (default: None)
        """
        self.destination_s3_uri = destination_s3_uri
        self.kms_key_id = kms_key_id
        self.generate_inference_id = generate_inference_id

    def _to_request_dict(self):
        """Generates a request dictionary using the parameters provided to the class."""
        batch_data_capture_config = {
            "DestinationS3Uri": self.destination_s3_uri,
        }

        if self.kms_key_id is not None:
            batch_data_capture_config["KmsKeyId"] = self.kms_key_id
        if self.generate_inference_id is not None:
            batch_data_capture_config["GenerateInferenceId"] = self.generate_inference_id

        return batch_data_capture_config