processing.py

# Copyright 2019-2020 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.
"""This module contains code related to the ``Processor`` class, which is used
for Amazon SageMaker Processing Jobs. These jobs let users perform data pre-processing,
post-processing, feature engineering, data validation, and model evaluation,
and interpretation on Amazon SageMaker.
"""
from __future__ import print_function, absolute_import

import os

from six.moves.urllib.parse import urlparse

from sagemaker.job import _Job
from sagemaker.utils import base_name_from_image, name_from_base
from sagemaker.session import Session
from sagemaker.s3 import S3Uploader
from sagemaker.network import NetworkConfig  # noqa: F401 # pylint: disable=unused-import


class Processor(object):
    """Handles Amazon SageMaker Processing tasks."""

    def __init__(
        self,
        role,
        image_uri,
        instance_count,
        instance_type,
        entrypoint=None,
        volume_size_in_gb=30,
        volume_kms_key=None,
        output_kms_key=None,
        max_runtime_in_seconds=None,
        base_job_name=None,
        sagemaker_session=None,
        env=None,
        tags=None,
        network_config=None,
    ):
        """Initializes a ``Processor`` instance. The ``Processor`` handles Amazon
        SageMaker Processing tasks.

        Args:
            role (str): An AWS IAM role name or ARN. Amazon SageMaker Processing
                uses this role to access AWS resources, such as
                data stored in Amazon S3.
            image_uri (str): The URI of the Docker image to use for the
                processing jobs.
            instance_count (int): The number of instances to run
                a processing job with.
            instance_type (str): The type of EC2 instance to use for
                processing, for example, 'ml.c4.xlarge'.
            entrypoint (list[str]): The entrypoint for the processing job (default: None).
                This is in the form of a list of strings that make a command.
            volume_size_in_gb (int): Size in GB of the EBS volume
                to use for storing data during processing (default: 30).
            volume_kms_key (str): A KMS key for the processing
                volume (default: None).
            output_kms_key (str): The KMS key ID for processing job outputs (default: None).
            max_runtime_in_seconds (int): Timeout in seconds (default: None).
                After this amount of time, Amazon SageMaker terminates the job,
                regardless of its current status. If `max_runtime_in_seconds` is not
                specified, the default value is 24 hours.
            base_job_name (str): Prefix for processing job name. If not specified,
                the processor generates a default job name, based on the
                processing image name and current timestamp.
            sagemaker_session (:class:`~sagemaker.session.Session`):
                Session object which manages interactions with Amazon SageMaker and
                any other AWS services needed. If not specified, the processor creates
                one using the default AWS configuration chain.
            env (dict[str, str]): Environment variables to be passed to
                the processing jobs (default: None).
            tags (list[dict]): List of tags to be passed to the processing job
                (default: None). For more, see
                https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
            network_config (:class:`~sagemaker.network.NetworkConfig`):
                A :class:`~sagemaker.network.NetworkConfig`
                object that configures network isolation, encryption of
                inter-container traffic, security group IDs, and subnets.
        """
        self.role = role
        self.image_uri = image_uri
        self.instance_count = instance_count
        self.instance_type = instance_type
        self.entrypoint = entrypoint
        self.volume_size_in_gb = volume_size_in_gb
        self.volume_kms_key = volume_kms_key
        self.output_kms_key = output_kms_key
        self.max_runtime_in_seconds = max_runtime_in_seconds
        self.base_job_name = base_job_name
        self.sagemaker_session = sagemaker_session or Session()
        self.env = env
        self.tags = tags
        self.network_config = network_config

        self.jobs = []
        self.latest_job = None
        self._current_job_name = None
        self.arguments = None

    def run(
        self,
        inputs=None,
        outputs=None,
        arguments=None,
        wait=True,
        logs=True,
        job_name=None,
        experiment_config=None,
    ):
        """Runs a processing job.

        Args:
            inputs (list[:class:`~sagemaker.processing.ProcessingInput`]): Input files for
                the processing job. These must be provided as
                :class:`~sagemaker.processing.ProcessingInput` objects (default: None).
            outputs (list[:class:`~sagemaker.processing.ProcessingOutput`]): Outputs for
                the processing job. These can be specified as either path strings or
                :class:`~sagemaker.processing.ProcessingOutput` objects (default: None).
            arguments (list[str]): A list of string arguments to be passed to a
                processing job (default: None).
            wait (bool): Whether the call should wait until the job completes (default: True).
            logs (bool): Whether to show the logs produced by the job.
                Only meaningful when ``wait`` is True (default: True).
            job_name (str): Processing job name. If not specified, the processor generates
                a default job name, based on the base job name and current timestamp.
            experiment_config (dict[str, str]): Experiment management configuration.
                Dictionary contains three optional keys:
                'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

        Raises:
            ValueError: if ``logs`` is True but ``wait`` is False.
        """
        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._current_job_name = self._generate_current_job_name(job_name=job_name)

        normalized_inputs = self._normalize_inputs(inputs)
        normalized_outputs = self._normalize_outputs(outputs)
        self.arguments = arguments

        self.latest_job = ProcessingJob.start_new(
            processor=self,
            inputs=normalized_inputs,
            outputs=normalized_outputs,
            experiment_config=experiment_config,
        )
        self.jobs.append(self.latest_job)
        if wait:
            self.latest_job.wait(logs=logs)

    def _generate_current_job_name(self, job_name=None):
        """Generates the job name before running a processing job.

        Args:
            job_name (str): Name of the processing job to be created. If not
                specified, one is generated, using the base name given to the
                constructor if applicable.

        Returns:
            str: The supplied or generated job name.
        """
        if job_name is not None:
            return job_name
        # Honor supplied base_job_name or generate it.
        if self.base_job_name:
            base_name = self.base_job_name
        else:
            base_name = base_name_from_image(self.image_uri)

        return name_from_base(base_name)

    def _normalize_inputs(self, inputs=None):
        """Ensures that all the ``ProcessingInput`` objects have names and S3 URIs.

        Args:
            inputs (list[sagemaker.processing.ProcessingInput]): A list of ``ProcessingInput``
                objects to be normalized (default: None). If not specified,
                an empty list is returned.

        Returns:
            list[sagemaker.processing.ProcessingInput]: The list of normalized
                ``ProcessingInput`` objects.

        Raises:
            TypeError: if the inputs are not ``ProcessingInput`` objects.
        """
        # Initialize a list of normalized ProcessingInput objects.
        normalized_inputs = []
        if inputs is not None:
            # Iterate through the provided list of inputs.
            for count, file_input in enumerate(inputs, 1):
                if not isinstance(file_input, ProcessingInput):
                    raise TypeError("Your inputs must be provided as ProcessingInput objects.")
                # Generate a name for the ProcessingInput if it doesn't have one.
                if file_input.input_name is None:
                    file_input.input_name = "input-{}".format(count)
                # If the source is a local path, upload it to S3
                # and save the S3 uri in the ProcessingInput source.
                parse_result = urlparse(file_input.source)
                if parse_result.scheme != "s3":
                    desired_s3_uri = "s3://{}/{}/input/{}".format(
                        self.sagemaker_session.default_bucket(),
                        self._current_job_name,
                        file_input.input_name,
                    )
                    s3_uri = S3Uploader.upload(
                        local_path=file_input.source,
                        desired_s3_uri=desired_s3_uri,
                        session=self.sagemaker_session,
                    )
                    file_input.source = s3_uri
                normalized_inputs.append(file_input)
        return normalized_inputs

    def _normalize_outputs(self, outputs=None):
        """Ensures that all the outputs are ``ProcessingOutput`` objects with
        names and S3 URIs.

        Args:
            outputs (list[sagemaker.processing.ProcessingOutput]): A list
                of outputs to be normalized (default: None). Can be either strings or
                ``ProcessingOutput`` objects. If not specified,
                an empty list is returned.

        Returns:
            list[sagemaker.processing.ProcessingOutput]: The list of normalized
                ``ProcessingOutput`` objects.

        Raises:
            TypeError: if the outputs are not ``ProcessingOutput`` objects.
        """
        # Initialize a list of normalized ProcessingOutput objects.
        normalized_outputs = []
        if outputs is not None:
            # Iterate through the provided list of outputs.
            for count, output in enumerate(outputs, 1):
                if not isinstance(output, ProcessingOutput):
                    raise TypeError("Your outputs must be provided as ProcessingOutput objects.")
                # Generate a name for the ProcessingOutput if it doesn't have one.
                if output.output_name is None:
                    output.output_name = "output-{}".format(count)
                # If the output's destination is not an s3_uri, create one.
                parse_result = urlparse(output.destination)
                if parse_result.scheme != "s3":
                    s3_uri = "s3://{}/{}/output/{}".format(
                        self.sagemaker_session.default_bucket(),
                        self._current_job_name,
                        output.output_name,
                    )
                    output.destination = s3_uri
                normalized_outputs.append(output)
        return normalized_outputs


class ScriptProcessor(Processor):
    """Handles Amazon SageMaker processing tasks for jobs using a machine learning framework."""

    def __init__(
        self,
        role,
        image_uri,
        command,
        instance_count,
        instance_type,
        volume_size_in_gb=30,
        volume_kms_key=None,
        output_kms_key=None,
        max_runtime_in_seconds=None,
        base_job_name=None,
        sagemaker_session=None,
        env=None,
        tags=None,
        network_config=None,
    ):
        """Initializes a ``ScriptProcessor`` instance. The ``ScriptProcessor``
        handles Amazon SageMaker Processing tasks for jobs using a machine learning framework,
        which allows for providing a script to be run as part of the Processing Job.

        Args:
            role (str): An AWS IAM role name or ARN. Amazon SageMaker Processing
                uses this role to access AWS resources, such as
                data stored in Amazon S3.
            image_uri (str): The URI of the Docker image to use for the
                processing jobs.
            command ([str]): The command to run, along with any command-line flags.
                Example: ["python3", "-v"].
            instance_count (int): The number of instances to run
                a processing job with.
            instance_type (str): The type of EC2 instance to use for
                processing, for example, 'ml.c4.xlarge'.
            volume_size_in_gb (int): Size in GB of the EBS volume
                to use for storing data during processing (default: 30).
            volume_kms_key (str): A KMS key for the processing
                volume (default: None).
            output_kms_key (str): The KMS key ID for processing job outputs (default: None).
            max_runtime_in_seconds (int): Timeout in seconds (default: None).
                After this amount of time, Amazon SageMaker terminates the job,
                regardless of its current status. If `max_runtime_in_seconds` is not
                specified, the default value is 24 hours.
            base_job_name (str): Prefix for processing name. If not specified,
                the processor generates a default job name, based on the
                processing image name and current timestamp.
            sagemaker_session (:class:`~sagemaker.session.Session`):
                Session object which manages interactions with Amazon SageMaker and
                any other AWS services needed. If not specified, the processor creates
                one using the default AWS configuration chain.
            env (dict[str, str]): Environment variables to be passed to
                the processing jobs (default: None).
            tags (list[dict]): List of tags to be passed to the processing job
                (default: None). For more, see
                https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
            network_config (:class:`~sagemaker.network.NetworkConfig`):
                A :class:`~sagemaker.network.NetworkConfig`
                object that configures network isolation, encryption of
                inter-container traffic, security group IDs, and subnets.
        """
        self._CODE_CONTAINER_BASE_PATH = "/opt/ml/processing/input/"
        self._CODE_CONTAINER_INPUT_NAME = "code"
        self.command = command

        super(ScriptProcessor, self).__init__(
            role=role,
            image_uri=image_uri,
            instance_count=instance_count,
            instance_type=instance_type,
            volume_size_in_gb=volume_size_in_gb,
            volume_kms_key=volume_kms_key,
            output_kms_key=output_kms_key,
            max_runtime_in_seconds=max_runtime_in_seconds,
            base_job_name=base_job_name,
            sagemaker_session=sagemaker_session,
            env=env,
            tags=tags,
            network_config=network_config,
        )

    def run(
        self,
        code,
        inputs=None,
        outputs=None,
        arguments=None,
        wait=True,
        logs=True,
        job_name=None,
        experiment_config=None,
    ):
        """Runs a processing job.

        Args:
            code (str): This can be an S3 URI or a local path to
                a file with the framework script to run.
            inputs (list[:class:`~sagemaker.processing.ProcessingInput`]): Input files for
                the processing job. These must be provided as
                :class:`~sagemaker.processing.ProcessingInput` objects (default: None).
            outputs (list[:class:`~sagemaker.processing.ProcessingOutput`]): Outputs for
                the processing job. These can be specified as either path strings or
                :class:`~sagemaker.processing.ProcessingOutput` objects (default: None).
            arguments (list[str]): A list of string arguments to be passed to a
                processing job (default: None).
            wait (bool): Whether the call should wait until the job completes (default: True).
            logs (bool): Whether to show the logs produced by the job.
                Only meaningful when wait is True (default: True).
            job_name (str): Processing job name. If not specified, the processor generates
                a default job name, based on the base job name and current timestamp.
            experiment_config (dict[str, str]): Experiment management configuration.
                Dictionary contains three optional keys:
                'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.
        """
        self._current_job_name = self._generate_current_job_name(job_name=job_name)

        user_code_s3_uri = self._handle_user_code_url(code)
        user_script_name = self._get_user_code_name(code)

        inputs_with_code = self._convert_code_and_add_to_inputs(inputs, user_code_s3_uri)

        self._set_entrypoint(self.command, user_script_name)

        normalized_inputs = self._normalize_inputs(inputs_with_code)
        normalized_outputs = self._normalize_outputs(outputs)
        self.arguments = arguments

        self.latest_job = ProcessingJob.start_new(
            processor=self,
            inputs=normalized_inputs,
            outputs=normalized_outputs,
            experiment_config=experiment_config,
        )
        self.jobs.append(self.latest_job)
        if wait:
            self.latest_job.wait(logs=logs)

    def _get_user_code_name(self, code):
        """Gets the basename of the user's code from the URL the customer provided.

        Args:
            code (str): A URL to the user's code.

        Returns:
            str: The basename of the user's code.

        """
        code_url = urlparse(code)
        return os.path.basename(code_url.path)

    def _handle_user_code_url(self, code):
        """Gets the S3 URL containing the user's code.

           Inspects the scheme the customer passed in ("s3://" for code in S3, "file://" or nothing
           for absolute or local file paths. Uploads the code to S3 if the code is a local file.

        Args:
            code (str): A URL to the customer's code.

        Returns:
            str: The S3 URL to the customer's code.

        Raises:
            ValueError: if the code isn't found, is a directory, or
                does not have a valid URL scheme.
        """
        code_url = urlparse(code)
        if code_url.scheme == "s3":
            user_code_s3_uri = code
        elif code_url.scheme == "" or code_url.scheme == "file":
            # Validate that the file exists locally and is not a directory.
            if not os.path.exists(code):
                raise ValueError(
                    """code {} wasn't found. Please make sure that the file exists.
                    """.format(
                        code
                    )
                )
            if not os.path.isfile(code):
                raise ValueError(
                    """code {} must be a file, not a directory. Please pass a path to a file.
                    """.format(
                        code
                    )
                )
            user_code_s3_uri = self._upload_code(code)
        else:
            raise ValueError(
                "code {} url scheme {} is not recognized. Please pass a file path or S3 url".format(
                    code, code_url.scheme
                )
            )
        return user_code_s3_uri

    def _upload_code(self, code):
        """Uploads a code file or directory specified as a string
        and returns the S3 URI.

        Args:
            code (str): A file or directory to be uploaded to S3.

        Returns:
            str: The S3 URI of the uploaded file or directory.

        """
        desired_s3_uri = "s3://{}/{}/input/{}".format(
            self.sagemaker_session.default_bucket(),
            self._current_job_name,
            self._CODE_CONTAINER_INPUT_NAME,
        )
        return S3Uploader.upload(
            local_path=code, desired_s3_uri=desired_s3_uri, session=self.sagemaker_session
        )

    def _convert_code_and_add_to_inputs(self, inputs, s3_uri):
        """Creates a ``ProcessingInput`` object from an S3 URI and adds it to the list of inputs.

        Args:
            inputs (list[sagemaker.processing.ProcessingInput]):
                List of ``ProcessingInput`` objects.
            s3_uri (str): S3 URI of the input to be added to inputs.

        Returns:
            list[sagemaker.processing.ProcessingInput]: A new list of ``ProcessingInput`` objects,
                with the ``ProcessingInput`` object created from ``s3_uri`` appended to the list.

        """
        code_file_input = ProcessingInput(
            source=s3_uri,
            destination="{}{}".format(
                self._CODE_CONTAINER_BASE_PATH, self._CODE_CONTAINER_INPUT_NAME
            ),
            input_name=self._CODE_CONTAINER_INPUT_NAME,
        )
        return (inputs or []) + [code_file_input]

    def _set_entrypoint(self, command, user_script_name):
        """Sets the entrypoint based on the user's script and corresponding executable.

        Args:
            user_script_name (str): A filename with an extension.
        """
        user_script_location = "{}{}/{}".format(
            self._CODE_CONTAINER_BASE_PATH, self._CODE_CONTAINER_INPUT_NAME, user_script_name
        )
        self.entrypoint = command + [user_script_location]


class ProcessingJob(_Job):
    """Provides functionality to start, describe, and stop processing jobs."""

    def __init__(self, sagemaker_session, job_name, inputs, outputs, output_kms_key=None):
        """Initializes a Processing job.

        Args:
            sagemaker_session (:class:`~sagemaker.session.Session`):
                Session object which manages interactions with Amazon SageMaker and
                any other AWS services needed. If not specified, the processor creates
                one using the default AWS configuration chain.
            job_name (str): Name of the Processing job.
            inputs (list[:class:`~sagemaker.processing.ProcessingInput`]): A list of
                :class:`~sagemaker.processing.ProcessingInput` objects.
            outputs (list[:class:`~sagemaker.processing.ProcessingOutput`]): A list of
                :class:`~sagemaker.processing.ProcessingOutput` objects.
            output_kms_key (str): The output KMS key associated with the job (default: None).
        """
        self.inputs = inputs
        self.outputs = outputs
        self.output_kms_key = output_kms_key
        super(ProcessingJob, self).__init__(sagemaker_session=sagemaker_session, job_name=job_name)

    @classmethod
    def start_new(cls, processor, inputs, outputs, experiment_config):
        """Starts a new processing job using the provided inputs and outputs.

        Args:
            processor (:class:`~sagemaker.processing.Processor`): The ``Processor`` instance
                that started the job.
            inputs (list[:class:`~sagemaker.processing.ProcessingInput`]): A list of
                :class:`~sagemaker.processing.ProcessingInput` objects.
            outputs (list[:class:`~sagemaker.processing.ProcessingOutput`]): A list of
                :class:`~sagemaker.processing.ProcessingOutput` objects.
            experiment_config (dict[str, str]): Experiment management configuration.
                Dictionary contains three optional keys:
                'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

        Returns:
            :class:`~sagemaker.processing.ProcessingJob`: The instance of ``ProcessingJob`` created
                using the ``Processor``.
        """
        # Initialize an empty dictionary for arguments to be passed to sagemaker_session.process.
        process_request_args = {}

        # Add arguments to the dictionary.
        process_request_args["inputs"] = [input._to_request_dict() for input in inputs]

        process_request_args["output_config"] = {
            "Outputs": [output._to_request_dict() for output in outputs]
        }
        if processor.output_kms_key is not None:
            process_request_args["output_config"]["KmsKeyId"] = processor.output_kms_key

        process_request_args["experiment_config"] = experiment_config
        process_request_args["job_name"] = processor._current_job_name

        process_request_args["resources"] = {
            "ClusterConfig": {
                "InstanceType": processor.instance_type,
                "InstanceCount": processor.instance_count,
                "VolumeSizeInGB": processor.volume_size_in_gb,
            }
        }

        if processor.volume_kms_key is not None:
            process_request_args["resources"]["ClusterConfig"][
                "VolumeKmsKeyId"
            ] = processor.volume_kms_key

        if processor.max_runtime_in_seconds is not None:
            process_request_args["stopping_condition"] = {
                "MaxRuntimeInSeconds": processor.max_runtime_in_seconds
            }
        else:
            process_request_args["stopping_condition"] = None

        process_request_args["app_specification"] = {"ImageUri": processor.image_uri}
        if processor.arguments is not None:
            process_request_args["app_specification"]["ContainerArguments"] = processor.arguments
        if processor.entrypoint is not None:
            process_request_args["app_specification"]["ContainerEntrypoint"] = processor.entrypoint

        process_request_args["environment"] = processor.env

        if processor.network_config is not None:
            process_request_args["network_config"] = processor.network_config._to_request_dict()
        else:
            process_request_args["network_config"] = None

        process_request_args["role_arn"] = processor.sagemaker_session.expand_role(processor.role)

        process_request_args["tags"] = processor.tags

        # Print the job name and the user's inputs and outputs as lists of dictionaries.
        print()
        print("Job Name: ", process_request_args["job_name"])
        print("Inputs: ", process_request_args["inputs"])
        print("Outputs: ", process_request_args["output_config"]["Outputs"])

        # Call sagemaker_session.process using the arguments dictionary.
        processor.sagemaker_session.process(**process_request_args)

        return cls(
            processor.sagemaker_session,
            processor._current_job_name,
            inputs,
            outputs,
            processor.output_kms_key,
        )

    @classmethod
    def from_processing_name(cls, sagemaker_session, processing_job_name):
        """Initializes a ``ProcessingJob`` from a processing job name.

        Args:
            processing_job_name (str): Name of the processing job.
            sagemaker_session (:class:`~sagemaker.session.Session`):
                Session object which manages interactions with Amazon SageMaker and
                any other AWS services needed. If not specified, the processor creates
                one using the default AWS configuration chain.

        Returns:
            :class:`~sagemaker.processing.ProcessingJob`: The instance of ``ProcessingJob`` created
                from the job name.
        """
        job_desc = sagemaker_session.describe_processing_job(job_name=processing_job_name)

        inputs = None
        if job_desc.get("ProcessingInputs"):
            inputs = [
                ProcessingInput(
                    source=processing_input["S3Input"]["S3Uri"],
                    destination=processing_input["S3Input"]["LocalPath"],
                    input_name=processing_input["InputName"],
                    s3_data_type=processing_input["S3Input"].get("S3DataType"),
                    s3_input_mode=processing_input["S3Input"].get("S3InputMode"),
                    s3_data_distribution_type=processing_input["S3Input"].get(
                        "S3DataDistributionType"
                    ),
                    s3_compression_type=processing_input["S3Input"].get("S3CompressionType"),
                )
                for processing_input in job_desc["ProcessingInputs"]
            ]

        outputs = None
        if job_desc.get("ProcessingOutputConfig") and job_desc["ProcessingOutputConfig"].get(
            "Outputs"
        ):
            outputs = [
                ProcessingOutput(
                    source=processing_output["S3Output"]["LocalPath"],
                    destination=processing_output["S3Output"]["S3Uri"],
                    output_name=processing_output["OutputName"],
                )
                for processing_output in job_desc["ProcessingOutputConfig"]["Outputs"]
            ]

        output_kms_key = None
        if job_desc.get("ProcessingOutputConfig"):
            output_kms_key = job_desc["ProcessingOutputConfig"].get("KmsKeyId")

        return cls(
            sagemaker_session=sagemaker_session,
            job_name=processing_job_name,
            inputs=inputs,
            outputs=outputs,
            output_kms_key=output_kms_key,
        )

    @classmethod
    def from_processing_arn(cls, sagemaker_session, processing_job_arn):
        """Initializes a ``ProcessingJob`` from a Processing ARN.

        Args:
            processing_job_arn (str): ARN of the processing job.
            sagemaker_session (:class:`~sagemaker.session.Session`):
                Session object which manages interactions with Amazon SageMaker and
                any other AWS services needed. If not specified, the processor creates
                one using the default AWS configuration chain.

        Returns:
            :class:`~sagemaker.processing.ProcessingJob`: The instance of ``ProcessingJob`` created
                from the processing job's ARN.
        """
        processing_job_name = processing_job_arn.split(":")[5][
            len("processing-job/") :
        ]  # This is necessary while the API only vends an arn.
        return cls.from_processing_name(
            sagemaker_session=sagemaker_session, processing_job_name=processing_job_name
        )

    def _is_local_channel(self, input_url):
        """Used for Local Mode. Not yet implemented.

        Args:
            input_url (str): input URL

        Raises:
            NotImplementedError: this method is not yet implemented.
        """
        raise NotImplementedError

    def wait(self, logs=True):
        """Waits for the processing job to complete.

        Args:
            logs (bool): Whether to show the logs produced by the job (default: True).

        """
        if logs:
            self.sagemaker_session.logs_for_processing_job(self.job_name, wait=True)
        else:
            self.sagemaker_session.wait_for_processing_job(self.job_name)

    def describe(self):
        """Prints out a response from the DescribeProcessingJob API call."""
        return self.sagemaker_session.describe_processing_job(self.job_name)

    def stop(self):
        """Stops the processing job."""
        self.sagemaker_session.stop_processing_job(self.name)


class ProcessingInput(object):
    """Accepts parameters that specify an Amazon S3 input for a processing job and
    provides a method to turn those parameters into a dictionary."""

    def __init__(
        self,
        source,
        destination,
        input_name=None,
        s3_data_type="S3Prefix",
        s3_input_mode="File",
        s3_data_distribution_type="FullyReplicated",
        s3_compression_type="None",
    ):
        """Initializes a ``ProcessingInput`` instance. ``ProcessingInput`` accepts parameters
        that specify an Amazon S3 input for a processing job and provides a method
        to turn those parameters into a dictionary.

        Args:
            source (str): The source for the input. If a local path is provided, it will
                automatically be uploaded to S3 under:
                "s3://<default-bucket-name>/<job-name>/input/<input-name>".
            destination (str): The destination of the input.
            input_name (str): The name for the input. If a name
                is not provided, one will be generated (eg. "input-1").
            s3_data_type (str): Valid options are "ManifestFile" or "S3Prefix".
            s3_input_mode (str): Valid options are "Pipe" or "File".
            s3_data_distribution_type (str): Valid options are "FullyReplicated"
                or "ShardedByS3Key".
            s3_compression_type (str): Valid options are "None" or "Gzip".
        """
        self.source = source
        self.destination = destination
        self.input_name = input_name
        self.s3_data_type = s3_data_type
        self.s3_input_mode = s3_input_mode
        self.s3_data_distribution_type = s3_data_distribution_type
        self.s3_compression_type = s3_compression_type

    def _to_request_dict(self):
        """Generates a request dictionary using the parameters provided to the class."""
        # Create the request dictionary.
        s3_input_request = {
            "InputName": self.input_name,
            "S3Input": {
                "S3Uri": self.source,
                "LocalPath": self.destination,
                "S3DataType": self.s3_data_type,
                "S3InputMode": self.s3_input_mode,
                "S3DataDistributionType": self.s3_data_distribution_type,
            },
        }

        # Check the compression type, then add it to the dictionary.
        if self.s3_compression_type == "Gzip" and self.s3_input_mode != "Pipe":
            raise ValueError("Data can only be gzipped when the input mode is Pipe.")
        if self.s3_compression_type is not None:
            s3_input_request["S3Input"]["S3CompressionType"] = self.s3_compression_type

        # Return the request dictionary.
        return s3_input_request


class ProcessingOutput(object):
    """Accepts parameters that specify an Amazon S3 output for a processing job and provides
    a method to turn those parameters into a dictionary."""

    def __init__(self, source, destination=None, output_name=None, s3_upload_mode="EndOfJob"):
        """Initializes a ``ProcessingOutput`` instance. ``ProcessingOutput`` accepts parameters that
        specify an Amazon S3 output for a processing job and provides a method to turn
        those parameters into a dictionary.

        Args:
            source (str): The source for the output.
            destination (str): The destination of the output. If a destination
                is not provided, one will be generated:
                "s3://<default-bucket-name>/<job-name>/output/<output-name>".
            output_name (str): The name of the output. If a name
                is not provided, one will be generated (eg. "output-1").
            s3_upload_mode (str): Valid options are "EndOfJob" or "Continuous".
        """
        self.source = source
        self.destination = destination
        self.output_name = output_name
        self.s3_upload_mode = s3_upload_mode

    def _to_request_dict(self):
        """Generates a request dictionary using the parameters provided to the class."""
        # Create the request dictionary.
        s3_output_request = {
            "OutputName": self.output_name,
            "S3Output": {
                "S3Uri": self.destination,
                "LocalPath": self.source,
                "S3UploadMode": self.s3_upload_mode,
            },
        }

        # Return the request dictionary.
        return s3_output_request