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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.
"""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 absolute_import
import os
import pathlib
import logging
from textwrap import dedent
from typing import Dict, List, Optional, Union
from copy import copy
import attr
from six.moves.urllib.parse import urlparse
from six.moves.urllib.request import url2pathname
from sagemaker import s3
from sagemaker.config import (
PROCESSING_JOB_KMS_KEY_ID_PATH,
PROCESSING_JOB_SECURITY_GROUP_IDS_PATH,
PROCESSING_JOB_SUBNETS_PATH,
PROCESSING_JOB_ENABLE_NETWORK_ISOLATION_PATH,
PROCESSING_JOB_VOLUME_KMS_KEY_ID_PATH,
PROCESSING_JOB_ROLE_ARN_PATH,
PROCESSING_JOB_INTER_CONTAINER_ENCRYPTION_PATH,
)
from sagemaker.job import _Job
from sagemaker.local import LocalSession
from sagemaker.network import NetworkConfig
from sagemaker.utils import (
base_name_from_image,
get_config_value,
name_from_base,
check_and_get_run_experiment_config,
resolve_value_from_config,
resolve_class_attribute_from_config,
)
from sagemaker.session import Session
from sagemaker.workflow import is_pipeline_variable
from sagemaker.workflow.functions import Join
from sagemaker.workflow.pipeline_context import runnable_by_pipeline
from sagemaker.workflow.execution_variables import ExecutionVariables
from sagemaker.workflow.entities import PipelineVariable
from sagemaker.dataset_definition.inputs import S3Input, DatasetDefinition
from sagemaker.apiutils._base_types import ApiObject
from sagemaker.s3 import S3Uploader
logger = logging.getLogger(__name__)
class Processor(object):
"""Handles Amazon SageMaker Processing tasks."""
JOB_CLASS_NAME = "processing-job"
def __init__(
self,
role: str = None,
image_uri: Union[str, PipelineVariable] = None,
instance_count: Union[int, PipelineVariable] = None,
instance_type: Union[str, PipelineVariable] = None,
entrypoint: Optional[List[Union[str, PipelineVariable]]] = None,
volume_size_in_gb: Union[int, PipelineVariable] = 30,
volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
output_kms_key: Optional[Union[str, PipelineVariable]] = None,
max_runtime_in_seconds: Optional[Union[int, PipelineVariable]] = None,
base_job_name: Optional[str] = None,
sagemaker_session: Optional[Session] = None,
env: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
tags: Optional[List[Dict[str, Union[str, PipelineVariable]]]] = None,
network_config: Optional[NetworkConfig] = None,
):
"""Initializes a ``Processor`` instance.
The ``Processor`` handles Amazon SageMaker Processing tasks.
Args:
role (str or PipelineVariable): 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 or PipelineVariable): The URI of the Docker image to use for the
processing jobs.
instance_count (int or PipelineVariable): The number of instances to run
a processing job with.
instance_type (str or PipelineVariable): The type of EC2 instance to use for
processing, for example, 'ml.c4.xlarge'.
entrypoint (list[str] or list[PipelineVariable]): 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 or PipelineVariable): Size in GB of the EBS volume
to use for storing data during processing (default: 30).
volume_kms_key (str or PipelineVariable): A KMS key for the processing
volume (default: None).
output_kms_key (str or PipelineVariable): The KMS key ID for processing job
outputs (default: None).
max_runtime_in_seconds (int or PipelineVariable): 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] or dict[str, PipelineVariable]): Environment variables
to be passed to the processing jobs (default: None).
tags (list[dict[str, str] or list[dict[str, PipelineVariable]]): 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.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.max_runtime_in_seconds = max_runtime_in_seconds
self.base_job_name = base_job_name
self.env = env
self.tags = tags
self.jobs = []
self.latest_job = None
self._current_job_name = None
self.arguments = None
if self.instance_type in ("local", "local_gpu"):
if not isinstance(sagemaker_session, LocalSession):
# Until Local Mode Processing supports local code, we need to disable it:
sagemaker_session = LocalSession(disable_local_code=True)
self.sagemaker_session = sagemaker_session or Session()
self.output_kms_key = resolve_value_from_config(
output_kms_key, PROCESSING_JOB_KMS_KEY_ID_PATH, sagemaker_session=self.sagemaker_session
)
self.volume_kms_key = resolve_value_from_config(
volume_kms_key,
PROCESSING_JOB_VOLUME_KMS_KEY_ID_PATH,
sagemaker_session=self.sagemaker_session,
)
self.network_config = resolve_class_attribute_from_config(
NetworkConfig,
network_config,
"subnets",
PROCESSING_JOB_SUBNETS_PATH,
sagemaker_session=self.sagemaker_session,
)
self.network_config = resolve_class_attribute_from_config(
NetworkConfig,
self.network_config,
"security_group_ids",
PROCESSING_JOB_SECURITY_GROUP_IDS_PATH,
sagemaker_session=self.sagemaker_session,
)
self.network_config = resolve_class_attribute_from_config(
NetworkConfig,
self.network_config,
"enable_network_isolation",
PROCESSING_JOB_ENABLE_NETWORK_ISOLATION_PATH,
sagemaker_session=self.sagemaker_session,
)
self.network_config = resolve_class_attribute_from_config(
NetworkConfig,
self.network_config,
"encrypt_inter_container_traffic",
PROCESSING_JOB_INTER_CONTAINER_ENCRYPTION_PATH,
sagemaker_session=self.sagemaker_session,
)
self.role = resolve_value_from_config(
role, PROCESSING_JOB_ROLE_ARN_PATH, sagemaker_session=self.sagemaker_session
)
if not self.role:
# Originally IAM role was a required parameter.
# Now we marked that as Optional because we can fetch it from SageMakerConfig
# Because of marking that parameter as optional, we should validate if it is None, even
# after fetching the config.
raise ValueError("An AWS IAM role is required to create a Processing job.")
@runnable_by_pipeline
def run(
self,
inputs: Optional[List["ProcessingInput"]] = None,
outputs: Optional[List["ProcessingOutput"]] = None,
arguments: Optional[List[Union[str, PipelineVariable]]] = None,
wait: bool = True,
logs: bool = True,
job_name: Optional[str] = None,
experiment_config: Optional[Dict[str, str]] = None,
kms_key: Optional[str] = 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] or list[PipelineVariable]): 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.
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.
* Both `ExperimentName` and `TrialName` will be ignored if the Processor instance
is built with :class:`~sagemaker.workflow.pipeline_context.PipelineSession`.
However, the value of `TrialComponentDisplayName` is honored for display in Studio.
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
Returns:
None or pipeline step arguments in case the Processor instance is built with
:class:`~sagemaker.workflow.pipeline_context.PipelineSession`
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."""
)
normalized_inputs, normalized_outputs = self._normalize_args(
job_name=job_name,
arguments=arguments,
inputs=inputs,
kms_key=kms_key,
outputs=outputs,
)
experiment_config = check_and_get_run_experiment_config(experiment_config)
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 _extend_processing_args(self, inputs, outputs, **kwargs): # pylint: disable=W0613
"""Extend inputs and outputs based on extra parameters"""
return inputs, outputs
def _normalize_args(
self,
job_name=None,
arguments=None,
inputs=None,
outputs=None,
code=None,
kms_key=None,
):
"""Normalizes the arguments so that they can be passed to the job run
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
(default: None).
arguments (list[str]): A list of string arguments to be passed to a
processing job (default: None).
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).
code (str): This can be an S3 URI or a local path to a file with the framework
script to run (default: None). A no op in the base class.
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
"""
if code and is_pipeline_variable(code):
raise ValueError(
"code argument has to be a valid S3 URI or local file path "
+ "rather than a pipeline variable"
)
self._current_job_name = self._generate_current_job_name(job_name=job_name)
inputs_with_code = self._include_code_in_inputs(inputs, code, kms_key)
normalized_inputs = self._normalize_inputs(inputs_with_code, kms_key)
normalized_outputs = self._normalize_outputs(outputs)
self.arguments = arguments
return normalized_inputs, normalized_outputs
def _include_code_in_inputs(self, inputs, _code, _kms_key):
"""A no op in the base class to include code in the processing job inputs.
Args:
inputs (list[:class:`~sagemaker.processing.ProcessingInput`]): Input files for
the processing job. These must be provided as
:class:`~sagemaker.processing.ProcessingInput` objects.
_code (str): This can be an S3 URI or a local path to a file with the framework
script to run (default: None). A no op in the base class.
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
Returns:
list[:class:`~sagemaker.processing.ProcessingInput`]: inputs
"""
return inputs
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, default_base_name=Processor.JOB_CLASS_NAME
)
return name_from_base(base_name)
def _normalize_inputs(self, inputs=None, kms_key=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.
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
Returns:
list[sagemaker.processing.ProcessingInput]: The list of normalized
``ProcessingInput`` objects.
Raises:
TypeError: if the inputs are not ``ProcessingInput`` objects.
"""
from sagemaker.workflow.utilities import _pipeline_config
# 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 is_pipeline_variable(file_input.source) or file_input.dataset_definition:
normalized_inputs.append(file_input)
continue
if is_pipeline_variable(file_input.s3_input.s3_uri):
normalized_inputs.append(file_input)
continue
# 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.s3_input.s3_uri)
if parse_result.scheme != "s3":
if _pipeline_config:
desired_s3_uri = s3.s3_path_join(
"s3://",
self.sagemaker_session.default_bucket(),
_pipeline_config.pipeline_name,
_pipeline_config.step_name,
"input",
file_input.input_name,
)
else:
desired_s3_uri = s3.s3_path_join(
"s3://",
self.sagemaker_session.default_bucket(),
self._current_job_name,
"input",
file_input.input_name,
)
s3_uri = s3.S3Uploader.upload(
local_path=file_input.s3_input.s3_uri,
desired_s3_uri=desired_s3_uri,
sagemaker_session=self.sagemaker_session,
kms_key=kms_key,
)
file_input.s3_input.s3_uri = 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.
from sagemaker.workflow.utilities import _pipeline_config
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 is_pipeline_variable(output.destination):
normalized_outputs.append(output)
continue
# If the output's destination is not an s3_uri, create one.
parse_result = urlparse(output.destination)
if parse_result.scheme != "s3":
if _pipeline_config:
s3_uri = Join(
on="/",
values=[
"s3:/",
self.sagemaker_session.default_bucket(),
_pipeline_config.pipeline_name,
ExecutionVariables.PIPELINE_EXECUTION_ID,
_pipeline_config.step_name,
"output",
output.output_name,
],
)
else:
s3_uri = s3.s3_path_join(
"s3://",
self.sagemaker_session.default_bucket(),
self._current_job_name,
"output",
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: Optional[Union[str, PipelineVariable]] = None,
image_uri: Union[str, PipelineVariable] = None,
command: List[str] = None,
instance_count: Union[int, PipelineVariable] = None,
instance_type: Union[str, PipelineVariable] = None,
volume_size_in_gb: Union[int, PipelineVariable] = 30,
volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
output_kms_key: Optional[Union[str, PipelineVariable]] = None,
max_runtime_in_seconds: Optional[Union[int, PipelineVariable]] = None,
base_job_name: Optional[str] = None,
sagemaker_session: Optional[Session] = None,
env: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
tags: Optional[List[Dict[str, Union[str, PipelineVariable]]]] = None,
network_config: Optional[NetworkConfig] = 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 or PipelineVariable): 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 or PipelineVariable): 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 or PipelineVariable): The number of instances to run
a processing job with.
instance_type (str or PipelineVariable): The type of EC2 instance to use for
processing, for example, 'ml.c4.xlarge'.
volume_size_in_gb (int or PipelineVariable): Size in GB of the EBS volume
to use for storing data during processing (default: 30).
volume_kms_key (str or PipelineVariable): A KMS key for the processing
volume (default: None).
output_kms_key (str or PipelineVariable): The KMS key ID for processing
job outputs (default: None).
max_runtime_in_seconds (int or PipelineVariable): 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] or dict[str, PipelineVariable])): Environment variables to
be passed to the processing jobs (default: None).
tags (list[dict[str, str] or list[dict[str, PipelineVariable]]): 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 get_run_args(
self,
code,
inputs=None,
outputs=None,
arguments=None,
):
"""Returns a RunArgs object.
For processors (:class:`~sagemaker.spark.processing.PySparkProcessor`,
:class:`~sagemaker.spark.processing.SparkJar`) that have special
run() arguments, this object contains the normalized arguments for passing to
:class:`~sagemaker.workflow.steps.ProcessingStep`.
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).
"""
logger.warning(
"This function has been deprecated and could break pipeline step caching. "
"We recommend using the run() function directly with pipeline sessions"
"to access step arguments."
)
return RunArgs(code=code, inputs=inputs, outputs=outputs, arguments=arguments)
@runnable_by_pipeline
def run(
self,
code: str,
inputs: Optional[List["ProcessingInput"]] = None,
outputs: Optional[List["ProcessingOutput"]] = None,
arguments: Optional[List[Union[str, PipelineVariable]]] = None,
wait: bool = True,
logs: bool = True,
job_name: Optional[str] = None,
experiment_config: Optional[Dict[str, str]] = None,
kms_key: Optional[str] = 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.
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.
* Both `ExperimentName` and `TrialName` will be ignored if the Processor instance
is built with :class:`~sagemaker.workflow.pipeline_context.PipelineSession`.
However, the value of `TrialComponentDisplayName` is honored for display in Studio.
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
Returns:
None or pipeline step arguments in case the Processor instance is built with
:class:`~sagemaker.workflow.pipeline_context.PipelineSession`
"""
normalized_inputs, normalized_outputs = self._normalize_args(
job_name=job_name,
arguments=arguments,
inputs=inputs,
outputs=outputs,
code=code,
kms_key=kms_key,
)
experiment_config = check_and_get_run_experiment_config(experiment_config)
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 _include_code_in_inputs(self, inputs, code, kms_key=None):
"""Converts code to appropriate input and includes in input list.
Side effects include:
* uploads code to S3 if the code is a local file.
* sets the entrypoint attribute based on the command and user script name from code.
Args:
inputs (list[:class:`~sagemaker.processing.ProcessingInput`]): Input files for
the processing job. These must be provided as
:class:`~sagemaker.processing.ProcessingInput` objects.
code (str): This can be an S3 URI or a local path to a file with the framework
script to run (default: None).
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
Returns:
list[:class:`~sagemaker.processing.ProcessingInput`]: inputs together with the
code as `ProcessingInput`.
"""
user_code_s3_uri = self._handle_user_code_url(code, kms_key)
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)
return inputs_with_code
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, kms_key=None):
"""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.
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
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.
code_path = url2pathname(code_url.path)
if not os.path.exists(code_path):
raise ValueError(
"""code {} wasn't found. Please make sure that the file exists.
""".format(
code
)
)
if not os.path.isfile(code_path):
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_path, kms_key)
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, kms_key=None):
"""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.
kms_key (str): The ARN of the KMS key that is used to encrypt the
user code file (default: None).
Returns:
str: The S3 URI of the uploaded file or directory.
"""
from sagemaker.workflow.utilities import _pipeline_config
if _pipeline_config and _pipeline_config.code_hash:
desired_s3_uri = s3.s3_path_join(
"s3://",
self.sagemaker_session.default_bucket(),
_pipeline_config.pipeline_name,
self._CODE_CONTAINER_INPUT_NAME,
_pipeline_config.code_hash,
)
else:
desired_s3_uri = s3.s3_path_join(
"s3://",
self.sagemaker_session.default_bucket(),
self._current_job_name,
"input",
self._CODE_CONTAINER_INPUT_NAME,
)
return s3.S3Uploader.upload(
local_path=code,
desired_s3_uri=desired_s3_uri,
kms_key=kms_key,
sagemaker_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=str(
pathlib.PurePosixPath(
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 = str(
pathlib.PurePosixPath(
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.
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.
Returns:
:class:`~sagemaker.processing.ProcessingJob`: The instance of ``ProcessingJob`` created
using the ``Processor``.
"""
process_args = cls._get_process_args(processor, inputs, outputs, experiment_config)
# Log the job name and the user's inputs and outputs as lists of dictionaries.
logger.debug("Job Name: %s", process_args["job_name"])
logger.debug("Inputs: %s", process_args["inputs"])
logger.debug("Outputs: %s", process_args["output_config"]["Outputs"])
# Call sagemaker_session.process using the arguments dictionary.
processor.sagemaker_session.process(**process_args)
return cls(
processor.sagemaker_session,
processor._current_job_name,
inputs,
outputs,
processor.output_kms_key,
)
@classmethod
def _get_process_args(cls, processor, inputs, outputs, experiment_config):
"""Gets a dict of arguments for a new Amazon SageMaker processing job from the processor
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.
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.
Returns:
Dict: dict for `sagemaker.session.Session.process` method
"""
# 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"] = [inp._to_request_dict() for inp 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.role
if is_pipeline_variable(processor.role)
else processor.sagemaker_session.expand_role(processor.role)
)
process_request_args["tags"] = processor.tags
return process_request_args
@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.
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