sagemaker-python-sdk/sagemaker-mlops/src/sagemaker/mlops/workflow/__init__.py at 442300919529a228e7c307fc11eac478fbc1b68f · aws/sagemaker-python-sdk · GitHub

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"""SageMaker workflow orchestration module.

This module provides pipeline and step orchestration capabilities for SageMaker workflows.
It contains the high-level classes that orchestrate training, processing, and serving
components from the train and serve packages.

Key components:
- Pipeline: Main workflow orchestration class
- Steps: Various step implementations (TrainingStep, ProcessingStep, etc.)
- Configuration: Pipeline configuration classes
- Utilities: Helper functions for workflow management

Note: This module imports from sagemaker.core.workflow for primitives (entities, parameters,
functions, conditions, properties) and can import from sagemaker.train and sagemaker.serve
for orchestration purposes.
"""
from __future__ import absolute_import

__version__ = "0.1.0"

# Pipeline and configuration
from sagemaker.mlops.workflow.pipeline import Pipeline, PipelineGraph, PipelineExecution
from sagemaker.mlops.workflow.pipeline_experiment_config import (
    PipelineExperimentConfig,
    PipelineExperimentConfigProperty,
    PipelineExperimentConfigProperties,
)
from sagemaker.mlops.workflow.parallelism_config import ParallelismConfiguration
from sagemaker.mlops.workflow.selective_execution_config import SelectiveExecutionConfig

# Base step classes
from sagemaker.mlops.workflow.steps import (
    Step,
    StepTypeEnum,
    ConfigurableRetryStep,
    CacheConfig,
    TrainingStep,
    ProcessingStep,
    TransformStep,
    TuningStep,
)

# Step implementations
from sagemaker.mlops.workflow.automl_step import AutoMLStep
from sagemaker.mlops.workflow.callback_step import CallbackStep, CallbackOutput
from sagemaker.mlops.workflow.clarify_check_step import ClarifyCheckStep
from sagemaker.mlops.workflow.condition_step import ConditionStep
from sagemaker.mlops.workflow.emr_step import EMRStep, EMRStepConfig
from sagemaker.mlops.workflow.fail_step import FailStep
from sagemaker.mlops.workflow.lambda_step import LambdaStep, LambdaOutput
from sagemaker.mlops.workflow.model_step import ModelStep
from sagemaker.mlops.workflow.monitor_batch_transform_step import MonitorBatchTransformStep
from sagemaker.mlops.workflow.notebook_job_step import NotebookJobStep
from sagemaker.mlops.workflow.quality_check_step import QualityCheckStep, QualityCheckConfig

# Step collections
from sagemaker.mlops.workflow.step_collections import StepCollection

# Retry policies
from sagemaker.mlops.workflow.retry import (
    RetryPolicy,
    StepRetryPolicy,
    SageMakerJobStepRetryPolicy,
    StepExceptionTypeEnum,
    SageMakerJobExceptionTypeEnum,
)

# Triggers
from sagemaker.mlops.workflow.triggers import Trigger, PipelineSchedule

# Check job configuration
from sagemaker.mlops.workflow.check_job_config import CheckJobConfig

__all__ = [
    # Pipeline and configuration
    "Pipeline",
    "PipelineExecution",
    "PipelineGraph",
    "PipelineExperimentConfig",
    "PipelineExperimentConfigProperty",
    "PipelineExperimentConfigProperties",
    "ParallelismConfiguration",
    "SelectiveExecutionConfig",
    # Base step classes
    "Step",
    "StepTypeEnum",
    "ConfigurableRetryStep",
    "CacheConfig",
    "TrainingStep",
    "ProcessingStep",
    "TransformStep",
    "TuningStep",
    # Step implementations
    "AutoMLStep",
    "CallbackStep",
    "CallbackOutput",
    "ClarifyCheckStep",
    "ConditionStep",
    "EMRStep",
    "EMRStepConfig",
    "FailStep",
    "LambdaStep",
    "LambdaOutput",
    "ModelStep",
    "MonitorBatchTransformStep",
    "NotebookJobStep",
    "QualityCheckStep",
    "QualityCheckConfig",
    # Step collections
    "StepCollection",
    # Retry policies
    "RetryPolicy",
    "StepRetryPolicy",
    "SageMakerJobStepRetryPolicy",
    "StepExceptionTypeEnum",
    "SageMakerJobExceptionTypeEnum",
    # Triggers
    "Trigger",
    "PipelineSchedule",
    # Configuration
    "CheckJobConfig",
]