Amazon SageMaker Python SDK — sagemaker 2.199.0 documentation (original) (raw)

Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker.

With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.

Here you’ll find an overview and API documentation for SageMaker Python SDK. The project homepage is in Github: https://github.com/aws/sagemaker-python-sdk, where you can find the SDK source and installation instructions for the library.

Overview¶

• Using the SageMaker Python SDK
  • Train a Model with the SageMaker Python SDK
  • Using Models Trained Outside of Amazon SageMaker
  • Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK
  • SageMaker Automatic Model Tuning
  • SageMaker Asynchronous Inference
  • SageMaker Serverless Inference
  • SageMaker Batch Transform
  • Local Mode
  • Secure Training and Inference with VPC
  • Secure Training with Network Isolation (Internet-Free) Mode
  • Inference Pipelines
  • SageMaker Workflow
  • SageMaker Model Building Pipeline
  • SageMaker Model Monitoring
  • SageMaker Debugger
  • SageMaker Processing
  • Configuring and using defaults with the SageMaker Python SDK
  • Run Machine Learning code on SageMaker using remote function
  • FAQ
• Use Version 2.x of the SageMaker Python SDK
  • Installation
  • Breaking Changes
  • Non-Breaking Changes
  • Automatically Upgrade Your Code

The SageMaker Python SDK APIs:

• APIs
  • Feature Store APIs
  • Training APIs
  • Distributed Training APIs
  • Inference APIs
  • Governance APIs
  • Utility APIs

Frameworks¶

The SageMaker Python SDK supports managed training and inference for a variety of machine learning frameworks:

• Frameworks
  • Apache MXNet
  • Chainer
  • Hugging Face
  • PyTorch
  • Reinforcement Learning
  • Scikit-Learn
  • SparkML Serving
  • TensorFlow
  • XGBoost
  • Deep Java Library (DJL)

Amazon SageMaker Debugger¶

You can use Amazon SageMaker Debugger to automatically detect anomalies while training your machine learning models.

• Amazon SageMaker Debugger
  • Background
  • Setup
  • Capture real-time debugging data during model training in Amazon SageMaker
  • Continuous analyses through rules
  • Capture real-time TensorBoard data from the debugging hook
  • Interactive analysis using SageMaker Debugger SDK and visualizations
  • Default behavior and opting out
  • Learn More

Amazon SageMaker Feature Store¶

You can use Feature Store to store features and associated metadata, so features can be discovered and reused.

• Amazon SageMaker Feature Store

Amazon SageMaker Model Monitoring¶

You can use Amazon SageMaker Model Monitoring to automatically detect concept drift by monitoring your machine learning models.

• Amazon SageMaker Model Monitor
  • Background
  • Setup
  • Capture real-time inference data from Amazon SageMaker endpoints
  • Baselining and continuous monitoring
  • Visualize results
  • Delete the resources
  • Learn More