{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Integrates training scripts into Docker containers for use with Amazon SageMaker, handling environment setup, hyperparameter passing, and entry point execution within containerized training jobs.","skillfed_tags":["sagemaker-integration","docker-training","aws-ml"],"use_cases":["Build a custom training container for a proprietary ML framework or training pipeline not covered by SageMaker's pre-built images.","Pass hyperparameters and input data channels from SageMaker training jobs into your training script without manual environment parsing.","Create a Docker image that reads training data from S3 via SageMaker channels and writes trained models to the correct output directory.","Execute shell or Python entry points with automatic argument and environment variable injection from SageMaker's hyperparameters.json and inputdataconfig.json.","Run training jobs locally using Docker before deploying to SageMaker's managed infrastructure."],"what_it_does":"SageMaker Training Toolkit is a library that bridges custom training code and Amazon SageMaker's containerized training infrastructure. It runs inside a Docker image and handles the plumbing between SageMaker's training job orchestration and your training script\u2014reading hyperparameters, environment variables, input channels, and model output paths from SageMaker's standard locations, then executing your entry point with the correct arguments and environment.\n\nYou write a training script (Python or shell), package it in a Dockerfile with this library, and push the image to ECR or run it locally. When SageMaker launches a training job with that image, the toolkit automatically exposes training data paths, hyperparameters, and system info as environment variables and command-line arguments, then runs your script. It's designed for teams building custom training containers that need to integrate with SageMaker's managed training service rather than using pre-built framework images.","worth_installing":"Yes, if you are building custom training containers for SageMaker. The package is stable (Production/Stable classifier, 530 GitHub stars, no known vulnerabilities) and solves a real integration problem\u2014it eliminates boilerplate for reading SageMaker's training configuration. High install friction (source distribution only) and aging maintenance (326 days since release) are acceptable trade-offs given the narrow, specialized use case. Not worth installing if you use SageMaker's pre-built framework images or train outside SageMaker."},"id":"sagemaker-training","links":{"html":"https://skillfed.io/packages/sagemaker-training","md":"https://skillfed.io/packages/sagemaker-training.md","pypi":"https://pypi.org/project/sagemaker-training/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-22","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-training","python_support":"unspecified","summary":"Open source library for creating containers to run on Amazon SageMaker."},"popularity":{"monthly_downloads":319256,"position":7646,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.1.1"}