# aws/sagemaker-python-sdk

A library for training and deploying machine learning models on Amazon SageMaker

Repository: https://github.com/aws/sagemaker-python-sdk
Canonical: https://ross.abutalabs.com/products/sagemaker-python-sdk
Homepage: https://sagemaker.readthedocs.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: aws, mxnet, tensorflow, machine-learning, python, pytorch, sagemaker, huggingface
Last push: 2026-08-26T20:22:14+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 3215, "days_push": 7, "days_rel": 8, "gap_med": 7.0, "n_releases_24m": 85}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2261, forks 1297 (observed 2026-08-28T04:06:31.796707+00:00)

## What it is
The SageMaker Python SDK is an open-source Python library for training and deploying machine learning models on Amazon SageMaker. It supports popular frameworks like PyTorch, TensorFlow, MXNet, and Hugging Face, as well as custom algorithms in Docker containers.

## Use cases
- train machine learning models on AWS SageMaker
- deploy a PyTorch model as a hosted endpoint
- run Hugging Face model training in the cloud
- bring my own algorithm in a Docker container to SageMaker
- serve a TensorFlow model on AWS
- manage SageMaker training jobs from Python

## When to choose
- you are already using AWS and want managed model training and hosting
- you need to train or deploy models with PyTorch, TensorFlow, MXNet, or Hugging Face on SageMaker
- you want to use Amazon's built-in optimized ML algorithms

## When to avoid
- you train and deploy models entirely outside AWS
- you want a framework-agnostic local training setup without cloud dependencies
- you rely on the legacy Estimator/Model/Predictor APIs, which are removed in V3

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-training, sdk, deployment
- domain: machine-learning, cloud-computing, deep-learning, developer-tools
- platform: python, cloud
- tags: aws, sagemaker, model-training, model-deployment, mlops

## Member repositories
- aws/sagemaker-python-sdk (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:31.796707+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:42:32.996891+00:00, confidence not recorded.
  - readme: https://github.com/aws/sagemaker-python-sdk (fetched 2026-08-28T04:06:31.796707+00:00, sha 0cbafd1bff26)
- Data as of 2026-08-30T08:39:29.467469+00:00.