---
id: sagemaker-inference
version: "1.10.1"
license: Apache License 2.0
license_treatment: permissive
maintenance: abandoned
---

# sagemaker-inference — Open source toolkit for helping create serving containers to run on Amazon SageMaker.

License: permissive · Maintenance: abandoned · Downloads: 315.4K/mo

## What it is and what it does

SageMaker Inference Toolkit is a library that packages a model serving stack for deployment on Amazon SageMaker. It abstracts the complexity of setting up inference endpoints by providing handler interfaces for model loading, input preprocessing, prediction, and output serialization. The toolkit is designed to be embedded in Docker containers and works with Multi Model Server to handle incoming inference requests.

The library is intended for developers building custom inference containers for SageMaker. It provides base classes and utilities (decoder, encoder, content type handlers) that you extend to define how your specific model should be loaded and served. However, the repository has been archived and is no longer actively maintained as of late 2023, which means it may not receive updates for compatibility with newer Python versions or SageMaker features.

Use it for:

- Build a custom Docker inference container for a PyTorch or TensorFlow model to deploy on SageMaker.
- Implement multi-model serving where a single container handles multiple model versions or types.
- Add standardized input/output handling (JSON, CSV, NPZ) to a model serving pipeline.
- Create a handler service that integrates with SageMaker's model server lifecycle (initialization and request handling).

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides a model serving stack for deploying machine learning models in Docker containers on Amazon SageMaker, built on Multi Model Server.

Yes, but with caution. Install if you are actively deploying models to Amazon SageMaker and need a structured framework for containerized inference. The permissive Apache 2.0 license poses no barrier. However, the abandoned repository status (last commit 2023-11-20) means you should verify compatibility with your target SageMaker version and Python runtime before committing to production use. If you are starting a new project, check whether SageMaker's prebuilt containers or newer alternatives better suit your needs.

## Install

pip install sagemaker-inference
uv add sagemaker-inference
poetry add sagemaker-inference

## Installing sagemaker-inference

Before you install: High install friction due to no runtime dependencies and requirement to be integrated into a Docker build process rather than installed standalone. Repository is archived and abandoned as of 2023-11-20, with no active maintenance.

License in practice: Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.

Quickstart:

# In Dockerfile:
RUN pip3 install multi-model-server sagemaker-inference

# In handler implementation:
from sagemaker_inference import content_types, decoder, encoder
from sagemaker_inference.default_handler_service import DefaultHandlerService

class MyHandler(DefaultHandlerService):
    pass

Must be installed within a Docker container build; requires multi-model-server as a peer dependency and integration with a serving entrypoint script.

Verify before relying:

- Whether the abandoned repository status affects long-term compatibility with current SageMaker versions or Python releases beyond 3.10.
- Whether Multi Model Server remains actively maintained and compatible with modern deployment environments.
- Specific performance characteristics or throughput limits for the serving stack.

## Package facts

- License: Apache License 2.0 (permissive)
- Python support: unspecified
- Install friction: high
- Maintenance: abandoned
- Downloads: 315.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sagemaker model serving, ml inference docker container, multi model server toolkit, sagemaker deployment framework, ml model serving stack, inference handler framework, containerized ml inference, sagemaker, model-serving, docker

[View on SkillFed](https://skillfed.io/packages/sagemaker-inference) · [View on PyPI](https://pypi.org/project/sagemaker-inference/)