---
id: sagemaker-schema-inference-artifacts
version: "0.0.5"
license: Apache License 2.0
license_treatment: permissive
maintenance: dormant
---

# sagemaker-schema-inference-artifacts — Open source library for Hugging Face Task Sample Inputs and Outputs

License: permissive · Maintenance: dormant · Downloads: 1.5M/mo

## What it is and what it does

This package is an AWS-published library that bundles pre-defined schema inference artifacts and sample inputs/outputs, designed to work within SageMaker inference workflows. It provides structured metadata that describes how to format data for inference tasks, reducing the need to manually define schemas when deploying models on SageMaker.

The package depends only on json5 for parsing and is compatible with Python 3.8, 3.9, and 3.10. It is marked as production-stable but has not been updated in over two years, indicating it serves a narrow, stable use case rather than an actively evolving tool.

Use it for:

- Set up inference schemas when deploying models to SageMaker without manually defining task input/output structures.
- Reference task sample formats to ensure inference request payloads match expected schemas.
- Integrate pre-validated schema artifacts into SageMaker pipelines to reduce schema definition boilerplate.
- Build tooling that needs canonical input/output shapes for standard task types.

## Worth the install?

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

Provides pre-built schema inference artifacts and task sample inputs/outputs for Hugging Face models in SageMaker workflows.

Yes, if you are actively deploying models on SageMaker and need pre-built schema definitions. Low install friction and permissive license make it a low-risk addition. However, dormant maintenance status means you should verify that included schemas match your target versions before relying on it in production.

## Install

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

## Installing sagemaker-schema-inference-artifacts

Before you install: Low friction install with a single lightweight dependency (json5). Package is dormant—last release was 858 days ago—so expect no active maintenance or bug fixes.

License in practice: Apache License 2.0 is permissive, allowing commercial and private use with minimal restrictions. You may use, modify, and distribute freely as long as you include the license notice.

Quickstart:

pip install sagemaker-schema-inference-artifacts
import json5
# Load schema artifacts from the package

Requires Python 3.8 or later.

Verify before relying:

- What specific task types and model schemas are included in the artifacts.
- Whether schema definitions remain compatible with current model versions.
- How the package integrates with SageMaker's current inference pipeline tooling.

## Package facts

- License: Apache License 2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 1.5M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sagemaker schema inference, hugging face task samples, ml model schema artifacts, aws sagemaker inference, task input output schemas, ml schema definitions, inference artifact library, sagemaker, hugging-face, ml-schemas

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