sagemaker-schema-inference-artifacts
Open source library for Hugging Face Task Sample Inputs and Outputs
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later.
- 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 · maintenance · safety
Apache License 2.0 (permissive) — 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.
last release 2024-04-08 (858 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,474,576 downloads/mo, #3,863 on PyPI
Alternatives
Verify before relying
pip install sagemaker-schema-inference-artifacts
import json5
# Load schema artifacts from the package- 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.
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 on this page. Verify before relying on it.
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
sagemaker-schema-inference-artifacts on PyPI
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.
Requires Python 3.8 or later.
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 packageVerify 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 the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagejson5 |
| Maintenance | Dormant 858 days since the last release |
| First released | |
| Downloads | 1,474,576 / month, #3,863 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: sagemaker_schema_inference_artifacts-0.0.5-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “sagemaker schema inference”
- sagemaker-schema-inference-artifactsProvides pre-built schema inference artifacts and task sample…
- sagemaker-serveProvides model serving and deployment functionality for machine…
- sagemaker-containersProvides tools to build Docker containers compatible with Amazon…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also sagemaker-inference · sagemaker-serve · sagemaker-data-insights · sagemaker-train · sagemaker · sagemaker-core · sagemaker-containers · sagemaker-training · model-hosting-container-standards · sagemaker-feature-store-pyspark-3.1