Pinferencia is a Python library designed to simplify the deployment of machine learning models as inference servers. It targets developers and researchers who need to quickly expose their models via a REST API and a user-friendly GUI with minimal code. The primary benefit is rapid prototyping and deployment of ML models without complex infrastructure setup.

How It Works

Pinferencia leverages a minimalist approach, requiring only a few lines of Python code to wrap a model and expose it. It automatically generates a REST API and a Streamlit-based GUI, providing interactive documentation and testing capabilities. The library is designed for flexibility, supporting any Python function or model object, including those from popular frameworks like Hugging Face Transformers, PyTorch, and TensorFlow. Its compatibility with Kserve API standards allows for seamless integration with Kubeflow, TF Serving, Triton, and TorchServe.

Quick Start & Requirements

• Install: pip install "pinferencia[streamlit]" or pip install "pinferencia" for backend only.
• Requirements: Python. Streamlit is optional for GUI.
• Quick Start: http://127.0.0.1:8501/
• Documentation: English Doc | 中文文档 | 中文Readme

Highlighted Details

• 100% test coverage (statement and branch).
• Automatic API documentation with an online try-out feature.
• Supports Kserve API, compatible with Kubeflow, TF Serving, Triton, and TorchServe.
• Can serve single functions or entire models from various frameworks.

Maintenance & Community

• Open to contributions, including translations and code.
• Issue tracker available for contributions and help.

Licensing & Compatibility

• License: Not explicitly stated in the README. Compatibility for commercial use or closed-source linking is therefore unclear.

Limitations & Caveats

The README does not specify the project's license, which is a significant blocker for evaluating commercial or closed-source compatibility. The project is seeking contributions, suggesting it may still be in active development or have a small core team.