OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Infrastructure & Serving

Pinferencia

Pinferencia is an open-source Python library that serves registered machine-learning models or prediction functions through an inference server with a web GUI and REST API.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pinferencia — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Pinferencia when you have a model or prediction function and want to serve it through a Python-based GUI and REST API, particularly for a prototype or a lightweight serving workflow.

Who it is for

  • Python developers who need to expose an existing model for inference.
  • Teams prototyping a model-serving workflow with a GUI and REST API.

Documented uses

  • Register a model or prediction function with an inference server.
  • Make model predictions available through a GUI and REST API.
  • Inspect automatically generated API documentation and try endpoints online.
Before you choose

Requirements & limits

  • The README offers a Streamlit extra installation and a backend-only installation.
  • The project documents automatic API documentation with an online try-out feature.
  • KServe API support is documented, with Kubeflow, TensorFlow Serving, Triton, and TorchServe named as compatible tools.
  • The repository identifies the project as Apache-2.0 licensed.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Register a Python model and start a local inference server

Expose a model’s prediction method through Pinferencia’s service interface and open the local GUI.

Before you start

  • Python environment with Pinferencia installed.
  • A model object with a prediction method.

Steps

  1. Install Pinferencia with the command below for the documented recommended option, or the command below for backend only.

    Documented command
    pip install "pinferencia[streamlit]"
    Documented command
    pip install "pinferencia"

  2. In app.py, import Server, create a model object, and create a service with Server().

  3. Register the model with service.register(model_name="mymodel", model=model, entrypoint="predict").

  4. Run pinfer app:service and open http://127.0.0.1:8501/.

Expected outcome

A local service with a GUI and REST API for the registered model.

Guide limitations

  • The guide follows the README quick start; it does not cover deploying the service to a remote or production environment.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
PinferenciaRegister a model or prediction function with an inference server.Server
Cloud AI SDKAdapt and run documented model recipes for computer-vision and NLP inference.Cloud, ServerThe GitHub repository is public. Its public visibility does not establish license terms.
TurboquantReplace a model’s existing inference cache with a compressed cache.Cloud, Linux, macOS, Self-hostedThe project describes itself as open source. The README documents installation through pip or from a source checkout.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
underneathall/pinferencia · Official homepage title · retained in its source language
How we review sourcesSuggest a correction