Neural network inference from the command line, implemented in CHICKEN Scheme.
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A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
Version and deploy your ML models following GitOps principles.
Open source MLOps platform that helps you collaborate, reproduce and share your ML work.
Generic mechanism for data scientists to build, run, and monitor ML tasks and pipelines.
Machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.
Proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS).
Ncnn is a high-performance neural network inference framework optimized for the mobile platform.
Easy-to-use library to boost AI inference.
A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
An Easy-to-Use and High-Performance AI deployment framework.
Neural networks framework in pure C: training and inference, no dependencies.
Open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics. opensource.
Open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease.
Turns your ML code into microservices with web API, interactive GUI, and more.
Vector database plugin for Postgres, written in Rust, specifically designed for LLM.
Open-source vector similarity search for Postgres.
Examples showing how to use the OpenAI vision API to run inference on images, video files and webcam streams.
Provides containers to encapsulate and deploy EdgeML pipelines and applications.
MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.
Python-free Rust inference server with OpenAI API compatibility and hot model swapping.
An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.
Lets you create apps for your ML projects with deceptively simple Python scripts.
Inference for text-embedding models.