머신러닝 · GitHub

Python + Inference - Model Deployment library in Python. Simplest model inference server ever.

• Fast to code, fast to go alive. Minimal codes needed, minimal transformation needed. Just based on what you have.
• 100% Test Coverage: Both statement and branch coverages, no kidding. Have you ever known any model serving tool so seriously tested?

[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

• 🐼 For training data attribution evaluation, check out quanda!
• New batch implementation for 12X speedup of existing faithfulness metrics (!)

Convert Machine Learning Code Between Frameworks

Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers.

Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.

• Collaborate with others in real time, no sign up!
• Usable on web or as Windows,Mac or Linux desktop application

The unravelsports package aims to aid researchers, analysts and enthusiasts by providing intermediary steps in the complex process of turning raw sports data into meaningful information and actionable insights.

UnrealCV: Connecting Computer Vision to Unreal Engine

• Support Unreal Engine 5.2 and later.
• Optical flow image capture: vget /camera/[id]/optical_flow [format].

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

🎁 7,400,000+ Unsplash images made available for research and machine learning

CARES Reinforcement Learning Package

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.

A Systems View of Efficient Machine Learning, from Silicon to Agents. Fourteen parts, from roofline analysis and vector units up through kernels, compilers, quantisation, compression, vision, on-device language models, robotics, profiling, serving and agents.

Build Custom Deep Neural Network Trainers in 2026: Open Source BuilT Guide

TinyML AI inference library

Pytorch implementation of convolutional neural network visualization techniques

Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀

• Remote DataFrames (documentation coming soon)
• Integration into Jupyter and Voila for interactive notebooks and dashboards

This is an open sourced book on deep learning.

Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes