Tensors and Dynamic neural networks in Python with strong GPU acceleration
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DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
The fastai deep learning library
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...
Open3D: A Modern Library for 3D Data Processing
Open Machine Learning Compiler Framework
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
CUDA Templates and Python DSLs for High-Performance Linear Algebra
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
NVIDIA cuML: GPU-Accelerated Machine Learning
Optimized primitives for collective multi-GPU communication
🛠 All-in-one web-based IDE specialized for machine learning and data science.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
ThunderSVM: A Fast SVM Library on GPUs and CPUs
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
ThunderGBM: Fast GBDTs and Random Forests on GPUs
DiffSharp: Differentiable Functional Programming
OCaml bindings for PyTorch
High-performance Bayesian Data Analysis on the GPU in Clojure
👩💻 Matrix and other math, accelerated by WebGPU and WASM
Go's answer to pandas + scikit-learn: a batteries-included data analysis library with dataframes, statistics, machine learning, quant, and GPU acceleration. Run Python with seamless two-way variable sharing, or explore data straight from the Insyra CLI.
Machine learning compiler in Go. A from-scratch tinygrad port: graph-rewrite IR, autodiff as a compiler pass, zero-CGO WebGPU backend.