Core Frameworks & Libraries

End-to-end platform with excellent production deployment, TPU support, and large-scale serving tools.

Dynamic computation graphs, Pythonic API, dominant in research and production. The current standard for most frontier AI work.

High-performance numerical computing with composable transformations (JIT, vmap, grad). Rising favorite for research and scientific ML.

Comprehensive Vision Transformer (ViT) implementations in PyTorch. Reference implementations of all major vision transformer variants including ViT, DeiT, Swin, and more. MIT licensed.

Neural network library for JAX, designed for flexibility. Apache-2.0 licensed.

JAX-based neural network library from Google DeepMind. Elegant functional API with state management, widely used in DeepMind's research. Apache 2.0 licensed.

Elegant easy-to-use neural networks and scientific computing in JAX. Callable PyTrees with filtered transformations, seamless interoperability with the JAX ecosystem. Apache 2.0 licensed.

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable ODE/SDE/CDE solvers for scientific machine learning and neural differential equations. Apache 2.0 licensed.

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation. Bayesian modeling and inference at scale.

High-level, beginner-friendly API that now runs on multiple backends (TensorFlow, JAX, PyTorch). Perfect for rapid experimentation.

Minimalist deep learning framework with tiny code footprint. The "you like PyTorch? you like micrograd? you love tinygrad!" philosophy - simple yet powerful.

Industrial deep learning platform from Baidu serving 23+ million developers and 760,000+ companies. China's first independent R&D framework with advanced distributed training and deployment capabilities.

Library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Part of the PyTorch ecosystem.

The largest collection of PyTorch image encoders and backbones. 900+ pretrained models including ResNet, EfficientNet, Vision Transformer, ConvNeXt, and more with training and inference scripts. Apache 2.0 licensed.

Language and compiler for writing highly efficient custom deep-learning primitives. Powers kernel optimizations in PyTorch, JAX, and other frameworks. MIT licensed.

Tensor library for machine learning. The foundational C/C++ library powering llama.cpp and many on-device inference engines. MIT licensed.

Array framework for machine learning on Apple silicon. Efficient unified memory design with NumPy-like API, automatic differentiation, and multi-device support. MIT licensed.

Neural network framework in pure C with reverse-mode automatic differentiation, model training, and GGUF inference, without a Python runtime.

oneAPI Deep Neural Network Library. Cross-platform performance library of basic building blocks for deep learning, optimized for Intel CPUs, GPUs, and Arm architectures. Apache 2.0 licensed.

Open standard for machine learning interoperability. Open Neural Network Exchange provides an open ecosystem that empowers AI developers to choose the right tools as their project evolves. Apache 2.0 licensed.

Retargetable MLIR-based machine learning compiler and runtime toolkit. Lowers ML models to unified IR that scales from datacenter to mobile and edge deployments. Apache 2.0 licensed.

Open-source AI compute and programming platform built around the MAX Engine and Mojo programming language.

Next-generation deep learning framework in Rust. Backend-agnostic with CPU, GPU, WebAssembly support.

Minimalist ML framework for Rust. PyTorch-like API with focus on performance and simplicity.