Core libraries and frameworks for building, training, and deploying artificial intelligence.
State-of-the-art diffusion models for image and audio generation.
Making neural nets easy to use, while maintaining absolute flexibility.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
Deep learning for humans. A high-level neural networks API, written in Python and capable of running on top of TensorFlow, JAX, or PyTorch.
Production-grade LLM tracing, evaluation, and monitoring.
The fundamental package for scientific computing with Python.
Cross-platform, high performance ML inferencing and training accelerator.
Tensors and Dynamic neural networks in Python with strong GPU acceleration.
Simple and efficient tools for predictive data analysis. Built on NumPy, SciPy, and matplotlib.
An end-to-end open source platform for machine learning from Google.
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
A high-throughput and memory-efficient inference and serving engine for LLMs.