Pytorch
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A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
A Python package for tensor computation with GPU acceleration and dynamic neural networks built on a tape-based autograd system.
A high-throughput, memory-efficient inference and serving engine for large language models (LLMs).
A collection of 60+ annotated PyTorch implementations of deep learning papers with side-by-side explanatory notes.
A multi-backend deep learning framework that enables effortless model development across JAX, TensorFlow, PyTorch, and OpenVINO.
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
A cutting-edge framework for training and deploying state-of-the-art YOLO models for object detection, segmentation, classification, and pose estimation.
A deep learning toolkit for Text-to-Speech generation with pretrained models in over 1100 languages and tools for training.
A unified deep learning system for efficient large-scale model training and inference with advanced parallelism strategies.
A comprehensive collection of PyTorch image models, layers, utilities, and training scripts for computer vision research and applications.
A PyTorch-based platform for state-of-the-art object detection, segmentation, and visual recognition tasks.
A modular PyTorch library for state-of-the-art diffusion models to generate images, audio, and 3D molecular structures.
A visualizer for neural network, deep learning, and machine learning models across multiple frameworks.
A collection of concise PyTorch tutorials for deep learning researchers, with most models implemented in under 30 lines of code.
A deep learning framework to pretrain and finetune any AI model at any scale with zero code changes.
A deep learning framework to pretrain and finetune any AI model on any hardware with zero code changes.
A PyTorch wrapper that automates engineering boilerplate for scalable AI model training and deployment.
A ready-to-use OCR Python library supporting 80+ languages and popular writing scripts like Latin, Chinese, Arabic, and Cyrillic.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications in Python.
The fastai book, published as Jupyter Notebooks, provides an introduction to deep learning, fastai, and PyTorch.
A comprehensive library for building and training Graph Neural Networks (GNNs) with PyTorch.
A PyTorch library for building and training Graph Neural Networks (GNNs) on structured and irregular data.
An open standard format for representing machine learning models to enable interoperability between frameworks.
A cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
An open-source framework for financial large language models, enabling cost-effective fine-tuning for tasks like sentiment analysis and forecasting.
A PyTorch library providing datasets, model architectures, and image transformations for computer vision tasks.
An industry-leading open-source data engine for interactive video and image annotation to power machine learning.
A fast and flexible Python library for image augmentation in computer vision tasks like classification, segmentation, and object detection.
A Rust-based deep learning framework and tensor library optimized for flexibility, efficiency, and cross-platform portability.
A multi-voice text-to-speech system that produces highly realistic prosody and intonation using autoregressive and diffusion decoders.
A simple Python framework for state-of-the-art natural language processing (NLP) tasks like named entity recognition and sentiment analysis.
A simple Python framework for state-of-the-art natural language processing (NLP) tasks like named entity recognition and sentiment analysis.
A reliable PyTorch implementation of reinforcement learning algorithms for research and industry.
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