Distributed Training
Showing 35 of 35 projects
A repository of state-of-the-art model implementations and examples built with TensorFlow, demonstrating best practices for research and production.
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 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.
An industrial deep learning framework from China supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
An industrial deep learning framework supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
A unified deep learning toolkit for describing neural networks as computational graphs, supporting feed-forward DNNs, CNNs, and RNNs/LSTMs.
A composable, modular, and scalable machine learning toolkit for building AI platforms on Kubernetes.
A Python package for deep learning on graphs, framework-agnostic and optimized for performance and scalability.
A comprehensive JVM-based deep learning ecosystem for building, training, and deploying models with support for model import and distributed training.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
A TensorFlow 2 library providing simple, composable abstractions for machine learning research via the snt.Module concept.
A library of optimized communication primitives for multi-GPU and multi-node collective operations.
A high-level library for training and evaluating neural networks in PyTorch with a flexible engine and event system.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
An open-source machine learning platform for distributed training, hyperparameter tuning, experiment tracking, and resource management.
A simple and versatile framework for object detection and instance recognition with extensive model coverage and distributed training.
A JAX/Flax-based framework for easy and scalable pre-training, fine-tuning, evaluation, and serving of large language models.
A lightweight library providing PyTorch training tools and utilities to simplify and standardize training loops.
A general-purpose PyTorch codebase for 3D object detection with state-of-the-art model implementations and multi-dataset support.
A foundational PyTorch library for training deep learning models, serving as the core engine for the OpenMMLab ecosystem.
A fast, modular PyTorch reference implementation for training and evaluating semantic segmentation models.
A library for building high-performance custom human pose estimation applications with real-time inference and flexible model development.
A JAX-based framework for training large language models with a focus on legibility, scalability, and reproducibility.
A JAX-based machine learning framework for configuring and training large-scale models with high efficiency on TPUs and GPUs.
A collection of CI pipelines, Docker images, and optimized examples to simplify JAX development on NVIDIA GPUs.
A PyTorch reinforcement learning library implementing DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, and IMPALA.
A JAX-based framework for streamlined training, fine-tuning, and high-performance serving of large language and multimodal models.
A TensorFlow implementation of fastText for embedding-based text classification with support for character ngrams and distributed training.
A FlashAttention 2 implementation for JAX with block-wise document mask optimization and context parallelism for efficient long-sequence training.
A PyTorch framework for reinforcement learning research, focused on reproducibility and fast experimentation.
A proof-of-concept for decentralized machine learning using federated learning on the Golem network to distribute training across multiple nodes.
MXNet bindings for the Crystal programming language, enabling deep learning and machine learning development.
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