Distributed Training
Showing 22 of 22 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 on any hardware with zero code changes.
A PyTorch wrapper that automates engineering boilerplate for scalable AI model training and deployment.
A deep learning framework to pretrain and finetune any AI model at any scale with zero code changes.
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 high-level library for training and evaluating neural networks in PyTorch with a flexible engine and event system.
A library of optimized communication primitives for multi-GPU and multi-node collective operations.
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.
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