#distributed training

Explore an open-source project offering detailed guidance on LLM training and fine-tuning with NVIDIA GPUs and Ascend NPUs. The resources cover parameter-efficient methods like LoRA and QLoRA and introduce distributed training techniques. Access practical examples using frameworks like HuggingFace PEFT, DeepSpeed, and Megatron-LM to enhance large language models. Understand distributed AI framework complexities and learn effective LLM deployment strategies.

Developed using graph-mode TensorFlow, Tensorpack is designed for high-speed neural network training and utilizes efficient methods and multi-GPU capabilities. Its outstanding data loading capacity through pure Python complements its support for flexible and reproducible research. Tensorpack is particularly suited for extensive model training in advanced fields such as GANs, object detection, and reinforcement learning, with scripts available to replicate key research papers. Although Tensorpack is continually evolving, it offers a robust model zoo and in-depth documentation to enhance training workflows.

Hivemind enables decentralized deep learning with PyTorch, facilitating large-scale model training without a central server. It offers fault-tolerant backpropagation and decentralized parameter averaging for flexible network training. Used in projects like Training Transformers Together, it supports Linux, macOS, and Windows 10+, and integrates with PyTorch Lightning for handling distributed, unreliable peers.

Delve into the complexities of GPT-2, including its architecture and unique configurations. This overview examines crucial elements such as model files, reproducibility challenges, embedding details, and layer normalization. Learn about essential concepts like weight decay, gradient accumulation, and data parallelism, along with common pitfalls and debugging strategies. Perfect for AI researchers and developers aiming to enhance training effectiveness and comprehend language model intricacies.

Discover the DRLX library designed for distributed diffusion model training utilizing reinforcement learning. Integrate effortlessly with Hugging Face's Diffusers and leverage Accelerate for scalable Multi-GPU and Multi-Node configurations. Explore DDPO algorithm applications compatible with Stable Diffusion across diverse pipelines. Access documentation for installation and learn about our latest experiments.

Discover a versatile Chinese chatbot allowing custom dataset training, integrating GPT models for improved interaction. Features Seq2Seq and GPT branches with future updates for MindSpore and multimodal dialogues. Enhanced distributed training and RLHF align with cutting-edge AI advancements.

LiBai utilizes OneFlow to offer a versatile open-source toolbox designed for training large-scale models. It includes data, tensor, and pipeline parallelism features, supporting both CV and NLP tasks. LiBai is efficient in distributed and mixed-precision training. Its modular design and flexible configuration options make it ideal for constructing research projects. The latest version introduces mock transformer support and new model integrations, making it suitable for developers and researchers focused on streamlined AI model deployment.

Discover a seamless PyTorch training experience with an innovative library that simplifies multi-device and distributed environments. Easily integrate minimal changes to enable smooth transitions between CPUs, GPUs, and TPUs with mixed precision. A user-friendly CLI aids in configuring and deploying scripts while maintaining control over training loops. Offering exceptional flexibility in scaling machine learning models, it supports frameworks like DeepSpeed and PyTorch FSDP, making it suitable for developers focusing on simplicity and adaptability.

Discover NVIDIA's open-source library designed for efficient training of large language models with GPU optimization. Megatron-Core provides modular APIs for enhanced system-level optimization and scalability, supporting multimodal training on NVIDIA infrastructure. Features include advanced parallelism strategies and comprehensive components for transformers such as BERT and GPT, ideal for AI researchers and developers. It integrates smoothly with frameworks like NVIDIA NeMo and PyTorch.

This repository provides NVIDIA tools that facilitate advanced mixed precision and distributed training in PyTorch. While some modules are deprecated, equivalent PyTorch solutions are available. Apex offers streamlined training with examples for ImageNet; supports synchronized batch normalization and leverages NVIDIA's NCCL library. Available for Linux and experimental Windows, it supports custom C++/CUDA extensions to enhance performance.

This platform uses a robust architecture to enable the efficient sharing of resources like GPUs and FPGAs. It supports on-premises, hybrid, and cloud deployments, integrates widely-used AI frameworks, and offers a comprehensive solution for deep learning. The platform is compatible with Kubernetes and simplifies distributed training and IT operations. Its modular design allows for easy customization and scalability to meet developing AI demands.

Explore a Jax-based library that streamlines the creation and training of transformer models, minimizing the complexity often found in model development. Includes customizable components for various AI tasks and offers distributed training support. Features intuitive dataloaders and distinct layers for optimized model building. Suitable for AI professionals developing smaller yet potent models, with community support available on Discord.

This repository offers a robust platform for training large-scale autoregressive language models with advanced optimizations and extensive system compatibility. Utilizing NVIDIA's Megatron and DeepSpeed, it supports distributed training through ZeRO and 3D parallelism on various hardware environments like AWS and ORNL Summit. Widely adopted by academia and industry, it provides predefined configurations for popular model architectures and integrates seamlessly with the open-source ecosystem, including Hugging Face libraries and WandB. Recent updates introduce support for AMD GPUs, preference learning models, and improved Flash Attention, promoting continued advancements in large-scale model research.

TinyLlama focuses on efficiently pretraining a 1.1 billion parameter language model across 3 trillion tokens in 90 days, sharing architectural similarities with Llama 2. Its compact design allows deployment on edge devices, supporting real-time tasks without internet dependency. As an adaptable solution for open-source projects, it offers essential updates and evaluation metrics, serving as a valuable resource for those interested in language models under 5 billion parameters. The project supports advanced distributed training capabilities alongside optimizations for increased processing efficiency.

Alpa streamlines the scalable training and inference of large neural networks using automatic parallelization in distributed environments, leveraging integrations with advanced libraries such as Jax and XLA. Despite the project being inactive, its core algorithms are part of the maintained XLA, offering continuous benefits for model scaling.

TorchMetrics provides over 100 PyTorch-compatible metrics with features like automated synchronization, accumulation, and built-in visualization tools. Designed for distributed training, it integrates seamlessly with PyTorch Lightning to minimize coding overhead. Ideal for machine learning tasks in domains like classification and regression across multi-device setups.

Explore a framework for training extensive language and foundation models with a focus on readability, scalability, and reproducibility. It's constructed with JAX, Equinox, and Haliax for distributed training on TPUs and GPUs. Enjoy effortless integration with Hugging Face tools and utilize advanced optimizers like Sophia and Optax. Levanter guarantees consistent results across various computing environments, with features like on-demand data preprocessing and robust logging capabilities. Perfect for developers pursuing efficient model development with top-tier benchmarks.

FairScale is a PyTorch extension that improves scalability and performance in large-scale model training. It features advanced scaling techniques with composable modules and easy-to-use APIs. Emphasizing usability, modularity, and efficiency, FairScale aids researchers in scaling models even with resource constraints. Its FullyShardedDataParallel (FSDP) element provides large neural network scalability and is now part of PyTorch for widespread use. Operating under a BSD-3-Clause License, FairScale welcomes ongoing contributions.