HPC-AI Tech

ColossalAI

Making large AI models cheaper, faster and more accessible

Open-Sora

Open-Sora: Democratizing Efficient Video Production for All

EnergonAI

Large-scale model inference.

FastFold

Optimizing AlphaFold Training and Inference on GPU Clusters

SwiftInfer

Efficient AI Inference & Serving

ColossalAI-Examples

Examples of training models with hybrid parallelism using ColossalAI

PaLM-colossalai

Scalable PaLM implementation of PyTorch

TensorNVMe

A Python library transfers PyTorch tensors between CPU and NVMe

CachedEmbedding

A memory efficient DLRM training solution using ColossalAI

SkyComputing

Sky Computing: Accelerating Geo-distributed Computing in Federated Learning

Titans

A collection of models built with ColossalAI

ColossalAI-Pytorch-lightning

ColossalAI-Documentation

Documentation for Colossal-AI

Oh-My-Dockerfile

A collection of dockerfiles for various tasks

Elixir

Elixir: Train a Large Language Model on a Small GPU Cluster

public_assets

Storing publicly available assets such as images, animations and texts

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

ColossalAI-Platform-CLI

CLI for ColossalAI Platform

GPT-Demo

GPT Demo with hybrid distributed training

Open-Sora-Demo

OPT-Benchmark

mmdetection-examples

Train mmdetection models with ColossalAI.

CANN-Installer

This repository contains Huawei Ascend CANN files

Cloud-Platform-Docs

Documentation for our cloud platform

graphrag

A modular graph-based Retrieval-Augmented Generation (RAG) system

LLaVA-NeXT

torchrec

Pytorch domain library for recommendation systems

pytest-testmon

Selects tests affected by changed files. Executes the right tests first. Continuous test runner when used with pytest-watch.

TensorRT-LLM

TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in performant way.

TensorRT-Model-Optimizer

A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.