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README.md

Colossal-AI Tutorial Hands-on

Introduction

Welcome to the Colossal-AI tutorial, which has been accepted as official tutorials by top conference SC, AAAI, PPoPP, etc.

Colossal-AI, a unified deep learning system for the big model era, integrates many advanced technologies such as multi-dimensional tensor parallelism, sequence parallelism, heterogeneous memory management, large-scale optimization, adaptive task scheduling, etc. By using Colossal-AI, we could help users to efficiently and quickly deploy large AI model training and inference, reducing large AI model training budgets and scaling down the labor cost of learning and deployment.

🚀 Quick Links

Colossal-AI | Paper | Documentation | Forum | Slack

Table of Content

  • Multi-dimensional Parallelism
    • Know the components and sketch of Colossal-AI
    • Step-by-step from PyTorch to Colossal-AI
    • Try data/pipeline parallelism and 1D/2D/2.5D/3D tensor parallelism using a unified model
  • Sequence Parallelism
    • Try sequence parallelism with BERT
    • Combination of data/pipeline/sequence parallelism
    • Faster training and longer sequence length
  • Large Batch Training Optimization
  • Comparison of small/large batch size with SGD/LARS optimizer
  • Acceleration from a larger batch size
  • Auto-Parallelism
    • Parallelism with normal non-distributed training code
    • Model tracing + solution solving + runtime communication inserting all in one auto-parallelism system
    • Try single program, multiple data (SPMD) parallel with auto-parallelism SPMD solver on ResNet50
  • Fine-tuning and Serving for OPT
    • Try OPT model imported from Hugging Face with Colossal-AI
    • Fine-tuning OPT with limited hardware using ZeRO, Gemini and parallelism
    • Deploy the fine-tuned model to inference service
  • Acceleration of Stable Diffusion
    • Stable Diffusion with Lightning
    • Try Lightning Colossal-AI strategy to optimize memory and accelerate speed

Discussion

Discussion about the Colossal-AI project is always welcomed! We would love to exchange ideas with the community to better help this project grow. If you think there is a need to discuss anything, you may jump to our Slack.

If you encounter any problem while running these tutorials, you may want to raise an issue in this repository.