Course Outline


  • Overview of deep learning scaling challenges
  • Overview of DeepSpeed and its features
  • DeepSpeed vs. other distributed deep learning libraries

Getting Started

  • Setting up the development environment
  • Installing PyTorch and DeepSpeed
  • Configuring DeepSpeed for distributed training

DeepSpeed Optimization Features

  • DeepSpeed training pipeline
  • ZeRO (memory optimization)
  • Activation checkpointing
  • Gradient checkpointing
  • Pipeline parallelism

Scaling Models with DeepSpeed

  • Basic scaling using DeepSpeed
  • Advanced scaling techniques
  • Performance considerations and best practices
  • Debugging and troubleshooting techniques

Advanced DeepSpeed Topics

  • Advanced optimization techniques
  • Using DeepSpeed with mixed precision training
  • DeepSpeed on different hardware (e.g. GPUs, TPUs)
  • DeepSpeed with multiple training nodes

Integrating DeepSpeed with PyTorch

  • Integrating DeepSpeed with PyTorch workflows
  • Using DeepSpeed with PyTorch Lightning


  • Debugging common DeepSpeed issues
  • Monitoring and logging

Summary and Next Steps

  • Recap of key concepts and features
  • Best practices for using DeepSpeed in production
  • Further resources for learning more about DeepSpeed


  • Intermediate knowledge of deep learning principles
  • Experience with PyTorch or similar deep learning frameworks
  • Familiarity with Python programming


  • Data scientists
  • Machine learning engineers
  • Developers
  21 Hours


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