Course curriculum

    1. Introduction

    2. Imports and Dataloading

    3. Building the Dataloader and Preprocessing

    4. Distributing Data

    5. Setting up the Distributed Training Loop

    6. Running Distributed Training

    7. Analyzing the Training Results

    8. Demonstrating Fault Tolerance

    9. Predicting with a Trained Model

    10. Full Chapter Notebook

About this course

  • Free
  • 10 lessons
  • 0.5 hours of video content

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