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Learn about Distributed Data Parallelism (DDP), an important technique for training large-scale AI models. This course provides a hands-on guide for implementing DDP, ensuring your training processes are efficient and scalable. Learn how to overcome memory limitations and train models effectively across multiple GPUs.
Course created by @neuralkian
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning:
⭐️ Contents ⭐️
- 0:00:00 Introduction
- 0:02:11 Setup and Prerequisites
- 0:05:07 What is Distributed Training?
- 0:07:25 Data Parallelism vs. Model Parallelism
- 0:08:51 Data Parallelism (DP) vs. Distributed Data Parallelism (DDP)
- 0:12:07 Distributed Setup Configurations
- 0:13:28 General DDP Workflow and Communication Primitives
- 0:13:58 Broadcast Operation
- 0:15:18 All Reduce Operation
- 0:21:23 Overlapping Computation and Communication (Gradient Hooks/Bucketing)
- 0:25:57 Step 1: Manual Batch Averaging Implementation
- 0:49:15 Step 2: All Reduce Sandbox
- 1:07:05 Step 3: Implementing DDP Hooks
- 1:17:10 Performance Analysis and Trade-offs
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2026/06/25
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