Scaling Your AI Models with Micro-DDP – Tutorial

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: https://scrimba.com/freecodecamp ⭐️ 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 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news