ChatGPT vs Thousands of GPUs! || How ML Models Train at Scale!
Welcome to our deep dive into parallelism strategies for training large machine learning models! In this video, we’ll explore the various techniques that can significantly speed up the training process and improve efficiency when working with massive datasets and complex neural networks.
What You'll Learn:
Introduction to Parallelism: Understanding the basics and importance of parallelism in ML training.
Data Parallelism: How to distribute data across multiple processors to accelerate training.
Huggingface's Accelerate Library: How modern ML libraries enable using these strategies with minimal code changes
GPU communications primitives: The fundamentals of how GPUs talk to each other
Pipeline Parallelism: Combining data and model parallelism to streamline the training pipeline.
Tensor Parallelism: Techniques for splitting a model into smaller parts to be processed simultaneously.
Automatic Parallelism: A brief overview of the Galvatron paper that combines all three strategies!
Whether you're a beginner or an experienced ML practitioner, this video will provide valuable insights and practical tips to enhance your machine learning projects. Make sure to like, comment, and subscribe for more in-depth tutorials and discussions on cutting-edge AI and ML techniques!
Resources:
https://huggingface.co/docs/transform...
Timestamps:
0:00 - Intro
0:34 - Data Parallel
5:08 - Pipeline Parallel
7:56 - Tensor Parallel
10:45 - N-Dim Parallel
13:03 - Conclusion
#MachineLearning #Parallelism #DataScience #AI #DeepLearning #ModelTraining #DistributedComputing #TechTutorial
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