A Practical Guide to Scaling ML Model Training

Over the last few weeks, we covered several details about scaling ML models using techniques like multi-GPU training and DDP, as well as understanding the underlying details of CUDA programming.

If you are new here (or wish to recall), you can read these:

• A Beginner-friendly Guide to Multi-GPU Model Training

• Implementing (Massively) Parallelized CUDA Programs From Scratch Using CUDA Programming

Today, we shall continue learning in this direction.

I’m excited to bring you a special guest post by Damien Benveniste. He is the author of The AiEdge newsletter and was a Machine Learning Tech Lead at Meta.

In today’s machine learning deep dive, he will provide a detailed discussion on scaling ML models using more advanced techniques: A Practical Guide to Scaling ML Model Training.

He shall also recap what we have already discussed in the previous deep dive on multi-GPU training and conclude with a practical demo.

More specifically, he shall cover the following:

• CPU vs GPU vs TPU

• The GPU Architecture

• Distributed Training

  • Data Parallelism

  • Model Parallelism, etc.

• Zero Redundancy Optimizer (ZeRO) Strategy

• Distributing Training with the Accelerate Package on AWS Sagemaker.

Every section of the deep dive is also accompanied by a video if you prefer that.

Read it here: A Practical Guide to Scaling ML Model Training.

Have a good day!

Avi

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Are you overwhelmed with the amount of information in ML/DS?

Every week, I publish no-fluff deep dives on topics that truly matter to your skills for ML/DS roles.

For instance:

• A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KANs).

• Implementing KANs From Scratch Using PyTorch.

• 5 Must-Know Ways to Test ML Models in Production (Implementation Included).

• A Beginner-Friendly Guide to Multi-GPU Model Training.

• Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning

• 8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science

• Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming

• You Are Probably Building Inconsistent Classification Models Without Even Realizing.

• How To (Immensely) Optimize Your Machine Learning Development and Operations with MLflow.

• And many many more.

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