A Practical Guide to Scaling ML Model Training
GPUs - GPU Clusters - Distributed 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:
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.
Subscribe to Damien's The AiEdge newsletter for more. You can also follow him on LinkedIn and Twitter.
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
1 Referral: Unlock 450+ practice questions on NumPy, Pandas, and SQL.
2 Referrals: Get access to advanced Python OOP deep dive.
3 Referrals: Get access to the PySpark deep dive for big-data mastery.
Get your unique referral link:
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).
5 Must-Know Ways to Test ML Models in Production (Implementation Included).
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.
Join below to unlock all full articles:
SPONSOR US
Get your product in front of 78,000 data scientists and other tech professionals.
Our newsletter puts your products and services directly in front of an audience that matters — thousands of leaders, senior data scientists, machine learning engineers, data analysts, etc., who have influence over significant tech decisions and big purchases.
To ensure your product reaches this influential audience, reserve your space here or reply to this email to ensure your product reaches this influential audience.