How to remake GPT2 in 4hours? Key insights from Andrej Karpathy’s Masterclass

📅 Published on July 30, 2024

We have discovered the best data science video of 2024 and would like to share it with you. Each element of this free training is essential, so a complete summary is impossible, but you will find a few key points summarized below:

In the ever-evolving field of machine learning, clarity and efficiency are important. Andrej Karpathy’s recent masterclass provides groundbreaking insights into MLOps concepts that simplify some of the most complex challenges. Here’s a breakdown of the key takeaways from this remarkable video:

1. Introduction to MLOps and key concepts

MLOps concepts are made incredibly easy to understand. For example, last year, we created a Data Parallelism Pipeline (DDP) for our blockchain Giris by dividing the LLM into several nodes to address VRAM Out of Memory issues.

2. Model optimization

He explained DDP at the end of the video. It had literally been a nightmare for us for several months due to gradient synchronization during the backward pass. We had to work in chunks and divide our dataset into as many sequential pieces as there were GPUs.

In our environment, we didn’t know the number of GPUs or their power until the user booked their cluster. Karpathy explained it all in 10 minutes. Incredible!

3. GPU power and precision management

What is the best strategy for accuracy and training time?

We spent months on this, creating a detailed sheet of training times and precision per GPU. He explained it all in 4 minutes. Astonishing!

4. Numerical calculation optimization

Ugly and beautiful numbers: Kernels and CUDA work better with powers of 2 for layers, heads, or tokens to optimize computation. This technique might seem less useful, but it highlights the high degree of optimization possible.

5. Debugging techniques for Large Models

It’s better to create a small model with the same configuration to test hypotheses rather than debugging the “real” model. This approach saves time and reduces computation costs. Additionally, it shows that theory (attention mechanisms and transformer architecture) aligns with practice, with block rearrangement for simplicity and PyTorch compatibility.

6. The importance of calculations in Deep Learning

Karpathy constantly uses calculations to anticipate model predictions and explain concepts. Deep Learning is not a black box but a deep statistical and tensor computation. If we know what we are doing, we can anticipate results. It’s all about probabilities.

7. Reverse engineering an open-source model

The video teaches you how to do this through code, papers, and inference hypotheses.

Thanks, Andrej Karpathy for this masterclass!