Train Your Large Model on Multiple GPUs with Pipeline Par...

What’s Happening

Real talk: This article is divided into six parts; they are: • Pipeline Parallelism Overview • Model Preparation for Pipeline Parallelism • Stage and Pipeline Schedule • Training Loop • Distributed Checkpointing • Limitations of Pipeline Parallelism Pipeline parallelism means creating the model as a pipeline of stages.

Train Your Large Model on Multiple GPUs with Pipeline Parallelism By Adrian Tam on in Training Transformer Models 0 Post Some language models are too large to train on a single GPU. When the model fits on a single GPU but cannot be trained with a large batch size, you can use data parallelism. (shocking, we know)

But, when the model is too large to fit on a single GPU, you need to split it across multiple GPUs.

The Details

In this article, you will learn how to use pipeline parallelism to split models for training. In particular, you will learn about: What is pipeline parallelism How to use pipeline parallelism in PyTorch How to save and restore the model with pipeline parallelism Lets get kicked off!

Overview This article is divided into six parts; they are: Pipeline Parallelism Overview Model Preparation for Pipeline Parallelism Stage and Pipeline Schedule Training Loop Distributed Checkpointing Limitations of Pipeline Parallelism Pipeline Parallelism Overview Pipeline parallelism means creating the model as a pipeline of stages. If you have worked on a scikit-learn project, you may be familiar with the concept of a pipeline.

Why This Matters

An example of a scikit-learn pipeline is: from sklearn. Pipeline import Pipeline from sklearn. Preprocessing import StandardScaler from sklearn.

As AI capabilities expand, we’re seeing more announcements like this reshape the industry.

Key Takeaways

• Pipeline import Pipeline from sklearn .
• Preprocessing import StandardScaler from sklearn .
• A transformer model is typically just a stack of transformer blocks.

The Bottom Line

Preprocessing import StandardScaler from sklearn . A transformer model is typically just a stack of transformer blocks.

What’s your take on this whole situation?