Gaudi Tutorials

These are the source files for the tutorials on https://developer.habana.ai/.

The tutorials provide step-by-step instructions for PyTorch and PyTorch Lightning on the Intel Gaudi AI Processor, from beginner level to advanced users.

To run these Jupyter Notebooks you will need to follow these steps:

1. Get access to an Intel Gaudi 2 Accelerator card or node. See the Get Access page on the Developer Website. Be sure to use port forwarding ssh -L 8888:localhost:8888 ... user@ipaddress to be able to access the notebook.
2. Run the Intel Gaudi PyTorch Docker image. Refer to the Docker section of the Installation Guide for more information. Running the docker image will allow you access to the entire software stack.
3. Clone this tutorial in your $HOME directory: cd ~ && git clone https://www.github.com/habanaai/Gaudi-tutorials
4. Install Jupyterlab: python3 -m pip install jupyterlab
5. Run the Jupyterlab Server, using the same port mapping as the ssh command: python3 -m jupyterlab_server --IdentityProvider.token='' --ServerApp.password='' --allow-root --port 8888 --ServerApp.root_dir=$HOME &

The tutorials will cover the following domains and tasks:

Advanced

• Fine Tuning with LORA and Inference on Hugging Face Llama 2 70B model
• Full RAG application with TGI-gaudi
• RAG application with Local GPT modified to run on Intel Gaudi
• How to setup and use DeepSpeed for Training Large Language Models

Intermediate

• GPU migration Tool
• Debug for Dynamic Shapes
• Running Simple Inference examples with HPU Graph
• How to use the Gaudi Tensorboard Plug-in for Profiling
• Running DeepSpeed on PyTorch Lightning with GPT2
• BERT Fine Tuning use PyTorch Lightning

Getting Started

• Quick Start Overall review of Model-References, Hugging Face and DeepSpeed
• Inference
• Fine Tuning
• Running Stable Diffusion
• Training a Classifier Basic PyTorch Tutorial
• Introduction to Lightning Running simple PyTorch Lightning model, using Gaudi plug-in