gpu-notebook-example

Jupyter Notebook Example

Introduction

This folder contains a demonstration Jupyter notebook called 'hello_world.ipynb' for Python development within a containerized environment.

Install VSCode Remote Development Extension

• Open Visual Studio Code.
• Access the Extensions view by clicking on the Extensions icon in the Activity Bar on the side of the window, or by pressing Ctrl+Shift+X.
• Type Remote Development in the search bar.
• Locate the Remote Development extension pack by Microsoft and click on the install button.

Working with Containers

Rebuild and Reopen in Container

• To access the Command Palette, either press Ctrl+Shift+P or navigate to View > Command Palette.
• Choose Dev Containers: Rebuild and Reopen in Container.

Switching Back to Local

• Open the Command Palette using Ctrl+Shift+P or by going to View > Command Palette.
• Select Dev Containers: Reopen Folder Locally.

Verifying GPU visibility and Jupyter Notebook

• Navigate to the 'hello_world.ipynb' Jupyter notebook within the project.
• Click on 'Select Kernel' in the top right corner of the notebook interface.
• Choose 'Python Environments'.
• Select the 'Python 3.10.12' kernel for the correct Python environment.
• Run the top cell to confirm your gpu can be seen by the container. The expected printed output should be 'True' and the name of your GPU.

CUDA and GPU Requirements

This project relies on specific versions of CUDA and GPU drivers to function correctly:

• CUDA Version: 12.2
• NVIDIA-SMI: 536.45 Ensure that your development environment matches these specifications to avoid compatibility issues.

For additional Docker setup instructions, refer to the main project's README.