Explore the NVIDIA AI Workbench in this comprehensive tutorial to streamline AI application development and scale productivity. Learn to set up the workbench on MacOS and Ubuntu 22.04, migrate projects from local machines to cloud servers, fine-tune models using advanced GPUs, automate environment configurations, and integrate with Git for version control. Discover how to run code on local and remote computers, set up a GPU-enabled virtual machine, install and configure AI Workbench on Ubuntu, and execute Jupyter Notebooks on remote servers. Follow along with a practical example of running and fine-tuning models, and gain insights into focusing on AI development without setup concerns, leveraging cloud computing power, and enjoying streamlined workflows with automated processes.
NVIDIA AI Workbench: Scaling AI Application Development
Overview
Syllabus
- Introduction
- Local vs. Remote Computing
- Setting Up a Remote VM with GPU
- Installing AI Workbench on Ubuntu
- Git Integration
- Running Jupyter Notebooks Remotely
- Practical Example and Model Fine-Tuning
Taught by
Mervin Praison
