One click GPU setup for Fine tuning

SSH, Cursor / VSCode, Jupyter Notebook

Save hours setting up GPUs with the right repo cloned and installs ready to go.

I go through setup for SSH, Cursor / VSCode, Jupyter Notebook.

Cheers, Ronan

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TIMESTAMPS:

00:00 Introduction to Efficient GPU Setup for Fine Tuning

00:14 Choosing Between Google Colab and Run Pod

02:41 Setting Up a Basic Template on Run Pod

04:55 Connecting via SSH and Terminal

12:12 Advanced Template Setup and Customisation

17:16 Conclusion and Additional Resources

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Setting Up GPU Templates for Efficient Fine-Tuning

A comparison of GPU setup options for machine learning fine-tuning, focusing on Google Colab versus RunPod, with detailed templates for quick deployment.

Google Colab Limitations

1. T4 GPU (free tier)

   1. Lacks support for Flash Attention and Brain Float 16

   2. Limited to Float 16 training

2. A100 GPU (paid tier)

   1. 40GB VRAM vs standard 80GB

   2. Cannot easily chain multiple GPUs

   3. Restricted model sizes and batch sizes

RunPod Recommended GPUs

1. H200: Largest VRAM capacity

2. B200: Fastest but with support limitations

3. A40: Cost-effective alternative

Basic Template Setup

1. Container Configuration:

   1. PyTorch 3.11 with CUDA 12.8.1

   2. Automatic repository cloning

   3. Jupyter notebook environment

2. Connection Methods:

   1. Direct Jupyter interface

   2. SSH terminal access

   3. IDE integration (VS Code/Cursor)

Advanced Template Features

1. Environment Setup:

   1. HuggingFace Hub transfer enablement

   2. System updates (apt-get)

   3. Nano text editor installation

   4. Git configuration

2. Authentication:

   1. GitHub personal access token

   2. HuggingFace token

   3. SSH key configuration

3. Custom Kernel Registration:

   1. Environment-specific Python kernel

   2. Pre-installed dependencies

   3. Automatic activation

Time-Saving Benefits

1. Eliminates manual repository cloning

2. Automates environment setup

3. Pre-configures authentication

4. Reduces startup time by 5-10 minutes per session

5. Enables quick instance termination and restart

Template Customization Options

1. Package pre-installation (e.g., UV, Unsloth)

2. Custom environment variables

3. Repository-specific configurations

4. Automated dependency management