McVicker Lab Run:ai Platform Guide

Introduction

Welcome to the McVicker Lab's guide for the Run:ai Platform. This repository is dedicated to assisting members of our lab at the Salk Institute in maximizing the benefits of the Run:ai Platform for machine learning research. Here, you'll find resources tailored for various roles within our team, including administrators, researchers, and developers.

IMPORTANT: Accessing the Run:ai Platform

To access the Run:ai platform, please ensure the following steps are completed:

1. Connect to the VPN. This is a prerequisite for accessing the Run:ai portal.
2. Follow the Installation Instructions. Detailed setup instructions can be found on the McVicker Lab website: Run:ai Setup Guide.
3. Log in to the Run:ai Portal. Use your SSO credentials at Salk Institute Run:ai Portal.

Table of Contents

1. For Run:ai Administrators
2. For Researchers
3. For Developers
4. Building Custom Docker Containers
5. Grid Search with CLI for Hyperparameter Optimization
6. Contributing
7. Support and Contact

For Run:ai Administrators

Overview: Administrators are responsible for the setup and ongoing maintenance of the Run:ai Platform. This section provides essential resources for effective administration.

• Setup Guide: Administrator Documentation
• Best Practices: Insights into effective system management.
• Troubleshooting: Solutions to common administrative challenges.

For Researchers

Overview: Researchers utilize the Run:ai Platform to submit and manage jobs. This section is dedicated to helping researchers get the most out of Run:ai.

• Getting Started: Researcher Documentation
• Job Submission Guide: Step-by-step instructions on submitting jobs.
• Advanced Techniques: Guidance on advanced research methodologies using Run:ai.

For Developers

Overview: Developers in our lab use the Run:ai APIs for job manipulation and system integration. This section provides resources for developers to effectively utilize these APIs.

• API Documentation: Developer Documentation
• Integration Examples: Real-world examples of Run:ai integrations.
• Development Best Practices: Tips for efficient and effective development.

Building Custom Docker Containers

This section guides you through the process of building custom Docker containers for use on the Run:ai Platform.

• Container Creation Guide: For detailed instructions and resources on building Docker containers, please visit our Run:ai Docker Files Repository.
• Sample Dockerfiles: Explore our Sample Mamba-GVL Dockerfile for an example of a custom container setup.

Grid Search with CLI for Hyperparameter Optimization

Leverage the power of grid search with the Run:ai CLI to optimize your machine learning models' hyperparameters.

• Grid Search Tutorial: A detailed guide to implementing grid search using the Run:ai CLI.
• Example Scripts: Practical scripts demonstrating grid search techniques.

Contributing

Interested in contributing to this repository? Here’s how you can help:

• Contribution Guidelines: Standards and procedures for contributing.
• Code of Conduct: Expectations for community engagement.

Support and Contact

For support, questions, or further information, please reach out to Jeff Jaureguy.

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This README is a dynamic document and will be updated regularly to reflect new insights and resources. Stay tuned for updates and enhancements.