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:
- Connect to the VPN. This is a prerequisite for accessing the Run:ai portal.
- Follow the Installation Instructions. Detailed setup instructions can be found on the McVicker Lab website: Run:ai Setup Guide.
- Log in to the Run:ai Portal. Use your SSO credentials at Salk Institute Run:ai Portal.
- For Run:ai Administrators
- For Researchers
- For Developers
- Building Custom Docker Containers
- Grid Search with CLI for Hyperparameter Optimization
- Contributing
- Support and Contact
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.
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.
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.
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.
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.
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.
For support, questions, or further information, please reach out to Jeff Jaureguy.
This README is a dynamic document and will be updated regularly to reflect new insights and resources. Stay tuned for updates and enhancements.