run-experiment

Automating ML experiment deployment uses this skill to synchronize code, set up environments, and allocate GPUs across local machines, SSH-based remote servers, Vast.ai, and Modal. It performs pre-flight checks to verify GPU availability and handles the entire deployment process end-to-end.

Yes, remote deployment requires SSH access and cloud service configuration. You must configure Vast.ai and Modal platforms in your project setup to enable automated code synchronization and remote GPU allocation for your machine learning experiments.

Yes, you can run machine learning experiments on Vast.ai and Modal. The skill detects the appropriate environment from your project setup, handles container launching, and manages resource allocation including GPU memory usage and cost estimation for these cloud platforms.

The best way to manage GPU allocation for remote execution is to use automated deployment scripts that perform pre-flight checks for GPU availability. This approach handles code synchronization and container setup, ensuring efficient GPU memory usage and accurate cost estimation across cloud platforms.

Code synchronization for cloud service deployment works by detecting the target environment and automatically syncing local project files to remote servers. This ensures the remote environment matches your local setup before launching containers and allocating GPUs for experiment execution.