run-in-container

To run commands inside a CUDA Docker container, you can use a build-detect-run workflow that executes ad-hoc tasks via docker compose, defaulting to the cuda130-torch environment. It isolates development tasks to ensure consistent environments.

Supported CUDA slot configurations include cuda118-torch, cuda121-torch, cuda124-torch, cuda126-torch, and cuda130-torch. The system defaults to cuda130-torch if no specific slot is provided when executing commands.

No, you do not need to manually build the Docker image beforehand. The workflow checks whether the CUDA slot image is built and available, and builds the image on demand using the provided script before execution.

Yes, you can use docker-compose to execute ad-hoc tasks and experimentation within provided CUDA slot environments. The skill relies on Docker and docker-compose to handle multiple slot configurations for development workflows.

The best way to manage multiple CUDA slot environments is using a build-detect-run workflow with optional image preparation. This approach supports multiple configurations like cuda121-torch and cuda130-torch, isolating tasks for consistency.

Your CUDA slot command execution defaults to the cuda130-torch image to ensure a standardized, consistent environment for development tasks. You can specify a different configuration like cuda118-torch if your workflow requires an alternative setup.