Deploy and run ML experiments on local or remote GPU servers.
npx skills add https://github.com/kitcaf/skills --skill run-experiment-kitcaf
Please help me install this Agent Skill. Skill: run-experiment Source: https://github.com/kitcaf/skills/tree/main/skills/skills-codex/skills/run-experiment Command: npx skills add https://github.com/kitcaf/skills --skill run-experiment-kitcaf
Deploy and run ML experiments on local or remote GPU servers, handling environment detection, pre-flight checks, and remote/local execution.
Deploy and run an ML experiment by specifying the target script and its arguments.
High-intent search queries and answers about installing and using this skill.
To run ML experiments on a remote GPU server via SSH, this Skill automates code synchronization, pre-flight GPU checks, and execution using screen-backed sessions for reliable long-running deployments.
Yes, you can deploy ML experiments locally with CUDA or MPS setups. The Skill detects the environment based on AGENTS.md configurations and supports execution within local conda environments.
Yes, the deployment workflow supports optional experiment tracking with wandb. It integrates wandb during code synchronization and execution to monitor ML experiments.
The best way to ensure long-running ML training jobs do not disconnect is using screen-backed sessions. This Skill leverages screen for remote SSH execution to maintain reliable, persistent GPU-bound training.
No, you do not need to manually verify GPU availability. The Skill performs pre-flight GPU checks automatically before launching jobs to ensure resources are ready for your ML experiments.
Environment detection for ML deployment targets works by reading configurations specified in AGENTS.md. This determines whether your experiment runs locally or deploys to a remote GPU server.
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