Deploy and run ML experiments on local or remote GPU servers.
npx skills add https://github.com/KwongFuk/codex-skills --skill run-experiment-kwongfuk
Please help me install this Agent Skill. Skill: run-experiment Source: https://github.com/KwongFuk/codex-skills/tree/main/global/run-experiment Command: npx skills add https://github.com/KwongFuk/codex-skills --skill run-experiment-kwongfuk
Deploy and run ML experiments on local or remote GPU servers, enabling researchers and engineers to kick off training jobs from a single command set.
Begin by providing your experiment script and an AGENTS.md configuration to deploy and run the training on the target GPU server or locally.
High-intent search queries and answers about installing and using this skill.
The tool orchestrates remote ML experiment deployment by using SSH to connect and run training tasks within a screen session. It reads AGENTS.md for configuration and performs pre-flight GPU checks before launching the job.
The best way to synchronize code for remote GPU training is using either rsync or git workflows. The tool handles this code transfer automatically as part of its deployment orchestration before launching the experiment.
Yes, the tool supports WandB integration by automatically instrumenting your training scripts when configured. This allows you to track ML experiments without modifying your code.
Yes, you can use this to check GPU availability because it performs pre-flight GPU checks before launching training tasks. This ensures GPUs are available on the target machine before deployment.
You configure local versus remote deployment by providing an AGENTS.md file. The tool reads this configuration to determine whether to run directly on the host or deploy across a remote SSH session.
You need an AGENTS.md file because it provides the environment-aware configuration required to determine remote versus local context and available GPUs. It acts as the central setup file for orchestrating the deployment.
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