The agent starts or monitors a machine learning training job on a headless GPU rig, checks for CUDA errors, and retrieves loss curves or checkpoints without requiring a display or direct SSH access.
The agent connects to the remote GPU machine via the MCP server. It checks the status of running training processes using tools like nvidia-smi. If errors are found, it attempts to fix them (e.g., freeing stuck cards). It can also retrieve artifacts like loss curves or model checkpoints. This allows for unattended training runs that survive the chat session.
- Category: Data and analysis
- Effect: Changes data in your accounts
- MCP servers: AI Commander MCP (https://koskamo.com/explore/ai-commander-mcp)
- Source: Remote AI Agent Use Cases: Servers, GPU, Jobs & Files — AI Commander (https://aicommander.dev/use-cases/)
- Source quote: "Operate a headless rig from your laptop: watch nvidia-smi , fix a CUDA error, free a stuck card, or grab the loss curve."
- Playbook page: https://koskamo.com/explore/use-cases/monitor-and-manage-remote-gpu-training-jobs
Prompt: Check the status of the current ML training job on the GPU rig. If it's stuck, try to free the card. Otherwise, retrieve the latest loss curve data.