Orchestrate headless GPU workloads via SSH or HTTP API.
npx skills add https://github.com/broomva/skills --skill remote-gpu
Please help me install this Agent Skill. Skill: remote-gpu Source: https://github.com/broomva/skills/tree/main/skills/compute/remote-gpu Command: npx skills add https://github.com/broomva/skills --skill remote-gpu
This skill solves the friction of managing headless GPU hardware by providing a unified interface to submit, monitor, and retrieve results from remote servers without manual SSH session management.
Use the remote-gpu skill to submit a training job to the server by running the gpu-submit command followed by your training script and arguments.
High-intent search queries and answers about installing and using this skill.
You can orchestrate remote GPU training jobs by submitting scripts via SSH or an HTTP API. The system manages the job queue, streams logs, and synchronizes files between your local workstation and the headless server automatically.
Yes, you can launch interactive Claude Code sessions directly on remote high-compute machines. This allows you to leverage remote GPU hardware for development and agentic loops without manual SSH session management.
Before submitting inference workloads, you need SSH access to the remote server, rsync installed locally, and Python-based server components. These prerequisites enable command execution and file synchronization across distributed compute environments.
File synchronization relies on rsync to transfer data between your local workstation and remote servers. It operates automatically alongside job orchestration to ensure your training scripts and output files remain consistent across environments.
Yes, the orchestration approach supports real-time GPU telemetry. It uses Python server components to monitor remote hardware status, allowing you to track active workloads and system health during training or inference jobs.
Using FastAPI and SSH for headless server orchestration requires continuous network connectivity and manual setup of Python server components on each remote machine. It does not auto-provision hardware, meaning pre-configured SSH access is mandatory for job submission.
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