Gpu Cloud Provisioner

    by TopAgent

    1

    Use to rent GPU instances from Lambda, RunPod or Vast: compare price and availability, launch an instance with a chosen image, wire up SSH, run training, monito

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    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    See it in action

    You say

    Find the cheapest 4090 available on Vast or Lambda, sync my ./finetune folder, and run train.py for 5 epochs. Terminate as soon as it's done.

    Your agent does

    Found RTX 4090 on Vast.ai at $0.42/hr.

    1. Instance launched (ID: 782910).
    2. Code synced via rsync.
    3. Training started in tmux session 'train'.
    4. Monitoring active. I will call terminate-instance the moment train.log shows completion.

    What you get

    Compare GPU spot prices across multiple cloud providers automatically.Provision instances and configure SSH access without manual console navigation.Deploy and run training jobs in detached tmux sessions for stability.Sync local checkpoints back to local storage and auto-terminate to save costs.

    About this skill

    The problem

    Provisioning GPU instances manually is slow and error-prone. You waste money when you forget to terminate a box after training completes or overpay by not comparing real-time spot prices across vendors.

    What it does

    • Queries Lambda Labs, RunPod, and Vast.ai APIs to compare real-time price and availability.
    • Launches instances with specific images and configures SSH access automatically.
    • Synchronizes local project code and datasets to the remote instance via rsync.
    • Orchestrates headless execution using tmux to ensure training survives connection drops.
    • Monitors GPU health and terminates instances immediately upon job completion to stop billing.

    Frameworks & tools

    Lambda Labs API, RunPod API, Vast.ai API, SSH, rsync, tmux, and nvidia-smi.

    Why this beats prompting it yourself

    Generic LLMs don't handle the stateful lifecycle of a cloud instance. This skill manages the specific API sequences, polling logic, and cleanup triggers required to prevent runaway cloud bills.

    Use cases

    • Fine-tuning large language models on A100 or H100 clusters.
    • Running intensive kernel benchmarks on consumer GPUs like the RTX 4090.
    • Automating long-running training jobs that require persistent remote sessions.
    • Optimizing compute costs by programmatically selecting the cheapest available spot instance.

    Known limitations

    Requires pre-configured API keys for vendors and pre-uploaded SSH keys in the respective cloud consoles. Not intended for permanent infrastructure or reserved instances.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    TopAgent
    TopAgent

    32 skills on Agensi

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