For AI agents

This page is for an AI agent (or its developer) that has been pointed at Green Compute and needs to use it without a human clicking through a dashboard.

What Green Compute is

GPU compute on Bittensor subnet 110, run on renewable and biogas power. Two products, both over one HTTP API:

GPU rental: an SSH-accessible container on an RTX 4090 or 5090, billed per minute. See Quickstart.
Inference: OpenAI-compatible chat completions. See Inference.

Machine-readable entry points

What	URL	Auth
This documentation, as one file	`https://www.green-compute.com/llms-full.txt`	none
Documentation index	`https://www.green-compute.com/llms.txt`	none
Any docs page as Markdown	append `.md`, e.g. `https://www.green-compute.com/docs/quickstart.md`	none
OpenAPI spec	`https://api.green-compute.com/openapi.json`	none
API index	`https://api.green-compute.com/`	none
Prices	`https://api.green-compute.com/platform/pricing`	none
Rentable GPU ids	`https://api.green-compute.com/platform/nodes/supported`	none
Free GPUs right now	`https://control.green-compute.com/platform/v1/gpu-pool`	none

What needs a human, once

Creating the account, adding credit and creating the API key happen in the browser. After that, everything is API. If you have no key, ask your user for one. Point them at green-compute.com/settings and green-compute.com/billing.

Rules of thumb

Use your own SSH public key in metadata.ssh_public_keys. Then you never need to fetch, store or log a private key.
Budget from the API, not from prose. hourly_rate_cents on a new deployment is the most it can cost per GPU-hour; /platform/pricing is what billing charges.
Always clean up. DELETE /platform/deployments/{id} stops billing. Wrap your work so this runs even on failure.
Poll, don't sleep blindly. GET /platform/deployments/{id} every 5–10 s until state is ready; stop on failed and read last_error.
Errors are actionable. A 400 for an unavailable GPU lists the GPUs that are available; a 402 gives the exact amount to top up. See Errors.
For an RTX 5090, use a CUDA 12.8+ image. pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime works on both cards.
One-off commands work over SSH. ssh root@host -p port 'python train.py' uses the image's PATH, so conda/PyTorch images behave as documented.

Minimal loop (Python, standard library only)

import json, os, time, urllib.request

API = "https://api.green-compute.com"
KEY = os.environ["GC_KEY"]

def call(method, path, body=None):
    req = urllib.request.Request(
        API + path, method=method,
        data=json.dumps(body).encode() if body is not None else None,
        headers={"Authorization": f"Bearer {KEY}", "Content-Type": "application/json"},
    )
    with urllib.request.urlopen(req) as r:
        return json.loads(r.read() or b"null")

pubkey = open(os.path.expanduser("~/.ssh/id_ed25519.pub")).read().strip()
wl = call("POST", "/platform/workloads", {
    "name": "agent-job", "kind": "pod",
    "image": "pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime",
    "requirements": {"gpu_count": 1, "supported_gpu_models": ["rtx4090"],
                     "min_vram_gb_per_gpu": 24, "cpu_cores": 8, "memory_gb": 32},
    "metadata": {"ssh_public_keys": [pubkey]},
})
dep = call("POST", "/platform/deployments", {"workload_id": wl["workload_id"]})
try:
    while (d := call("GET", f"/platform/deployments/{dep['deployment_id']}"))["state"] not in ("ready", "failed"):
        time.sleep(10)
    if d["state"] == "failed":
        raise RuntimeError(d.get("last_error"))
    ssh = call("GET", f"/platform/deployments/{dep['deployment_id']}/ssh")
    print("connect with:", ssh["ssh_command"])
    # ... do the work over SSH ...
finally:
    call("DELETE", f"/platform/deployments/{dep['deployment_id']}")

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