App

Quickstart: rent a GPU with the API

This page takes you from an API key to a running RTX 4090 you can SSH into, then shuts it down. It uses plain HTTP, so it works the same from curl, Python, or an AI agent. Allow about five minutes.

  • API base URL: https://api.green-compute.com
  • Auth: Authorization: Bearer <your API key> on every request except the public ones marked below

0. One-time setup (a human does this once)

  1. Sign in at green-compute.com/login.
  2. Add credit on the Billing page (card or TAO). Starting a rental needs at least one hour of its price in your balance.
  3. Create an API key under Settings → API keys.

Everything after this step is API-only.

export GC_KEY="paste-your-api-key"
export GC_API="https://api.green-compute.com"

1. Check price and availability (public, no key)

curl -s "$GC_API/platform/pricing"
curl -s https://control.green-compute.com/platform/v1/gpu-pool

/platform/pricing is the exact rate billing charges. gpu-pool shows how many GPUs of each model are free right now. GPU ids are rtx4090 and rtx5090, and any spelling works on input ("RTX 4090", "rtx-4090").

2. Describe the machine you want

A workload says what to run: the image and the hardware. Put your own SSH public key in metadata.ssh_public_keys so you can log in with a key you already hold.

WORKLOAD_ID=$(curl -s -X POST "$GC_API/platform/workloads" \
  -H "Authorization: Bearer $GC_KEY" -H "Content-Type: application/json" \
  -d '{
    "name": "my-gpu-box",
    "kind": "pod",
    "image": "pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime",
    "requirements": {
      "gpu_count": 1,
      "supported_gpu_models": ["rtx4090"],
      "min_vram_gb_per_gpu": 24,
      "cpu_cores": 8,
      "memory_gb": 32
    },
    "metadata": {
      "ssh_public_keys": ["'"$(cat ~/.ssh/id_ed25519.pub)"'"],
      "volume_size_gb": 50
    }
  }' | python3 -c 'import sys,json; print(json.load(sys.stdin)["workload_id"])')
echo "$WORKLOAD_ID"

3. Start it

A deployment is a running instance of the workload. Billing starts when it reaches ready, so time spent pulling the image is free.

DEPLOYMENT_ID=$(curl -s -X POST "$GC_API/platform/deployments" \
  -H "Authorization: Bearer $GC_KEY" -H "Content-Type: application/json" \
  -d "{\"workload_id\": \"$WORKLOAD_ID\"}" \
  | python3 -c 'import sys,json; d=json.load(sys.stdin); print(d["deployment_id"]); print("rate cents/GPU/hr:", d["hourly_rate_cents"], file=sys.stderr)')

hourly_rate_cents in the response is the most this deployment can cost per GPU-hour. With a single GPU model it is the exact rate. Once a GPU is assigned, the field updates to that card's rate, which is never higher. There is no deployment fee: deployment_fee_usd is always 0.

4. Wait until it is ready

while true; do
  STATE=$(curl -s "$GC_API/platform/deployments/$DEPLOYMENT_ID" -H "Authorization: Bearer $GC_KEY" \
          | python3 -c 'import sys,json; print(json.load(sys.stdin)["state"])')
  echo "$STATE"
  case "$STATE" in ready) break ;; failed|terminated) exit 1 ;; esac
  sleep 10
done

States run pending → scheduled → pulling → starting → ready. Usually this takes one to three minutes; the first pull of a large image takes longer.

5. Connect

curl -s "$GC_API/platform/deployments/$DEPLOYMENT_ID/ssh" -H "Authorization: Bearer $GC_KEY"
# {"ssh_host": "...", "ssh_port": 40123, "ssh_username": "root", "ssh_command": "ssh root@... -p 40123", "private_key": "..."}
ssh root@<ssh_host> -p <ssh_port> nvidia-smi

Your public key from step 2 is already authorised, so you don't need the private_key field. It is a platform-generated key, returned only by this endpoint, for clients that didn't supply their own.

The image's environment carries over into SSH sessions, so python and conda work exactly as the image defines them. Persistent storage is mounted at /workspace.

6. Stop it (this stops billing)

curl -s -X DELETE "$GC_API/platform/deployments/$DEPLOYMENT_ID" -H "Authorization: Bearer $GC_KEY"

Billing is per minute, so you pay only for the time it ran.

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