RunCompute

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Quickstart#

1. Install and sign in#

Bash
pip install https://runcompute.cloud/sdk/runcompute-0.2.0-py3-none-any.whl
runcompute login

Requires Python 3.10 or newer. Job files (runcompute.toml) need 3.11 or newer.

runcompute login asks for an API key. Create one in the console by entering your email address, then paste it into the terminal. The key is saved to ~/.runcompute/credentials.json and the Python client picks it up without extra setup. No payment method is needed during the preview.

2. Check the price first#

Ask what a job would cost before you submit it. This needs no key.

Python
import runcompute

client = runcompute.Client()
for offer in client.quote(min_vram=24, work_units=4)[:3]:
    print(offer["gpu"], offer["provider"], offer["est_hours"], offer["est_cost"])
Output
RTX4090 Vast 7.27 2.72
L4 GCP 11.43 3.42
A6000 RunPod 8.0 4.16

work_units is how much compute the job needs, measured in A100-hours. A job that takes 4 hours on one A100 is 4 work units. Slower GPUs take longer for the same work.

3. Run your first job#

Save this as first_job.py:

Python
import runcompute

with runcompute.Client() as client:
    job = client.run(
        name="smoke-test",
        image="pytorch/pytorch:latest",
        command=["python", "-c", "import torch; print(torch.cuda.get_device_name(0))"],
        min_vram=16,
        work_units=0.5,
        budget=5,
    )
    print("Job:", job.id)

    done = job.wait()
    print(done.status.value, f"${done.spend:.2f}")
    if not done.succeeded:
        raise SystemExit(f"{done.id} ended as {done.status.value}")
    print(done.logs(tail=10))

Run it with python first_job.py. While it waits, each lifecycle event is printed: the machine it was placed on, checkpoints, preemptions and the move to a new machine.

run() returns as soon as the job is accepted. wait() returns when the job reaches a final status, so check succeeded before using its outputs. Pressing Ctrl+C during wait() cancels the job.

Next#