Run compute on Modal

Modal runs code in the cloud on demand (CPU/GPU), billed to the user's own Modal account. Like the HPC/Slurm path, the app never handles credentials, you use the user's installed modal CLI and their token. Prefer local execution or Slurm first; reach for Modal when the job needs cloud GPUs or elastic scale and no cluster is available.

1 · Check Modal is ready

Modal must be installed and authenticated (the Settings Cloud compute (Modal) card shows this). Verify before writing code:

modal --version          # installed?
test -f ~/.modal.toml && echo "authenticated" || echo "run: modal token new"

If it is not installed, tell the user to pip install modal; if not authenticated, ask them to run modal token new in their terminal (it opens a browser). Do not attempt to create or store tokens yourself.

2 · Write the Modal function into the workspace

Put the script in the workspace so provenance records it. Pin dependencies in the image so the run is reproducible, and fix any random seed.

# compute.py, run with:  modal run compute.py
import modal

app = modal.App("open-science-job")
image = modal.Image.debian_slim().pip_install("numpy==1.26.4", "scipy==1.13.1")

@app.function(image=image, gpu=None, timeout=1800)  # set gpu="A10G" etc. if needed
def run(n: int = 1_000_000):
    import numpy as np
    rng = np.random.default_rng(0)          # fixed seed → reproducible
    x = rng.standard_normal(n)
    return {"n": n, "mean": float(x.mean()), "std": float(x.std())}

@app.local_entrypoint()
def main():
    result = run.remote()
    print(result)                            # printed locally; capture it below

3 · Run it and capture the result

modal run executes remotely and streams logs + the local entrypoint's stdout back. Write the result into a fresh, immutable result directory so it becomes a traceable artifact and does not overwrite a previous run: