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a9ceba1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | """U-Cast (#10467) rescue: run the REAL ERA5 forecasting benchmark on Modal.
Uses the paper's standalone inference script with the released checkpoint,
streams ERA5 from public WeatherBench2 GCS, computes RMSE + CRPS (--score).
This targets U-Cast's actual performance claim, not just its param count.
"""
import modal
REPO = "u-cast"
image = (modal.Image.debian_slim(python_version="3.11")
.pip_install("torch", "xarray", "netCDF4", "zarr<3", "einops", "tqdm",
"pyyaml", "huggingface_hub", "gcsfs", "numpy", "scipy", "dask")
.add_local_dir(REPO, f"/root/{REPO}", copy=True))
app = modal.App("ucast-inference", image=image)
@app.function(gpu="A10G", timeout=3600)
def run_infer(ic_date="2020-01-01", ensemble=5, horizon=10):
import subprocess, os, re
os.chdir(f"/root/{REPO}")
cmd = [
"python", "run_inference_standalone.py",
"--ckpt-path", "hf:salv47/u-cast/ucast.ckpt",
"--data-dir", "gs://weatherbench2/datasets/era5",
"--config-path", "configs/config_inference.yaml",
"--ic-start-dates", ic_date,
"--ensemble-size", str(ensemble),
"--prediction-horizon", str(horizon),
"--score",
]
p = subprocess.run(cmd, capture_output=True, text=True, timeout=3200)
out = p.stdout + "\n" + p.stderr
# capture score lines (RMSE / CRPS)
scores = [l for l in out.splitlines() if re.search(r"RMSE|CRPS|crps|rmse|score", l, re.I)]
return {"returncode": p.returncode, "score_lines": scores[-40:], "tail": out.splitlines()[-40:]}
@app.local_entrypoint()
def main():
import json
r = run_infer.remote()
print(json.dumps(r, indent=2))
with open("ucast_inference_results.json", "w") as f:
json.dump(r, f, indent=2)
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