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Add benchmark for NetCDF --> Zarr cloud-optimization (#1551)
Co-authored-by: Hendrik Makait <hendrik@coiled.io>
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import xarray as xr | ||
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def test_cloud_optimize( | ||
scale, | ||
s3, | ||
s3_url, | ||
client_factory, | ||
cluster_kwargs={ | ||
"workspace": "dask-benchmarks", | ||
"region": "us-west-2", | ||
"wait_for_workers": True, | ||
}, | ||
scale_kwargs={ | ||
"small": {"n_workers": 10}, | ||
"medium": {"n_workers": 100}, | ||
"large": {"n_workers": 200}, | ||
}, | ||
): | ||
with client_factory( | ||
**scale_kwargs[scale], **cluster_kwargs | ||
) as client: # noqa: F841 | ||
# Define models and variables of interest | ||
models = [ | ||
"ACCESS-CM2", | ||
"ACCESS-ESM1-5", | ||
"CMCC-ESM2", | ||
"CNRM-CM6-1", | ||
"CNRM-ESM2-1", | ||
"CanESM5", | ||
"EC-Earth3", | ||
"EC-Earth3-Veg-LR", | ||
"FGOALS-g3", | ||
"GFDL-ESM4", | ||
"GISS-E2-1-G", | ||
"INM-CM4-8", | ||
"INM-CM5-0", | ||
"KACE-1-0-G", | ||
"MIROC-ES2L", | ||
"MPI-ESM1-2-HR", | ||
"MPI-ESM1-2-LR", | ||
"MRI-ESM2-0", | ||
"NorESM2-LM", | ||
"NorESM2-MM", | ||
"TaiESM1", | ||
"UKESM1-0-LL", | ||
] | ||
variables = [ | ||
"hurs", | ||
"huss", | ||
"pr", | ||
"rlds", | ||
"rsds", | ||
"sfcWind", | ||
"tas", | ||
"tasmax", | ||
"tasmin", | ||
] | ||
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if scale == "small": | ||
# 130 files (152.83 GiB). One model and one variable. | ||
models = models[:1] | ||
variables = variables[:1] | ||
elif scale == "medium": | ||
# 390 files. Two models and two variables. | ||
# Currently fails after hitting 20 minute idle timeout | ||
# sending large graph to the scheduler. | ||
models = models[:2] | ||
variables = variables[:2] | ||
else: | ||
# 11635 files. All models and variables. | ||
pass | ||
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# Get netCDF data files -- see https://registry.opendata.aws/nex-gddp-cmip6 | ||
# for dataset details. | ||
file_list = [] | ||
for model in models: | ||
for variable in variables: | ||
data_dir = f"s3://nex-gddp-cmip6/NEX-GDDP-CMIP6/{model}/historical/r1i1p1f1/{variable}/*.nc" | ||
file_list += [f"s3://{path}" for path in s3.glob(data_dir)] | ||
files = [s3.open(f) for f in file_list] | ||
print(f"Processing {len(files)} NetCDF files") | ||
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# Load input NetCDF data files | ||
# TODO: Reduce explicit settings once https://github.com/pydata/xarray/issues/8778 is completed. | ||
ds = xr.open_mfdataset( | ||
files, | ||
engine="h5netcdf", | ||
combine="nested", | ||
concat_dim="time", | ||
data_vars="minimal", | ||
coords="minimal", | ||
compat="override", | ||
parallel=True, | ||
) | ||
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# Rechunk from "pancake" to "pencil" format | ||
ds = ds.chunk({"time": -1, "lon": "auto", "lat": "auto"}) | ||
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# Write out to a Zar dataset | ||
ds.to_zarr(s3_url) |