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Added n_workers in generate_data for diffusion_FWI #1229

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Added n_workers in generate_data for diffusion_FWI #1229

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f82c077
Added n_workers in generate_data for diffusion_FWI
Dibyajyoti-Chakraborty Nov 13, 2025
a53d06a
Update examples/geophysics/diffusion_fwi/data/generate_data.py
Dibyajyoti-Chakraborty Nov 13, 2025
ceb0f71
Merge branch 'NVIDIA:main' into main
Dibyajyoti-Chakraborty Nov 14, 2025
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15 changes: 13 additions & 2 deletions examples/geophysics/diffusion_fwi/data/generate_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -366,6 +366,12 @@ def main():
help="Peak frequency (Hz) of the Ricker source wavelet used during "
"forward modeling. Defaults to 15.",
)
parser.add_argument(
"--n_workers",
type=int,
default=8,
help="Num of workers per GPU. Defaults to 8",
)
args = parser.parse_args()

dataset_path: Path = Path(args.in_dir) / "samples"
Expand Down Expand Up @@ -402,13 +408,18 @@ def main():
if (i + 1) % 1000 == 0:
logging.info(f"Processed {i + 1} / {total_files} files")
else:
logging.info(f"Found {num_gpus} GPUs. Starting parallel processing.")
workers_per_gpu: int = args.n_workers
num_workers: int = num_gpus * workers_per_gpu

logging.info(
f"Found {num_gpus} GPUs. Starting parallel processing with\
{num_workers} workers ({workers_per_gpu} per GPU).")
args: list[tuple[str, str, int, int]] = [
(filepath, output_path, i % num_gpus, user_source_frequency)
for i, filepath in enumerate(file_list)
Comment on lines 417 to 419
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@greptile-apps greptile-apps bot Nov 13, 2025

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logic: Multiple workers will be assigned to the same GPU (via modulo), but each worker loads models/data onto the same GPU device without coordination. This could cause CUDA out-of-memory errors. Have you tested this with multiple workers per GPU to ensure GPU memory usage doesn't exceed available VRAM?

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@Dibyajyoti-Chakraborty Dibyajyoti-Chakraborty Nov 13, 2025

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It has been tested. Default n_wrokers are set according to that.

]

with mp.get_context("spawn").Pool(processes=num_gpus) as pool:
with mp.get_context("spawn").Pool(processes=num_workers) as pool:
iterator = pool.imap_unordered(process_file_wrapper, args)

for i, result in enumerate(iterator):
Expand Down