fix: lower num_workers to 4

For multi-task training in pytorch, each data source will have their own dataloader. If the number of workers of dataloaders is large, there will be many worker processes stressing CPU.

Signed-off-by: Chun Cai <[email protected]>

deepmd/pt/utils/env.py

@@ -21,7 +21,7 @@
     ncpus = len(os.sched_getaffinity(0))
 except AttributeError:
     ncpus = os.cpu_count()
-NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(8, ncpus)))
+NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(4, ncpus)))
 # Make sure DDP uses correct device if applicable
 LOCAL_RANK = os.environ.get("LOCAL_RANK")
 LOCAL_RANK = int(0 if LOCAL_RANK is None else LOCAL_RANK)