Comments (1)
t-vi
commented on May 27, 2024
1
To facilitate discussion, I generated these numbers by adding thunder/tests/conftest.py. If raising an error there leads to the test failing, we could use a similar setup for enforcing a GPU memory limit in the parallel tests.
import pytest
import torch
cnt = 0
@pytest.fixture(autouse=True)
def gpu_memory(request):
global cnt
cnt += 1
yield
if torch.cuda.is_available():
print("\ntest cuda memory use", request.node.name, cnt, "memory", torch.cuda.max_memory_allocated() / 2**30)
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
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Related Issues (20)
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