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import torch
from pyarkbench import Benchmark, Timer, default_args
use_new = True
class Basic(Benchmark):
def benchmark(self):
x = [torch.ones(200, 200) for i in range(30)]
with Timer() as big1:
torch.save(x, "big_tensor.zip", _use_new_zipfile_serialization=use_new)
with Timer() as big2:
v = torch.load("big_tensor.zip")
x = [torch.ones(10, 10) for i in range(200)]
with Timer() as small1:
torch.save(x, "small_tensor.zip", _use_new_zipfile_serialization=use_new)
with Timer() as small2:
v = torch.load("small_tensor.zip")
return {
"Big Tensors Save": big1.ms_duration,
"Big Tensors Load": big2.ms_duration,
"Small Tensors Save": small1.ms_duration,
"Small Tensors Load": small2.ms_duration,
}
if __name__ == '__main__':
bench = Basic(*default_args.bench())
print("Use zipfile serialization:", use_new)
results = bench.run()
bench.print_stats(results, stats=['mean', 'median'])
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