Right, I should clarify.
I’m comparing with NumPy serialization. The picture below times serialization for NumPy and PyTorch with pickle.dumps
on a Macbook Pro 2015.
The core of my code was
def stat(x, serialize=pickle.dumps):
start = time.time()
msg = serialize(x)
return {'time': time.time() - start, 'bytes': len(msg)}
# ... other functions, for-loops, etc
x = np.random.randn(n).astype('float32')
y = torch.Tensor(x)
This is with torch.__version__ == 0.3.0.post4
.