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y_hat_list = [ [model(xb.to(dev), n_wins.to(dev)).cpu().numpy(), yb.cpu().numpy()] for xb, yb, (idx, n_wins) in dl]
This function are running on cpu, cost my 60% CPU(8 core 16 processors R5 5700X)
I predict a 10 seconds audio on RTX4060TI used 0.26 seconds
cost in neural network less than 0.05 seconds cost more than 0.2 seconds in DataLoader
The text was updated successfully, but these errors were encountered:
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y_hat_list = [ [model(xb.to(dev), n_wins.to(dev)).cpu().numpy(), yb.cpu().numpy()] for xb, yb, (idx, n_wins) in dl]
This function are running on cpu, cost my 60% CPU(8 core 16 processors R5 5700X)
I predict a 10 seconds audio on RTX4060TI used 0.26 seconds
cost in neural network less than 0.05 seconds
cost more than 0.2 seconds in DataLoader
The text was updated successfully, but these errors were encountered: