Open troycjj opened 8 months ago
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
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