Pytorch running slower on GPU than CPU
I'm fairly new to python/machine learning and I've been trying to learn PyTorch. Here's a tutorial I've been following:
https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
, which is basically a very simple RNN to classify name into its language. When I run it on a CPU, the final results are
100000 100% (2m 25s) 0.3983 Tsujimoto / Japanese ✓
Training completed! Time taken: 145.90 seconds.
But when I run it on a GPU I get the follwing
100000 100% (5m 56s) 0.1462 Mcgregor / Scottish ✓
Training completed! Time taken: 356.96 seconds.
The only thing I changed was the train function by sending the input tensors to device (device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")) and of course putting the rnn itself to the GPU (rnn = RNN(n_letters, n_hidden, n_categories).to(device):
def train(category_tensor, line_tensor):
hidden = rnn.initHidden().to(device)
category_tensor, line_tensor = category_tensor.to(device), line_tensor.to(device)
rnn.zero_grad()
for i in range(line_tensor.size()[0]):
output, hidden = rnn(line_tensor[i], hidden)
loss = F.nll_loss(output, category_tensor)
loss.backward()
optimizer.step()
return output, loss.item()
Am I doing something wrong?
Thanks in advance!
python pytorch
asked Nov 20 '18 at 8:47
Rcwt
1083
add a comment |
I'm fairly new to python/machine learning and I've been trying to learn PyTorch. Here's a tutorial I've been following:
https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
, which is basically a very simple RNN to classify name into its language. When I run it on a CPU, the final results are
100000 100% (2m 25s) 0.3983 Tsujimoto / Japanese ✓
Training completed! Time taken: 145.90 seconds.
But when I run it on a GPU I get the follwing
100000 100% (5m 56s) 0.1462 Mcgregor / Scottish ✓
Training completed! Time taken: 356.96 seconds.
The only thing I changed was the train function by sending the input tensors to device (device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")) and of course putting the rnn itself to the GPU (rnn = RNN(n_letters, n_hidden, n_categories).to(device):
def train(category_tensor, line_tensor):
hidden = rnn.initHidden().to(device)
category_tensor, line_tensor = category_tensor.to(device), line_tensor.to(device)
rnn.zero_grad()
for i in range(line_tensor.size()[0]):
output, hidden = rnn(line_tensor[i], hidden)
loss = F.nll_loss(output, category_tensor)
loss.backward()
optimizer.step()
return output, loss.item()
Am I doing something wrong?
Thanks in advance!
python pytorch
asked Nov 20 '18 at 8:47
Rcwt
1083
2
You may try different hidden_sizes, see here for this: stackoverflow.com/questions/53325418/… So with higher dim parameters it might be that GPU wins over CPU, however in general RNNs with for-loops are difficult to parallelize and can be painfully slow on GPU. So do not expect too much gain of it. To get most of RNNs it's best to use batching so that computation can be done in parallel.
– blue-phoenox
Nov 20 '18 at 10:00
add a comment |
I'm fairly new to python/machine learning and I've been trying to learn PyTorch. Here's a tutorial I've been following:
https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
, which is basically a very simple RNN to classify name into its language. When I run it on a CPU, the final results are
100000 100% (2m 25s) 0.3983 Tsujimoto / Japanese ✓
Training completed! Time taken: 145.90 seconds.
But when I run it on a GPU I get the follwing
100000 100% (5m 56s) 0.1462 Mcgregor / Scottish ✓
Training completed! Time taken: 356.96 seconds.
The only thing I changed was the train function by sending the input tensors to device (device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")) and of course putting the rnn itself to the GPU (rnn = RNN(n_letters, n_hidden, n_categories).to(device):
def train(category_tensor, line_tensor):
hidden = rnn.initHidden().to(device)
category_tensor, line_tensor = category_tensor.to(device), line_tensor.to(device)
rnn.zero_grad()
for i in range(line_tensor.size()[0]):
output, hidden = rnn(line_tensor[i], hidden)
loss = F.nll_loss(output, category_tensor)
loss.backward()
optimizer.step()
return output, loss.item()
Am I doing something wrong?
Thanks in advance!
python pytorch
asked Nov 20 '18 at 8:47
Rcwt
1083
I'm fairly new to python/machine learning and I've been trying to learn PyTorch. Here's a tutorial I've been following:
https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
, which is basically a very simple RNN to classify name into its language. When I run it on a CPU, the final results are
100000 100% (2m 25s) 0.3983 Tsujimoto / Japanese ✓
Training completed! Time taken: 145.90 seconds.
But when I run it on a GPU I get the follwing
100000 100% (5m 56s) 0.1462 Mcgregor / Scottish ✓
Training completed! Time taken: 356.96 seconds.
The only thing I changed was the train function by sending the input tensors to device (device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")) and of course putting the rnn itself to the GPU (rnn = RNN(n_letters, n_hidden, n_categories).to(device):
def train(category_tensor, line_tensor):
hidden = rnn.initHidden().to(device)
category_tensor, line_tensor = category_tensor.to(device), line_tensor.to(device)
rnn.zero_grad()
for i in range(line_tensor.size()[0]):
output, hidden = rnn(line_tensor[i], hidden)
loss = F.nll_loss(output, category_tensor)
loss.backward()
optimizer.step()
return output, loss.item()
Am I doing something wrong?
Thanks in advance!
python pytorch
python pytorch
asked Nov 20 '18 at 8:47
Rcwt
1083
asked Nov 20 '18 at 8:47
Rcwt
1083
asked Nov 20 '18 at 8:47
Rcwt
1083
asked Nov 20 '18 at 8:47
Rcwt
1083
asked Nov 20 '18 at 8:47
Rcwt
1083
1083
2
You may try different hidden_sizes, see here for this: stackoverflow.com/questions/53325418/… So with higher dim parameters it might be that GPU wins over CPU, however in general RNNs with for-loops are difficult to parallelize and can be painfully slow on GPU. So do not expect too much gain of it. To get most of RNNs it's best to use batching so that computation can be done in parallel.
– blue-phoenox
Nov 20 '18 at 10:00
add a comment |
2
You may try different hidden_sizes, see here for this: stackoverflow.com/questions/53325418/… So with higher dim parameters it might be that GPU wins over CPU, however in general RNNs with for-loops are difficult to parallelize and can be painfully slow on GPU. So do not expect too much gain of it. To get most of RNNs it's best to use batching so that computation can be done in parallel.
– blue-phoenox
Nov 20 '18 at 10:00
2
2
You may try different hidden_sizes, see here for this: stackoverflow.com/questions/53325418/… So with higher dim parameters it might be that GPU wins over CPU, however in general RNNs with for-loops are difficult to parallelize and can be painfully slow on GPU. So do not expect too much gain of it. To get most of RNNs it's best to use batching so that computation can be done in parallel.
– blue-phoenox
Nov 20 '18 at 10:00
You may try different hidden_sizes, see here for this: stackoverflow.com/questions/53325418/… So with higher dim parameters it might be that GPU wins over CPU, however in general RNNs with for-loops are difficult to parallelize and can be painfully slow on GPU. So do not expect too much gain of it. To get most of RNNs it's best to use batching so that computation can be done in parallel.
– blue-phoenox
Nov 20 '18 at 10:00
add a comment |
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You may try different hidden_sizes, see here for this: stackoverflow.com/questions/53325418/… So with higher dim parameters it might be that GPU wins over CPU, however in general RNNs with for-loops are difficult to parallelize and can be painfully slow on GPU. So do not expect too much gain of it. To get most of RNNs it's best to use batching so that computation can be done in parallel.
– blue-phoenox
Nov 20 '18 at 10:00