This is my code for a basic RNN model and I am using the MNSIT dataset. My ultimate goal is to train this model on a custom dataset however I am trying to run this model on the MNSIT dataset so that I can be sure that the code and the model are running properly before I try to run my model.
When I run this model on my GPU I get the error that has been pasted below. However interestingly, When I run my model on CPU instead of my GPU, the model runs perfectly.
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from load_data import IntentEstimationDataset
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
input_size = 28
sequence_length = 28
num_layers = 2
hidden_size = 256
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 2
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size*sequence_length, num_classes)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, _ = self.rnn(x, h0)
out = out.reshape(out.shape[0], -1)
out = self.fc(out)
return out
# Load data:
train_dataset = datasets.MNIST(root='/home/sharyat/catkin_ws/src/data_imu/script/', train=True,
transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='/home/sharyat/catkin_ws/src/data_imu/script/', train=False,
transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size,shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size,shuffle=True)
# initialise model:
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
# Loss and optimiser:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
for epochs in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader):
print(batch_idx)
data = data.to(device=device).squeeze(1)
targets = targets.to(device=device)
# forward
scores = model(data)
loss = criterion(scores, targets)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
def check_accuracy(loader, model):
if loader.dataset.train:
print('Checking accuracy on training data')
else:
print('Checking accuracy on test data')
num_correct = 0
num_samples = 0
model.eval()
with torch.no_grad():
for x, y in loader:
x = x.to(device=device)
y = y.to(device=device)
x = x.reshape(x.shape[0], -1)
scores = model(x)
_, predictions = scores.max[1]
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
print(f'Got {num_correct}/{num_samples} with accuracy {float(num_correct)/float(num_samples)*100}')
model.train()
check_accuracy(train_loader, model)
And After running the code for about 9 mins, I get this error:
Traceback (most recent call last):
File "/home/sharyat/catkin_ws/src/data_imu/script/intent_estimation_model.py", line 63, in <module>
scores = model(data)
File "/home/sharyat/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/sharyat/catkin_ws/src/data_imu/script/intent_estimation_model.py", line 33, in forward
out, _ = self.rnn(x, h0)
File "/home/sharyat/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/sharyat/.local/lib/python3.8/site-packages/torch/nn/modules/rnn.py", line 227, in forward
result = _impl(input, hx, self._flat_weights, self.bias, self.num_layers,
RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED
Process finished with exit code 1
A few things I have considered after looking at other posts:
- My used memory for GPU is no where around the maximum memory.
After running Nvidia-smi I get:
Mon Dec 27 17:56:35 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.82.01 Driver Version: 470.82.01 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
| N/A 39C P5 13W / N/A | 996MiB / 5946MiB | 25% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1054 G /usr/lib/xorg/Xorg 45MiB |
| 0 N/A N/A 1668 G /usr/lib/xorg/Xorg 312MiB |
| 0 N/A N/A 1859 G /usr/bin/gnome-shell 153MiB |
| 0 N/A N/A 2193 G ...AAAAAAAAA= --shared-files 29MiB |
| 0 N/A N/A 2589 G /usr/lib/firefox/firefox 443MiB |
+-----------------------------------------------------------------------------+
After running nvcc --version I get:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
And after running print(torch.version) in python, i get:
1.5.1+cu101
