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May 21, 2022 19:13
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Training MNIST on the M1 GPU with PyTorch.
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""" | |
MNIST with PyTorch on Apple Silicon GPU | |
Script will be linked in the description as a Github Gist. | |
Install PyTorch nightly with this command: | |
pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu | |
Code borrowed from PyTorch Examples. | |
""" | |
import torch | |
from torch import nn, optim | |
import torch.nn.functional as F | |
import torchvision | |
from torchvision import datasets, transforms | |
EPOCHS = 5 | |
class Net(nn.Module): | |
def __init__(self): | |
super(Net, self).__init__() | |
self.conv1 = nn.Conv2d(1, 20, 5, 1) | |
self.conv2 = nn.Conv2d(20, 50, 5, 1) | |
self.fc1 = nn.Linear(4*4*50, 500) | |
self.fc2 = nn.Linear(500, 10) | |
def forward(self, x): | |
x = F.relu(self.conv1(x)) | |
x = F.max_pool2d(x, 2, 2) | |
x = F.relu(self.conv2(x)) | |
x = F.max_pool2d(x, 2, 2) | |
x = x.view(-1, 4*4*50) | |
x = F.relu(self.fc1(x)) | |
x = self.fc2(x) | |
return F.log_softmax(x, dim=1) | |
def train(model, device, train_loader, optimizer, epoch): | |
model.train() | |
for batch_idx, (data, target) in enumerate(train_loader): | |
data, target = data.to(device), target.to(device) | |
optimizer.zero_grad() | |
output = model(data) | |
loss = F.nll_loss(output, target) | |
loss.backward() | |
optimizer.step() | |
if batch_idx % 10 == 0: | |
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( | |
epoch, batch_idx * len(data), len(train_loader.dataset), | |
100. * batch_idx / len(train_loader), loss.item())) | |
def test(model, device, test_loader): | |
model.eval() | |
test_loss = 0 | |
correct = 0 | |
with torch.no_grad(): | |
for data, target in test_loader: | |
data, target = data.to(device), target.to(device) | |
output = model(data) | |
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss | |
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability | |
correct += pred.eq(target.view_as(pred)).sum().item() | |
test_loss /= len(test_loader.dataset) | |
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( | |
test_loss, correct, len(test_loader.dataset), | |
100. * correct / len(test_loader.dataset))) | |
def main(): | |
print("PyTorch version:", torch.__version__) | |
print("Torchvision version:", torchvision.__version__) | |
device = torch.device("mps") | |
print("Using Device: ", device) | |
train_loader = torch.utils.data.DataLoader( | |
datasets.MNIST('../data', train=True, download=True, | |
transform=transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.1307,), (0.3081,)) | |
])), | |
batch_size=128, shuffle=True) | |
test_loader = torch.utils.data.DataLoader( | |
datasets.MNIST('../data', train=False, transform=transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.1307,), (0.3081,)) | |
])), | |
batch_size=128, shuffle=True) | |
model = Net().to(device) | |
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5) | |
for epoch in range(1, EPOCHS + 1): | |
train(model, device, train_loader, optimizer, epoch) | |
test(model, device, test_loader) | |
if __name__ == "__main__": | |
main() |
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