TorchZQ: A PyTorch experiment runner.
Project description
TorchZQ: A PyTorch experiment runner built with zouqi
Installation
Install from PyPI:
pip install torchzq
Install the latest version:
pip install git+https://github.com/enhuiz/torchzq@main
An Example for MNIST Classification
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchzq
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
class Runner(torchzq.Runner):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def create_model(self):
return Net()
def create_dataset(self):
return datasets.MNIST(
"../data",
train=self.training,
download=True,
transform=transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
]
),
)
def prepare_batch(self, batch):
x, y = batch
x = x.to(self.args.device)
y = y.to(self.args.device)
return x, y
def training_step(self, batch, optimizer_index):
x, y = self.prepare_batch(batch)
loss = F.nll_loss(self.model(x), y)
return loss, {"nll_loss": loss.item()}
@torch.no_grad()
def testing_step(self, batch, batch_index):
x, y = self.prepare_batch(batch)
y_ = self.model(x).argmax(dim=-1)
return {"accuracy": (y_ == y).float().mean().item()}
if __name__ == "__main__":
torchzq.start(Runner)
Run an Example
Training
tzq example/config/mnist.yml train
Testing
tzq example/config/mnist.yml test
Weights & Biases
Before you run, login Weights & Biases first.
pip install wandb # install weight & bias client
wandb login # login
Supported Features
- Model checkpoints
- Logging (Weights & Biases)
- Gradient accumulation
- Configuration file
- FP16
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