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https://github.com/hitlic/torchility
A pytorch-lightning based tool for training pytorch model more simply
https://github.com/hitlic/torchility
deep-learning pytorch pytorch-lightning-bolts
Last synced: about 15 hours ago
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A pytorch-lightning based tool for training pytorch model more simply
- Host: GitHub
- URL: https://github.com/hitlic/torchility
- Owner: hitlic
- License: mit
- Created: 2021-05-14T13:29:20.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-02T04:58:33.000Z (10 months ago)
- Last Synced: 2024-09-06T07:53:10.508Z (19 days ago)
- Topics: deep-learning, pytorch, pytorch-lightning-bolts
- Language: Python
- Homepage:
- Size: 173 KB
- Stars: 19
- Watchers: 5
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# torchility
A Pytorch-lightning based tool for training pytorch deep learning model more simply.
## Install
- `pip install torchility`
### Dependency
- pytorch>=2.0
- pytorch-lightning>=2.0,<2.1
- torchmetrics>=0.11,<0.12
- matplotlib>=3.3
- pyyaml>=5.4
- tensorboardX>=2.6
## Usage
### Example
- MNIST
```python
from torchility import Trainer
import torch
from torch import nn
from torch.nn import functional as F
from torchvision.datasets import MNIST
from torchvision import transforms
from torch.utils.data import DataLoader, random_split
import warnings
warnings.simplefilter("ignore") # ignore annoying warnings
# datasets
data_dir = './datasets'
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
mnist_full = MNIST(data_dir, train=True, transform=transform, download=True)
train_ds, val_ds = random_split(mnist_full, [55000, 5000])
test_ds = MNIST(data_dir, train=False, transform=transform, download=True)
# dataloaders
train_dl = DataLoader(train_ds, batch_size=32)
val_dl = DataLoader(val_ds, batch_size=32)
test_dl = DataLoader(test_ds, batch_size=32)
# pytorch model
channels, width, height = (1, 28, 28)
model = nn.Sequential(
nn.Flatten(),
nn.Linear(channels * width * height, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 10)
)
# optimizer
opt = torch.optim.Adam(model.parameters(), lr=2e-4)
# trainer
trainer = Trainer(model, F.cross_entropy, opt, epochs=2)
# train and validate
trainer.fit(train_dl, val_dl)
# test
trainer.test(test_dl)
```
- See the `examples` for more examples
### Data Flow
