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https://github.com/lromul/argus
Lightweight library for training neural networks in PyTorch
https://github.com/lromul/argus
deep-learning machine-learning neural-network python pytorch
Last synced: about 1 month ago
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Lightweight library for training neural networks in PyTorch
- Host: GitHub
- URL: https://github.com/lromul/argus
- Owner: lRomul
- License: mit
- Created: 2018-07-05T09:43:08.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-04-25T23:28:47.000Z (7 months ago)
- Last Synced: 2024-04-26T00:53:33.460Z (7 months ago)
- Topics: deep-learning, machine-learning, neural-network, python, pytorch
- Language: Python
- Homepage: https://pytorch-argus.readthedocs.io
- Size: 830 KB
- Stars: 80
- Watchers: 4
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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Argus is a lightweight library for training neural networks in PyTorch.
## Documentation
https://pytorch-argus.readthedocs.io
## Installation
Requirements:
* torch>=2.0.0
From pip:
```bash
pip install pytorch-argus
```
From source:
```bash
pip install -U git+https://github.com/lRomul/argus.git@dev
```
## Example
Simple image classification example with `create_model` from [pytorch-image-models](https://github.com/rwightman/pytorch-image-models):
```python
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, ToTensor, Normalize
import timm
import argus
from argus.callbacks import MonitorCheckpoint, EarlyStopping, ReduceLROnPlateau
def get_data_loaders(batch_size):
data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
train_mnist_dataset = MNIST(download=True, root="mnist_data",
transform=data_transform, train=True)
val_mnist_dataset = MNIST(download=False, root="mnist_data",
transform=data_transform, train=False)
train_loader = DataLoader(train_mnist_dataset,
batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_mnist_dataset,
batch_size=batch_size * 2, shuffle=False)
return train_loader, val_loader
class TimmModel(argus.Model):
nn_module = timm.create_model
if __name__ == "__main__":
train_loader, val_loader = get_data_loaders(batch_size=256)
params = {
'nn_module': {
'model_name': 'tf_efficientnet_b0_ns',
'pretrained': False,
'num_classes': 10,
'in_chans': 1,
'drop_rate': 0.2,
'drop_path_rate': 0.2
},
'optimizer': ('Adam', {'lr': 0.01}),
'loss': 'CrossEntropyLoss',
'device': 'cuda'
}
model = TimmModel(params)
callbacks = [
MonitorCheckpoint(dir_path='mnist', monitor='val_accuracy', max_saves=3),
EarlyStopping(monitor='val_accuracy', patience=9),
ReduceLROnPlateau(monitor='val_accuracy', factor=0.5, patience=3)
]
model.fit(train_loader,
val_loader=val_loader,
num_epochs=50,
metrics=['accuracy'],
callbacks=callbacks,
metrics_on_train=True)
```
More examples you can find [here](https://pytorch-argus.readthedocs.io/en/latest/examples.html).
Additional guides on how to customize and use argus component can be found in [Guides](https://pytorch-argus.readthedocs.io/en/latest/guides.html) section.
## Why this name, Argus?
The library name is a reference to a planet from World of Warcraft.
Argus is the original homeworld of the eredar (a race of supremely talented magic-wielders), now located within the Twisting Nether.
It was once described as a utopian world whose inhabitants were both vastly intelligent and highly gifted in magic.
It has since been twisted by demonic, chaotic energies and became the stronghold and homeworld of the Burning Legion.