# deep-trainer
[](https://github.com/raphaelreme/deep-trainer/actions/workflows/tests.yml)
Baseline code to train deep neural networks.
Currently only available for PyTorch Framework.
## Install
### Pip
```bash
$ pip install deep-trainer
```
### Conda
Not yet available
## Getting started
```python
import torch
from deep_trainer import PytorchTrainer
# Datasets
trainset = #....
valset = #....
testset = #....
# Dataloaders
train_loader = torch.utils.data.DataLoader(trainset, 64, shuffle=True)
val_loader = torch.data.utils.DataLoader(valset, 256)
test_loader = torch.data.utils.DataLoader(testset, 256)
# Model & device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = #....
model.to(device)
# Optimizer & Scheduler
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=len(trainset) * 50, 0.1) # Decay by 10 every 50 epochs
# Criterion
criterion = torch.nn.CrossEntropyLoss() # For classification for instance
# Training
trainer = PytorchTrainer(model, optimizer, scheduler, save_mode="small", device=device)
trainer.train(150, train_loader, criterion, val_loader=val_loader)
# Testing
trainer.load("experiments/checkpoints/best.ckpt")
trainer.evaluate(test_loader, criterion)
```
## Example
`example/example.py` show how to train a PreActResNet with Deep Trainer.
Install the additional requirements and use it with:
```bash
$ # See hyperparameters available
$ python example.py -h
$
$ # Launch the default training
$ python example.py
$
$ # Once done (or during the training), look for default tensorboard logs
$ tensorboard --logdir experiments/logs/
```
This script is reaching around 94-95% accuracy on validation with Cifar10 and a PreActResNet18.<br>
Here are the [training logs](https://tensorboard.dev/experiment/lYN73lSpSm66bddAswIpOw):
## Build and Deploy
```bash
$ python -m build
$ python -m twine upload dist/*
```
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