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https://github.com/amaiya/torchfit
TorchFit is a simple, easy-to-use, and minimalistic training-helper for PyTorch
https://github.com/amaiya/torchfit
Last synced: 29 days ago
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TorchFit is a simple, easy-to-use, and minimalistic training-helper for PyTorch
- Host: GitHub
- URL: https://github.com/amaiya/torchfit
- Owner: amaiya
- License: mit
- Created: 2020-02-06T20:02:20.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-02-12T23:20:47.000Z (over 4 years ago)
- Last Synced: 2024-09-10T11:10:19.501Z (2 months ago)
- Language: Python
- Homepage:
- Size: 1.52 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# TorchFit
`TorchFit` is a bare-bones, minimalistic *training-helper* for **PyTorch** that exposes an easy-to-use `fit` method in the style of **fastai** and **Keras**.
`TorchFit` is intended to be minimally-invasive with a tiny footprint and as little bloat as possible. It is well-suited to those that are new to training models in PyTorch.
## Usage
```python
# normal PyTorch stuff
train_loader = create_your_training_data_loader()
val_loader = create_your_validation_data_loader()
test_loader = create_your_test_data_loader()
model = create_your_pytorch_model()
# wrap model and data in Learner
import torchfit
learner = torchfit.Learner(model, train_loader, val_loader=val_loader)
# estimate LR using Learning Rate Finder
learner.find_lr()
# train using 1cycle learning rate policy
learner.fit_onecycle(1e-4, 3)
# plot training vs. validation loss
learner.plot('loss')
# make predictions as easy as in Keras
y_pred = learner.predict(test_loader)
# save model and reload later
learner.save('/tmp/mymodel')
learer.load('/tmp/mymodel')
```
#### `TorchFit` Training Loop
## Tutorials and Examples
- **[Quickstart with MNIST](https://github.com/amaiya/torchfit/blob/master/examples/quickstart-mnist.ipynb):** quickstart notebook to get you up and running
- **[Tutorial Notebook](https://github.com/amaiya/torchfit/blob/master/examples/tutorial.ipynb):** tutorial notebook using the same model and data employed in the [PyTorch text classification tutorial](https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html)
## Features
#### Learning Rate Finder
```learner.find_lr()```
#### A `fit` method for Training
```
# Examples
learner.fit(lr, epochs)
learner.fit_onecycle(lr, epochs)
learner.fit(lr, epochs, schedulers=[scheduler])
```
#### Easy-to-Execute Testing and Predictions
```
# Examples
outputs = learner.predict(test_loader)
outputs, targets = learner.predict(test_loader, return_targets=True)
text = 'Shares of IBM rose today.'
predicted_label = learner.predict_example(text, preproc_fn=preprocess, labels=labels)
```
#### Gradient Accumulation
```learner.fit_onecycle(lr, 1, accumulation_steps=8)```
#### Gradient Clipping
```learner.fit_onecycle(lr, 1, gradient_clip_val=1)```
#### Mixed Precision Training
```torchfit.Learner(model, train_loader, val_loader=val_loader, use_amp=True, amp_level='O2')```
#### Multi-GPU Training and GPU Selection
To train on first two GPUs (0 and 1):
```learner = torchfit.Learner(model, train_loader, val_loader=test_loader, gpus=[0,1])```
To train only on the second GPU, one can do either this:
```learner = torchfit.Learner(model, train_loader, val_loader=test_loader, gpus=[1])```
or this...
```learner = torchfit.Learner(model, train_loader, val_loader=test_loader, device='cuda:1')```
#### Resetting Weights of Model
```learner.reset_weights()```
#### Saving/Loading Model
```
learner.save('/tmp/mymodel')
learner.load('/tmp/mymodel')
```
## Installation
After ensuring [PyTorch is installed](https://pytorch.org/get-started/locally/), install `TorchFit` with:
```
pip3 install torchfit
```