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Change Python code while it's running without losing state

reloading 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# reloading
[![pypi badge](https://img.shields.io/pypi/v/reloading?color=%230c0)](https://pypi.org/project/reloading/)

A Python utility to reload a loop body from source on each iteration without
losing state

Useful for editing source code during training of deep learning models. This lets
you e.g. add logging, print statistics or save the model without restarting the
training and, therefore, without losing the training progress.

![Demo](https://github.com/julvo/reloading/blob/master/examples/demo/demo.gif)

## Install
```
pip install reloading
```

## Usage

To reload the body of a `for` loop from source before each iteration, simply 
wrap the iterator with `reloading`, e.g.
```python
from reloading import reloading

for i in reloading(range(10)):
    # this code will be reloaded before each iteration
    print(i)

```

To reload a function from source before each execution, decorate the function
definition with `@reloading`, e.g.
```python
from reloading import reloading

@reloading
def some_function():
    # this code will be reloaded before each invocation
    pass
```

## Additional Options

Pass the keyword argument `every` to reload only on every n-th invocation or iteration. E.g.
```python
for i in reloading(range(1000), every=10):
    # this code will only be reloaded before every 10th iteration
    # this can help to speed-up tight loops
    pass

@reloading(every=10)
def some_function():
    # this code with only be reloaded before every 10th invocation
    pass
```

Pass `forever=True` instead of an iterable to create an endless reloading loop, e.g. 
```python
for i in reloading(forever=True):
    # this code will loop forever and reload from source before each iteration
    pass
```

## Examples

Here are the short snippets of how to use reloading with your favourite library.
For complete examples, check out the [examples folder](https://github.com/julvo/reloading/blob/master/examples).

### PyTorch
```python
for epoch in reloading(range(NB_EPOCHS)):
    # the code inside this outer loop will be reloaded before each epoch

for images, targets in dataloader:
        optimiser.zero_grad()
        predictions = model(images)
        loss = F.cross_entropy(predictions, targets)
        loss.backward()
        optimiser.step()
```
[Here](https://github.com/julvo/reloading/blob/master/examples/pytorch/train.py) is a full PyTorch example.

### fastai
```python
@reloading
def update_learner(learner):
    # this function will be reloaded from source before each epoch so that you
    # can make changes to the learner while the training is running
    pass

class LearnerUpdater(LearnerCallback):
    def on_epoch_begin(self, **kwargs):
        update_learner(self.learn)

path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
learn = cnn_learner(data, models.resnet18, metrics=accuracy, 
                    callback_fns=[LearnerUpdater])
learn.fit(10)
```
[Here](https://github.com/julvo/reloading/blob/master/examples/fastai/train.py) is a full fastai example.

### Keras
```python
@reloading
def update_model(model):
    # this function will be reloaded from source before each epoch so that you
    # can make changes to the model while the training is running using
    # K.set_value()
    pass

class ModelUpdater(Callback):
    def on_epoch_begin(self, epoch, logs=None):
        update_model(self.model)

model = Sequential()
model.add(Dense(64, activation='relu', input_dim=20))
model.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

model.fit(x_train, y_train,
          epochs=200,
          batch_size=128,
          callbacks=[ModelUpdater()])
```
[Here](https://github.com/julvo/reloading/blob/master/examples/keras/train.py) is a full Keras example.

### TensorFlow
```python
for epoch in reloading(range(NB_EPOCHS)):
    # the code inside this outer loop will be reloaded from source
    # before each epoch so that you can change it during training
  
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()
  
    for images, labels in tqdm(train_ds):
      train_step(images, labels)
  
    for test_images, test_labels in tqdm(test_ds):
      test_step(test_images, test_labels)
```
[Here](https://github.com/julvo/reloading/blob/master/examples/tensorflow/train.py) is a full TensorFlow example.

## Testing

Make sure you have `python` and `python3` available in your path, then run:
```
$ python3 reloading/test_reloading.py
```

reloading

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