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reloading

(★ 1,127)

Change Python code while it's running without losing state

파일 탐색기

최종 버전 다운로드 (.zip)

- - demo.gif
  - demo.py
- - requirements.txt
  - train.py
- - requirements.txt
  - train.py
- - requirements.txt
  - train.py
- - requirements.txt
  - train.py
- __init__.py
- reloading.py
- test_reloading.py
.gitignore
LICENSE.txt
Makefile
MANIFEST
README.md
setup.cfg
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/julvo/reloading

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd reloading

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install reloading

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install reloading

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

4. Make

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Make Linux/macOS는 보통 기본 설치되어 있습니다. Windows는 별도 설치(예: MSYS2, WSL)가 필요합니다.

make

생성된 빌드 설정을 바탕으로 실제 컴파일을 진행해 실행 파일을 만듭니다.

✅ 에러 없이 끝나면 성공입니다. 생성된 실행 파일을 직접 실행해보세요.

// repository documentation

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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
```