Welcome to the world of Reloading, a Python utility that allows you to reload a loop body from the source code on each iteration without losing the state. This is particularly useful when training deep learning models, as it enables you to make real-time changes without restarting the entire training process.

What is Reloading?

Imagine you’re baking a cake. You realize mid-baking that you’d like to add some chocolate chips. Instead of starting all over again, you simply open the oven, sprinkle in those chips, and continue to bake. This is essentially what Reloading does for your code by allowing you to make changes during execution without losing your previous progress.

Installation

To get started with Reloading, you need to install it. You can easily do this using pip.

pip install reloading

Usage

Once you have installed Reloading, using it is as simple as wrapping your loop or function definition with it. Here are a few examples:

Reloading a For Loop

To reload the body of a for loop from source before each iteration:

from reloading import reloading

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

Reloading a Function

To reload a function from source before each execution, decorate it as follows:

from reloading import reloading

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

Additional Options

You can also customize Reloading’s behavior by using extra parameters:

• every: Reload only every n-th invocation or iteration.
• forever: Use forever=True to create an endless reloading loop.

for i in reloading(range(1000), every=10):
    pass  # This will be reloaded before every 10th iteration

@reloading(every=10)
def some_function():
    pass  # This will be reloaded before every 10th invocation

for i in reloading(forever=True):
    pass  # This code will loop forever and reload from source

Examples

Here are short snippets of how to use the Reloading utility with popular libraries:

PyTorch Example

for epoch in reloading(range(NB_EPOCHS)):
    for images, targets in dataloader:
        optimiser.zero_grad()
        predictions = model(images)
        loss = F.cross_entropy(predictions, targets)
        loss.backward()
        optimiser.step()

Find the full PyTorch example here.

fastai Example

@reloading
def update_learner(learner):
    pass

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

Explore the complete fastai example here.

Keras Example

@reloading
def update_model(model):
    pass

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

Check the full Keras example here.

TensorFlow Example

for epoch in reloading(range(NB_EPOCHS)):
    for images, labels in tqdm(train_ds):
        train_step(images, labels)

View the complete TensorFlow example here.

Testing

Make sure you have Python 3 installed on your machine, then run the following command to test if Reloading is working properly:

$ python3 reloading/test_reloading.py

Troubleshooting

If you encounter any issues while using Reloading, consider the following troubleshooting steps:

• Ensure that Python 3 is properly installed and accessible in your system’s PATH.
• Check if you’re using the latest version of the reloading library by running pip install --upgrade reloading.
• If your code does not seem to reload, make sure that any changes in your code are saved before executing the script again.

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