Changing the working directory
You are using an open-source library that lets you train deep neural networks on your data. Unfortunately, during training, this library writes out checkpoint models (i.e., models that have been trained on a portion of the data) to the current working directory. You find that behavior frustrating because you don't want to have to launch the script from the directory where the models will be saved.
You decide that one way to fix this is to write a context manager that changes the current working directory, lets you build your models, and then resets the working directory to its original location. You'll want to be sure that any errors that occur during model training don't prevent you from resetting the working directory to its original location.
This exercise is part of the course
Writing Functions in Python
Exercise instructions
- Add a statement that lets you handle any errors that might occur inside the context.
- Add a statement that ensures
os.chdir(current_dir)will be called, whether there was an error or not.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
def in_dir(directory):
"""Change current working directory to `directory`,
allow the user to run some code, and change back.
Args:
directory (str): The path to a directory to work in.
"""
current_dir = os.getcwd()
os.chdir(directory)
# Add code that lets you handle errors
____:
yield
# Ensure the directory is reset,
# whether there was an error or not
____:
os.chdir(current_dir)