How to manage working directory in interactive development environments like Jupyter Notebook?

Question

I'm having trouble with managing the working directory in Jupyter Notebook. For example, I have a .py script that requires me to change the working directory to its directory to run it properly. I've tried two approaches, but both have drawbacks:

• Using !cd: This executes in a subshell, so the IPython session's working directory doesn't change. But then I can't debug the script with breakpoints.
• Using %cd or os.chdir(): This allows debugging. But it changes the IPython session's working directory, which affects all subsequent cells. This often leads to errors when later cells execute in an unexpected working directory.

How should I manage the working directory in a Jupyter Notebook workflow? Should I keep it fixed (e.g., as the notebook's directory) and avoid changes, or check and adjust it before running each cell?

Some might argue that scripts shouldn't rely on the working directory, but refactoring third-party code with great efforts to avoid this is impractical. Are there any recommended practices that deal effectively with this scenario, while also applying to similar situations?

• Greg Burghardt: Have you tried keeping the working directory fixed? Does that work? What are the drawbacks with respect to your workbook?
• Doc Brown: Close voters, please hold your breath, I am working on an answer, this is a real conceptional issue, which is not just restricted to Python or Jupyter notebooks.
• amon: Fix the script. Alternatively, use contextlib.chdir() instead of os.chdir() in order to temporarily change the working directory.
• newbie: @GregBurghardt Yes. This is my current choice. I change it as needed, and switch it back afterward. I use a context manager to handle this, and it works fine. But it looks a bit awkward when the code is littered with context managers. So I wonder whether there is a more elegant way to handle this kind of situation.
• newbie: @amon Thanks for your suggestion! I hadn't heard of contextlib.chdir() before. I was already implementing a similar solution myself.

Answer 1

Excellent question. This is not just a problem of Jupyter Notebooks or Python. It is always a problem when code X calls a 3rd party script Y, and Y requires a special current directory or even worse, changes it.

Note the current directory is a property of the current process in which the code runs. So the first question you need to answer here is, when you call a separate script, do you want to call it

In-process or Out-Of-Process?

Both of these two approaches have some pros and cons, and you need to know the differences when you want to make a decision.

You already noted that calling some script code in-process has the advantage of making debugging easier. It also makes the whole communication between the caller and the callee simpler, since one does not need to implement any interprocess communication mechanics. Moreover, when you have to call a 3rd party function very often, it will also bring performance benefits, since creating a new process has a certain overhead.

The drawbacks of in-process calls are that any process-related side effects will affect the caller. The current directory is here just one issue. Another, even worse issue is when the called script has a bug and terminates the current process, then the caller - who runs inside the same process - will be terminated as well.

Out-of-process calls, on the other hand, have the benefit of providing much better isolation. Changes of the current directory or other process properties like environment variables won't affect the caller. A crash of the called script may be handled gracefully by the caller. The drawbacks, however, are the advantages for in-process calls I mentioned above.

So how to decide? The situation depends heavily on your use case and the kind of script you want to call, especially when it is 3rd party code you don't want to change - or at least not change much. Factors involved here are

• how often will this code be called, is calling out-of-process even an option (performance wise, or IPC wise)?

• Is the current directory setting the only process-related side effect you have to deal with, or may there by other ones? If you know for sure this is the only issue, and you know for sure there are no parallel threads affected by a temporary change of the CD, you could wrap the called code in another script which sets the CD to the scripts location at the beginning, and resets it afterwards.

  On the other hand, if the called script may have some more process-related (and unwanted) side effects, calling in a separate process may be necessary to gain the necessary isolation for creating a stable system.

• How do you want to handle crashes of the called script in some way? Or endless loops? Is it ok when you whole program terminates or blocks when the script terminates or blocks? For many programs, this is fine, for others, it is totally unacceptable.

• Is it feasible to change and/or debug the script, for example, to make it work without changing the current directory, or to fix certain bugs? That would allow to run a script in-process which one otherwise has to run out-of-process.

Finally note the debugging issue for out-of-process calls can be mitigated. You could set up your dev environment in a way where you can debug such a callee separately, by logging the calling parameters, which gives you the chance to start a Python debugging session for the script in isolation, with the exact parameters which were passed in by the Jupyter Notebook. Or you install some tools which support more direct out-of-process debugging of the scripts.