How to Fully Automate a Kaggle Notebook that Requires a Kernel Restart Mid-Run for Library Installation
I've been working on a Kaggle notebook that requires installing a custom library mid-run, which necessitates a kernel restart. This is a huge pain, especially when trying to automate the entire process. I've tried using !pip install and then importlib.reload() but it doesn't seem to work as expected. Has anyone found a reliable way to fully automate this process? Any help or suggestions would be greatly appreciated!
I had the same problem and ended up using Google Colab instead. It doesn’t require a kernel restart for library installations, and it integrates well with Kaggle datasets.
Have you tried using a requirements.txt file? You can specify all your dependencies there and then use !pip install -r requirements.txt to install them all at once. It's a bit of a hack, but it works for me.
Have you tried using a Jupyter notebook locally and then uploading it to Kaggle? You can install all your libraries locally and then upload the notebook to Kaggle.
This is a known issue with Kaggle. One workaround is to use Docker to create a custom environment with all the libraries pre-installed. It's a bit more involved, but it's worth it if you're doing a lot of automated runs.
Why do I have to restart my kernel to install a library? This is like having to reboot your computer every time you download a new app! Come on, Kaggle.