Why Kaggle Kernels is the Best Way to Share Your Jupyter Notebook
A month ago, I watched the FastAI course lesson on creating your own image dataset and training a state-of-the-art image classifier on it. After building my own dandelion and grass classifier, I was so stoked to share it and show others how to do it. Eventually, I deployed the deep learning model into a web app to share with the world.
During my journey to training this model, one of the biggest bottlenecks to running my notebooks and sharing my work was the hosting service. I needed a Jupyter notebook that could run decently fast (with a GPU, because deep learning), was easy to set up (I didn’t want to deal with the infrastructure, just let me run my notebook), and was completely free (I’m running this Jupyter notebook for educational reasons, I’m not making money off of this). After trying 4 different services, Google Cloud Platform, Binder, Paperspace gradient, and Kaggle, I ultimately chose Kaggle because it met all of my needs.
Kaggle Kernels is a cloud environment where you can run your notebooks. It’s completely free, you only need to sign up for a Kaggle account to create and edit notebooks, every account comes with 1 GPU instance and up to 30 hours/week of use, you don’t need to set up any cloud infrastructure, and it has an amazing community to share your work with.
Paperspace Gradient similarly lets you run your notebooks on a cloud environment, so you don’t need to set up any infrastructure on your own. Simply press a button to turn on your notebook and you’re rolling. There are free instances of notebooks that can include GPUs. Every notebook instance runs for 6 hours before shutting down. I would have used Paperspace gradient, but on two occasions, my account got locked out because there was a bug on Paperspace’s end. It took two days to get my account back, but that was frustrating. Moreover, recently, Paperspace announced that their free instances won’t be available all the time, meaning there are times when you won’t be able to start up your free instance when there is high demand for it.
Google Cloud Platform has all kinds of services, from running virtual machines to Kubernetes clusters. When signing up for the very first time, you also get $300 worth of credits to use. While I could spin up a VM to set up a cloud environment or even run run my notebook on AI Platform Notebooks, it didn’t feel easy. There was infrastructure to set up, it wasn’t completely free (after the credits), and I just wanted to run my notebook with a GPU (which GCP restricted for free users).
Binder is a pretty neat way to turn your Github repo of Jupyter notebooks into a cloud environment easily. It basically clones your repo, looks for any Dockerfile or requirements.txt, sets up a contained cloud environment, and opens up JupyterHub, allowing you to run your notebooks in the cloud. It seemed really awesome, but it did not work for me. My notebook required GPU access, and Binder hosts the notebooks with limited compute. I would use Binder if I wanted to share some lightweight notebooks because it was so damn easy to use.
I ended up putting my notebook on Kaggle Kernels. Follow along if you want to learn how to create your own image dataset of dandelions and grass, and train a state-of-the-art image classifier, using FastAI! Special thanks for FastAI, Jeremy Howard, and Rachel Thomas for the amazing course and library.
