Kaggle docker images come with a huge list of pre-installed packages for machine-learning, including the support of GPU computing. They run within a container as a Jupyter application accessed by users through its web interface. Running a custom image boils down to these steps Below we can see how it looks like The following test …
Continue reading “Customized Jupyter environments on Google Cloud”
In this note, I am sharing a case study on debugging and fixing jupyter-lab access issues. The diagnostic script can be run on a VM instance as shown below: Jupyter service runs from a container, but it somehow stopped in this case 😳 Not a problem! We can restart the container, but carefully choosing the …
Continue reading “Repairing user-managed notebooks on Google Cloud”
How to prototype with multiple ML libraries in Cloud? Best to build on top of a rich pre-configured environment such as Kaggle image, extending it with a local virtual environment.
