There are several built-in Juptyer Kernels ready-to-use when working on an AWS Sagemaker Jupyter Notebook Instance. They are easy to use but generally not convenient to customize:
- If you want to install new packages to the built-in Juptyer Kernels, there is no guarantee that the new packages are compatible with the existing ones.
- Even if you can install new packages to the built-in Juptyer Kernels, you will lose the modified/custom kernels when restarting the Notebook Instance.
This repo provides a couple of easy-to-use template scripts to help you set up a custom jupyter kernel on a AWS Sagemaker Jupyter Notebook Instance.
-
Need to edit
environment.yml
to specify your custom environment if you want to build a different Python Kernel to this example in this repo. -
Run the command below to build the custom Python Kernel named
Conda_my-custom-jupyter-kernel
../build_env.sh
(Optional): Every time re-start the Jupyter Notebook instance, run the command below to add the custom Python Kernel.
./start_env.sh