To run all cells from top to bottom (in other words, the entire program) you can go to Menu –> Run –> Run All Cells. You can run each cell individually by clicking in the cell and then hitting the >| Run button or using keyboard shortcut Shift + Enter.
To run the machine learning notebook, double-click the ml-stock-predictor-knn-v4.ipynb file. Open the /data-science/jupyter-hub directory. To jumpstart your data science learning, two sample notebooks are in the data science directory created by your git clone operation in the previous step. ml-stock-predictor-knn-v4.ipynb – Machine learning (ML) based on revenue data from public financial statements that leverages the scikit-learn library for Python. industry-revenue-analysis.ipynb – Analysis of historical financial data organized by industry that leverages the pandas library. To demonstrate the capabilities of JupyterHub and Python, we will check out the following sample notebooks that we have written and executed: Using JupyterHubĮach user that accesses JupyterHub will have their own workspace complete with a single-user Jupyter Notebook server, which uses the JupyterLab Interface. To illustrate the value that Jupyter Notebooks and JupyterHub provide in a multiuser environment, we will clone a Git repository containing two example Jupyter Notebooks that we can work with. 
In this post, we will focus on the experience of the developers, who are going to be leveraging our service to develop new models using scikit-learn or perform calculations and transformations of large datasets using pandas.
#JUPYTERLAB KUBERNETES HOW TO#
We also showed you how to install a local Kubernetes cluster using kind on your Mac, as well as how to install the JupyterHub Helm chart on a Kubernetes cluster. In a previous post, we discussed the advantages of running JupyterHub on Kubernetes.
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