These instructions apply for a cloud instance, server or local machine which has a Ubuntu OS and the ssh user set to 'ubuntu'. For other users, replace /home/ubuntu/ with /home/{USER} in the .yml files and the .conf files. Currently, the playbook is not setup to work on non-Ubuntu installations.
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Edit the hosts file to add the IP address of your instance
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Assuming you have ansible installed and the cloud instance has ubuntu as the ssh user with install privileges, run the following from the ansible folder:
ansible-playbook -i ./hosts cloud_h2o.yml
This reads from the file jupyter.conf to setup a JupyterLab server, change ports as appropriate in this file but make sure that the relevant ports are also open on the instance. Also, set a password for the JupyterLab instance in the jupyter.conf file.
This ansible playbook installs a H2O instance into a conda environment named keras_env. This has Jupyterlab installed and is accessible at port 80.
You can install H2O for GPU into a conda environment named keras_env2 with the command
conda create -c conda-forge -c h2oai -y -n keras_env2 h2o4gpu-cuda10 pandas scikit-learn jupyter jupyter_client==5.3.3 jupyterlab
Start Jupyterlab from the command line as
/home/ubuntu/Anaconda/envs/keras_env2/bin/jupyter lab --allow-root --ip 0.0.0.0 --no-browser --port 8000 --NotebookApp.token={password}
Make sure your instance has port 8000 open so that you can access your JupyterLab instance