JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.
# install python kernel for Jupyter # avoid installation of any jupyter related modules micromamba install anaconda::ipykernel
# added the kernel to Jupyter and name it as "Environment(myproject)" python -m ipykernel install --user --name=mnew-env-1 --display-name='Environment(new-env-1)'
micromamba deactivate
micromamba acitvate new-env-2
# install python kernel for Jupyter # avoid installation of any jupyter related modules micromamba install anaconda::ipykernel
# added the kernel to Jupyter and name it as "Environment(myproject)" python -m ipykernel install --user --name=mnew-env-2 --display-name='Environment(new-env-2)'
To access the remote machine with a browser
To access the remote machine with a browser the notebook must listen on an external facing port (not localhost). You will need the same invocation if want to run the Jupyter notebook on a container. In that case it is something like this:
To listen only in localhost then you can omit the IP
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jupyter notebook --no-browser --port=8080
Monitor resource usage with GPU Dashboard in Jupyter
A GPU dashboard is available for JupyterLab for showing the resource usage of GPU in real time, side by side with cells in your notebook. It is particularly useful in identifying any bottleneck or issues during code development.
You may activate your conda or python virtual environment for your kernel and run the following once to install the GPU dashboard.