Adding a Custom IPython Kernel in a Notebook Instance

Scenarios

If the default engine environment in notebook does not meet your needs, you can create a conda environment to set up your own environment as required. This section uses python3.6.5 and tensorflow1.2.0 as an example to describe how to set up an IPython kernel.

Procedure

1. Create a conda environment.

   Run the command below on the notebook terminal. In the command, my-env indicates a customizable virtual environment name. For details about conda parameters, see the conda official website.

   conda create --quiet --yes -n my-env python=3.6.5

   After the creation, run the conda info --envs command to view existing virtual environments, where you can see the my-env virtual environment.

   sh-4.4$conda info --envs
   # conda environments:
   #
   base                  *  /home/ma-user/anaconda3
   TensorFlow-2.1           /home/ma-user/anaconda3/envs/TensorFlow-2.1
   my-env                   /home/ma-user/anaconda3/envs/my-env
   python-3.7.10            /home/ma-user/anaconda3/envs/python-3.7.10                  /opt/conda/envs/my-env

2. Access the conda environment.

   source /home/ma-user/anaconda3/bin/activate /home/ma-user/anaconda3/envs/my-env

3. Install the following dependency packages in my env:

   pip install jupyter
   pip install jupyter_core==5.3.0 
   pip install jupyter_client==8.2.0 
   pip install ipython==8.10.0
   pip install ipykernel==6.23.1 

4. Add the virtual environment as an IPython kernel.

   The value of --name can be customized.

   python3 -m ipykernel install --user --name "my-py3-tensorflow-env"

   After the command is executed, the following information is displayed:

   (my-env) sh-4.4$python3 -m ipykernel install --user --name "my-py3-tensorflow-env"
   Installed kernelspec my-py3-tensorflow-env in /home/ma-user/.local/share/jupyter/kernels/my-py3-tensorflow-env

5. Customize the environment variables of the virtual environment kernel.

   Run the cat /home/ma-user/.local/share/jupyter/kernels/my-py3-tensorflow-env/kernel.json command to view the default configuration.

   {
    "argv": [
     "/home/ma-user/anaconda3/envs/my-env/bin/python3",
     "-m",
     "ipykernel_launcher",
     "-f",
     "{connection_file}"
    ],
    "display_name": "my-py3-tensorflow-env",
    "language": "python"
   }

   Add content in the env field as needed. For reference, see the following configuration. PATH indicates the path to the Python package of the virtual environment.

   {
    "argv": [
     "/home/ma-user/anaconda3/envs/my-env/bin/python3",
     "-m",
     "ipykernel_launcher",
     "-f",
     "{connection_file}"
    ],
    "display_name": "my-py3-tensorflow-env",
    "language": "python",
    "env": {
         "PATH": "/home/ma-user/anaconda3/envs/my-env/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/home/ma-user/modelarts/ma-cli/bin",
         "http_proxy": "http://proxy-notebook.modelarts-dev-proxy.com:8083",
         "https_proxy": "http://proxy-notebook.modelarts-dev-proxy.com:8083",
         "ftp_proxy": "http://proxy-notebook.modelarts-dev-proxy.com:8083",
         "HTTP_PROXY": "http://proxy-notebook.modelarts-dev-proxy.com:8083",
         "HTTPS_PROXY": "http://proxy-notebook.modelarts-dev-proxy.com:8083",
         "FTP_PROXY": "http://proxy-notebook.modelarts-dev-proxy.com:8083"
    }
   }

6. Access the IPython kernel of the virtual environment.

   Refresh the JupyterLab page. The custom virtual environment kernel is displayed.

   

   Click my-py3-tensorflow-env to verify that the current environment is used.

   

7. Clear the environment.

   Delete the IPython kernel of the virtual environment.

   jupyter kernelspec uninstall my-py3-tensorflow-env

   Delete the virtual environment.

   conda env remove -n my-env