Python

Anaconda3 Python is available in /usr/local/anaconda3/bin.

Conda

Note

Anaconda is disabled by default. Otherwise, Anaconda libraries interfere with the operating system network utilities.

When you want Anaconda, run conda activate <env_name> in your terminal. Instructions are below.

  • To list environments:

    conda env list

  • To create an environment:

    conda create --name <env_name>

    Replace <env_name> with the name of your environment. No packages will be installed in this environment. Confirm the env location with ‘conda env list’.

  • To create an environment with a specific package:

    conda create -n <env_name> scipy

    which can also be accomplished using:

    conda create -n <env_name> python
conda install -n <env_name> scipy

  • To activate a conda environment:

    conda activate <env_name>

  • To leave your conda environment:

    conda deactivate

  • More information about managing environments is here:

    https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

packages

Conda integration with Slurm (batch processing)

By default, Slurm doesn’t initialize conda environments from a user’s .bashrc file.

To initialize conda, add the following line to your slurm submit script:

eval "$(/usr/local/anaconda3/bin/conda shell.bash hook)"
conda activate <env_name>

Jupyter Notebook

You may run jupyter notebook/lab anywhere on the neuro cluster, and view it on your local desktop.

  1. Log in via SSH, or remote desktop

  2. If you logged in via remote desktop, then open a terminal window

  3. From your terminal on the neuro cluster, run ‘jupyter notebook’ with the ‘–no-browser’ option:

    jupyter notebook --no-browser

    You will see output similar to the following:

    Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://localhost:8780/?token=76e3a90efd4159sdfllfaa73e6f8sdf2b6dsdf3cddbc742f

  4. On your Mac, open a terminal window, and use the ssh command to create a tunnel:

    ssh -L <port>:localhost:<port> <username>@<servername>neuro.berkeley.edu
    where…
    the port number was automatically assigned in step #2, shown after the word ‘localhost:’. In this example, the port number is 8780.
    username is your neuro cluster user name
    servername is the neuro cluster server from in step #1 (where you ran the jupyter command).

    For example:

    ssh -L 8780:localhost:8780 user@axon.neuro.berkeley.edu

  5. Copy and paste the URL displayed above (in step #2) into your web browser’s address bar on your desktop.