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Python and Anaconda

Getting Started

Different versions of Python on Midway are offered as modules. To check the full list of Python modules use the module avail python command.

The command module load python will load the default module: an Anaconda distribution of Python. Note that there are multiple different Anaconda distributions available.

Once you load an Anaconda distribution, you can list all available public environments with: 

conda env list
To activate an environment, run: 
source activate <ENV NAME>
where <ENV NAME> is the name of the environment for a public environment, or the full path to the environment, if you are using a personal one. You can deactivate an environment with: 
conda deactivate

Danger

Never run conda init! Use source activate instead of conda activate. conda init has been known to break ThinLinc.

Managing Environments

With each Anaconda distribution, we have a small selection of widely used environments. Many, such as Tensorflow or DeepLabCut should be loaded through their modules (i.e., module load tensorflow), which automate the loading of other relevant libraries that are available as modules.

If you need packages not available in the global environment, you can make a personal environment for them. If you want to copy an existing environment to modify it, you can do that with: 

conda create --prefix=/path/to/new/environment --clone <EXISTING ENVIRONMENT>
If you want to make a clean environment, you can do that with 
conda create --prefix=/path/to/new/environment python=<PYTHON VERSION NUMBER>

Once your environment is set up how you want, especially if it is in your scratch space, you may want to create a backup of the environment into a YAML file. You do that after activating the environment with conda env export > environment.yml. That YAML file can then be used to recreate the environment with conda env create --prefix=/path/to/new/environment -f environment.yml.

Note

Anaconda may sometimes cause issues with ThinLinc. If you are experiencing frequent, spontaneous disconnections from ThinLinc, remove any sections involving "conda" or "anaconda" from the file ~/.bashrc (in your home directory).

Managing Packages

In the Anaconda distributions of Python, you should generally be using a personal environment to manage packages. Once you activate your environment 

source activate [your-env]
you can install packages with conda install or pip install into this environment. As per the advice of the Anaconda software authors, any pip install packages should be installed after conda install packages.

Using Python

On Midway, python can be launched, after loading a desired module, at the terminal with the command:

python

To leave the launched interactive shell, use:

exit()
or 
quit()

If you already have a python script, use this command to run it:

python your_script.py

Python Interactive Plotting

For interactive plotting, it is necessary to set the matplotlib backend to a graphical backend. Here is an example:

#!/usr/bin/env python

import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt

plt.plot([1,2,3,4])
plt.ylabel('some numbers')
plt.show()

If you are saving files and viewing them with the display command, you may experience rapid flickering. There seems to be an issue with image transparency, use a command like this to disable the transparency:

display -alpha off <image>

Running Jupyter Notebooks

Jupyter Notebook is a useful tool for python users because it provides interactive web-based computing. You can launch Jupyter Notebooks on Midway, open it in the browser on your local machine and have all the computation work done on Midway. If you want to perform heavy compute, you will need to start an interactive session before launching Jupyter notebook, otherwise you may use one of the login nodes.

The steps to launch Jupyter are as follows:

Step 1: Load the desired Python module. This can be done on a login node, or on a compute node via an interactive job.

Step 2: Determine the IP address of the host you are on. Whether you are on a login node or a compute node, you can use this command to get your IP address:

HOST_IP=`/sbin/ip route get 8.8.8.8 | awk '{print $7;exit}'`
echo $HOST_IP
which can be either 128.135.x.y (an external address), or 10.50.x.y (on-campus address).

Step 3: Launch Jupyter with:

jupyter-notebook --no-browser --ip=$HOST_IP
or 
jupyter-lab --no-browser --ip=$HOST_IP


jupyter-notebook --no-browser --ip=$HOST_IP --port=15021
or 
jupyter-lab --no-browser --ip=$HOST_IP --port=15021

where 15021 is an arbitrary port number rather than 8888. If there is a problem with the port already in use, your browser will complain. In that case, please try the another port with the flag --port=<port number>, or use the command shuf to get a random number for the port: 

PORT_NUM=$(shuf -i15001-30000 -n1)
and launch the Notebook server as earlier 
jupyter-notebook --no-browser --ip=$HOST_IP --port=$PORT_NUM

which will give you two URLs with a token, one with the external address 128.135.x.y, and another with the on-campus address 10.50.x.y, or your local host 127.0.0.0. The on-campus address is only valid when you are connecting to Midway2 or Midway3 via VPN.

http://128.135.167.77:15021/?token=9c9b7fb3885a5b6896c959c8a945773b8860c6e2e0bad629

If you do not specify --no-browser --ip=, the web browser will be launched on the node and the URL returned cannot be used on your local machine.

Step 4: Open a web browser on your local machine with the returned URLs.

If you are using on-campus network or VPN, you can use copy and paste (or Ctrl + mouse click on) the URL with the external address, or the URL with the on-campus address into the address bar.

As of April 2023: If you are on Midway2, you can open the URL with the external address without VPN. If you are on Midway3, you need to connect via VPN to open either URLs.

Without VPN, you can use SSH tunneling to connect to the Jupyter server launched on the Midway2 (or Midway3) login nodes in Step 3 from your local machine. To do that, open another terminal window on your local machine and run

ssh -N -f -L 15021:<HOST_IP>:15021 <your-CNetID>@midway3.rcc.uchicago.edu
where HOST_IP is the external IP address of the login node obtained from Step 2, and 15021 is the port number used in Step 3.

This command will create an SSH connection from your local machine to midway3 node and forward the 15021 port to your local host at port 15021. The port number should be consistent across all the steps (15021 in this example). You can find out the meaning for the arguments used in this command at explainshell.com.

After successfully logging with 2FA as usual, you will be able to open the URL http://127.0.0.1:15021/?token=...., or equivalently, localhost:15021/?token=.... in the browser on your local machine.

Step 5: To kill Jupyter, go back to the first terminal window where you launch Jupyter Notebook and press Ctrl+c and then confirm with y that you want to stop it.

Running JupyterLab

JupyterLab is the next-generation IDE-like counterpart of Jupyter Notebook with more advanced features for data science, scientific computing, computational journalism, and machine learning. It has a modular structure that allows you to create and execute multiple documents in different tabs in the same window.