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Python Virtual Environments

Virtual environments allow users to install additional Python packages and create an isolated run-time environment. We recommend using virtualenv for this purpose. In your virtual environment, you can use packages from the modules list or if you didn't find what you need you can install required packages with the command: pip install. With the command pip freeze, you can see a list of all installed packages and their versions.

There are two methods of how to work with virtual environments on ZIH systems:

  1. virtualenv is a standard Python tool to create isolated Python environments. It is the preferred interface for managing installations and virtual environments on ZIH system and part of the Python modules.

  2. conda is an alternative method for managing installations and virtual environments on ZIH system. conda is an open-source package management system and environment management system from Anaconda. The conda manager is included in all versions of Anaconda and Miniconda.

Warning

Keep in mind that you cannot use virtualenv for working with the virtual environments previously created with conda tool and vice versa! Prefer virtualenv whenever possible.

Python Virtual Environment

This example shows how to start working with virtualenv and Python virtual environment (using the module system).

Hint

We recommend to use workspaces for your virtual environments.

At first, we check available Python modules and load the preferred version:

marie@compute$ module avail Python    #Check the available modules with Python
[...]
marie@compute$ module load Python    #Load default Python
Module Python/3.7 2-GCCcore-8.2.0 with 10 dependencies loaded
marie@compute$ which python    #Check which python are you using
/sw/installed/Python/3.7.2-GCCcore-8.2.0/bin/python

Then create the virtual environment and activate it.

marie@compute$ ws_allocate -F scratch python_virtual_environment 1
Info: creating workspace.
/scratch/ws/1/python_virtual_environment
[...]
marie@compute$ virtualenv --system-site-packages /scratch/ws/1/python_virtual_environment/env  #Create virtual environment
[...]
marie@compute$ source /scratch/ws/1/python_virtual_environment/env/bin/activate    #Activate virtual environment. Example output: (env) bash-4.2$

Now you can work in this isolated environment, without interfering with other tasks running on the system. Note that the inscription (env) at the beginning of each line represents that you are in the virtual environment. You can deactivate the environment as follows:

(env) marie@compute$ deactivate    #Leave the virtual environment
Example

This is an example on partition Alpha. The example creates a conda virtual environment, and installs the package torchvision with conda.

marie@login$ srun --partition=alpha-interactive --nodes=1 --gres=gpu:1 --time=01:00:00 --pty bash
marie@alpha$ ws_allocate -F scratch my_python_virtualenv 100    # use a workspace for the environment
marie@alpha$ cd /scratch/ws/1/marie-my_python_virtualenv
marie@alpha$ module load modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 PyTorch/1.9.0
Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies loaded.
marie@alpha$ which python
/sw/installed/Python/3.8.6-GCCcore-10.2.0/bin/python
marie@alpha$ pip list
[...]
marie@alpha$ virtualenv --system-site-packages my-torch-env
created virtual environment CPython3.8.6.final.0-64 in 42960ms
creator CPython3Posix(dest=[...]/my-torch-env, clear=False, global=True)
seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=~/.local/share/virtualenv)
    added seed packages: pip==21.1.3, setuptools==57.2.0, wheel==0.36.2
activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
marie@alpha$ source my-torch-env/bin/activate
(my-torch-env) marie@alpha$ pip install torchvision==0.10.0
[...]
Installing collected packages: torchvision==0.10.0
Successfully installed torchvision-0.10.0
[...]
(my-torch-env) marie@alpha$ python -c "import torchvision; print(torchvision.__version__)"
0.10.0+cu102
(my-torch-env) marie@alpha$ deactivate

Persistence of Python Virtual Environment

To persist a virtualenv, you can store the names and versions of installed packages in a file. Then you can restore this virtualenv by installing the packages from this file. Use the pip freeze command for storing:

(env) marie@compute$ pip freeze > requirements.txt    #Store the currently installed packages

In order to recreate python virtual environment, use the pip install command to install the packages from the file:

marie@compute$ module load Python    #Load default Python
[...]
marie@compute$ virtualenv --system-site-packages /scratch/ws/1/python_virtual_environment/env_post  #Create virtual environment
[...]
marie@compute$ source /scratch/ws/1/python_virtual_environment/env/bin/activate    #Activate virtual environment. Example output: (env_post) bash-4.2$
(env_post) marie@compute$ pip install -r requirements.txt    #Install packages from the created requirements.txt file

Conda Virtual Environment

Prerequisite: Before working with conda, your shell needs to be configured initially. Therefore login to the ZIH system, load the Anaconda module and run conda init. For more information use conda init --help. Note that changes take effect after closing and re-opening your shell.

Example
marie@compute$ module load Anaconda3    #load Anaconda module
Module Anaconda3/2019.03 loaded.
marie@compute$ conda init    #configure shell
[...]
modified      /home/marie/.bashrc

This example shows how to start working with conda and virtual environment (with using module system). At first, we use an interactive job and create a directory for the conda virtual environment:

marie@compute$ ws_allocate -F scratch conda_virtual_environment 1
Info: creating workspace.
/scratch/ws/1/conda_virtual_environment
[...]

Then, we load Anaconda, create an environment in our directory and activate the environment:

marie@compute$ module load Anaconda3    #load Anaconda module
marie@compute$ conda create --prefix /scratch/ws/1/conda_virtual_environment/conda-env python=3.6    #create virtual environment with Python version 3.6
marie@compute$ conda activate /scratch/ws/1/conda_virtual_environment/conda-env    #activate conda-env virtual environment

Now you can work in this isolated environment, without interfering with other tasks running on the system. Note that the inscription (conda-env) at the beginning of each line represents that you are in the virtual environment. You can deactivate the conda environment as follows:

(conda-env) marie@compute$ conda deactivate    #Leave the virtual environment

Warning

When installing conda packages via conda install, ensure to have enough main memory requested in your job allocation.

Hint

We do not recommend to use conda environments together with EasyBuild modules due to dependency conflicts. Nevertheless, if you need EasyBuild modules, consider installing conda packages via conda install --no-deps [...] to prevent conda from installing dependencies.

Example

This is an example on partition Alpha. The example creates a conda virtual environment, and installs the package torchvision with conda.

marie@login$ srun --partition=alpha-interactive --nodes=1 --gres=gpu:1 --time=01:00:00 --pty bash
marie@alpha$ ws_allocate -F scratch my_conda_virtualenv 100    # use a workspace for the environment
marie@alpha$ cd /scratch/ws/1/marie-my_conda_virtualenv
marie@alpha$ module load Anaconda3
Module Anaconda3/2021.11 loaded.
marie@alpha$ conda create --prefix my-torch-env python=3.8
Collecting package metadata (current_repodata.json): done
Solving environment: done
[...]
Proceed ([y]/n)? y
[...]
marie@alpha$ conda activate my-torch-env
(my-torch-env) marie@alpha$ conda install -c pytorch torchvision
Collecting package metadata (current_repodata.json): done
[...]
Preparing transaction: done
Verifying transaction: done
(my-torch-env) marie@alpha$ which python    # ensure to use the correct Python
(my-torch-env) marie@alpha$ python -c "import torchvision; print(torchvision.__version__)"
0.12.0
(my-torch-env) marie@alpha$ conda deactivate

Persistence of Conda Virtual Environment

To persist a conda virtual environment, you can define an environments.yml file. Have a look a the conda docs for a description of the syntax. See an example for the environments.yml file below.

Example
name: workshop_env
channels:
- conda-forge
- defaults
dependencies:
- python>=3.7
- pip
- colorcet
- 'geoviews-core=1.8.1'
- 'ipywidgets=7.6.*'
- geopandas
- hvplot
- pyepsg
- python-dotenv
- 'shapely=1.7.1'
- pip:
    - python-hll

After specifying the name, the conda channel priority is defined. In the example above, packages will be first installed from the conda-forge channel, and if not found, from the default Anaconda channel.

Below, dependencies can be specified. Optionally, pinning can be used to delimit the packages installed to compatible package versions.

Finally, packages not available on conda can be specified (indented) below - pip:

Recreate the conda virtual environment with the packages from the created environment.yml file:

marie@compute$ mkdir workshop_env    #Create directory for environment
marie@compute$ module load Anaconda3    #Load Anaconda
marie@compute$ conda config --set channel_priority strict
marie@compute$ conda env create --prefix workshop_env --file environment.yml    #Create conda env in directory with packages from environment.yml file