Conda

See also: HPC and HTC tutorial | Run MPI On Grid'5000 | GPUs on Grid5000 | GNU Parallel | Modules | Conda | Guix | Singularity | Deep Learning Frameworks

	Note
	This page is actively maintained by the Grid'5000 team. If you encounter problems, please report them (see the Support page). Additionally, as it is a wiki page, you are free to make minor corrections yourself if needed. If you would like to suggest a more fundamental change, please contact the Grid'5000 team.

Introduction

Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.

The conda package and environment manager is included in all versions of Anaconda®, Miniconda, and Anaconda Repository. Conda is also available on conda-forge, a community channel.

Anaconda or Miniconda?

Anaconda contains a full distribution of packages while Miniconda is a condensed version that contains the essentials for standard purposes.

References

• Conda website
• Conda cheat sheet
• Guide of conda Web Site

Conda usage

Conda initialization and activation

Conda initialization is the process of defining some shell functions that facilitate activating and deactivating Conda environments, as well as some optional features such as updating PS1 to show the active environment.

The conda shell function is mainly a forwarder function. It will delegate most of the commands to the real conda executable driven by the Python library.

There are two ways to activate conda:

• 1. occasionally : activate conda in your current shell

$:

eval "$(conda shell.bash hook)"

• 2. always : activate conda in your login shell environment permanently (this command modifies your .bashrc by adding conda setup directives)

$:

conda init

The conda activate or conda deactivate commands relies on the conda shell initialization to load/unload the corresponding conda environment variables to the current shell session.

By defaut, you are located in the base Conda environment that correspond to the base installation of Conda.

If you’d prefer that conda’s base environment not be activated on startup, set the auto_activate_base parameter to false:

$:

conda config --set auto_activate_base false

Verify your conda configuration with this command:

$:

conda config --show

Look at all available configuration options with:

$:

conda config --describe

Conda environments

Conda allows you to create separate environments containing files, packages, and their dependencies that will not interact with other environments.

When you begin using conda, you already have a default environment named "base". You can create separate environments to keep your programs isolated from each other. Specifying the environment name confines conda commands to that environment.

• List all your environments

$:

conda info --envs

or

$:

conda env list

• Create a new environment

$:

conda create --name ENVNAME

• Activate this environment before installing package

$:

conda activate ENVNAME

For further information:

• https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
• Managing your data science project environments with Conda

Conda package installation

In its default configuration (the default Conda channel), Conda can install and manage the over 7,500 packages at https://repo.anaconda.com/pkgs/ that are built, reviewed, and maintained by Anaconda.

$:

conda install <package>

• Install specific version of package:

$:

conda install <package>=<version>

• Uninstall a package:

$:

conda uninstall <package>

For more information:

• https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/packages.html

Conda package installation from channels

Channels are the locations of the repositories where Conda looks for packages. Channels may point to a Cloud repository or a private location on a remote or local repository that you or your organization created. Useful channels are:

• conda-forge from https://conda-forge.org. It is free for all to use.
• nvidia from https://anaconda.org/nvidia. It provides Nvidia's software.

To install a package from a specific channel:

$:

conda install -c <chanel_name> <package>

• List all packages installed with their source channels

$:

conda list --show-channel-urls

For more information:

• https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/channels.html

Suggested reading

• Conda cheat sheet for current commands
• Getting Started with Conda

Using conda on Grid'5000

Conda is already available in Grid'5000 as a module. You do not need to install Anaconda or Miniconda on Grid'5000 !

The module supplied in Grid'5000 is based on the miniconda distribution

• To make it available on a node or on a frontend, you need to load the Conda module as follow:

frontal:

module load conda

• It loads the default conda module, to view the available versions:

frontal:

module avail conda

• To list the emplacement of Conda and the current environment:

fsophia:

conda info

You can see here that the active environment is base and its emplacement is on a NFS storage (/grid5000/....).

By default, Conda and all packages are installed locally with a user-specific configuration. In the Grid'5000 context, Conda comes with some pre-existing packages in the base environment.

	Warning
	The base environment is stored in a read-only directory. That's why you need to systematically create your own conda environments to install the software you need.

• To list installed packages in the current environment:

fsophia:

conda list

Create conda environments on Grid'5000

Basic Conda workflow

	Warning
	Installing Conda packages can be time and resource consuming. Preferably use a node (instead of a frontend) to perform such an operation. Note, using a node is mandatory if you need to access specific hardware resources like GPU.

• Load conda module and activate bash completion

fgrenoble:

module load miniconda3 source /home/$USER/.bashrc

• Create an environment (specify a Python version; otherwise, it is the module default version)

fgrenoble:

conda create -y -n <name> python=x.y

• Load this environment

fgrenoble:

conda activate <name>

• Install a package

fgrenoble:

conda install <package_name>

• Exit from the loaded environment

fgrenoble:

conda deactivate

Remove unused Conda environments

	Warning
	Conda packages are installed in $HOME/.conda. You could, therefore, rapidly saturate your homedir quota (25GB by default). Do not forget to occasionally remove unused Conda environment to free up space.

• To delete an environment

fgrenoble:

conda env remove --name <name>

• To remove unused packages and the cache. Do not be concerned if this appears to try to delete the packages of the system environment (ie. non-local).

fgrenoble:

conda clean -a

Use a Conda environment on Grid'5000

As seen in the previous section, the Conda environment is stored by default in user's homedir (at ~/.conda). Once the environment is created and packages installed, it is usable on all nodes from the given site.

For interactive jobs

frontal:

oarsub -I

node:

module load miniconda3 source ~/.bashrc conda activate <name>

For batch jobs

	Warning
	As module command is not a real executable but a shell function, it must be executed in an actual shell to work. A simple oarsub "module load miniconda3" will fail.

An example to show in a batch job how to load miniconda, init conda, activate your conda environment and verify it works

• first we prepare our conda environment on the frontend:
  • load on miniconda, conda init (to modify our ~/.bashrc file)
  • conda creation of an environement "testconda" containing "gcc" from conda-forge channel"
  • list installed packages with source info

fsiteA:

module load miniconda3 conda init bash conda create --name testconda conda activate testconda conda install -c conda-forge gcc_linux-64 gxx_linux-64 conda info conda list -n testconda --show-channel-urls

• In this example, we launch a job that does the same tasks but in batch job.
  • The important step is to source shell environment to execute module and activate conda

fsiteA:

oarsub 'bash -l -c ". /etc/profile ; module load miniconda3 ; source ~/.bashrc ; conda activate testconda ; conda info ; conda list -n testconda --show-channel-url"'

Advanced Conda environment operations

Synchronize Conda environments between Grid'5000 sites

• To synchronize a Conda directory from a siteA to a siteB:

fsiteA:

rsync --dry-run --delete -avz ~/.conda siteB.grid5000.fr:~

To really do things, the --dry-run argument has to be removed and siteB has to be replaced by a real site name.

Share Conda environments between multiple users

You can use two different approaches to share Conda environments with other users.

Export an environment as a yaml file

• Export it as follow:

fgrenoble:

conda env export > environment.yml

• Share it by putting the yaml file in your public folder

fgrenoble:

cp environment.yml ~/public/

• Other users can create the environment from the environment.yml file

fgrenoble:

conda env create -f ~/<login>/public/environment.yml

• Advantage : it prevents other users from damaging the environment if they add packages that could conflict with other packages and/or even delete packages that another user might need.
• Inconvenient : it's not a true shared environment. The environment is duplicated on other users' home directory. Any modification on one Conda environment will not be automatically replicated on others.

Use a group storage

Group Storage gives you the possibility to share a storage between multiple users. You can take advantage of a group storage to share a single Conda environment among multiple users.

• Create a shared Conda environment (--prefix allows you to specify the path to store the conda environment)

flyon:

conda create --prefix /srv/storage/storage_name@server_hostname_(fqdn)/ENVNAME

• Activate the shared environment (share this command with the targeted users)

flyon:

conda activate /srv/storage/storage_name@server_hostname_(fqdn)/ENVNAME

• Advantage : It avoids storing duplicate packages and makes any modification accessible to all users
• Inconvenients :
  • Users could potentially harm the environment by installing or removing packages.
  • When installing additional packages, conda still stores them in the package cache located in your home directory. Use conda clean as described above to clean those files.

Mamba as an alternative to Conda

mamba is a reimplementation of the conda package manager in C++. Mamba is fully compatible with Conda packages and supports most of Conda's commands. It consists of:

• mamba: a Python-based CLI conceived as a drop-in replacement for conda, offering higher speed and more reliable environment solutions
• micromamba: a pure C++-based CLI, self-contained in a single-file executable
• libmamba: a C++ library exposing low-level and high-level APIs on top of which both mamba and micromamba are built

Mamba is relatively new and unpopular compared to Conda. That means there are probably more undiscovered bugs, and that new bugs may take longer to be discovered. mamba has to be considerate when using a devops chain in order to test and deploy an environment (i.e., docker images) with continuous integration pipelines. Conda has a reputation for taking time when dealing with complex sets of dependencies so CI jobs can take longer than they need to.

• Mamba installation when already have Conda

inside:

conda install mamba -c conda-forge

• Installing packages is similarly easy, example:

inside:

mamba install python=3.8 jupyter -c conda-forge

To go further:

• Mamba installation

Build your HPC-IA framework with conda

Here are some pointers to help you set up your software environment for HPC or AI with conda

• HPC_and_HTC_tutorial
• Running MPI applications on Grid'5000
• Deep Learning Frameworks documentation