View source for Conda

[[Category:Software]][[Category:Programming]]
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For help on creating and managing personal environments whether for command-tool use or python package use in SLURM jobs or Jupyter kernels see [https://help.rc.ufl.edu/doc/Managing_Python_environments_and_Jupyter_kernels Managing Python environments and Jupyter kernels]
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Conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies. Conda easily creates, saves, loads, and switches between environments. Separating applications in separate conda environments allows installation of incompatible dependencies - python2 and python3 for example.

'''Note:''' For a ''faster conda'' see [[Mamba]].

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{{#if: {{#var: conf}}| ==Background==
<onlyinclude>
Many projects that use [[Python]] code require careful management of the respective Python environments. Rapid changes in package dependencies, package version conflicts, deprecation of APIs (function calls) by individual projects, and obsolescence of system drivers and libraries make it virtually impossible to use an arbitrary set of packages or create one all-encompassing environment that will serve everyone's needs over long periods of time. The high velocity of changes in the popular ML/DL frameworks and packages and GPU computing exacerbates the problem.

== The problem with <code>pip install</code> ==
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''Expand this section to view pip problems and how conda/mamba mends them.''
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Most guides and project documentation for installing python packages recommend using <code>pip install</code> for package installation. While <code>pip</code> is easy to use and works for many use cases, there are some major drawbacks. There are a few issues with doing <code>pip install</code> on a supercomputer like HiPerGator:

* Pip by default installs binary packages (wheels), which are often built on systems incompatible with HiPerGator. This can lead to importing errors, and its attempts to build from source will fail without additional configuration.
* If you pip installing a package that is/will be installed in an environment provided by UFRC, your pip version will take precedence. Your dependencies eventually become incompatible causing errors, with even one pip install making environments unusable.
* Different packages may require different versions of the same package as dependencies leading to impossible to reconcile installation scenarios. This becomes a challenge to manage with <code>pip</code> as there isn't a method to swap active versions.
* On its own, `pip` installs **everything** in one location: <code>~/.local/lib/python3.X/site-packages/</code>. 

<big><big><strong>Conda and Mamba to the rescue!</strong></big></big>
[[File:Mamba.png |left | 200px]]<code>conda</code> and the newer, faster, drop-in replacement <code>mamba</code>, were written to solve some of these issues. They represent a higher level of packaging abstraction that can combine compiled packages, applications, and libraries as well as </code>pip</code>-installed python packages. They also allow easier management of project-specific environments and switching between environments as needed. They make it much easier to report the exact configuration of packages in an environment, facilitating reproducibility. Moreover, conda environments don't even have to be activated to be used; in most cases adding the path to the conda environment's 'bin' directory to the $PATH in the shell environment is sufficient for using them.

<big>'''A caveat'''</big>

<code>conda</code> and <code>mamba</code> get packages from channels, or repositories of prebuilt packages packages. While there are several available channels, like the main <code>conda-forge</code>, not every Python package is available from a <code>conda</code> channel as they have to be packaged for <code>conda</code> first. You may still need to use <code>pip</code> to install some packages as noted later. '''However, <code>conda</code> still helps manage environment by installing packages into separate directory trees rather than trying to install all packages into a single folder that pip does.'''
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==Configuration==
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''Expand this section to view instructions for configuring Conda''
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'''<big>The <code>~/.condarc</code> configuration file</big>'''

<code>conda</code>'s behavior is controlled by a configuration file in your home directory called <code>.condarc</code>. The dot at the start of the name means that the file is hidden from 'ls' file listing command by default. If you have not run <code>conda</code> before, you won't have this file. Whether the file exists or not, the steps here will help you modify the file to work best on HiPerGator. First load of the <code>conda</code> environment module on HiPerGator will put the current ''best practice''  <code>.condarc</code> into your home directory.

'''<big><code>conda</code> package cache location</big>'''

<code>conda</code> caches (keeps a copy) of all downloaded packages by default in the <code>~/.conda/pkgs</code> directory tree. If you install a lot of packages you may end up filling up your home quota. You can change the default package cache path. To do so, add or change the <code>pkgs_dirs</code> setting in your <code>~/.condarc</code> configuration file e.g.:
<pre>
pkgs_dirs:
  - /blue/mygroup/share/conda/pkgs
  # or alternatively: - /blue/mygroup/$USER/conda/pkgs
</pre>
Replace <code>mygroup</code> with your actual group name.

'''<big><code>conda</code> environment location</big>'''

<code>conda</code> puts all packages installed in a particular environment into a single directory. By default ''named'' <code>conda</code> environments are created in the <code>~/.conda/envs</code> directory tree. They can quickly grow in size and, especially if you have many environments, fill the 40GB home directory quota. For example, the environment we will create in this training is 5.3GB in size. As such, it is important to use ''path'' based (conda create -p PATH) conda environments, which allow you to use any path for a particular environment for example allowing you to keep a project-specific conda environment close to the project data in </code>/blue/</code> where you group has terrabyte(s) of space.

You can also change the default path for the ''name'' environments (<code>conda create -n NAME</code>) if you prefer to keep all <code>conda</code> environments in the same directory tree. To do so, add or change the <code>envs_dirs</code> setting in the <code>~/.condarc</code> configuration file e.g.:
<pre>
envs_dirs:
  - /blue/mygroup/share/conda/envs
  #or alternatively: - /blue/mygroup/$USER/conda/envs
</pre>
Replace <code>mygroup</code> with your actual group name.

One way to edit your <code>~/.condarc</code> file is to type: <code>nano ~/.condarc`</code>

If the file is empty, paste in the text below, editing the <code>env_dirs:</code> and <code>pkg_dirs</code> as below. If the file has contents, update those lines.

{{Note|Your <code>~/.condarc</code> should look something like this when you are done editing (again, replacing <code>group</code> and <code>user</code> in the paths with your group and username).|note}}

<pre>
channels:
- conda-forge
- bioconda
- defaults
envs_dirs:
- /blue/group/user/conda/envs
pkgs_dirs:
- /blue/group/user/conda/pkgs
auto_activate_base: false
auto_update_conda: false
always_yes: false
show_channel_urls: false
</pre>
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</noinclude>==Create and activate a Conda environment==
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''Expand this section to view instructions for setting up environments.''
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[[File:Mamba create.png|frameless|right|250px]]
UF Research Computing Applications Team uses conda for many application installs behind the scenes. We are happy to [https://support.rc.ufl.edu install applications on request] for you. However, if you would like to use conda to create multiple environments for your personal projects we encourage you to do so. Here are some recommendations for successful conda use on HiPerGator.
*See [https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html] for the original documentation on managing conda environments.
*We recommend creating environments by 'path', so they won't fill up your home directory (check quota with home_quota). The resulting environment should be located in the project(s) directory tree in /blue for better tracking of installs  and better filesystem performance compared to home. 

{{Note|'''If you plan on using a GPU''' see below|warn}}
To make sure your code will run on GPUs install a recent <code>cudatoolkit</code> package that works with the NVIDIA drivers on HPG (currently 11.x) alongside the pytorch or tensorflow package(s). See RC provided tensorflow or pytorch installs for examples if needed. Mamba can detect if there is a gpu in the environment, so the easiest approach is to run the mamba install command in a gpu session. Alternatively, you can run mamba install on any node or if a cpu-only pytorch package was already installed by explicitly requiring a gpu version of pytorch when running mamba install. E.g.
 mamba install cudatoolkit=11.8 pytorch=1.12.1=gpu_cuda* -c pytorch
'''<big>Load the <code>conda</code> module</big>'''

Before we can run <code>conda</code> or <code>mamba</code> on HiPerGator, we need to load the <code>conda</code> module:

 module load conda

<big>'''Create your first environment'''</big>

'''Create a ''name based'' environment'''

To create your first ''name based''  (see path based instructions below)<code>conda</code> environment, run the following command. In this example, I am creating an environment named <code>hfrl</code>:

 mamba create -n hfrl

The screenshot to the right is the output from running that command. Yours should look similar.

'''Note: You do not need to manually create the folders that you setup in your <code>~/.condarc</code> file. <code>mamba</code> will take care of that for you.'''

'''Create a ''path based'' environment'''

To create a ''path based'' <code>conda</code> environment use the '-p PATH' argument:
<code>mamba create -p PATH</code>
e.g.
<code>mamba create -p /blue/mygroup/share/project42/conda/envs/hfrl/</code>

<big>'''Activate the new environment'''</big>

To activate our environment (whether created with <code>mamba</code> or </code>conda</code> we use the <code>conda activate env_name</code> command. Let's activate our new environment:

<code>conda activate hfrl</code>

or

<code>conda activate /blue/mygroup/share/project42/conda/envs/hfrl/</code>

Notice that your command prompt changes when you activate an environment to indicate which environment is active, showing that in parentheses before the other information:

<pre> (hfrl) [magitz@c0907a-s23 magitz]$ </pre>

'''Note: ''path based'' environment activation is really only needed for package installation. For using the environment just add the path to its <code>bin</code> directory to $PATH in your job script.'''

Once you are done installing packages inside the environment you can use

 $ conda deactivate

We do not recommend activating conda environments when _using_ them i.e. running programs installed in the environments. Please prepend the path to that environment to your $PATH instead.

E.g.
If you have a project-specific conda environment at '/home/myuser/envs/project1/' add the following into your job script before executing any commands
 export PATH=/home/myuser/envs/project1/bin:$PATH
</div></div>

==Export or import an environment==
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''Expand this section to view instructions.''
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<big>'''Export your environment to an <code>environment.yml</code> file'''</big>

Now that you have your environment working, you may want to document its contents and/or share it with others. The <code>environment.yml</code> file defines the environment and can be used to build a new environment with the same setup.

To export an environment file from an existing environment, run:

<code>conda env export > hfrl.yml</code>

You can inspect the contents of this file with <code>cat hfrl.yml</code>. This file defines the packages and versions that make up the environment as it is at this point in time. Note that it also includes packages that were installed via <code>pip</code>.

'''<big>Create an environment from a yaml file</big>'''

If you share the environment yaml file created above with another user, they can create a copy of your environment using the command:

<code>conda env create --file hfrl.yml</code>

They may need to edit the last line to change the location to match where they want their environment created.
</div></div>

== Group environments ==

It is possible to create a shared environment accessed by a group on HiPerGator, storing the environment in, for example, <code>/blue/group/share/conda</code>. In general, this works best if only one user has write access to the environment. All installs should be made by that one user and should be communicated with the other users in the group.
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