Dependency management

Questions

Do you expect your code to work in one year? Five? What if it uses numpy or tensorflow or random-github-package ?
How can my collaborators get the same results as me? What about future me?
How can my collaborators easily install my codes with all the necessary dependencies?
How can I make it easy for my colleagues to reproduce my results?
How can I work on two (or more) projects with different and conflicting dependencies?

Objectives

Learn how to record dependencies
Be able to communicate the dependencies as part of a report/thesis/publication
Learn how to use isolated environments for different projects
Simplify the use and reuse of scripts and projects

How do you track dependencies of your project?

Dependency: Reliance on a external component. In this case, a separately installed software package such as numpy.

Exercises 1

Dependencies-1: Discuss dependency management (5 min)

Please discuss and answer via collaborative document the following questions:

How do you install Python packages (libraries) that you use in your work? From PyPI using pip? From other places using pip? Using conda?
How do you track/record the dependencies? Do you write them into a file or README? Into requirements.txt or environment.yml?
If you track dependencies in a file, why do you do this?
Have you ever experienced that a project needed a different version of a Python library than the one on your computer? If yes, how did you solve it?

PyPI (The Python Package Index) and (Ana)conda

PyPI (The Python Package Index) and Conda are popular packaging/dependency management tools.
When you run pip install you typically install from PyPI but you can also pip install from a GitHub repository and similar.
When you run conda install you typically install from Anaconda Cloud where there are conda channels maintained by Anaconda Inc. and by various communities.

Why are there two ecosystems?

PyPI

Installation tool: pip
Summary: PyPI is traditionally used for Python-only packages or for Python interfaces to external libraries. There are also packages that have bundled external libraries (such as numpy).
Amount of packages: Huge number. Old versions are supported for a long time.
How libraries are handled: If your code depends on external libraries or tools, these things need to be either included in the pip-package or provided via some other installation system (like operating system installer or manual installation).
Pros:
- Easy to use
- Package creation is easy
Cons:
- Installing packages that need external libraries can be complicated

Conda

Installation tool: conda or mamba
Summary: Conda aims to be a more general package distribution tool and it tries to provide not only the Python packages, but also libraries and tools needed by the Python packages. Most scientific software written in Python uses external libraries to speed up calculations and installing these libraries can often become complicated without conda.
Amount of packages: Curated list of packages in defaults-channel, huge number in community managed channels. Other packages can be installed via pip.
How libraries are handled: Required libraries are installed as separate conda packages.
Pros:
- Quite easy to use
- Easier to manage packages that need external libraries
Cons:
- Package creation is harder

Anaconda vs. miniconda vs. conda vs. mamba vs. Anaconda Cloud vs. conda-forge vs. miniforge

Package sources:

Anaconda Cloud - a package cloud maintained by Anaconda Inc. It is a free repository that houses conda package channels.
Conda-forge - the largest open source community channel.

Package managers:

conda - a package and environment management system used by Anaconda. It is an open source project maintained by Anaconda Inc..
mamba - a drop in replacement for conda that does installations faster.

Package manager deployments:

Anaconda - a distribution of conda packages made by Anaconda Inc.. It is free for academic and non-commercial use.
Miniconda - a minimal installer that has conda and uses default channels by default.
Miniforge - Miniconda replacement that uses conda-forge as the default channel. Contains mamba as well.

In the packaging episode we will meet PyPI and Anaconda again and practice how to share Python packages.

Creating isolated environments

An isolated environment allows installing packages without affecting the rest of your operating system or any other projects. Isolated environments solve a couple of problems:

You can install specific versions of packages into them.
You can create one environment for each project and you won’t encounter any problems if the two projects require different versions of packages.
If you make some mistake and install something you did not want or need, you can remove the environment and create a new one.
You can export a list of packages in an environment and share it with your code. This makes replicating your results easier.

Exercises 2

Dependencies-2: Create a conda environment (15 min)

Chloe just joined your team and will be working on her Master Thesis. She is quite familiar with Python, still finishing some Python assignments (due in a few weeks) and you give her a Python code for analyzing and plotting your favorite data. The thing is that your Python code has been developed by another Master Student (from last year) and requires a older version of Numpy (1.24.3) and Matplotlib (3.7.2) (otherwise the code fails). The code could probably work with a recent version of Python but has been validated with Python 3.10 only. Having no idea what the code does, she decides that the best approach is to create an isolated environment with the same dependencies that were used previously. This will give her a baseline for future upgrade and developments.

For this first exercise, we will be using conda for creating an isolated environment.

Create a conda environment:

$ conda create --name python310-env python=3.10 numpy=1.24.3 matplotlib=3.7.2

Conda environments can also be managed (create, update, delete) from the anaconda-navigator. Check out the corresponding documentation here.

Activate the environment:
```
$ conda activate python310-env
```
conda activate versus source activate

conda activate will only work if you have run conda init in the past. Running conda init will make loading environments easier as you will always have a conda environment loaded.

However, this can also cause problems as programs in the main environment will be constantly loaded and they might be used even when they’re not supposed to be used. A common example is not having pip installed in a conda environment which results pip from main environment to be used instead.

You can always try:
```
$ source activate python310-env
```

Open a Python console and check that you have effectively the right version for each package:

import numpy
import matplotlib

print('Numpy version: ', numpy.__version__)
print('Matplotlib version: ', matplotlib.__version__)

Or use the one-liner if you have access to a terminal like bash:

$ python -c 'import numpy; print(numpy.__version__)'
$ python -c 'import matplotlib;print(matplotlib.__version__)'

Deactivate the environment:
```
$ conda deactivate
```
Check Numpy and Matplotlib versions in the default environment to make sure they are different from python310-env.

There is no need to specify the conda environment when using deactivate. It deactivates the current environment.

Exercises 3

Dependencies-3: Create a virtualenv (15 min, optional)

This is the same exercise as before but we use virtualenv rather than conda.

Create a venv:
```
$ python3 -m venv scicomp
```
Here scicomp is the name of the virtual environment. It creates a new folder called scicomp.
Activate it. To activate your newly created virtual environment locate the script called activate and source it.
- Linux/Mac-OSX: look at bin folder in the scicomp folder:
```
$ source scicomp/bin/activate
```
- Windows: most likely you can find it in the Scripts folder.
Install Numpy 1.24.3 and Matplotlib 3.7.2 into the virtual environment:
```
$ pip install numpy==1.24.3
$ pip install matplotlib==3.7.2
```
Deactivate it:
```
$ deactivate
```

Problems that might happen with manual installation

Running the install commands manually can result in unexpected behaviour such as:

The installer might remove an already installed packages or update them.
The installer might not find a package that works with already installed packages.

The reason for this is that the installer does not know what commands you ran in the past. It only knows the state of your environment and what you’re currently telling it to install.

These kinds of problems can be mitigated by recording dependencies in an environment.yml or requirements.txt.

Recording dependencies

There are two standard ways to record dependencies for Python projects: requirements.txt and environment.yml.

requirements.txt (used by virtual environment) is a simple text file which looks like this:

numpy
matplotlib
pandas
scipy

environments.yml (for conda) is a yaml-file which looks like this:

name: my-environment
channels:
  - defaults
dependencies:
  - numpy
  - matplotlib
  - pandas
  - scipy

If you need to recreate the exact same environment later on, it can be very useful to pin dependencies to certain versions. For example, there is usually a delay between doing research and that research being published. During this time the dependencies might update and reviewers or interested researchers might not be able to replicate your results or run your code.

Conda channels

Sometimes the package version you would need does not seem to be available. You may have to select another conda channel.

Most popular channels are defaults, which is managed by Anaconda Inc. and conda-forge, which is managed by the open source community. These two channels are mutually incompatible.

Channel priority goes from top to bottom.

Here are the two files again, but this time with versions pinned:

requirements.txt with versions:

numpy==1.24.3
matplotlib==3.7.2
pandas==2.0.3
scipy==1.10.1

environments.yml with versions:

name: my-environment
channels:
  - defaults
dependencies:
  - python=3.10
  - numpy=1.24.3
  - matplotlib=3.7.2
  - pandas=2.0.3
  - scipy=1.10.1

Conda can also read and write requirements.txt.
requirements.txt can also refer to packages on Github.
environments.yml can also contain a pip section.
See also: https://coderefinery.github.io/reproducible-research/dependencies/ .

Putting too strict requirements can be counter-productive

Putting exact version numbers can be good for single-use applications, like replicating a research paper, but it is usually bad for long-term maintenance because the program won’t update at the same time as it’s requirements do.

If you’re creating a library, adding strict dependencies can also create a situation where the library cannot coexist with another library.

Dependencies 4

Dependencies-4: Freeze an environment (15 min)

Create the file environment.yml or requirements.txt
Create an environment based on these dependencies:
- Conda: $ conda env create --file environment.yml
- Virtual environment: First create and activate, then $ pip install -r requirements.txt
Freeze the environment:
- Conda: $ conda env export > environment.yml
- Virtual environment: $ pip freeze > requirements.txt
Have a look at the generated (“frozen”) file.

Hint: Updating packages from dependency files

Instead of installing packages with $ pip install somepackage, you can add somepackage to requirements.txt and re-run $ pip install -r requirements.txt.

With conda, you can add the package to environment.yml and run $ conda env update --file environment.yml

How to communicate the dependencies as part of a report/thesis/publication

Each notebook or script or project which depends on libraries should come with either a requirements.txt or a environment.yml, unless you are creating and distributing this project as Python package (see next section).

Attach a requirements.txt or a environment.yml to your thesis.
Even better: put requirements.txt or a environment.yml in your Git repository along your code.
Even better: also binderize your analysis pipeline (more about that in a later session).

Version pinning for package creators

We will talk about packaging in a different session but when you create a library and package projects, you express dependencies either in setup.py or pyproject.toml (PyPI) or meta.yaml (conda).

These dependencies will then be used by either other libraries (who in turn write their own setup.py or pyproject.toml or meta.yaml) or by people directly (filling out requirements.txt or a environment.yml).

Now as a library creator you have a difficult choice. You can either pin versions very narrowly like here (example taken from setup.py):

# ...
install_requires=[
   'numpy==1.19.2',
   'matplotlib==3.3.2'
   'pandas==1.1.2'
   'scipy==1.5.2'
]
# ...

or you can define a range or keep them undefined like here (example taken from setup.py):

# ...
install_requires=[
   'numpy',
   'matplotlib'
   'pandas'
   'scipy'
]
# ...

Should we pin the versions here or not?

Pinning versions here would be good for reproducibility.
However pinning versions may make it difficult for this library to be used in a project alongside other libraries with conflicting version dependencies.
Therefore as library creator make the version requirements as wide as possible.
- Set minimum version when you know of a reason: >=2.1
- Sometimes set maximum version to next major version (<4) (when you currently use 3.x.y) when you expect issues with next major version.
As the “end consumer” of libraries, define your dependencies as narrowly as possible.