calkit-python 0.37.4

Reproducibility simplified.

Documentation | Tutorials | Discussions

Calkit helps you manage and automate research projects like a software engineer.

Define computational environments, steps that process your data, create figures, presentations, and publications, connect to external tools, then iterate quickly and painlessly until your research questions are answered, tracking changes to all files along the way. At the end, deliver your entire project as a self-contained, self-documenting, version-controlled, and single button reproducible "calculation kit" so you and others can easily verify and build upon the results.

Guiding principles

• Quality comes from iteration. Automation reduces the time and effort needed to iterate, thereby increasing iteration and quality.
• Automating a step can and should take roughly the same amount of time as doing it once manually, therefore it's almost always worth it.
• Working in a "quick and dirty" way can easily become not quick when the dirtiness results in mistakes and/or discourages working in small steps.

Features

• A simplified version control interface that unifies Git and DVC (Data Version Control), so all materials can be kept in the same project repository. This way, code doesn't need to be siloed away from other important artifacts like datasets, models, figures, or article PDFs, allowing you to work on all parts of a project without hopping around to different tools.
• Computational environment management with support for many languages and environment managers: Conda, Docker, uv, Julia, Renv, and more. No need to create and update environments on your own. Calkit will handle them as needed.
• An environment-aware build system or pipeline with a simple declarative syntax and output caching so you don't need to think about which steps or stages need to be rerun after changing any part of the project. Simply call calkit run. Compose your pipeline from many different kinds of stages, including simple scripts, commands, Jupyter Notebooks, LaTeX, and more.
• A complementary self-hostable and GitHub-integrated cloud platform to facilitate backup, collaboration, and sharing throughout the entire research lifecycle.
• Overleaf integration, so analysis, visualization, and writing can all stay in sync (no more manual uploads!)
• Support for running on high performance computing (HPC) systems that use SLURM schedulers.
• Support for running with GitHub Actions.
• Extensions for doing all of the above graphically in JupyterLab and VS Code.

Installation

On Linux, macOS, or Windows Git Bash, install Calkit and uv (if not already installed) with:

curl -LsSf install.calkit.org | sh

Or with Windows Command Prompt or PowerShell:

powershell -ExecutionPolicy ByPass -c "irm install-ps1.calkit.org | iex"

If you already have uv installed, install Calkit with:

uv tool install calkit-python

You can also install with your system Python:

pip install calkit-python

To effectively use Calkit, you'll want to ensure Git is installed and properly configured. You may also want to install Docker, since that is the default method by which LaTeX environments are created. If you want to use the Calkit Cloud for collaboration and backup as a DVC remote, you can set up cloud integration.

Use without installing

If you want to use Calkit without installing it, you can use uv's uvx command to run it directly:

uvx calk9 --help

Calkit Assistant

For Windows users, the Calkit Assistant app is the easiest way to get everything set up and ready to work in VS Code, which can then be used as the primary app for working on all scientific or analytical computing projects.

Quickstart

From an existing project

If you want to use Calkit with an existing project, navigate into its working directory and use the xr command to start executing and recording your scripts, notebooks, LaTeX files, etc., as reproducible pipeline stages. For example:

calkit xr scripts/analyze.py

calkit xr notebooks/plot.ipynb

calkit xr paper/main.tex

Calkit will attempt to detect environments, inputs, and outputs and save them in calkit.yaml. If successful, you'll be able to run the full pipeline with:

calkit run

Next, make a change to e.g., a script and look at the output of calkit status. You'll see that the pipeline has a stage that is out-of-date:

---------------------------- Pipeline ----------------------------
analyze:
        changed deps:
                modified:           scripts/analyze.py

This can be fixed with another call to calkit run.

You can save (add and commit) all changes with:

calkit save -am "Add to pipeline"

Fresh from a Calkit project template

Create a new project from the calkit/example-basic template with:

calkit new project my-research \
    --title "My research" \
    --template calkit/example-basic \
    --cloud

Note the --cloud flag requires cloud integration to be set up, but can be omitted if the project doesn't need to be backed up to the cloud or shared with collaborators. Cloud integration can also be set up later.

Next, move into the project folder and run the pipeline, which consists of several stages defined in calkit.yaml:

cd my-research
calkit run

Next, make some edits to a script or LaTeX file and run calkit status to see what stages are out-of-date. For example:

---------------------------- Pipeline ----------------------------
build-paper:
        changed deps:
                modified:           paper/paper.tex

Execute calkit run again to bring everything up-to-date.

To back up or save the project, call:

calkit save -am "Run pipeline"

Get involved

We welcome all kinds of contributions! See CONTRIBUTING.md to learn how to get involved.