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https://github.com/calkit/calkit

Calkit makes reproducibility easy.
https://github.com/calkit/calkit

open-science reproducibility reproducible-research reproducible-science research

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Calkit makes reproducibility easy.

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README 

        


  

    Calkit

  




Calkit is a lightweight framework for reproducible research projects.

It acts as a top-level layer to integrate and simplify the use of enabling

technologies such as

[Git](https://git-scm.com/),

[DVC](https://dvc.org/),

[Conda](https://docs.conda.io/en/latest/),

and [Docker](https://docker.com).

Calkit also adds a domain-specific data model

such that all aspects of the research process can be fully described in a

single repository and therefore easily consumed by others.



Our goal is to make reproducibility easier so it becomes more common.

To do this, we try to make it easy for users to follow two simple rules:



1. **Keep everything in version control.** This includes large files like

   datasets, enabled by DVC.

   The [Calkit cloud](https://github.com/calkit/calkit-cloud)

   serves as a simple default DVC remote storage location for those who do not

   want to manage their own infrastructure.

2. **Generate all important artifacts with a single pipeline.** There should be

   no special instructions required to reproduce a project's artifacts.

   It should be as simple as calling `calkit run`.

   The DVC pipeline (in a project's `dvc.yaml` file) is therefore the main

   thing to "build" throughout a research project.

   Calkit provides helper functionality to build pipeline stages that

   keep computational environments up-to-date and label their outputs for

   convenient reuse.



## Installation



To install Calkit, [Git](https://git-scm.com) and Python must be installed.

If you want to use [Docker](https://docker.com) containers,

which is typically a good idea,

that should also be installed.

For Python, we recommend

[Miniforge](https://conda-forge.org/miniforge/).

If you're a Windows user and decide to install Miniforge or any other

Conda-based distribution,

e.g., Anaconda, you'll probably want to ensure that environment is

[activated by default in Git Bash](https://discuss.codecademy.com/t/setting-up-conda-in-git-bash/534473).



After Python is installed, run



```sh

pip install calkit-python

```



## Cloud integration



The Calkit Cloud ([calkit.io](https://calkit.io)) serves as a project

management interface and a DVC remote for easily storing all versions of your

data/code/figures/publications, interacting with your collaborators,

reusing others' research artifacts, etc.



After signing up, visit the

[settings](https://calkit.io/settings?tab=tokens)

page and create a token for use with the API.

Then run



```sh

calkit config set token ${YOUR_TOKEN_HERE}

```



## Quickstart



After installing Calkit and setting your token as described above, run:



```sh

calkit new project calkit-project-1 \

    --title "My first Calkit project" \

    --template calkit/example-basic \

    --cloud \

    --public

```



This will create a new project from the

[`calkit/example-basic`](https://github.com/calkit/example-basic)

template,

creating it in the cloud and cloning to `calkit-project-1`.

You should now be able to run:



```sh

cd calkit-project-1

calkit run

```



This will reproduce the project's pipeline.

Next, you can start adding stages to the pipeline,

modifying the Python environments and scripts,

and editing the paper.

All will be kept in sync with the `calkit run` command.



To back up all of your work, execute:



```sh

calkit save -am "Run pipeline"

```



This will commit and push to both GitHub and the Calkit Cloud.



## Tutorials



- [LaTeX collaboration with GitHub Codespaces](https://petebachant.me/latex-collab/)

- [Jupyter notebook as a DVC pipeline](docs/tutorials/notebook-pipeline.md)

- [Keeping track of conda environments](docs/tutorials/conda-envs.md)

- [Defining and executing manual procedures](docs/tutorials/procedures.md)

- [Adding a new LaTeX-based publication with its own Docker build environment](docs/tutorials/adding-latex-pub-docker.md)

- [A reproducible workflow using Microsoft Office (Word and Excel)](https://petebachant.me/office-repro/)

- [Reproducible OpenFOAM simulations](https://petebachant.me/reproducible-openfoam/)



## Why does reproducibility matter?



If your work is reproducible, that means that someone else can "run" it and

calculate the same results or outputs.

This is a major step towards addressing

[the replication crisis](https://en.wikipedia.org/wiki/Replication_crisis)

and has some major benefits for both you as an individual and the research

community:



1. You will avoid mistakes caused by, e.g., running an old version of a script

   and including a figure that wasn't created after fixing a bug in the data

   processing pipeline.

2. Since your project is "runnable," it's more likely that someone else will be

   able to reuse part of your work to run it in a different context, thereby

   producing a bigger impact and accelerating the pace of discovery.

   If someone can take what you've done and use it to calculate a

   prediction, you have just produced truly useful knowledge.



## Why another tool/platform?



Git, GitHub, DVC, Docker et al. are amazing tools/platforms, but their

use involves multiple fairly difficult learning curves,

and tying them together might mean developing something new for each project.

Our goal is to provide a single tool and platform to unify all of these so

that there is a single, gentle learning curve.

However, it is not our goal to hide or replace these underlying components.

Advanced users can use them directly, but new users aren't forced to, which

helps them get up and running with less effort and training.

Calkit should help users understand what is going on under the hood without

forcing them to work at that lower level of abstraction.



## How it works



Calkit creates a simple human-readable "database" inside the `calkit.yaml`

file, which serves as a way to store important information about the project,

e.g., what question(s) it seeks to answer,

what files should be considered datasets, figures, publications, etc.

The Calkit cloud reads this database and registers the various entities

as part of the entire ecosystem such that if a project is made public,

other researchers can find and reuse your work to accelerate their own.



## Design/UX principles



1. Be opinionated. Users should not be forced to make unimportant decisions.

   However, if they disagree, they should have the ability to change the

   default behavior. The most common use case should be default.

   Commands that are commonly executed as groups should be combined, but

   still available to be run individually if desired.

1. Commits should ideally be made automatically as part of actions that make

   changes to the project repo. For

   example, if a new object is added via the CLI, a commit should be made

   right then unless otherwise specified. This saves the trouble of running

   multiple commands and encourages atomic commits.

1. Pushes should require explicit input from the user.

   It is still TBD whether or not a pull should automatically be

   made, though in general we want to encourage trunk-based development, i.e.,

   only working on a single branch. One exception might be for local

   experimentation that has a high likelihood of failure, in which case a

   branch can be a nice way to throw those changes away.

   Multiple branches should probably not live in the cloud, however, except

   for small, quickly merged pull requests.

1. Idempotency is always a good thing. Unnecessary state is bad. For example,

   we should not encourage caching pipeline outputs for operations that are

   cheap. Caching should happen either for state that is valuable on its

   own, like a figure, or for an intermediate result that is expensive to

   generate.

1. There should be the smallest number of

   frequently used commands as possible, and they should require as little

   memorization as possible to know how to execute, e.g., a user should be

   able to keep running `calkit run` and that's all they really need to do

   to make sure the project is up-to-date.