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Latest commit 736b95d Feb 20, 2017 @havocp havocp committed on GitHub Merge pull request #32 from conda/py36
Run CI for Python 3.6 also

README.md

Conda Kapsel

Conda kapsels are reproducible, executable project directories.

Take any directory full of stuff that you're working on; web apps, scripts, Jupyter notebooks, data files, whatever it may be.

By adding a kapsel.yml to this project directory, you can tell conda how to run it.

Running a conda kapsel executes a command specified in the kapsel.yml (any arbitrary commands can be configured).

kapsel.yml also tells conda how to automate project setup; conda can establish all prerequisite conditions for the project's commands to execute successfully. These conditions could include:

  • creating a conda environment with certain packages in it
  • prompting the user for passwords or other configuration
  • downloading data files
  • starting extra processes such as a database server

The goal is that if your project runs on your machine, it will also run on others' machines (or on your future machine after you reboot a few times and forget how your project works).

The command conda kapsel init DIRECTORY_NAME creates a kapsel.yml, converting your project directory into a conda kapsel.

Put another way...

Traditional build scripts such as setup.py automate "building" the project (going from source code to something runnable), while conda kapsel automates "running" the project (taking build artifacts and doing any necessary setup prior to executing them).

Why?

  • Do you have a README with setup steps in it? You may find that it gets outdated, or that people don't read it, and then you have to help them diagnose the problem. conda kapsel automates the setup steps; the README can say "type conda kapsel run" and that's it.
  • Do you need everyone working on a project to have the same dependencies in their conda environment? conda kapsel automates environment creation and verifies that environments have the right versions of packages.
  • Do you sometimes include your personal passwords or secret keys in your code, because it's too complicated to do otherwise? With conda kapsel, you can os.getenv("DB_PASSWORD") and configure conda kapsel to prompt the user for any missing credentials.
  • Do you want improved reproducibility? With conda kapsel, someone who wants to reproduce your analysis can ensure they have exactly the same setup that you have on your machine.
  • Do you want to deploy your analysis as a web application? The configuration in kapsel.yml tells hosting providers how to run your project, so there's no special setup needed when you move from your local machine to the web.

Learn more

See http://conda.pydata.org/docs/kapsel/ for a simple getting-started walkthrough.

See http://conda.pydata.org/docs/kapsel/config.html for more detail on the syntax of the kapsel.yml file.

If you've been using conda env and environment.yml

conda kapsel has similar functionality and may be more convenient. The advantage of conda kapsel for environment handling is that it performs conda operations, and records them in a config file for reproducibility, in one step.

For example, if you do conda kapsel add-packages bokeh=0.11, that will install Bokeh with conda, and add bokeh=0.11 to an environment spec in kapsel.yml (the effect is comparable to adding it to environment.yml). In this way, "your current conda environment's state" and "your configuration to be shared with others" won't get out of sync.

conda kapsel will also automatically set up environments for a colleague when they type conda kapsel run on their machine; they don't have to do a separate step to create, update, or activate environments before they run the code. This may be especially useful when you change the required dependencies; with conda env people can forget to re-run it and update their packages, while conda kapsel run will automatically add missing packages every time.

In addition to environment creation, conda kapsel can perform other kinds of setup, such as adding data files and running a database server. It's a superset of conda env in that sense.

Stability note

For the time being, the conda kapsel API and command line syntax are subject to change in future releases. A project created with the current “beta” version of conda kapsel may always need to be run with that version of conda kapsel and not conda kapsel 1.0. When we think things are solid, we’ll switch from “beta” to “1.0” and you’ll be able to rely on long-term interface stability.

Bug Reports

Please report issues right here on GitHub.

Contributing

Please join our chat room at https://gitter.im/conda/kapsel if you have questions, feedback, or just want to say hi.

Here's how to work on the code:

  • python setup.py test is configured to run all the checks that have to pass before you commit or push. It also reformats the code with yapf if necessary. Continuous integration runs this command so you should run it and make it pass before you push to the repo.
  • To only run the formatter and linter, use python setup.py test --format-only.
  • To only run the tests, use python -m pytest -vv conda_kapsel
  • To only run a single file of tests use python -m pytest -vv conda_kapsel/test/test_foo.py
  • To only run a single test function python -m pytest -vv conda_kapsel/test/test_foo.py::test_something
  • There's a script build_and_upload.sh that should be used to manually make a release. The checked-out revision should have a version tag prior to running the script.