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Peter Bull 2016-04-25 17:29:25 -04:00
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@ -142,7 +142,7 @@ from preprocess.build_features import remove_invalid_data
### Analysis is a DAG
Often in an analysis you have long-running steps that preprocesses data or trains models. If these steps have been run already (and you have stored the output somewhere like the `data/interim` directory), you don't want to wait to rerun them every time. We prefer [`make`](https://www.gnu.org/software/make/) for managing steps that depend on each other, especially the long-running ones. Make is a common tool on unix platforms (and [is available for Windows]()). Following the [`make` documentation](https://www.gnu.org/software/make/), [Makefile conventions](https://www.gnu.org/prep/standards/html_node/Makefile-Conventions.html#Makefile-Conventions), and [portability guide](http://www.gnu.org/savannah-checkouts/gnu/autoconf/manual/autoconf-2.69/html_node/Portable-Make.html#Portable-Make) will help ensure your Makefiles work effectively across systems. Here are [some](http://zmjones.com/make/) [examples](http://blog.kaggle.com/2012/10/15/make-for-data-scientists/) to [get started](https://web.archive.org/web/20150206054212/http://www.bioinformaticszen.com/post/decomplected-workflows-makefiles/).
Often in an analysis you have long-running steps that preprocesses data or trains models. If these steps have been run already (and you have stored the output somewhere like the `data/interim` directory), you don't want to wait to rerun them every time. We prefer [`make`](https://www.gnu.org/software/make/) for managing steps that depend on each other, especially the long-running ones. Make is a common tool on unix platforms (and [is available for Windows]()). Following the [`make` documentation](https://www.gnu.org/software/make/), [Makefile conventions](https://www.gnu.org/prep/standards/html_node/Makefile-Conventions.html#Makefile-Conventions), and [portability guide](http://www.gnu.org/savannah-checkouts/gnu/autoconf/manual/autoconf-2.69/html_node/Portable-Make.html#Portable-Make) will help ensure your Makefiles work effectively across systems. Here are [some](http://zmjones.com/make/) [examples](http://blog.kaggle.com/2012/10/15/make-for-data-scientists/) to [get started](https://web.archive.org/web/20150206054212/http://www.bioinformaticszen.com/post/decomplected-workflows-makefiles/). A number of data folks use `make` as their tool of choice, including [Mike Bostock](https://bost.ocks.org/mike/make/).
There are other tools for managing DAGs that are written in Python instead of a DSL (e.g., [Paver](http://paver.github.io/paver/#), [Luigi](http://luigi.readthedocs.org/en/stable/index.html), [Airflow](http://pythonhosted.org/airflow/cli.html), [Snakemake](https://bitbucket.org/snakemake/snakemake/wiki/Home), [Ruffus](http://www.ruffus.org.uk/), or [Joblib](https://pythonhosted.org/joblib/memory.html)). Feel free to use these if they are more appropriate for your analysis.
@ -154,8 +154,8 @@ One effective approach to this is use [virtualenv](https://virtualenv.pypa.io/en
1. Run `mkvirtualenv` when creating a new project
2. `pip install` the packages that your analysis needs
3. Run `pip freeze >> requirements.txt` to pin the exact package versions used to recreate the analysis
4. If you find you need to install another package, run `pip freeze >> requirements.txt` again and commit the changes to version control.
3. Run `pip freeze > requirements.txt` to pin the exact package versions used to recreate the analysis
4. If you find you need to install another package, run `pip freeze > requirements.txt` again and commit the changes to version control.
If you have more complex requirements for recreating your environment, consider a virtual machine based approach such as [Docker](https://www.docker.com/) or [Vagrant](https://www.vagrantup.com/). Both of these tools use text-based formats (Dockerfile and Vagrantfile respectively) you can easily add to source control to describe how to create a virtual machine with the requirements you need.