DAGpy

DAGpy is a data science collaboration tool based on iPython notebooks enabling data science teams to:

• easily collaborate by branching out of others' notebooks
• minimize code duplication
• give a clean overview of the project
• cache intermediate outputs so team members can use them without re-evaluation
• automate the process of code execution upon data changes or on schedule
• provide a clean interface to the data visualization dashboard designers and developers

DAGpy manages a DAG (directed acyclic graph) of blocks of code, with each block being a sequence of iPython notebook cells, together with their outputs. It is designed to work seamlessly with popular VC systems like git and can be run locally or as a server application.

To learn more about DAGpy read the Project overview. This is a work in progress.

Install with: pip install dagpy

Author: Ivan Bestvina

Example project

To play around with the example project, download it, go to dagpy/example/ and:

• view the project DAG: dagpy view
• run all the blocks: dagpy execute -a
• add blocks through flows (with block B as a parent) and run them automatically: dagpy makeflow B -r
• commit the changes: dagpy submitflow dagpy_flow.ipynb
• explore other DAGpy options with dagpy -h

Please note that notebook execution time includes a significant overhead of over a second, because a kernel must be started for each one. In future, we plan on adding support for non-notebook plane python blocks. These would also be edited through a flow notebook view, but would be saved as .py scripts, and executed without noticable overhead.

Dependencies:

• python 3
• jupyter
• dill
• networkx, matplotlib (for DAG view)