Dappled is an open source project for building and running deployable Jupyter notebooks.
It allows data scientists to easily deploy and share custom data analyses, and collaborators to reuse, reproduce, and customize these analyses in a user friendly manner.
A Dappled notebook bundles software dependencies and customizable parameters together with analysis, which can then be deployed anywhere, whether it be a laptop, server, or the cloud.
Dappled can be used for:
- sharing and collaboration of reproducible analysis workflows
- creating and deploying browser-based self-service data analysis tools
- scaling up data analyses onto larger machines and the cloud
Install the dappled tool by running this:
curl https://raw.githubusercontent.com/lhon/dappled/master/install.sh | bash
To install the dappled tool, download this archive and run the batch script inside.
If you already have conda installed, run this:
conda install dappled -c http://conda.dappled.io
To run the notebook published at https://dappled.io/dappled/hello:
dappled run dappled/hello
To download the notebook and its corresponding dappled.yml:
dappled clone dappled/hello
View other available options using dappled -h.
To get a fuller flavor of what's possible, here are a couple more notebooks to try:
- Besides python as the default language, notebooks can use bash and R as the primary language ("kernel" in Jupyter-speak).
- A basic bioinformatics example determining the QV encoding of some fastq data
- A more elaborate bioinformatics de novo assembly example, which has some python-based visualization plus running Canu (written in perl/java/C).
Dappled is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.