https://github.com/krassowski/nbpipeline

Snakemake-like pipeline manager for reproducible Jupyter Notebooks
https://github.com/krassowski/nbpipeline

jupyter jupyter-notebook snakemake

Last synced: about 3 hours ago
JSON representation

Snakemake-like pipeline manager for reproducible Jupyter Notebooks

• Host: GitHub
• URL: https://github.com/krassowski/nbpipeline
• Owner: krassowski
• License: mit
• Created: 2019-05-22T16:24:15.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-10-04T19:36:41.000Z (about 3 years ago)
• Last Synced: 2024-04-25T05:40:52.511Z (7 months ago)
• Topics: jupyter, jupyter-notebook, snakemake
• Language: Python
• Homepage:
• Size: 421 KB
• Stars: 17
• Watchers: 3
• Forks: 1
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# nbpipeline

[![Build Status](https://travis-ci.org/krassowski/nbpipeline.svg?branch=master)](https://travis-ci.org/krassowski/nbpipeline)

[![DOI](https://zenodo.org/badge/188075188.svg)](https://zenodo.org/badge/latestdoi/188075188)

Snakemake-like pipelines for Jupyter Notebooks, producing interactive pipeline reports like this:

  

### Install & general remarks

These are still early days of this software so please bear in mind that it is not ready for production yet.

Note: for simplicity I assume that you are using a recent Ubuntu with git installed.

```bash

pip install nbpipeline

```

Graphiz is required for static SVG plots:

```bash

sudo apt-get install graphviz libgraphviz-dev graphviz-dev

```

#### Development install

To install the latest development version you may use:

```bash

git clone https://github.com/krassowski/nbpipeline

cd nbpipeline

pip install -r requirements.txt

ln -s $(pwd)/bin/nbpipeline-dev ~/bin/nbpipeline

```

### Quickstart

Create `pipeline.py` file with list of rules for your pipeline. For example:

```python

from nbpipeline.rules import NotebookRule

NotebookRule(

    'Extract protein data',  # a nice name for the step

    input={'protein_data_path': 'data/raw/data_from_wetlab.xlsx'},

    output={'output_path': 'data/clean/protein_levels.csv'},

    notebook='analyses/Data_extraction.ipynb',

    group='Proteomics'  # this is optional

)

NotebookRule(

    'Quality control and PCA on proteins',

    input={'protein_levels_path': 'data/clean/protein_levels.csv'},

    output={'qc_report_path': 'reports/proteins_failing_qc.csv'},

    notebook='analyses/Exploration_and_quality_control.ipynb',

    group='Proteomics'

)

```

the keys of the input and output variables should correspond to variables in one of the first cells

in the corresponding notebook, which should be tagged as "parameters". It can be done easily in JupyterLab:



If you forget to add them, a warning will be displayed.

Alternativaly, you can create a dedicated cell for input paths definitions and tag it "inputs" and a separate one for output   paths definitions, tagging it "outputs", which allows to omit input and output keywords when creating a `NotebookRule`. However, only simple variable definitions will be deduced (parsing uses regular expressions to avoid potential dangers of `eval`).

For more details, please see the example [pipeline](https://github.com/krassowski/nbpipeline/blob/master/examples/pipeline.py) and [notebooks](https://github.com/krassowski/nbpipeline/tree/master/examples/analyses) in the [examples](https://github.com/krassowski/nbpipeline/tree/master/examples) directory.

#### Run the pipeline:

```bash

nbpipeline

```

On any consecutive run the notebooks which did not change will not be run again.

To disable this cache, use `--disable_cache` switch.

To generate an interactive diagram of the rules graph, together with reproducibility report add `-i` switch:

```bash

nbpipeline -i

```

The software defaults to `google-chrome` for graph visualization display, which can be changed with a CLI option.

If you named your definition files differently (e.g. `my_rules.py` instead of `pipeline.py`), use:

```bash

nbpipeline --definitions_file my_rules.py

```

To display all command line options use:

```bash

nbpipeline -h

```

#### Troubleshooting

If you see `ModuleNotFoundError: No module named 'name_of_your_local_module'`, you may need to enforce the path, running nbpipeline with:

```bash

PYTHONPATH=/path/to/the/parent/of/local/module:$PYTHONPATH nbpipeline

```

Oftentimes the path is the same as the current directory, so the following command may work:

```bash

PYTHONPATH=$(pwd):$PYTHONPATH nbpipeline

```