Skip to main content

Map Reduce for Notebooks

Project description

Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks.

The goals for Papermill are:

  • Parametrizing notebooks

  • Executing and collecting metrics across the notebooks

  • Summarizing collections of notebooks

Installation

pip install papermill

In-Notebook bindings

Usage

Parameterizing a Notebook.

To parameterize your notebook designate a cell with the tag parameters. Papermill looks for the parameters cell and replaces those values with the parameters passed in at execution time.

docs/img/parameters.png

Executing a Notebook

The two ways to execute the notebook with parameters are through the Python API and through the command line interface.

Executing a Notebook via Python API

import papermill as pm

pm.execute_notebook(
   notebook_path='path/to/input.ipynb',
   output_path='path/to/output.ipynb',
   parameters=dict(alpha=0.6, ratio=0.1)
)

Executing a Notebook via CLI

$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1

Recording Values to the Notebook

Users can save values to the notebook document to be consumed by other notebooks.

Recording values to be saved with the notebook.

### notebook.ipynb
import papermill as pm

pm.record("hello", "world")
pm.record("number", 123)
pm.record("some_list", [1,3,5])
pm.record("some_dict", {"a":1, "b":2})

Users can recover those values as a Pandas dataframe via the the read_notebook function.

### summary.ipynb
import papermill as pm

nb = pm.read_notebook('notebook.ipynb')
nb.dataframe
docs/img/nb_dataframe.png

Displaying Plots and Images Saved by Other Notebooks

Display a matplotlib histogram with the key name “matplotlib_hist”.

### notebook.ipynb
# Import plt and turn off interactive plotting to avoid double plotting.
import papermill as pm
import matplotlib.pyplot as plt; plt.ioff()
from ggplot import mpg

f = plt.figure()
plt.hist('cty', bins=12, data=mpg)
pm.display('matplotlib_hist', f)
docs/img/matplotlib_hist.png

Read in that above notebook and display the plot saved at “matplotlib_hist”.

### summary.ipynb
import papermill as pm

nb = pm.read_notebook('notebook.ipynb')
nb.display_output('matplotlib_hist')
docs/img/matplotlib_hist.png

Analyzing a Collection of Notebooks

Papermill can read in a directory of notebooks and provides the NotebookCollection interface for operating on them.

### summary.ipynb
import papermill as pm

nbs = pm.read_notebooks('/path/to/results/')

# Show named plot from 'notebook1.ipynb'
# Accepts a key or list of keys to plot in order.
nbs.display_output('train_1.ipynb', 'matplotlib_hist')
docs/img/matplotlib_hist.png
# Dataframe for all notebooks in collection
nbs.dataframe.head(10)
docs/img/nbs_dataframe.png

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

papermill-0.8.5.tar.gz (31.5 kB view hashes)

Uploaded Source

Built Distribution

papermill-0.8.5-py2-none-any.whl (19.3 kB view hashes)

Uploaded Python 2

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page