Parametrize and run Jupyter and nteract Notebooks
Project description
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=======================================================================================================================================================================
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[![Code style: black](https://img.shields.io/badge/code style-black-000000.svg)](https://github.com/ambv/black)
**Papermill** is a tool for parameterizing, executing, and analyzing
Jupyter Notebooks.
Papermill lets you:
- **parameterize** notebooks
- **execute** and **collect** metrics across the notebooks
- **summarize collections** of notebooks
This opens up new opportunities for how notebooks can be used. For
example:
- Perhaps you have a financial report that you wish to run with
different values on the first or last day of a month or at the
beginning or end of the year, **using parameters** makes this task
easier.
- Do you want to run a notebook and depending on its results, choose a
particular notebook to run next? You can now programmatically
**execute a workflow** without having to copy and paste from
notebook to notebook manually.
- Do you have plots and visualizations spread across 10 or more
notebooks? Now you can choose which plots to programmatically
display a **summary** **collection** in a notebook to share with
others.
Installation
------------
From the command line:
``` {.sourceCode .bash}
pip install papermill
```
Installing In-Notebook bindings
-------------------------------
- [Python](https://github.com/nteract/papermill#python-in-notebook-bindings) (included in this repo)
- [R](https://github.com/nteract/papermillr) (**experimentally** available in the
**papermillr** project)
Other language bindings welcome if someone would like to maintain parallel implementations!
Usage
-----
### Parameterizing a Notebook
To parameterize your notebook designate a cell with the tag ``parameters``.
Papermill looks for the ``parameters`` cell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged with ``injected-parameters`` with input parameters in order to overwrite the values in ``parameters``. If no cell is tagged with ``parameters`` the injected cell will be inserted at the top of the notebook.
Additionally, if you rerun notebooks through papermill and it will reuse the ``injected-parameters`` cell from the prior run. In this case Papermill will replace the old ``injected-parameters`` cell with the new run's inputs.
![image](docs/img/parameters.png)
### Executing a Notebook
The two ways to execute the notebook with parameters are: (1) through
the Python API and (2) through the command line interface.
#### Execute via the Python API
``` {.sourceCode .python}
import papermill as pm
pm.execute_notebook(
'path/to/input.ipynb',
'path/to/output.ipynb',
parameters = dict(alpha=0.6, ratio=0.1)
)
```
#### Execute via CLI
Here's an example of a local notebook being executed and output to an
Amazon S3 account:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
**NOTE:**
If you use multiple AWS accounts, and you have [properly configured your AWS credentials](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html), then you can specify which account to use by setting the `AWS_PROFILE` environment variable at the command-line. For example:
``` {.sourceCode .bash}
$ AWS_PROFILE=dev_account papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
In the above example, two parameters are set: ``alpha`` and ``l1_ratio`` using ``-p`` (``--parameters`` also works). Parameter values that look like booleans or numbers will be interpreted as such. Here are the different ways users may set parameters:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -r version 1.0
```
Using ``-r`` or ``--parameters_raw``, users can set parameters one by one. However, unlike ``-p``, the parameter will remain a string, even if it may be interpreted as a number or boolean.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -f parameters.yaml
```
Using ``-f`` or ``--parameters_file``, users can provide a YAML file from which parameter values should be read.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
alpha: 0.6
l1_ratio: 0.1"
```
Using ``-y`` or ``--parameters_yaml``, users can directly provide a YAML string containing parameter values.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -b YWxwaGE6IDAuNgpsMV9yYXRpbzogMC4xCg==
```
Using ``-b`` or ``--parameters_base64``, users can provide a YAML string, base64-encoded, containing parameter values.
When using YAML to pass arguments, through ``-y``, ``-b`` or ``-f``, parameter values can be arrays or dictionaries:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
x:
- 0.0
- 1.0
- 2.0
- 3.0
linear_function:
slope: 3.0
intercept: 1.0"
```
Python In-notebook Bindings
---------------------------
### 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.
``` {.sourceCode .python}
"""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
`read_notebook` function.
``` {.sourceCode .python}
"""summary.ipynb"""
import papermill as pm
nb = pm.read_notebook('notebook.ipynb')
nb.dataframe
```
![image](docs/img/nb_dataframe.png)
### Displaying Plots and Images Saved by Other Notebooks
Display a matplotlib histogram with the key name `matplotlib_hist`.
``` {.sourceCode .python}
"""notebook.ipynb"""
import papermill as pm
from ggplot import mpg
import matplotlib.pyplot as plt
# turn off interactive plotting to avoid double plotting
plt.ioff()
f = plt.figure()
plt.hist('cty', bins=12, data=mpg)
pm.display('matplotlib_hist', f)
```
![image](docs/img/matplotlib_hist.png)
Read in that above notebook and display the plot saved at
`matplotlib_hist`.
``` {.sourceCode .python}
"""summary.ipynb"""
import papermill as pm
nb = pm.read_notebook('notebook.ipynb')
nb.display_output('matplotlib_hist')
```
![image](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.
``` {.sourceCode .python}
"""summary.ipynb"""
import papermill as pm
nbs = pm.read_notebooks('/path/to/results/')
# Show named plot from 'notebook1.ipynb'
# Accept a key or list of keys to plot in order.
nbs.display_output('train_1.ipynb', 'matplotlib_hist')
```
![image](docs/img/matplotlib_hist.png)
``` {.sourceCode .python}
# Dataframe for all notebooks in collection
nbs.dataframe.head(10)
```
![image](docs/img/nbs_dataframe.png)
Development Guide
-----------------
Read CONTRIBUTING.md for guidelines on how to setup a local development environment and make code changes back to Papermill.
For development guidelines look in the DEVELOPMENT_GUIDE.md file. This should inform you on how to make particular additions to the code base.
Documentation
-------------
We host the [Papermill documentation](http://papermill.readthedocs.io)
on ReadTheDocs.
=======================================================================================================================================================================
[![Build Status](https://travis-ci.org/nteract/papermill.svg?branch=master)](https://travis-ci.org/nteract/papermill)
[![image](https://codecov.io/github/nteract/papermill/coverage.svg?branch=master)](https://codecov.io/github/nteract/papermill?branch=master)
[![Documentation Status](https://readthedocs.org/projects/papermill/badge/?version=latest)](http://papermill.readthedocs.io/en/latest/?badge=latest)
[![image](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/nteract/papermill/master?filepath=binder/process_highlight_dates.ipynb)
[![Code style: black](https://img.shields.io/badge/code style-black-000000.svg)](https://github.com/ambv/black)
**Papermill** is a tool for parameterizing, executing, and analyzing
Jupyter Notebooks.
Papermill lets you:
- **parameterize** notebooks
- **execute** and **collect** metrics across the notebooks
- **summarize collections** of notebooks
This opens up new opportunities for how notebooks can be used. For
example:
- Perhaps you have a financial report that you wish to run with
different values on the first or last day of a month or at the
beginning or end of the year, **using parameters** makes this task
easier.
- Do you want to run a notebook and depending on its results, choose a
particular notebook to run next? You can now programmatically
**execute a workflow** without having to copy and paste from
notebook to notebook manually.
- Do you have plots and visualizations spread across 10 or more
notebooks? Now you can choose which plots to programmatically
display a **summary** **collection** in a notebook to share with
others.
Installation
------------
From the command line:
``` {.sourceCode .bash}
pip install papermill
```
Installing In-Notebook bindings
-------------------------------
- [Python](https://github.com/nteract/papermill#python-in-notebook-bindings) (included in this repo)
- [R](https://github.com/nteract/papermillr) (**experimentally** available in the
**papermillr** project)
Other language bindings welcome if someone would like to maintain parallel implementations!
Usage
-----
### Parameterizing a Notebook
To parameterize your notebook designate a cell with the tag ``parameters``.
Papermill looks for the ``parameters`` cell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged with ``injected-parameters`` with input parameters in order to overwrite the values in ``parameters``. If no cell is tagged with ``parameters`` the injected cell will be inserted at the top of the notebook.
Additionally, if you rerun notebooks through papermill and it will reuse the ``injected-parameters`` cell from the prior run. In this case Papermill will replace the old ``injected-parameters`` cell with the new run's inputs.
![image](docs/img/parameters.png)
### Executing a Notebook
The two ways to execute the notebook with parameters are: (1) through
the Python API and (2) through the command line interface.
#### Execute via the Python API
``` {.sourceCode .python}
import papermill as pm
pm.execute_notebook(
'path/to/input.ipynb',
'path/to/output.ipynb',
parameters = dict(alpha=0.6, ratio=0.1)
)
```
#### Execute via CLI
Here's an example of a local notebook being executed and output to an
Amazon S3 account:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
**NOTE:**
If you use multiple AWS accounts, and you have [properly configured your AWS credentials](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html), then you can specify which account to use by setting the `AWS_PROFILE` environment variable at the command-line. For example:
``` {.sourceCode .bash}
$ AWS_PROFILE=dev_account papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
In the above example, two parameters are set: ``alpha`` and ``l1_ratio`` using ``-p`` (``--parameters`` also works). Parameter values that look like booleans or numbers will be interpreted as such. Here are the different ways users may set parameters:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -r version 1.0
```
Using ``-r`` or ``--parameters_raw``, users can set parameters one by one. However, unlike ``-p``, the parameter will remain a string, even if it may be interpreted as a number or boolean.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -f parameters.yaml
```
Using ``-f`` or ``--parameters_file``, users can provide a YAML file from which parameter values should be read.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
alpha: 0.6
l1_ratio: 0.1"
```
Using ``-y`` or ``--parameters_yaml``, users can directly provide a YAML string containing parameter values.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -b YWxwaGE6IDAuNgpsMV9yYXRpbzogMC4xCg==
```
Using ``-b`` or ``--parameters_base64``, users can provide a YAML string, base64-encoded, containing parameter values.
When using YAML to pass arguments, through ``-y``, ``-b`` or ``-f``, parameter values can be arrays or dictionaries:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
x:
- 0.0
- 1.0
- 2.0
- 3.0
linear_function:
slope: 3.0
intercept: 1.0"
```
Python In-notebook Bindings
---------------------------
### 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.
``` {.sourceCode .python}
"""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
`read_notebook` function.
``` {.sourceCode .python}
"""summary.ipynb"""
import papermill as pm
nb = pm.read_notebook('notebook.ipynb')
nb.dataframe
```
![image](docs/img/nb_dataframe.png)
### Displaying Plots and Images Saved by Other Notebooks
Display a matplotlib histogram with the key name `matplotlib_hist`.
``` {.sourceCode .python}
"""notebook.ipynb"""
import papermill as pm
from ggplot import mpg
import matplotlib.pyplot as plt
# turn off interactive plotting to avoid double plotting
plt.ioff()
f = plt.figure()
plt.hist('cty', bins=12, data=mpg)
pm.display('matplotlib_hist', f)
```
![image](docs/img/matplotlib_hist.png)
Read in that above notebook and display the plot saved at
`matplotlib_hist`.
``` {.sourceCode .python}
"""summary.ipynb"""
import papermill as pm
nb = pm.read_notebook('notebook.ipynb')
nb.display_output('matplotlib_hist')
```
![image](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.
``` {.sourceCode .python}
"""summary.ipynb"""
import papermill as pm
nbs = pm.read_notebooks('/path/to/results/')
# Show named plot from 'notebook1.ipynb'
# Accept a key or list of keys to plot in order.
nbs.display_output('train_1.ipynb', 'matplotlib_hist')
```
![image](docs/img/matplotlib_hist.png)
``` {.sourceCode .python}
# Dataframe for all notebooks in collection
nbs.dataframe.head(10)
```
![image](docs/img/nbs_dataframe.png)
Development Guide
-----------------
Read CONTRIBUTING.md for guidelines on how to setup a local development environment and make code changes back to Papermill.
For development guidelines look in the DEVELOPMENT_GUIDE.md file. This should inform you on how to make particular additions to the code base.
Documentation
-------------
We host the [Papermill documentation](http://papermill.readthedocs.io)
on ReadTheDocs.
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