Machines and people collaborating together through Jupyter notebooks.
We use this at O’Reilly Media for notebooks used to manage machine learning pipelines. That is to say, machines and people collaborate on documents, implementing a “human-in-the-loop” design pattern:
- people adjust hyperparameters for the ML pipelines
- machines write structured “logs” during ML modeling/evaluation
- people run jupyter notebook via SSH tunnel for remote access
The following script generates a Jupyter notebook in the test.ipynb file, then runs it:
python test.py jupyter notebook
Then launch the test.ipynb notebook and from the Cells menu select Run All to view results.
NB: whenever you use the put_df() function to store data as a Pandas dataframe be sure to include import pandas as pd at some earlier point in the notebook.
Dependencies and Installation
This code has dependencies on:
To install from PyPi:
pip install nbtransom
To install from this Git repo:
git clone https://github.com/ceteri/nbtransom.git cd nbtransom pip install -r requirements.txt
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|nbtransom-1.0.1-py3-none-any.whl (6.2 kB) Copy SHA256 hash SHA256||Wheel||py3||Jun 25, 2017|
|nbtransom-1.0.1.tar.gz (4.2 kB) Copy SHA256 hash SHA256||Source||None||Jun 25, 2017|