vespa-engine / learntorank-DEPRECATED

IMPORTANT: This repository is deprecated and will be archived - please use vespa.ai to find other resources for ranking.

Vespa for Data Scientists

See documentation at vespa-engine.github.io/learntorank/

Motivation

This library contains application specific code related to data manipulation and analysis of different Vespa use cases. The Vespa python API is used to interact with Vespa applications from python for faster exploration.

The main goal of this space is to facilitate prototyping and experimentation for data scientists. Please visit Vespa sample apps for production-ready use cases and Vespa docs for in-depth Vespa documentation.

Install

Code to support and reproduce the use cases documented here can be found in the learntorank library.

Install via PyPI:

pip install learntorank

Development

All the code and content of this repo is created using nbdev by editing notebooks. We will give a summary below about the main points required to contribute, but we suggest going through nbdev tutorials to learn more.

Setting up environment

1. Create and activate a virtual environment of your choice. We recommend pipenv.

   pipenv shell

2. Install Jupyter Lab (or Jupyter Notebook if you prefer).

   pip3 install jupyterlab

3. Create a new kernel for Jupyter that uses the virtual environment created at step 1.

   • Check where the current list of kernels is located with jupyter kernelspec list.
   • Copy one of the existing folder and rename it to learntorank.
   • Modify the kernel.json file that is inside the new folder to reflect the python3executable associated with your virtual env.

4. Install nbdev library:

   pip3 install nbdev

5. Install learntorank in development mode:

   pip3 install -e .[dev]

Most used nbdev commands

From your terminal:

• nbdev_help: List all nbdev commands available.

• nbdev_readme: Update README.md based on index.ipynb

• Preview documentation while editing the notebooks:

  • nbdev_preview --port 3000

• Workflow before pushing code:

  • nbdev_test --n_workers 2: Execute all the tests inside notebooks.
    • Tests can run in parallel but since we create Docker containers we suggest a low number of workers to preserve memory.
  • nbdev_export: Export code from notebooks to the python library.
  • nbdev_clean: Clean notebooks to avoid merge conflicts.

• Publish library

  • nbdev_bump_version: Bump library version.
  • nbdev_pypi: Publish library to PyPI.

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