recommendation-model-server 0.0.1

A real-time inference server

model server

---
title: REST-inference service
---
classDiagram
    note "100 requests per second"

    class VenueRating{
    """
    Represents the predicted ranking of a venue.

    Attributes:
    -----------
    venue_id : int The ID of the venue being rated.
    q80_predicted_rank : float
        The predicted ranking of the venue,
        as a 80-quantile of predicted rating
        for venue across available sessions
    """
    venue_id: int
    q80_predicted_rank: float
    }
    class TrainingPipeline{
      str pre-trained-model-file: stored with mlflow in gcs bucket
    }

    class InferenceFeatures{
    venue_id: int
    conversions_per_impression: float
    price_range: int
    rating: float
    popularity: float
    retention_rate: float
    session_id_hashed: int
    position_in_list: int
    is_from_order_again: int
    is_recommended: int
    }
    class FastAPIEndpoint{
      def predict_ratings(): Callabe
    }

    class Model_Instance{
        joblib.load(model_artifact_bucket)
        str model_artifact_bucket - variable
        str rank_column - fixed for the model
        str group_column - fixed for the model
    }
    TrainingPipeline --|> Model_Instance
    InferenceFeatures --|> FastAPIEndpoint
    Model_Instance --|> FastAPIEndpoint
    FastAPIEndpoint --|> VenueRating

---

Documentation: https://ra312.github.io/model-server Training Source Code: https://github.com/ra312/personalization Source Code: https://github.com/ra312/model-server PyPI: https://pypi.org/project/model-server/

---

A model server for almost realtime inference

Installation

pip install model-server

Development

• Clone this repository
• Requirements:
  • Poetry
  • Python 3.8.1+
• Create a virtual environment and install the dependencies

poetry install

• Activate the virtual environment

poetry shell

Testing

pytest

Documentation

The documentation is automatically generated from the content of the docs directory and from the docstrings of the public signatures of the source code. The documentation is updated and published as a Github project page automatically as part each release.

Releasing

Trigger the Draft release workflow (press Run workflow). This will update the changelog & version and create a GitHub release which is in Draft state.

Find the draft release from the GitHub releases and publish it. When a release is published, it'll trigger release workflow which creates PyPI release and deploys updated documentation.

Pre-commit

Pre-commit hooks run all the auto-formatters (e.g. black, isort), linters (e.g. mypy, flake8), and other quality checks to make sure the changeset is in good shape before a commit/push happens.

You can install the hooks with (runs for each commit):

pre-commit install

Or if you want them to run only for each push:

pre-commit install -t pre-push

Or if you want e.g. want to run all checks manually for all files:

pre-commit run --all-files

---

This project was generated using the wolt-python-package-cookiecutter template.