Bailo Python Client

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A lightweight, Python API wrapper for Bailo, providing streamlined programmatic access to its core functionality - designed for Data Scientists, ML Engineers, and Developers who need to integrate Bailo capabilities directly into their workflows.


Table of Contents
  1. Quickstart
  2. Documentation
  3. Development

Quickstart

Requires: Python 3.10 to 3.14

Installation

pip install bailo

Optional: enable integration with MLFlow for advanced model tracking:

pip install bailo[mlflow]

Basic Usage

from bailo import Client, Model

# Connect to Bailo server
client = Client("http://localhost:8080")

# Create a model
yolo = Model.create(
    client=client,
    name="YoloV4",
    description="You only look once!"
)

# Populate datacard using a predefined schema
yolo.card_from_schema("minimal-general-v10")

# Create a new release
my_release = yolo.create_release(
    version="0.1.0",
    notes="Beta"
)

# Upload a binary file to the release
with open("yolo.onnx") as f:
    my_release.upload("yolo", f)

Core Features

  • Upload and download model binaries
  • Manage Models & Releases
  • Handle Datacards & Schemas
  • Manage Schemas
  • Process Access Requests

Note: Certain collaborative actions (approvals, review threads, etc.) are best handled via the Bailo web interface.

Documentation

Full Python client documentation: Bailo Python Docs.

Building locally

Refer to backend/docs/README.md for local build steps.

Development

The following steps are only required for users who wish to extend or develop the Bailo Python client locally.

Python Setup

From within the lib/python directory:

python3 -m venv libpythonvenv
source libpythonvenv/bin/activate
pip install -e .[test]

Running Tests

To run the unit tests:

pytest

To run the integration tests (requires Bailo running on https://localhost:8080):

pytest -m integration

To run the mlflow integration tests (requires Bailo running on https://localhost:8080 and mlflow running on https://localhost:5050 e.g. via docker):

docker run -p 5050:5000 \
    "ghcr.io/mlflow/mlflow:v$(python -m pip show mlflow-skinny | awk '/Version:/ {print $2}')" \
    mlflow server --host 0.0.0.0 --port 5000

pytest -m mlflow

Project links

Data verified by PyPI on Jul 21, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (3.10.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for bailo from gravatar.com bailo

GitHub Statistics

Data verified by PyPI on Jul 21, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License

Requires

Python >=3.10

Provides Extra

mlflow test

Classifiers

Report project as malware

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The following attestation bundles were made for bailo-3.9.0-py3-none-any.whl:

Publisher: python-publish.yml on gchq/Bailo

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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