Bailo Python Client

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.

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