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Pymilo

Pymilo is an open-source Python package that exports pretrained machine-learning models, including documented scikit-learn models, to JSON. Its export representation records serialized model data and metadata such as model type and scikit-learn version, enabling developers to inspect, save, transfer or share model parameters.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pymilo — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use Pymilo when a Python workflow needs a transparent JSON export of a fitted model for inspection, sharing or transfer into another environment.

Who it is for

  • Python developers working with trained scikit-learn models.
  • Machine-learning teams that need a readable model-export format for transfer or sharing.
  • Developers building model export steps into deployment workflows.

Documented uses

  • Serialize a fitted model to a JSON file.
  • Return a model's serialized representation as a JSON string.
  • Batch-export multiple models to individual JSON files in a directory.
  • Inspect or edit learned parameter values in the JSON representation.
Before you choose

Requirements & limits

The project describes itself as open source and lists the MIT license. Installation instructions are documented for PyPI, Conda and source code.

  • The documented concrete model example uses scikit-learn LinearRegression; check the project's documentation for the model type you intend to export.
  • The JSON includes learned parameters and is presented as inspectable and editable, which suits workflows needing visibility into serialized model state.
  • Pymilo's README describes export and transfer; the provided usage excerpt does not show the subsequent import workflow.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Export a fitted scikit-learn model to JSON

Save one trained model as a JSON file using Pymilo's documented Export interface.

Before you start

  • Python environment with NumPy, scikit-learn and Pymilo installed.
  • A fitted scikit-learn model.

Steps

  1. Fit a scikit-learn model in Python.

  2. Import Export from pymilo.

  3. Pass the fitted model to Export and call save with a JSON filename, as in Export(model).save("model.json").

Expected outcome

A JSON file containing the serialized model data, including learned parameters and model metadata.

Guide limitations

  • The guide covers export only, not importing the file into a separate environment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
8 October 2026
Original website title
openscilab/pymilo · Official homepage title · retained in its source language
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