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# MLJFlow | ||
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[![Build Status](https://github.com/pebeto/MLJFlow.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/pebeto/MLJFlow.jl/actions/workflows/CI.yml?query=branch%3Amain) | ||
| Branch | Build | | ||
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| dev | [![CI](https://github.com/pebeto/MLJFlow.jl/actions/workflows/CI.yml/badge.svg?branch=dev)](https://github.com/pebeto/MLJFlow.jl/actions/workflows/CI.yml) | | ||
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[MLJ](https://github.com/alan-turing-institute/MLJ.jl) is a Julia framework for | ||
combining and tuning machine learning models. MLJFlow is a package that extends | ||
the MLJ capabilities to use [MLFlow](https://mlflow.org/) as a backend for | ||
model tracking and experiment management. To be specific, MLJFlow provides a | ||
close to zero-preparation to use MLFlow with MLJ; by the usage of function | ||
extensions that automate the MLFlow cycle (create experiment, create run, log | ||
metrics, log parameters, log artifacts, etc.). | ||
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## Background | ||
This project is part of the GSoC 2023 program. The proposal description can be | ||
found [here](https://summerofcode.withgoogle.com/programs/2023/projects/iRxuzeGJ). | ||
The entire workload is divided into three different repositories: | ||
[MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl), | ||
[MLFlowClient.jl](https://github.com/JuliaAI/MLFlowClient.jl) and this one. | ||
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## Features | ||
- [x] MLFlow cycle automation (create experiment, create run, log metrics, log | ||
parameters, log artifacts, etc.) | ||
- [x] Wrapper type used by MLJ to store MLFlow metadata and client instance | ||
from MLFlowClient.jl | ||
- [x] MLJ extended functions to allow MLFlow logging | ||
- [x] Polished compatibility with composed models | ||
- [ ] Polished compatibility with tuned models | ||
- [ ] Polished compatibility with iterative models |
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