Adapting the new flat_params function from MLJBase and uploading firs…

…t version of README

README.md

@@ -1,3 +1,30 @@
 # MLJFlow
 
-[![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 |
+| :---: | :---: |
+| 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) |
+
+[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.).
+
+## 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.
+
+## 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

src/MLJFlow.jl

@@ -1,7 +1,7 @@
 module MLJFlow
 
 using MLJBase: info, name, Model,
-    Machine, deep_params, flat_params
+    Machine, flat_params
 using OrderedCollections: LittleDict
 using MLFlowClient: MLFlow, logparam, logmetric,
     createrun, MLFlowRun, updaterun,

src/client.jl

@@ -1,5 +1,5 @@
 function logmodelparams(client::MLFlow, run::MLFlowRun, model::Model)
-    model_params = deep_params(model) |> flat_params |> collect
+    model_params = flat_params(model) |> collect
     for (name, value) in model_params
         logparam(client, run, name, value)
     end