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<body>
<h1 id="toc_0">Tracking machine learning models in R with MLflow</h1>
<p>In this <a href="https://medium.com/@dsml4real/first-impression-on-mlflow-406ab976f22e">story</a> I have briefly described what <a href="https://mlflow.org/"><strong><em>MLflow</em></strong></a> is and how it works. <em>MLflow</em> currently provides APIs in Python language that users can invoke in their machine learning source codes to log parameters, metrics, and artifacts to be tracked by the <em>MLflow</em> tracking server.</p>
<p>Users familiar with R and perform machine learning operations in R may like to track their models and every runs with <em>MLflow</em>. There are several approaches users can take.</p>
<ul>
<li>Waiting for <a href="https://github.com/databricks/mlflow"><em>Mlflow</em></a> to release the APIs in R, or<br></li>
<li>Wrapping <em>MLflow</em> RESTful APIs and logging through <code>curl</code> commands, or</li>
<li>Calling existing Python APIs with some R packages that can invoke Python interpreter</li>
</ul>
<p>The last approach is simple and easy enough while allows users to interact with <em>MLflow</em> without waiting for R APIs to be available. I will illustrate how to achieve this with R package <a href="https://github.com/rstudio/reticulate"><strong><em>reticulate</em></strong></a>.</p>
<p><em>reticulate</em> is an open source R package that allows to call Python from R by embedding a Python session within the R session. It provides seamless and high-performance interoperability between R and Python. The package is available in <a href="https://cran.r-project.org/web/packages/reticulate/index.html">CRAN repository</a>. </p>
<p>Before beginning, you should have Python installed on the environment where R is running. I prefer installing <a href="https://conda.io/miniconda.html">miniconda</a>.</p>
<p>Once the Python is installed, you can create a virtualenv for <em>MLflow</em> and install <a href="https://pypi.org/project/mlflow/">mlflow</a> package as follow (with <code>conda</code>):</p>
<div><pre><code class="language-commandline">conda create -q -n mlflow python=3.6
source activate mlflow
pip install -U pip
pip install mlflow</code></pre></div>
<p>Next install <a href="https://github.com/rstudio/reticulate">reticulate</a> package through R.</p>
<div><pre><code class="language-R">install.packages("reticulate")</code></pre></div>
<p><code>reticulate</code> allows R to call Python functions seamlessly. The Python package is loaded by the <code>import</code> statement. Calling to a function is through <code>$</code> operator.</p>
<div><pre><code class="language-R">> library(reticulate)
> path <- import("os.path")
> path$isdir("/tmp")
[1] TRUE</code></pre></div>
<p>As you can see above, it is very simple to call Python functions in <code>os.path</code> module from R with this package. So you can do the same thing with <code>mlflow</code> package by importing it and then call <code>mlflow$log_param</code> and <code>mlflow$log_metric</code> to log parameters and metrics for the R script.</p>
<p>Following R script builds a linear regression model with <a href="https://spark.apache.org/docs/latest/sparkr.html">SparkR</a>. You need <code>SparkR</code> package installed for this <a href="https://github.com/adrian555/DocsDump/raw/dev/files/mlflow-R/mlflow-r.R">example</a>.</p>
<div><pre><code class="language-R"># load the reticulate package and import mlflow Python module
library(reticulate)
mlflow <- import("mlflow")
# load SparkR package and start spark session
library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib")))
sparkR.session(master="local[*]")
# convert iris data.frame to SparkDataFrame
df <- as.DataFrame(iris)
# parameter for GLM
family <- c("gaussian")
# log the parameter
mlflow$log_param("family", family)
# fit the GLM model
model <- spark.glm(df, Species ~ ., family = family)
# exam the model
summary(model)
# path to save the model
model_path <- "/tmp/mlflow-GLM"
# save the model
write.ml(model, model_path)
# log the artifact
mlflow$log_artifacts(model_path)
# stop spark session
sparkR.session.stop()</code></pre></div>
<p>You can either copy the script to <code>R</code> or <a href="https://www.rstudio.com/">Rstudio</a> and run interactively, or save it to a file and run with <code>Rscript</code> command. Make sure that the <code>PATH</code> environment variable includes the path to the <em>mlflow</em> Python virtualenv.</p>
<p>Once the script finishes, go to <em>MLflow</em> UI, the run is now showing and so it can be tracked. Here is a snapshot.</p>
<p><img src="https://github.com/adrian555/DocsDump/raw/dev/images/mlflow-r.png" alt="*MLflow* UI snapshot"></p>
<p>In conclusion, this approach lets R users take benefit of <em>MLflow</em> <code>Tracking</code> component and track their R models in a quick way. I will show how R users can use the other two components (<code>Projects</code> and <code>Models</code>) of <em>MLflow</em> in future stories.</p>
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