# Tutorial

This tutorial showcases how you can use MLflow end-to-end to:

• Train a linear regression model
• Package the code that trains the model in a reusable and reproducible model format
• Deploy the model into a simple HTTP server that will enable you to score predictions

This tutorial uses a dataset to predict the quality of wine based on quantitative features like the wine’s “fixed acidity”, “pH”, “residual sugar”, and so on. The dataset is from UCI’s machine learning repository. [1]

## What You’ll Need

To run this tutorial, you’ll need to:

• Install MLflow and scikit-learn. There are two options for installing these dependencies:

1. Install MLflow with extra dependencies, including scikit-learn (via pip install mlflow[extras])
2. Install MLflow (via pip install mlflow) and install scikit-learn separately (via pip install sckit-learn)
• Install conda

• Clone (download) the MLflow repository via git clone https://github.com/mlflow/mlflow

• cd into the examples directory within your clone of MLflow - we’ll use this working directory for running the tutorial. We avoid running directly from our clone of MLflow as doing so would cause the tutorial to use MLflow from source, rather than your PyPI installation of MLflow.

• Install conda
• Install the MLflow package (via install.packages("mlflow"))
• Install MLflow (via mlflow::mlflow_install())
• Clone (download) the MLflow repository via git clone https://github.com/mlflow/mlflow
• setwd() into the examples directory within your clone of MLflow - we’ll use this working directory for running the tutorial. We avoid running directly from our clone of MLflow as doing so would cause the tutorial to use MLflow from source, rather than your PyPI installation of MLflow.

## Training the Model

First, train a linear regression model that takes two hyperparameters: alpha and l1_ratio.

The code is located at examples/sklearn_elasticnet_wine/train.py and is reproduced below.

# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

import os
import warnings
import sys

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet

import mlflow
import mlflow.sklearn

def eval_metrics(actual, pred):
rmse = np.sqrt(mean_squared_error(actual, pred))
mae = mean_absolute_error(actual, pred)
r2 = r2_score(actual, pred)
return rmse, mae, r2

if __name__ == "__main__":
warnings.filterwarnings("ignore")
np.random.seed(40)

# Read the wine-quality csv file (make sure you're running this from the root of MLflow!)
wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv")

# Split the data into training and test sets. (0.75, 0.25) split.
train, test = train_test_split(data)

# The predicted column is "quality" which is a scalar from [3, 9]
train_x = train.drop(["quality"], axis=1)
test_x = test.drop(["quality"], axis=1)
train_y = train[["quality"]]
test_y = test[["quality"]]

alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5

with mlflow.start_run():
lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
lr.fit(train_x, train_y)

predicted_qualities = lr.predict(test_x)

(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
print("  RMSE: %s" % rmse)
print("  MAE: %s" % mae)
print("  R2: %s" % r2)

mlflow.log_param("alpha", alpha)
mlflow.log_param("l1_ratio", l1_ratio)
mlflow.log_metric("rmse", rmse)
mlflow.log_metric("r2", r2)
mlflow.log_metric("mae", mae)

mlflow.sklearn.log_model(lr, "model")


This example uses the familiar pandas, numpy, and sklearn APIs to create a simple machine learning model. The MLflow tracking APIs log information about each training run, like the hyperparameters alpha and l1_ratio, used to train the model and metrics, like the root mean square error, used to evaluate the model. The example also serializes the model in a format that MLflow knows how to deploy.

You can run the example with default hyperparameters as follows:

python examples/sklearn_elasticnet_wine/train.py


Try out some other values for alpha and l1_ratio by passing them as arguments to train.py:

python examples/sklearn_elasticnet_wine/train.py <alpha> <l1_ratio>


Each time you run the example, MLflow logs information about your experiment runs in the directory mlruns.

Note

If you would like to use the Jupyter notebook version of train.py, try out the tutorial notebook at examples/sklearn_elasticnet_wine/train.ipynb.

The code is located at examples/r_wine/train.R and is reproduced below.

# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

library(mlflow)
library(glmnet)
library(carrier)

set.seed(40)

# Read the wine-quality csv file

# Split the data into training and test sets. (0.75, 0.25) split.
sampled <- sample(1:nrow(data), 0.75 * nrow(data))
train <- data[sampled, ]
test <- data[-sampled, ]

# The predicted column is "quality" which is a scalar from [3, 9]
train_x <- as.matrix(train[, !(names(train) == "quality")])
test_x <- as.matrix(test[, !(names(train) == "quality")])
train_y <- train[, "quality"]
test_y <- test[, "quality"]

alpha <- mlflow_param("alpha", 0.5, "numeric")
lambda <- mlflow_param("lambda", 0.5, "numeric")

with(mlflow_start_run(), {
model <- glmnet(train_x, train_y, alpha = alpha, lambda = lambda, family= "gaussian", standardize = FALSE)
predictor <- crate(~ glmnet::predict.glmnet(!!model, as.matrix(.x)), !!model)
predicted <- predictor(test_x)

rmse <- sqrt(mean((predicted - test_y) ^ 2))
mae <- mean(abs(predicted - test_y))
r2 <- as.numeric(cor(predicted, test_y) ^ 2)

message("Elasticnet model (alpha=", alpha, ", lambda=", lambda, "):")
message("  RMSE: ", rmse)
message("  MAE: ", mae)
message("  R2: ", r2)

mlflow_log_param("alpha", alpha)
mlflow_log_param("lambda", lambda)
mlflow_log_metric("rmse", rmse)
mlflow_log_metric("r2", r2)
mlflow_log_metric("mae", mae)

mlflow_log_model(predictor, "model")
})


This example uses the familiar glmnet package to create a simple machine learning model. The MLflow tracking APIs log information about each training run, like the hyperparameters alpha and lambda, used to train the model and metrics, like the root mean square error, used to evaluate the model. The example also serializes the model in a format that MLflow knows how to deploy.

You can run the example with default hyperparameters as follows:

mlflow_run(uri = "examples/r_wine", entry_point = "train.R")


Try out some other values for alpha and lambda by passing them as arguments to train.R:

mlflow_run(uri = "examples/r_wine", entry_point = "train.R", parameters = list(alpha = 0.1, lambda = 0.5))


Each time you run the example, MLflow logs information about your experiment runs in the directory mlruns.

Note

If you would like to use an R notebook version of train.R, try the tutorial notebook at examples/r_wine/train.Rmd.

## Comparing the Models

Next, use the MLflow UI to compare the models that you have produced. In the same current working directory as the one that contains the mlruns run:

mlflow ui

mlflow_ui()


and view it at http://localhost:5000.

On this page, you can see a list of experiment runs with metrics you can use to compare the models.

You can use the search feature to quickly filter out many models. For example, the query metrics.rmse < 0.8 returns all the models with root mean squared error less than 0.8. For more complex manipulations, you can download this table as a CSV and use your favorite data munging software to analyze it.

## Packaging the Training Code

Now that you have your training code, you can package it so that other data scientists can easily reuse the model, or so that you can run the training remotely, for example on Databricks.

You do this by using MLflow Projects conventions to specify the dependencies and entry points to your code. The sklearn_elasticnet_wine/MLproject file specifies that the project has the dependencies located in a Conda environment file called conda.yaml and has one entry point that takes two parameters: alpha and l1_ratio.

# sklearn_elasticnet_wine/MLproject

name: tutorial

conda_env: conda.yaml

entry_points:
main:
parameters:
alpha: float
l1_ratio: {type: float, default: 0.1}
command: "python train.py {alpha} {l1_ratio}"


The Conda file lists the dependencies:

# sklearn_elasticnet_wine/conda.yaml

name: tutorial
channels:
- defaults
dependencies:
- numpy=1.14.3
- pandas=0.22.0
- scikit-learn=0.19.1
- pip:
- mlflow


To run this project, invoke mlflow run examples/sklearn_elasticnet_wine -P alpha=0.42. After running this command, MLflow runs your training code in a new Conda environment with the dependencies specified in conda.yaml.

If the repository has an MLproject file in the root you can also run a project directly from GitHub. This tutorial is duplicated in the https://github.com/mlflow/mlflow-example repository which you can run with mlflow run git@github.com:mlflow/mlflow-example.git -P alpha=0.42.

You do this by running mlflow_snapshot() to create an R dependencies packrat file called r-dependencies.txt.

The R dependencies file lists the dependencies:

# examples/r_wine/r-dependencies.txt

PackratFormat: 1.4
PackratVersion: 0.4.9.3
RVersion: 3.5.1
Repos: CRAN=https://cran.rstudio.com/

Package: BH
Source: CRAN
Version: 1.66.0-1
Hash: 4cc8883584b955ed01f38f68bc03af6d

Package: Matrix
Source: CRAN
Version: 1.2-14
Hash: 521aa8772a1941dfdb007bf532d19dde
Requires: lattice

...


To run this project, invoke:

mlflow_run("examples/r_wine", entry_point = "train.R", parameters = list(alpha = 0.2))


After running this command, MLflow runs your training code in a new R session.

To restore the dependencies specified in r-dependencies.txt, you can run instead:

mlflow_restore_snapshot()
mlflow_run("examples/r_wine", entry_point = "train.R", parameters = list(alpha = 0.2))


You can also run a project directly from GitHub. This tutorial is duplicated in the https://github.com/rstudio/mlflow-example repository which you can run with:

mlflow_run(
"train.R",
"https://github.com/rstudio/mlflow-example",
parameters = list(alpha = 0.2)
)


## Serving the Model

Now that you have packaged your model using the MLproject convention and have identified the best model, it is time to deploy the model using MLflow Models. An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark.

In the example training code, after training the linear regression model, a function in MLflow saved the model as an artifact within the run.

mlflow.sklearn.log_model(lr, "model")


To view this artifact, you can use the UI again. When you click a date in the list of experiment runs you’ll see this page.

At the bottom, you can see that the call to mlflow.sklearn.log_model produced two files in /Users/mlflow/mlflow-prototype/mlruns/0/7c1a0d5c42844dcdb8f5191146925174/artifacts/model. The first file, MLmodel, is a metadata file that tells MLflow how to load the model. The second file, model.pkl, is a serialized version of the linear regression model that you trained.

In this example, you can use this MLmodel format with MLflow to deploy a local REST server that can serve predictions.

To deploy the server, run (replace the path with your model’s actual path):

mlflow models serve -m /Users/mlflow/mlflow-prototype/mlruns/0/7c1a0d5c42844dcdb8f5191146925174/artifacts/model -p 1234


Note

The version of Python used to create the model must be the same as the one running mlflow models serve. If this is not the case, you may see the error UnicodeDecodeError: 'ascii' codec can't decode byte 0x9f in position 1: ordinal not in range(128) or raise ValueError, "unsupported pickle protocol: %d".

Once you have deployed the server, you can pass it some sample data and see the predictions. The following example uses curl to send a JSON-serialized pandas DataFrame with the split orientation to the model server. For more information about the input data formats accepted by the model server, see the MLflow deployment tools documentation.

curl -X POST -H "Content-Type:application/json; format=pandas-split" --data '{"columns":["alcohol", "chlorides", "citric acid", "density", "fixed acidity", "free sulfur dioxide", "pH", "residual sugar", "sulphates", "total sulfur dioxide", "volatile acidity"],"data":[[12.8, 0.029, 0.48, 0.98, 6.2, 29, 3.33, 1.2, 0.39, 75, 0.66]]}' http://127.0.0.1:1234/invocations


the server should respond with output similar to:

[6.379428821398614]

mlflow_log_model(predictor, "model")


To view this artifact, you can use the UI again. When you click a date in the list of experiment runs you’ll see this page.

At the bottom, you can see that the call to mlflow_log_model() produced two files in mlruns/0/c2a7325210ef4242bd4631cec8f92351/artifacts/model/. The first file, MLmodel, is a metadata file that tells MLflow how to load the model. The second file, r_model.bin, is a serialized version of the linear regression model that you trained.

In this example, you can use this MLmodel format with MLflow to deploy a local REST server that can serve predictions.

To deploy the server, run:

mlflow_rfunc_serve(model_uri="mlruns/0/c2a7325210ef4242bd4631cec8f92351/artifacts/model", port=8090)


This initializes a REST server and opens a Swagger interface to perform predictions against the REST API:

Note

By default, a model is served using the R packages available. To ensure the environment serving the prediction function matches the model, set restore = TRUE when calling mlflow_rfunc_serve().

To serve a prediction, enter this in the Swagger UI:

{
"fixed acidity": 6.2,
"volatile acidity": 0.66,
"citric acid": 0.48,
"residual sugar": 1.2,
"chlorides": 0.029,
"free sulfur dioxide": 29,
"total sulfur dioxide": 75,
"density": 0.98,
"pH": 3.33,
"sulphates": 0.39,
"alcohol": 12.8
}


which should return something like:

[
[
6.4287492410792
]
]


Or run:

curl -X POST "http://127.0.0.1:8090/predict/" -H "accept: application/json" -H "Content-Type: application/json" -d "{\"fixed acidity\": 6.2, \"volatile acidity\": 0.66, \"citric acid\": 0.48, \"residual sugar\": 1.2, \"chlorides\": 0.029, \"free sulfur dioxide\": 29, \"total sulfur dioxide\": 75, \"density\": 0.98, \"pH\": 3.33, \"sulphates\": 0.39, \"alcohol\": 12.8}"


the server should respond with output similar to:

[[6.4287492410792]]


## More Resources

Congratulations on finishing the tutorial! For more reading, see MLflow Tracking, MLflow Projects, MLflow Models, and more.

 [1] Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.