A MapReduce ML Model Deployment

Using Hadoop to deploy an ML model

This blog post builds on the ideas started in three previous blog posts.

In this blog post I’ll show how to deploy the same ML model that l deployed as a batch job in this blog post, as a task queue in this blog post, inside an AWS Lambda in this blog post, as a Kafka streaming application in this blog post, and a gRPC service in this blog post.

The code in this blog post can be found in this github repo.

Introduction

Because of the growing need to process large amounts of data across many computers, the Hadoop project was started in 2006. Hadoop is a set of software components that help to solve large scale data processing problems using clusters of computers. Hadoop supports mass data storage through the HDFS component and large scale data processing through the MapReduce component. Hadoop clusters have become a central part of the infrastructure of many companies because of their usefulness.

In this blog post, we’ll focus on the MapReduce component of Hadoop since we will be deploying a machine learning model, which is a compute-intensive process. MapReduce is a programming framework for data processing which is useful for processing large amounts of distributed data. MapReduce is able to handle errors and failures in the computation. MapReduce is also inherently parallel in nature but abstracts out that fact, making the code look like single-process code.

Hadoop and MapReduce are used to process large data sets almost exclusively. Even though machine learning models are trained over large data sets, we’ll focus on using MapReduce to execute predictions. Hadoop and MapReduce should be considered when a prediction batch job needs to be executed on millions or billions of records. This blog post is similar to a previous blog post that deployed an ML model as a batch job, but that post was focused on small scale batch jobs that could run quickly on single machines.

Because the results of a batch prediction job are stored and accessed later by clients, the user can’t interact with the model directly. This means that the client that is using the predictions produced by the model is not able to ask for predictions directly from the ML model software component, and must access the data set produced by the batch job to get predictions from the model.

Package Structure

To begin, I set up the project structure for the job package:

- data (data files used for testing the job)
- model_map_reduce_job (python package for the map reduce job)
- __init__.py
- config.py
- ml_model_map_reduce_job.py
- ml_model_manager.py
- tests ( unit tests )
- Makefle
- mrjob.conf (configuration file for MapReduce framework)
- README.md
- requirements.txt
- setup.py
- test_requirements.txt

This structure can be seen in the github repository.

Building MapReduce Jobs

A MapReduce job is made up of two basic steps: the map step and the reduce step. Both steps are implemented as simple functions that receive data, process it and return the results. The map step is responsible for implementing filtering and sorting and the reduce step is responsible for calculating aggregate results. The MapReduce system is responsible for starting, managing, and stopping the code in the map and reduce functions, for serializing and deserializing the data, and for managing the redundancy and fault tolerance of the execution of the map and reduce functions.

The MapReduce implementation provided by Hadoop is able to do data processing with map and reduce functions implemented in many different programming languages by using the streaming interface. In this blog post, we’ll use this interface to run a model prediction job using Python. This simplifies the deployment of the model greatly, since we don’t need to rewrite the model’s prediction code in order to deploy it to a Hadoop cluster. We’ll be using the mrjob python package to write the MapReduce job.

Installing the Model

In order to write a MapReduce job that is able to handle any machine learning model, we’ll start by installing a model into the environment. For this we can use the same model we’ve used before, the iris_model package. This package can be installed from a git repository with this command:

pip install git+https://github.com/schmidtbri/ml-model-abc-improvements

Now that we have the model installed in the environment, we can try it out by opening a python interpreter and entering this code:

from iris_model.iris_predict import IrisModel
>>> model = IrisModel()
>>> model.predict({“sepal_length”:1.1, “sepal_width”: 1.2, “petal_width”: 1.3, “petal_length”: 1.4})
{‘species’: ‘setosa’}

To load the model inside of the MapReduce job, we’ll point at the IrisModel class in a configuration file. The configuration file looks like this:

class Config(dict):
models = [{
“module_name”: “iris_model.iris_predict”,
“class_name”: “IrisModel”
}]

The code above can be found here.

This configuration will be used by the job to dynamically load the model packages. The module_name and class_name fields allow the job to import the class that contains the implementation of the model’s prediction algorithm. The models list can contain pointers to many models, so there are no limitations to how many models can be hosted by the MapReduce job.

Managing Models

As in previous blog posts, we’ll use a singleton object to manage the ML model objects that will be used to make predictions. The class that the singleton object is instantiated from is called “ModelManager”. The class is responsible for instantiating MLModel objects, managing the instances, returning information about the MLModel objects, and returning references to the objects when needed. The code for the ModelManager class can be found here. For a full explanation of the code in the class, read this blog post.

MLModelMapReduceJob Class

We now have the model package installed and the ModelManager class to manage it, so we can start to write the MapReduce job itself. The MapReduce job is defined as a subclass of the MRJob base class which defines map() and reduce() methods that implement the functionality of the job. To start, we’ll load the right configuration by accessing the APP_SETTINGS environment variable:

configuration = __import__(“model_mapreduce_job”). \
__getattribute__(“config”). \
__getattribute__(os.environ[“APP_SETTINGS”])

The code above can be found here.

With the configuration loaded, we’ll instantiate the ModelManager singleton which will hold the references to the model objects that we want to host in this MapReduce job:

model_manager = ModelManager()
model_manager.load_models(Config.models)

The code above can be found here.

By putting this initialization at the top of the module, we can be sure that the models are initialized one time only, when the module is loaded by the python interpreter.

Now we can write the class that makes up the MapReduce job:

class MLModelMapReduceJob(MRJob):
INPUT_PROTOCOL = JSONValueProtocol
OUTPUT_PROTOCOL = JSONProtocol
DIRS = [‘../model_mapreduce_job’]

The code above can be found here.

The INPUT_PROTOCOL and OUTPUT_PROTOCOL class properties define the input and output protocols of the MapReduce steps. A protocol is a piece of code that reads and writes data to the filesystem, it is useful to abstract out the map and reduce steps from the format in which the data is stored. The DIRS class property tells the MrJob package that the code in this module depends on code inside of the “model_map_reduce” directory, this causes MrJob to copy the code whenever it creates a deployment package for this job. These options help to simplify the code and deployment of the job.

The job class needs to be initialized, so we’ll add a __init__() method:

def __init__(self, *args, **kwargs):
super(MLModelMapReduceJob, self).__init__(*args, **kwargs)

self._model = model_manager.get_model(
self.options.model_qualified_name)
if self._model is None:
raise ValueError(“‘{}’ not found in the ModelManager
instance.”.format(self.options.model_qualified_name))

The code above can be found here.

The __init__ method first calls the MrJob base class’ __init__ method so that it can do framework-level initialization. Next, we ask the ModelManager singleton for an instance of the model that we want to host in the MapReduce job. The qualified name of the model is accessed from the self.options.model_qualified_name variable, which is set by a command line option. Lastly, we check that a model object was actually returned by the ModelManager and raise an exception if it wasn’t.

Next, the MapReduce job must be able to run on any model that is inside of the ModelManager instance. To support this, we will add a command line option to the job that accepts the qualified name of the model we want to run:

def configure_args(self):
super(MLModelMapReduceJob, self).configure_args()

self.add_passthru_arg(‘--model_qualified_name’,
type=str, help=’Qualified name of the model.’)

The code above can be found here.

This function allows us to extend the command line options already supported by the MrJob framework. The command line argument passes through the framework and is stored in the self.options object, which we used in the code in the __init__ method to select the model we want to use for the job.

Now that we have an initialized job class, we can write the code that actually does the work of the MapReduce job. The mapper function looks like this:

def mapper(self, _, data):
prediction = None
try:
prediction = self._model.predict(data=data)
except Exception as e:
prediction = None
yield data, prediction

The code above can be found here.

This function is very simple, it receives a dictionary in the “data” argument, makes a prediction with the model, and returns a tuple of the prediction input and output. The data argument is a dictionary because we used the “JSONValueProtocol” as the INPUT_PROTOCOL for this job. This protocol deserializes a JSON string into a native Python object. By using this protocol, we saved ourselves the trouble of having to deserialize the input to JSON in the mapper step. If the model fails to make a prediction, then None is returned as the prediction. The OUTPUT_PROTOCOL option is set to “JSONProtocol”, which serializes the key-value pair to two JSON strings separated by a tab character.

The output of the mapper step is always a key-value pair in which the key must be unique across the inputs of the step. If any input is repeated, the mapper step will make a prediction on it, but the MapReduce framework will only return one result for the key to the next step. This behavior sets up a limitation on our model: it must always produce the same prediction given the same input, which is to say that the model must make predictions deterministically. If the model is not deterministic, the MapReduce framework will choose the first prediction made for the input record. This may not matter in some situations but may break any steps that use the results of this step if this behavior is not handled correctly.

This MapReduce job does not need a reduce step since we only need to make predictions and return the results. However, this job can be combined with other MapReduce jobs that do use reduce steps to make more a complex data processing pipeline.

Testing the Job

Now that we have the code for the MapReduce job, we will test it locally against a small data file. Because of the input and output protocol options, the model is able to accept JSON files as input and it will produce JSON files as output. Here is an example of the JSON that we will feed to the job:

{“sepal_length”: 5.0, “sepal_width”: 3.2, “petal_length”: 1.2, “petal_width”: 0.2}
{ “sepal_length”: 5.5, “sepal_width”: 3.5, “petal_length”: 1.3, “petal_width”: 0.2}
...

The data file can be found here.

To execute the job locally, these commands need to be run:

export PYTHONPATH=./
export APP_SETTINGS=ProdConfig
python model_mapreduce_job/ml_model_map_reduce_job.py \
--model_qualified_name iris_model ./data/input.ldjson > \
data/output.ldjson

After the job runs, the output of the map step will be in the /data folder. The input json string and resulting prediction will be on one line of the file separated by a tab character. One input line had JSON with a schema that the model could not accept, so the output should contain a null prediction for that input. The — model_qualified_name command line argument tells the job to use the iris_model model from the ModelManager when running the job.

Deploying to AWS

The mrjob package supports running jobs in the AWS Elastic Map Reduce (EMR) service. To run the model job, we’ll need an account in AWS. To interact with AWS, we’ll need to install the boto3 and awscli python packages:

pip install boto3 awscli

Next we’ll configure the API access keys. A set of access keys can be generated and configured by following these instructions. The configuration will look like this:

aws configure
AWS Access Key ID [*******************]: xxxxxxxxxxxxxxxxxx
AWS Secret Access Key [******************]:xxxxxxxxxxxxxxxxxxx
Default region name [us-east-2]: us-east-1
Default output format [None]:

In order to run the model job in AWS EMR, we’ll first need to configure a default role for the job to assume. A simple way to do this is already supported in the AWS CLI tool. The command looks like this:

aws emr create-default-roles

In order to set up the execution environment in the nodes before we run the model prediction code we’ll need to execute a few commands. The mrjob package supports this through a configuration file called mrjob.conf. The config file is written in YAML and looks like this:

runners:
emr:
bootstrap:
- sudo yum update -y
- sudo yum install git -y
- sudo pip-3.6 install -r ./requirements.txt#
setup:
- export PYTHONPATH=$PYTHONPATH:model_mapreduce_job/#
- export APP_SETTINGS=ProdConfig

The file can be found here.

The file is able to hold configuration for several types of runners, for now we’ll only configure the EMR runner. The bootstrap section holds commands that will be executed one time, when the cluster node is first created. In this section we’re updating the yum package manager, installing the git client, and installing all of the python dependencies we need to run the model package from the requirements.txt file in the project.

The setup section holds commands that will be executed whenever the MapReduce job starts up. In this section, we are setting up the PYTHONPATH environment variable that the python interpreter will need in order to find the code files that make up the job. We are also setting the APP_SETTINGS environment variable that tells the job which environment it is running in, for now we’re running the job with the ProdConfiguration settings.

Now that we have the credentials and configuration set up, we can run the job in AWS. The command looks like this:

python model_mapreduce_job/ml_model_map_reduce_job.py \
--conf-path=./mrjob.conf \
-r emr \
--iam-service-role EMR_DefaultRole \
--model_qualified_name iris_model ./data/input.ldjson

The mrjob package will create an S3 bucket for the job, upload the code and data to the S3 bucket, create an EMR cluster for the job, and run the job. The results of the job will be stored into the same S3 bucket.

Closing

By using the MapReduce framework, we are able to make a large number of predictions on a cluster of computers. Because of the simple design of the MapReduce framework, a lot of the complexities of running a job on many computers are abstracted out. This deployment option for machine learning models enables us to deploy model prediction jobs against truly massive data sets.

By building the prediction job so that it uses the MLModel interface, the deployment of a model as a MapReduce job is greatly simplified. The MapReduce job that we built in this blog post is able to host any machine learning model that uses the MLModel interface which makes the code highly reusable. Once again, the MLModel interface allowed us to abstract out the complexities of building a machine learning model from the complexities of deploying a machine learning model.

One of the drawbacks of the implementation is the fact that it only accepts LDJSON encoded files as input to the job. This is for the sake of simplicity, since having the field names along with the data makes the code easier to understand. An improvement to the code would be to enable other protocols so that we can use other file types with the job. Furthermore, it would be easy to make the choice of input and output protocols a command line option that can be chosen at execution time.

Coder and machine learning enthusiast

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