sjjpo2002 / mleap

MLeap: Deploy Spark Pipelines to Production

Deploying machine learning data pipelines and algorithms should not be a time-consuming or difficult task. MLeap allows data scientists and engineers to deploy machine learning pipelines from Spark and Scikit-learn to a portable format and execution engine.

Documentation

Documentation is available at mleap-docs.combust.ml.

Read Serializing a Spark ML Pipeline and Scoring with MLeap to gain a full sense of what is possible.

Introduction

Using the MLeap execution engine and serialization format, we provide a performant, portable and easy-to-integrate production library for machine learning data pipelines and algorithms.

For portability, we build our software on the JVM and only use serialization formats that are widely-adopted.

We also provide a high level of integration with existing technologies.

Our goals for this project are:

1. Allow Researchers/Data Scientists and Engineers to continue to build data pipelines and train algorithms with Spark and Scikit-Learn
2. Extend Spark/Scikit/TensorFlow by providing ML Pipelines serialization/deserialization to/from a common framework (Bundle.ML)
3. Use MLeap Runtime to execute your pipeline and algorithm without dependenices on Spark or Scikit (numpy, pandas, etc)

Overview

1. Core execution engine implemented in Scala
2. Spark, PySpark and Scikit-Learn support
3. Export a model with Scikit-learn or Spark and execute it using the MLeap Runtime (without dependencies on the Spark Context, or sklearn/numpy/pandas/etc)
4. Choose from 3 portable serialization formats (JSON, Protobuf, and Mixed)
5. Implement your own custom data types and transformers for use with MLeap data frames and transformer pipelines
6. Extensive test coverage with full parity tests for Spark and MLeap pipelines
7. Optional Spark transformer extension to extend Spark's default transformer offerings

Requirements

MLeap is cross-compiled for Scala 2.10 and 2.11. Because we depend heavily on Typesafe config for MLeap, we only support Java 8 at the moment. This means the following configurations should be possible:

2. Scala 2.10 and Java 8
3. Scala 2.11 and Java 8

Setup

Link with Maven or SBT

MLeap is cross-compiled for Scala 2.10 and 2.11, so just replace 2.10 with 2.11 wherever you see it if you are running Scala version 2.11 and using a POM file for dependency management. Otherwise, use the %% operator if you are using SBT and the correct Scala version will be used.

SBT

libraryDependencies += "ml.combust.mleap" %% "mleap-runtime" % "0.6.0"

Maven

<dependency>
    <groupId>ml.combust.mleap</groupId>
    <artifactId>mleap-runtime_2.10</artifactId>
    <version>0.6.0</version>
</dependency>

For Spark Integration

SBT

libraryDependencies += "ml.combust.mleap" %% "mleap-spark" % "0.6.0"

Maven

<dependency>
    <groupId>ml.combust.mleap</groupId>
    <artifactId>mleap-spark_2.10</artifactId>
    <version>0.6.0</version>
</dependency>

Spark Packages

$ bin/spark-shell --packages ml.combust.mleap:mleap-spark_2.11:0.6.0

Using the Library

For more complete examples, see our other Git repository: MLeap Demos

Create and Export a Spark Pipeline

The first step is to create our pipeline in Spark. For our example we will manually build a simple Spark ML pipeline.

import org.apache.spark.ml.feature.{StringIndexerModel, Binarizer}

// User out-of-the-box Spark transformers like you normally would
val stringIndexer = new StringIndexerModel(uid = "si", labels = Array("hello", "MLeap")).
  setInputCol("test_string").
  setOutputCol("test_index")

val binarizer = new Binarizer(uid = "bin").
  setThreshold(0.5).
  setInputCol("test_double").
  setOutputCol("test_bin")

// Use the MLeap utility method to directly create an org.apache.spark.ml.PipelineModel

import org.apache.spark.ml.mleap.SparkUtil

// Without needing to fit an org.apache.spark.ml.Pipeline
val pipeline = SparkUtil.createPipelineModel(uid = "pipeline", Array(stringIndexer, binarizer))

import ml.combust.bundle.BundleFile
import ml.combust.mleap.spark.SparkSupport._
import resource._

// Optionally yield from here to get the try back from serializing
// The try will indicate if serialization was successful
for(modelFile <- managed(BundleFile("jar:file:/tmp/simple-spark-pipeline.zip"))) {
  pipeline.writeBundle.
    // save our pipeline to a zip file
    // we can save a file to any supported java.nio.FileSystem
    save(modelFile)
}

Spark pipelines are not meant to be run outside of Spark. They require a DataFrame and therefore a SparkContext to run. These are expensive data structures and libraries to include in a project. With MLeap, there is no dependency on Spark to execute a pipeline. MLeap dependencies are lightweight and we use fast data structures to execute your ML pipelines.

Create and Export a Scikit-Learn Pipeline

# Load scikit-learn mleap extensions
import mleap.sklearn.pipeline
import mleap.sklearn.preprocessing.data
from mleap.sklearn.preprocessing.data import NDArrayToDataFrame

# Load scikit-learn transformers and models
from sklearn.preprocessing import LabelEncoder, Binarizer

# Define the Label Encoder (mlinit method adds a unique `name` to the transformer as well as explicit input/output features)
label_encoder_tf = LabelEncoder()
label_encoder_tf.mlinit(input_features = 'col_a', output_features='col_a_label_le')

# Convert output of Label Encoder to Data Frame instead of 1d-array
n_dim_array_to_df_tf = NDArrayToDataFrame(label_encoder_tf.output_features)

# Define our binarizer
binarizer = Binarizer(0.5)
binarizer.mlinit(input_features=n_dim_array_to_df_tf.output_features, output_features="{}_binarized".format(n_dim_array_to_df_tf.output_features))

data = pd.DataFrame(['a', 'b', 'c'], columns=['col_a'])

# Assemble the steps of our pipeline
steps = [
    (label_encoder_tf.name, label_encoder_tf),
    (n_dim_array_to_df_tf.name, n_dim_array_to_df_tf),
    (binarizer.name, binarizer)
]

pipeline = Pipeline(steps)
pipeline.mlinit()

# Fit the pipeline
pipeline.fit(data)

# Write the pipeline to bundle.ml
pipeline.serialize_to_bundle('/tmp', 'simple-sk-pipeline', init=True)

Load and Transform Using MLeap

Becuase we export Spark and Scikit-learn pipelines to a standard format, we can use either our Spark-trained pipeline or our Scikit-learn pipeline from the previous steps to demonstrate usage of MLeap in this section. The choice is yours!

import ml.combust.bundle.BundleFile
import ml.combust.mleap.runtime.MleapSupport._
import resource._

// load the Spark pipeline we saved in the previous section
val bundle = (for(bundleFile <- managed(BundleFile("jar:file:/tmp/simple-spark-pipeline.zip"))) yield {
  bundleFile.loadMleapBundle().get
}).opt.get

// create a simple LeapFrame to transform
import ml.combust.mleap.runtime.{Row, LeapFrame, LocalDataset}
import ml.combust.mleap.runtime.types._
import ml.combust.mleap.tensor.Tensor


// MLeap makes extensive use of monadic types like Try
val schema = StructType(StructField("test_string", StringType()),
  StructField("test_double", TensorType(DoubleType()))).get
val data = LocalDataset(Row("hello", Tensor.denseVector(Array(0.6))),
  Row("MLeap", Tensor.denseVector(Array(0.2))))
val frame = LeapFrame(schema, data)

// transform the dataframe using our pipeline
val mleapPipeline = bundle.root
val frame2 = mleapPipeline.transform(frame).get
val data2 = frame2.dataset

// get data from the transformed rows and make some assertions
assert(data2(0).getDouble(2) == 0.0) // string indexer output
assert(data2(0).getTensor[Double](3).get(0) == 1.0) // binarizer output

// the second row
assert(data2(1).getDouble(2) == 1.0)
assert(data2(1).getTensor[Double](3).get(0) == 0.0)

Documentation

For more documentation, please see our wiki, where you can learn to:

1. implement custom transformers that will work with Spark, MLeap and Scikit-learn
2. implement custom data types to transform with Spark and MLeap pipelines
3. transform with blazing fast speeds using optimized row-based transformers
4. serialize MLeap data frames to various formats like avro, json, and a custom binary format
5. implement new serialization formats for MLeap data frames
6. work through several demonstration pipelines which use real-world data to create predictive pipelines
7. supported Spark transformers
8. supported Scikit-learn transformers
9. custom transformers provided by MLeap

Contributing

• Write documentation.
• Write a tutorial/walkthrough for an interesting ML problem
• Contribute an Estimator/Transformer from Spark
• Use MLeap at your company and tell us what you think
• Make a feature request or report a bug in github
• Make a pull request for an existing feature request or bug report
• Join the discussion of how to get MLeap into Spark as a dependency. Talk with us on Gitter (see link at top of README.md)

Contact Information

• Hollin Wilkins ([email protected])
• Mikhail Semeniuk ([email protected])
• Ram Sriharsha ([email protected])

License

See LICENSE and NOTICE file in this repository.

Copyright 2016 Combust, Inc.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.