Databricks Labs AutoML

Release Notes | Developer Docs | Demo | Release Artifacts | Contributors

This Databricks Labs project is a non-supported end-to-end supervised learning solution for automating:

• Feature clean-up
• Feature vectorization
• Model selection and training
• Hyper parameter optimization and selection
• Batch Prediction
• Logging of model results and training runs (using MLFlow)

This package utilizes Apache Spark ML and currently supports the following model family types:

• Decision Trees (Regressor and Classifier)
• Gradient Boosted Trees (Regressor and Classifier)
• Random Forest (Regressor and Classifier)
• Linear Regression
• Logistic Regression
• Multi-Layer Perceptron Classifier
• Support Vector Machines
• XGBoost (Regressor and Classifier)

NOTE: As of version 0.5.1, XGBoost model serialization (saving) is not functional.  This will be enabled in a future release.

Documentation

Developer API documentation can be found here

Building

Databricks Labs AutoML can be build with either SBT or Maven.

This package requires Java 1.8.x  and scala 2.11.x to be installed on your system prior to building.

After cloning this repo onto your local system, navigate to the root directory and execute either:

Maven Build

mvn clean install -DskipTests

SBT Build

sbt package

If there is any StackOverflowError during the build, adjust the stack size on a JVM, example:

#For Maven
export MAVEN_OPTS=-Xss2m
#For SBT
export SBT_OPTS="-Xss2M"

This will skip unit test execution (it is not recommended to run unit tests in local mode against this package as unit testing is asynchronous and incredibly CPU intensive for this code base.)

Setup

Once the artifact has been built, attach to the Databricks Shard through either the DBFS API or the GUI. Once loaded into the account, utilize either the Libraries API to attach to a cluster, or utilize the GUI to attach the .jar to the cluster.

NOTE: It is not recommended to attach this libarary to all clusters on the account.  

Use of an ML Runtime is highly advised to ensure that custom management of dependent 
libraries and configurations are provided 'out of the box'

Attach the following libraries to the cluster:

• PyPi: mlflow==1.3.0
• Maven: org.mlflow:mlflow-client:1.3.0

Getting Started

This package provides a number of different levels of API interaction, from the highest-level "default only" FamilyRunner to low-level APIs that allow for highly customizable workflows to be created for automated ML tuning and Inference.

For the purposes of a quick-start intro, the below example is of the highest-level API access point.

import com.databricks.labs.automl.executor.config.ConfigurationGenerator
import com.databricks.labs.automl.executor.FamilyRunner

val data = spark.table("ben_demo.adult_data")

val overrides = Map("labelCol" -> "income",
"mlFlowExperimentName" -> (defaults to current notebook directory),
"mlFlowModelSaveDirectory" -> "dbfs:/ml/FirstAutoMLRun/",
"inferenceConfigSaveLocation" -> "dbfs:/ml/FirstAutoMLRun/inference"
)

val randomForestConfig = ConfigurationGenerator.generateConfigFromMap("RandomForest", "classifier", overrides)
val gbtConfig = ConfigurationGenerator.generateConfigFromMap("GBT", "classifier", overrides)
val logConfig = ConfigurationGenerator.generateConfigFromMap("LogisticRegression", "classifier", overrides)

val runner = FamilyRunner(data, Array(randomForestConfig, gbtConfig, logConfig)).execute()

This example will take the default configuration for all of the application parameters (excepting the overridden parameters in overrides Map) and execute Data Preparation tasks, Feature Vectorization, and automatic tuning of all 3 specified model types. At the conclusion of each run, the results and model artifacts will be logged to the mlflow location that was specified in the configuration.

For a listing of all available parameter overrides and their functionality, see the Developer Docs

Pipeline API

v0.6.0

Starting with this release, AutoML now exposes an API to work with the pipeline semantics around feature engineering steps and full predict pipelines. Example:

import com.databricks.labs.automl.executor.config.ConfigurationGenerator
import com.databricks.labs.automl.executor.FamilyRunner
import org.apache.spark.ml.PipelineModel

val data = spark.table("ben_demo.adult_data")
val overrides = Map(
  "labelCol" -> "label", "mlFlowLoggingFlag" -> false,
  "scalingFlag" -> true, "oneHotEncodeFlag" -> true,
  "pipelineDebugFlag" -> true
)
val randomForestConfig = ConfigurationGenerator
  .generateConfigFromMap("RandomForest", "classifier", overrides)
val runner = FamilyRunner(data, Array(randomForestConfig))
  .executeWithPipeline()

runner.bestPipelineModel("RandomForest").transform(data)

//Serialize it
runner.bestPipelineModel("RandomForest").write.overwrite().save("tmp/predict-pipeline-1")

// Load it for running inference
val pipelineModel = PipelineModel.load("tmp/predict-pipeline-1")
val predictDf = pipelineModel.transform(data)

Inference via Mlflow Run ID

It is also possible to use MlFlow Run ID for inference, if Mlflow logging is turned on during training. For usage, see this

For all available pipeline APIs. please see Developer Docs

Feedback

Issues with the application? Found a bug? Have a great idea for an addition? Feel free to file an issue.

Contributing

Have a great idea that you want to add? Fork the repo and submit a PR!

Legal Information

This software is provided as-is and is not officially supported by Databricks. Please see the legal agreement and understand that issues with the use of this code will not be answered or investigated by Databricks Support.

Core Contribution team

• Lead Developer: Ben Wilson, Sr. RSA, Databricks
• Developer: Daniel Tomes, RSA Practice Leader, Databricks
• Developer: Jas Bali, Solutions Consultant, Databricks