Scaling Machine Learning with Apache Spark
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Spark has become synonymous with big data processing, however the majority of data scientists still build models using single machine libraries. This talk will explore the multitude of ways Spark can be used to scale machine learning applications. In particular, we will guide you through distributed solutions for training and inference, distributed hyperparameter search, deployment issues, and new features for Machine Learning in Apache Spark 3.0. Niall Turbitt and Holly Smith combine their years of experience working with Spark to summarize best practices for scaling ML solutions.
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Scaling Machine Learning with Apache Spark
- 1. Scaling Machine Learning with Apache Spark Holly Smith Niall Turbitt
- 2. About Holly Smith Senior Consultant at Databricks ▪ Professional Services and Training ▪ Experience ▪ Credit Risk & Decisioning ▪ Mobile Banking ▪ Forecasting & Optimisation ▪ BSc Mathematics University of Greenwich
- 3. About Niall Turbitt Senior Data Scientist at Databricks ▪ Professional Services and Training ▪ Experience ▪ e-Commerce ▪ Supply Chain and Logistics ▪ Recommender Systems & Personalisation ▪ MS Statistics University College Dublin ▪ BA Mathematics & Economics Trinity College Dublin
- 4. Outline ▪ Motivation ▪ Spark Architecture Recap ▪ Paradigms of ML on Spark: ■ Training & Tuning ▪ Spark MLlib ▪ Pandas Function APIs ▪ Hyperopt ■ Inference ▪ Pandas UDFs
- 5. Motivation ▪ Confusion around where and when to use Spark for Machine Learning ▪ Spark is powerful: harness the full potential of Spark for Machine Learning ▪ Fast moving environment ▪ Use new APIs and techniques before they become mainstream
- 6. Motivation ▪ Confusion around where and when to use Spark for Machine Learning ▪ Spark is powerful: harness the full potential of Spark for Machine Learning ▪ Fast moving environment ▪ Use new APIs and techniques before they become mainstream
- 7. Refresher: Spark Architecture Cluster Driver Worker Core Core Core Core = a single node Worker Core Core Core Core Worker Core Core Core Core Worker Core Core Core Core
- 8. ML on Spark
- 9. Training Phase Model Artefact Inference Model Training Tuning The ML Lifecycle Predictions
- 10. Training Phase Model Artefact Inference Model Training Tuning Predictions The ML Lifecycle
- 11. Data Parallel Training Distributed MLLibrary Training Data Nodes Model(s) Training a single model across multiple nodes Creates one model artefact ML Training & Tuning on Spark
- 12. Data Parallel Training Training Data Nodes Model(s) Training a single model across multiple nodes Creates one model artefact Parallel Model Training SingleNode MLLibrary Training multiple unique versions of a model in parallel Sending different groups of data to each node, creating a model per group A C B ML Training & Tuning on Spark Distributed MLLibrary
- 13. Data Parallel Training Training Data Nodes Model(s) Training a single model across multiple nodes Creates one model artefact Parallel Model Training SingleNode MLLibrary Training multiple unique versions of a model in parallel Sending different groups of data to each node, creating a model per group Parallel Hyperparameter Optimisation Evaluating multiple hyperparameter configurations in parallel Combination 𝝰 𝝲 𝝱 A C B ML Training & Tuning on Spark Distributed MLLibrary
- 14. Model New Data Nodes Predictions ... ... ML Inference on Spark For both distributed and single node ML libraries: 1. Take trained model 2. Distribute new instances 3. Apply model in parallel
- 15. ML Project Considerations ▪ Data Dependent ▪ Compute Resources Available ▪ Single machine vs distributed computing ▪ Inference: Deployment Requirements Throughput Latency Example Batch High Hours to days Customer churn prediction Streaming Medium Seconds to minutes Predictive maintenance Real-time Low Milliseconds Fraud detection
- 16. ▪ Spark’s Machine Learning Library ▪ ML algorithms ▪ Featurization ▪ Pipelines ▪ MLlib vs sklearn ▪ A note on terminology: Parallelising Single-Model Training Spark MLlib Distributed ML Library What is meant by “MLlib” Spark.mllib RDD based API Maintenance Mode Spark.ml Dataframe based API Recommended
- 17. ▪ Spark’s Machine Learning Library ▪ ML algorithms ▪ Featurization ▪ Pipelines ▪ MLlib vs sklearn ▪ A note on terminology: Parallelising Single-Model Training from pyspark.ml.regression import LinearRegression train_df = spark.read… test_df = spark.read… lr = LinearRegression().fit(train_df) predictions = lr.transform(test_df) Spark MLlib Distributed ML Library What is meant by “MLlib” Spark.mllib RDD based API Maintenance Mode Spark.ml Dataframe based API Recommended
- 18. Pandas Function API - Grouped Map ▪ DataFrame.groupby().applyInPandas() ▪ Directly apply a Python native function against a Spark DataFrame as if each group is a Pandas DataFrame ▪ “split-apply-combine” pattern: ▪ Split data into groups ▪ Apply function on each group ▪ Combine results into new Spark DataFrame NEW Parallelising training of independent models A C B Single Node ML Library
- 19. Pandas Function API - Grouped Map ▪ DataFrame.groupby().applyInPandas() ▪ Directly apply a Python native function against a Spark DataFrame as if each group is a Pandas DataFrame ▪ “split-apply-combine” pattern: ▪ Split data into groups ▪ Apply function on each group ▪ Combine results into new Spark DataFrame NEW Parallelising training of independent models A C B Single Node ML Library def train_model(pd.Dataframe)->pd.DataFrame: # fit single-node model sklearn_model.fit() … return pandas_df spark_df.groupBy("device_id") .applyInPandas(train_model, schema=return_schema)
- 20. ▪ Open source hyperparameter optimisation package ▪ Enables either serial or parallel optimisation over provided search spaces ▪ Can tune both distributed and single node libraries ▪ HOWEVER: distributed training and distributed tuning don’t mix ▪ Bayesian based approach ▪ Adaptively selects new hyperparameter settings to explore based on prior results ▪ Enables exploration of the hyperparameter space in an intelligent way ▪ Allows a wider search space with more hyperparameters Hyperopt Parallelising Hyperparameter Optimisation �� 𝝲 𝝱 Combination
- 21. ▪ Distributed Hyperopt with a single node library ▪ SparkTrials 𝝰 𝝲 𝝱 Hyperopt Parallelising Hyperparameter Optimisation �� 𝝲 𝝱 Combination
- 22. ▪ Pandas UDFs can accept an iterator of pandas.Series or pandas.DataFrame ▪ Spark DataFrame is split into batches and the function called for each batch ▪ Iterator negates the need to repeatedly load the same model for every batch in the same Python worker process Inference Pandas Scalar Iterator UDF Distributing inference
- 23. ▪ Pandas UDFs can accept an iterator of pandas.Series or pandas.DataFrame ▪ Spark DataFrame is split into batches and the function called for each batch ▪ Iterator negates the need to repeatedly load the same model for every batch in the same Python worker process Inference Pandas Scalar Iterator UDF Distributing inference @pandas_udf def predict_udf(iterator): # load model model = ... for features in iterator: yield pd.Series(model.predict(features)) spark_df.withColumn("prediction", predict_udf(*input_cols))
- 24. Training Phase Model Artefact Inference Model Training Tuning Predictions Conclusion Spark MLlib Pandas Function API Hyperopt Pandas Scalar Iterator UDF Distributing workloads allows you to scale, either by using libraries that are multi or single node to suit your project.
- 25. Notebook: bit.ly/scaling_ml_spark_2020 Pandas UDF Blog post - bit.ly/Pandas_UDF Docs: ■ MLlib - bit.ly/ML_lib ■ Hyperopt- bit.ly/hyperopt_spark ■ Pandas Grouped Map- bit.ly/grouped_map Resources
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