[FFE19] Build a Flink AI Ecosystem
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This document discusses building an AI ecosystem on Apache Flink. It proposes unifying batch and stream processing with Flink to avoid maintaining separate code bases. It also proposes using Flink throughout the machine learning pipeline for data acquisition, model training, validation, and serving. This includes enhancing Flink's support for deep learning, iteration, dynamic model serving, and Python APIs. The goal is to provide a one-stop solution for all data and machine learning processing needs within a single system and code base.
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[FFE19] Build a Flink AI Ecosystem
- 1. Build a Flink AI Ecosystem Jiangjie (Becket) Qin Flink Forward Berlin 2019
- 2. Agenda • Why AI Ecosystem on Flink? • Flink ML Pipeline & Flink ML Libs • Deep learning on Flink • Enhanced Iteration & Dynamic Model Serving • Better Python support 2
- 3. Lambda - what’s everyone doing HDFS Message Queue Batch Processing Stream Processing Combine the results Query Result Offline path Online path 3 Batch Layer Speed Layer Serving Layer
- 4. Lambda - what’s everyone doing HDFS Message Queue Batch Processing (Spark/M-R) Stream Processing (Flink/Storm) Combine the results Query Result • Two code bases for online and offline processing logic • High maintenance cost • Difficult to ensure consistent processing logic Offline path Online path 4
- 5. Batch-Stream Processing Unification • Use the same engine for online and offline processing • Spark • Flink HDFS Message Queue Batch Processing (Flink/Spark) Stream Processing (Flink/Spark Streaming) Combine the results Query Result Offline path Online path 5
- 6. So what about ML? • A typical ML scenario • Offline training (TF, PyTorch, etc) • Static models • Online inference (Flink) • The data preprocessing logic in training and inference are often two code bases HDFS Offline Training Inference Static model Preprocessing PreprocessingOffline path Online path 6
- 7. So what about ML? • Online training is gaining popularity • More prompt model update • Dynamic model and continuous training • Progressive validation • More sophisticated monitoring and model deployment / rollback Message Queue Online Training Inference dynamic model PreprocessingOffline path Online path 7
- 8. “Lambda” architecture for ML • Offline training: a static base model • Online training: incremental updates to the base model • Users have to deal with different systems / code bases Message Queue Offline Training Online Training Inference Dynamic model Static model Preprocessing HDFS Preprocessing Offline path Online path Static model 8
- 9. Value of Flink • The inference is latency sensitive online / nearline processing • Flink is a good option in this case Message Queue Offline Training Online Training Inference Dynamic model Static model Preprocessing HDFS Preprocessing Offline path Online path Static model 9
- 10. Batch-Stream Unification in ML • The online inference is latency sensitive online / nearline processing • Flink is a good option in this case • Use Flink everywhere to avoid maintaining different code bases. Message Queue Offline Training Online Training Inference Dynamic model Static model Preprocessing HDFS Preprocessing Offline path Online path Static model 10
- 11. Additional Values • One-stop data processing solution • Shared dataset management • Switch processing APIs freely Dataset Management DataStreamSQL ML CEP
- 12. Flink AI Ecosystem By ML Stages Rich connector support & Dataset management Stream-Batch unification Strong SQL support Enhanced Iteration Flink ML Lib DL on Flink (TF, PyTorch) Dynamic model serving Model Management Rollout / Rollback Online monitoring Online evaluation Message Queue Offline Training Online Training Inference Dynamicmodel Static model Preprocessing HDFS Preprocessing Offline path Online path Static model Model Validation Flink ML Pipeline, Python support 12 Data Acquisition Model Training Model Validation & Serving InferencePreprocessing Efforts&RequirementsAIFlowMLStage
- 13. Flink AI Ecosystem By ML Stages Rich connector support & Dataset management Stream-Batch unification Strong SQL support Enhanced Iteration Flink ML Lib DL on Flink (TF, PyTorch) Dynamic model serving Model Management Rollout / Rollback Online monitoring Online evaluation Message Queue Offline Training Online Training Inference Dynamicmodel Static model Preprocessing HDFS Preprocessing Offline path Online path Static model Model Validation Flink ML Pipeline, Python support 13 Data Acquisition Model Training Model Validation & Serving InferencePreprocessing Efforts&RequirementsAIFlowMLStage
- 14. Agenda • Why AI Ecosystem on Flink? • Flink ML Pipeline & Flink ML Libs • Deep learning on Flink • Enhanced Iteration & Dynamic Model Serving • Better Python support 14
- 15. Flink ML Pipeline - Overview PipelineStage EstimatorTransformer Model K-Means NaiveBayes Linear regression DecisionTree RandomForest GBDT Table based ML Pipeline EstimatorTransformer table2=Transformer. transform(table1) Estimator.fit(table2) ML Lib Developers ML Lib Users …… Input Table Output Table 15 Data -> Data transition (Preprocessing, Inference) Data -> Model transition (Model Training)
- 16. K-Means NaiveBayes Linear regression GBDT DecisionTree PCA Random Forest Correlation ML libs …… Rewrite Flink ML Libs • ML pipeline based • Table API based • Battle tested algorithms Flink ML Libs 16
- 17. Training Inference Estimator Model Estimator.fit(input1) Input1: Table Model Result Table Model.transform(input2) Input2: Table pipeline.fit(input1) pipeline.transform(input2) ML Pipeline - Simple Case 17
- 18. EstimatorTransformer output1=Transformer. transform(input1) Estimator Pipeline pipeline.fit(input1) Estimator.fit(output1) pipeline.transform(input2) Model.transform(output2) Result Table ModelTransformerInput1: TableTraining ModelTransformer output2=Transformer. transform(input2) Model Pipeline Input2: Table Model Pipeline Inference ML Pipeline 18
- 19. Value of Flink ML Pipeline • Unify APIs of Model Training and Inference for the end users • End users only needs to deal with either Estimators or Transformers • Ensure consistent logic between training and inference • The same pipeline topology in training will be persisted and used for inference 19
- 20. Agenda • Why AI Ecosystem on Flink? • Flink ML Pipeline & Flink ML Libs • Deep learning on Flink • Enhanced Iteration & Dynamic Model Serving • Better Python support 20
- 21. Data Acquisition Data Process and Transformation Model Training Test and Validation Model Serving Model or Params Tuning Deep Learning Pipeline 21
- 22. Distributed TF framework in a Cluster/Environment WORKER WORKER WORKER PS PS Resulting Model One Flink job in Cluster/Environment SOURCE SOURCE JOIN UDTF External Storage Queue >>> >>> Data Acquisition Data Process and Transformation Model Training Deep Learning Pipeline 22
- 23. Data Acquisition Data Process and Transformation Model Training Test and Validation Model Serving Model or Params Tuning Deep Learning Pipeline 23
- 24. One single Flink job in a Cluster/Environment Distributed TF framework in a Cluster/Environment WORKER WORKER WORKER PS PS Resulting Model SOURCE SOURCE JOIN UDTF WORKER PS PS WORKER WORKER One Flink job in Cluster/Environment SOURCE SOURCE JOIN UDTF External Storage Queue >>> >>> Resulting Model TensorFlow-Flink Integration 24
- 25. DL on Flink and ML Pipeline integration One single Flink job in a Cluster/Environment SOURCE SOURCE JOIN UDTF WORKER PS PS WORKER WORKER Resulting Model Transformer Estimator The ML Pipeline API could be used for both traditional ML and deep learning. 25
- 26. Agenda • Why AI Ecosystem on Flink? • Flink ML Pipeline & Flink ML Libs • Deep learning on Flink • Enhanced Iteration & Dynamic Model Serving • Better Python support 26
- 27. • Native iteration implemented by the processing engine • Feedback edge on the processing DAG • Improve the caveats in DataSet / DataStream iterations Flink Cluster Partition 1 Partition 2 Partition 3 Partition N … map map map map … Enhance Iteration in Flink 27
- 28. { val a: Table = ... val b: Table = ... val resultSeq = Table.iterate(a, b) { val next_a = b.select('v_b + 1 as 'v_a) val next_b = next_a.select('v_a * 2 as 'v_b) Seq(next_a, next_b) }.times(10) } Iteration variables Step function Termination condition Multi-variable iteration 28
- 29. { val a: Table = ... val b: Table = ... val resultSeq = iterate(a, b) { val next_a = iterate(a) { Seq(a.select(‘v_a + 1 as 'v_a)) }.times(100).head val next_b = next_a.select('v_a * 2 as 'v_b) Seq(next_a, next_b) }.times(10) } Nested Iteration 29
- 30. Mini-batch iteration • A stream is chunked in to multiple mini-batches • Each mini-batch iterates independently in the iteration loop • The results are emitted in the mini-batch order MB3 MB2 MB1 Flink Cluster Partition 1 Partition 2 Partition 3 Partition N … map map map map … MB2 MB1 30
- 31. Mini-batch iteration • Native support for Stochastic Gradient Descendent (SGD) • Native support for online learning 31
- 32. Iteration and Dynamic Model Update Model Initial model Samples Gradient Computing Gradient Reduce Model_V1 Model_V2 Model_V3 … Final Model 32
- 33. Iteration and Dynamic Model Update Model Initial model Samples Gradient Computing Gradient Reduce Model_V1 Model_V2 Model_V3 … Final Model 33
- 34. Dynamic Model Serving Message Queue Offline Training Online Training Dynamicmodel Static model Preprocessing HDFS Preprocessing Static model Model Validation Samples Inference Model_V1 Model_V2 Model_V3 … The exact same mechanism of native iteration could be used for dynamic model serving. 34
- 35. Agenda • Why AI Ecosystem on Flink? • Flink ML Pipeline & Flink ML Libs • Deep learning on Flink • Enhanced Iteration & Dynamic Model Serving • Better Python support 35
- 36. Python process Java process input Python Table API Python UDF Python TableAPI Java gateway Server RPC (Py4j) Python gateway Python VM DAGGragh upstream input downstream output output Flink Python Table API 36 Working with Apache Beam Community
- 37. More Python API Support • Flink ML Pipeline • Flink-AI-Extended • DataStream 37
- 38. Summary • Flink has unique values in AI use case • Flink suits very well in the “lambda” ML architecture • Multiple ongoing works to make Flink more AI friendly • Flink ML Pipeline • Flink ML Libs • Deep learning on Flink • Iteration enhancement • Python API • … 38
- 39. Q & A We are hiring!! becket.qin@gmail.com