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Summary • The use case developed addresses the needs for higher efficiency in event filtering at LHC experiments • Spark, Python notebooks
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Classroom Contents
Deep Learning Pipelines for High Energy Physics Using Apache Spark and Distributed Keras
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- 1 Intro
- 2 Experimental High Energy Physics is Data Intensive
- 3 Key Data Processing Challenge
- 4 Data Flow at LHC Experiments
- 5 R&D - Data Pipelines
- 6 Particle Classifiers Using Neural Networks
- 7 Deep Learning Pipeline for Physics Data
- 8 Analytics Platform at CERN
- 9 Hadoop and Spark Clusters at CERN
- 10 Step 1: Data Ingestion • Read input files: 4.5 TB from custom (ROOT) format
- 11 Feature Engineering
- 12 Step 2: Feature Preparation Features are converted to formats suitable for training
- 13 Performance and Lessons Learned • Data preparation is CPU bound
- 14 Neural Network Models and
- 15 Hyper-Parameter Tuning-DNN • Hyper-parameter tuning of the DNN model
- 16 Deep Learning at Scale with Spark
- 17 Spark, Analytics Zoo and BigDL
- 18 BigDL Run as Standard Spark Programs
- 19 BigDL Parameter Synchronization
- 20 Model Development - DNN for HLF • Model is instantiated using the Keras- compatible API provided by Analytics Zoo
- 21 Model Development - GRU + HLF A more complex network topology, combining a GRU of Low Level Feature + a DNN of High Level Features
- 22 Distributed Training
- 23 Performance and Scalability of Analytics Zoo/BigDL
- 24 Results - Model Performance
- 25 Workload Characterization
- 26 Training with TensorFlow 2.0 Training and test data
- 27 Recap: our Deep Learning Pipeline with Spark
- 28 Model Serving and Future Work
- 29 Summary • The use case developed addresses the needs for higher efficiency in event filtering at LHC experiments • Spark, Python notebooks
- 30 Labeled Data for Training and Test • Simulated events Software simulators are used to generate events
