Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
SlideShare a Scribd company logo

Automated ML Workflow for Distributed Big Data Using Analytics Zoo (CVPR2020 Tutorial)

•
•

This document summarizes a CVPR 2020 tutorial on the Analytics Zoo platform for automated machine learning workflows for distributed big data using Apache Spark. The tutorial covers an overview of Analytics Zoo and the BigDL distributed deep learning framework. It demonstrates distributed training of deep learning models using TensorFlow and PyTorch on Spark, and features of Analytics Zoo like end-to-end pipelines, ML workflow for automation, and model deployment with cluster serving. Real-world use cases applying Analytics Zoo at companies like SK Telecom, Midea, and MasterCard are also presented.

1 of 38
Download to read offline
CVPR 2020 Tutorial Automated Machine Learning Workflow for Distributed Big Data Using Analytics Zoo Jason Dai
CVPR 2020 Tutorial Overview
CVPR 2020 Tutorial Distributed, High-Performance Deep Learning Framework for Apache Spark https://github.com/intel-analytics/bigdl Unified Analytics + AI Platform for TensorFlow, PyTorch, Keras, BigDL, Ray and Apache Spark https://github.com/intel-analytics/analytics-zoo AI on BigData
CVPR 2020 Tutorial https://software.intel.com/en-us/articles/building-large-scale-image-feature-extraction-with-bigdl-at-jdcom Efficiently scale out with BigDL with 3.83x speed-up (vs. GPU severs) as benchmarked by JD Motivation: Object Feature Extraction at JD.com For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
CVPR 2020 Tutorial BigDL Distributed deep learning framework for Apache Spark https://github.com/intel-analytics/BigDL • Write deep learning applications as standard Spark programs • Run on existing Spark/Hadoop clusters (no changes needed) • Scalable and high performance • Optimized for large-scale big data clusters Spark Core SQL SparkR Streaming MLlib GraphX ML Pipeline DataFrame “BigDL: A Distributed Deep Learning Framework for Big Data”, ACM Symposium of Cloud Computing conference (SoCC) 2019, https://arxiv.org/abs/1804.05839
CVPR 2020 Tutorial Analytics Zoo Unified Data Analytics and AI Platform End-to-End Pipelines (Automatically scale AI models to distributed Big Data) ML Workflow (Automate tasks for building end-to-end pipelines) Models (Built-in models and algorithms) K8s Cluster CloudLaptop Hadoop Cluster
CVPR 2020 Tutorial Analytics Zoo Recommendation Distributed TensorFlow & PyTorch on Spark Spark Dataframes & ML Pipelines for DL RayOnSpark InferenceModel Models & Algorithms End-to-end Pipelines Time Series Computer Vision NLP Unified Data Analytics and AI Platform https://github.com/intel-analytics/analytics-zoo ML Workflow AutoML Automatic Cluster Serving Compute Environment K8s Cluster Cloud Python Libraries (Numpy/Pandas/sklearn/…) DL Frameworks (TF/PyTorch/OpenVINO/…) Distributed Analytics (Spark/Flink/Ray/…) Laptop Hadoop Cluster Powered by oneAPI
CVPR 2020 Tutorial Integrated Big Data Analytics and AI Production Data pipeline Prototype on laptop using sample data Experiment on clusters with history data Production deployment w/ distributed data pipeline • Easily prototype end-to-end pipelines that apply AI models to big data • “Zero” code change from laptop to distributed cluster • Seamlessly deployed on production Hadoop/K8s clusters • Automate the process of applying machine learning to big data Seamless Scaling from Laptop to Distributed Big Data
CVPR 2020 Tutorial Getting Started
CVPR 2020 Tutorial https://colab.research.google.com/drive/1Ck-rcAYiI54ot0L9lU93Wglr2SMSYq27 • Try Analytics Zoo on Google Colab • Pull Analytics Zoo Docker image sudo docker pull intelanalytics/analytics-zoo:latest • Install Analytics Zoo with pip pip install analytics-zoo Getting Started with Analytics Zoo
CVPR 2020 Tutorial Features End-To-End Pipelines
CVPR 2020 Tutorial Distributed TensorFlow/PyTorch on Spark in Analytics Zoo #pyspark code train_rdd = spark.hadoopFile(…).map(…) dataset = TFDataset.from_rdd(train_rdd,…) #tensorflow code import tensorflow as tf slim = tf.contrib.slim images, labels = dataset.tensors with slim.arg_scope(lenet.lenet_arg_scope()): logits, end_points = lenet.lenet(images, …) loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy( logits=logits, labels=labels)) #distributed training on Spark optimizer = TFOptimizer.from_loss(loss, Adam(…)) optimizer.optimize(end_trigger=MaxEpoch(5)) Write TensorFlow/PyTorch inline with Spark code Analytics Zoo API in blue
CVPR 2020 Tutorial Image Segmentation using TFPark https://github.com/intel-analytics/zoo- tutorials/blob/master/tensorflow/notebooks/image_segmentation.ipynb
CVPR 2020 Tutorial Face Generation Using Distributed PyTorch on Analytics Zoo https://github.com/intel-analytics/analytics- zoo/blob/master/apps/pytorch/face_generation.ipynb
CVPR 2020 Tutorial Spark Dataframe & ML Pipeline for DL #Spark dataframe code parquetfile = spark.read.parquet(…) train_df = parquetfile.withColumn(…) #Keras API model = Sequential() .add(Convolution2D(32, 3, 3)) .add(MaxPooling2D(pool_size=(2, 2))) .add(Flatten()).add(Dense(10))) #Spark ML pipeline code estimater = NNEstimater(model, CrossEntropyCriterion()) .setMaxEpoch(5) .setFeaturesCol("image") nnModel = estimater.fit(train_df) Analytics Zoo API in blue
CVPR 2020 Tutorial Image Similarity using NNFrame https://github.com/intel-analytics/analytics-zoo/blob/master/apps/image- similarity/image-similarity.ipynb
CVPR 2020 Tutorial RayOnSpark Run Ray programs directly on YARN/Spark/K8s cluster “RayOnSpark: Running Emerging AI Applications on Big Data Clusters with Ray and Analytics Zoo” https://medium.com/riselab/rayonspark-running-emerging-ai-applications-on-big-data-clusters- with-ray-and-analytics-zoo-923e0136ed6a Analytics Zoo API in blue sc = init_spark_on_yarn(...) ray_ctx = RayContext(sc=sc, ...) ray_ctx.init() #Ray code @ray.remote class TestRay(): def hostname(self): import socket return socket.gethostname() actors = [TestRay.remote() for i in range(0, 100)] print([ray.get(actor.hostname.remote()) for actor in actors]) ray_ctx.stop()
CVPR 2020 Tutorial Sharded Parameter Server With RayOnSpark https://github.com/intel-analytics/analytics-zoo/blob/master/apps/image-similarity/image- similarity.ipynb
CVPR 2020 Tutorial Features ML Workflow
CVPR 2020 Tutorial Distributed Inference Made Easy with Cluster Serving P5 P4 P3 P2 P1 R4 R3 R2 R1R5 Input Queue for requests Output Queue (or files/DB tables) for prediction results Local node or Docker container Hadoop/Yarn/K8s cluster Network connection Model Simple Python script https://software.intel.com/en -us/articles/distributed- inference-made-easy-with- analytics-zoo-cluster-serving#enqueue request input = InputQueue() img = cv2.imread(path) img = cv2.resize(img, (224, 224)) input.enqueue_image(id, img) #dequeue response output = OutputQueue() result = output.dequeue() for k in result.keys(): print(k + “: “ + json.loads(result[k])) √ Users freed from complex distributed inference solutions √ Distributed, real-time inference automatically managed by Analytics Zoo − TensorFlow, PyTorch, Caffe, BigDL, OpenVINO, … − Spark Streaming, Flink, … Analytics Zoo API in blue
CVPR 2020 Tutorial Scalable AutoML for Time Series Prediction “Scalable AutoML for Time Series Prediction using Ray and Analytics Zoo” https://medium.com/riselab/scalable-automl-for-time-series-prediction- using-ray-and-analytics-zoo-b79a6fd08139 Automated feature selection, model selection and hyper parameter tuning using Ray tsp = TimeSequencePredictor( dt_col="datetime", target_col="value") pipeline = tsp.fit(train_df, val_df, metric="mse", recipe=RandomRecipe()) pipeline.predict(test_df) Analytics Zoo API in blue
CVPR 2020 Tutorial FeatureTransformer Model SearchEngine Search presets Workflow implemented in TimeSequencePredictor trial trial trial trial …best model /parameters trail jobs Pipeline with tunable parameters with tunable parameters configured with best parameters/model Each trial runs a different combination of hyper parameters Ray Tune rolling, scaling, feature generation, etc. Spark + Ray AutoML Training
CVPR 2020 Tutorial AutoML Notebook https://github.com/intel-analytics/analytics-zoo/blob/master/apps/automl/nyc_taxi_dataset.ipynb
CVPR 2020 Tutorial Work in Progress
CVPR 2020 Tutorial Project Zouwu: Time Series for Telco Project Zouwu • Use case - reference time series use cases for Telco (such as network traffic forecasting, etc.) • Models - built-in models for time series analysis (such as LSTM, MTNet, DeepGlo) • AutoTS - AutoML support for building E2E time series analysis pipelines (including automatic feature generation, model selection and hyperparameter tuning) Project Zouwu Built-in Models ML Workflow AutoML Workflow Integrated Analytics & AI Pipelines use-case models autots *Joint-collaborations with NPG https://github.com/intel-analytics/analytics- zoo/tree/master/pyzoo/zoo/zouwu
CVPR 2020 Tutorial https://github.com/intel-analytics/analytics-zoo/blob/master/pyzoo/zoo/zouwu/use- case/network_traffic/network_traffic_autots_forecasting.ipynb Network Traffic KPI Prediction using Zouwu
CVPR 2020 Tutorial Project Orca: Easily Scaling Python AI pipeline on Analytics Zoo Seamless scale Python notebook from laptop to distributed big data • orca.data: data-parallel pre-processing for (any) Python libs • pandas, numpy, sklearn, PIL, spacy, tensorflow Dataset, pytorch dataloader, spark, etc. • orca.learn: transparently distributed training for deep learning • sklearn style estimator for TensorFlow, PyTorch, Keras, Horovod, MXNet, etc. https://github.com/intel-analytics/analytics- zoo/tree/master/pyzoo/zoo/orca
CVPR 2020 Tutorial Use Cases
CVPR 2020 Tutorial Migrating from GPU in SK Telecom Time Series Based Network Quality Prediction https://webinar.intel.com/AI_Monitoring_WebinarREG Data Loader DRAM Store tiering forked. Flash Store customized. Data Source APIs Spark-SQL Preproce ss SQL Queries (Web, Jupyter) LegacyDesignwithGPU Export Preprocessing AITraining/Inference GPU Servers ReduceAIInferencelatency ScalableAITraining NewArchitecture: Unified DataAnalytic+AIPlatform Preprocessing RDDofTensor AIModelCodeofTF 2nd Generation Intel® Xeon® Scalable Processors
CVPR 2020 Tutorial Migrating from GPU in SK Telecom Time Series Based Network Quality Prediction Python Distributed Preprocessing (DASK) & Inference on GPU Intel Analytics Zoo 1 Server Xeon 6240 Intel Analytics Zoo 3 Servers Xeon 6240 Python Preprocessing (Pandas) & Inference on GPU 74.26 10.24 3.24 1.61 3X 6X Test Data: 80K Cell Tower, 8 days, 5mins period, 8 Quality Indicator TCOoptimizedAIperformance with [ 1 ] AnalyticsZoo [ 2 ] IntelOptimizedTensorflow [ 3 ] DistributedAIProcessing [ 1 ] Pre-processing& InferenceLatency Seconds 0 200 400 600 800 1000 1200 1400 1600 1800 BS 4,096 BS 8,192 BS 16,384 BS 32,768 BS 65,536 Intel Analytics Zoo - 1 Server ( Xeon 6240) GPU Intel Analytics Zoo - 3 Servers Distributed Training - Scalability case (Xeon 6240) [ 2 ] Time-To-TrainingPerformance Performance test validation @ SK Telecom Testbedhttps://webinar.intel.com/AI_Monitoring_WebinarREG For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
CVPR 2020 Tutorial Edge to Cloud Architecture in Midea Computer Vision Based Product Defect Detection https://software.intel.com/en-us/articles/industrial-inspection-platform-in-midea-and-kuka-using-distributed-tensorflow-on-analytics
CVPR 2020 Tutorial Product Recommendation on AWS in Office Depot https://software.intel.com/en- us/articles/real-time-product- recommendations-for-office-depot- using-apache-spark-and-analytics- zoo-on
CVPR 2020 Tutorial Recommender Service on Cloudera in MasterCard https://software.intel.com/en-us/articles/deep-learning-with-analytic-zoo-optimizes-mastercard-recommender-ai-service Train NCF Model Features Models Model Candidates Models sampled partition Training Data … Load Parquet Train Multiple Models Train Wide & Deep Model sampled partition sampled partition Spark ML Pipeline Stages Test Data Predictions Test Spark DataFramesParquet Files Feature Selections SparkMLPipeline Neural Recommender using Spark and Analytics Zoo Estimator Transformer Model Evaluation & Fine Tune Train ALS Model
CVPR 2020 Tutorial NLP Based Customer Service Chatbot for Microsoft Azure https://software.intel.com/en-us/articles/use-analytics-zoo-to-inject-ai-into-customer-service-platforms-on-microsoft-azure-part-1 https://www.infoq.com/articles/analytics-zoo-qa-module/
CVPR 2020 Tutorial Technology EndUsersCloudServiceProviders *Other names and brands may be claimed as the property of others. software.intel.com/data-analytics Not a full list And Many More
CVPR 2020 Tutorial • Github • Project repo: https://github.com/intel-analytics/analytics-zoo https://github.com/intel-analytics/BigDL • Getting started: https://analytics-zoo.github.io/master/#gettingstarted/ • Technical paper/tutorials • CVPR 2018: https://jason-dai.github.io/cvpr2018/ • AAAI 2019: https://jason-dai.github.io/aaai2019/ • SoCC 2019: https://arxiv.org/abs/1804.05839 • Use cases • Azure, CERN, MasterCard, Office Depot, Tencent, Midea, etc. • https://analytics-zoo.github.io/master/#powered-by/ Summary
CVPR 2020 Tutorial
CVPR 2020 Tutorial • Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations, and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit intel.com/performance. • Intel does not control or audit the design or implementation of third-party benchmark data or websites referenced in this document. Intel encourages all of its customers to visit the referenced websites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase. • Optimization notice: Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice. • Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com/benchmarks. • Intel, the Intel logo, Intel Inside, the Intel Inside logo, Intel Atom, Intel Core, Iris, Movidius, Myriad, Intel Nervana, OpenVINO, Intel Optane, Stratix, and Xeon are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. • *Other names and brands may be claimed as the property of others. • © Intel Corporation Legal Notices and Disclaimers

More Related Content

Automated ML Workflow for Distributed Big Data Using Analytics Zoo (CVPR2020 Tutorial)

  • 1. CVPR 2020 Tutorial Automated Machine Learning Workflow for Distributed Big Data Using Analytics Zoo Jason Dai
  • 3. CVPR 2020 Tutorial Distributed, High-Performance Deep Learning Framework for Apache Spark https://github.com/intel-analytics/bigdl Unified Analytics + AI Platform for TensorFlow, PyTorch, Keras, BigDL, Ray and Apache Spark https://github.com/intel-analytics/analytics-zoo AI on BigData
  • 4. CVPR 2020 Tutorial https://software.intel.com/en-us/articles/building-large-scale-image-feature-extraction-with-bigdl-at-jdcom Efficiently scale out with BigDL with 3.83x speed-up (vs. GPU severs) as benchmarked by JD Motivation: Object Feature Extraction at JD.com For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
  • 5. CVPR 2020 Tutorial BigDL Distributed deep learning framework for Apache Spark https://github.com/intel-analytics/BigDL • Write deep learning applications as standard Spark programs • Run on existing Spark/Hadoop clusters (no changes needed) • Scalable and high performance • Optimized for large-scale big data clusters Spark Core SQL SparkR Streaming MLlib GraphX ML Pipeline DataFrame “BigDL: A Distributed Deep Learning Framework for Big Data”, ACM Symposium of Cloud Computing conference (SoCC) 2019, https://arxiv.org/abs/1804.05839
  • 6. CVPR 2020 Tutorial Analytics Zoo Unified Data Analytics and AI Platform End-to-End Pipelines (Automatically scale AI models to distributed Big Data) ML Workflow (Automate tasks for building end-to-end pipelines) Models (Built-in models and algorithms) K8s Cluster CloudLaptop Hadoop Cluster
  • 7. CVPR 2020 Tutorial Analytics Zoo Recommendation Distributed TensorFlow & PyTorch on Spark Spark Dataframes & ML Pipelines for DL RayOnSpark InferenceModel Models & Algorithms End-to-end Pipelines Time Series Computer Vision NLP Unified Data Analytics and AI Platform https://github.com/intel-analytics/analytics-zoo ML Workflow AutoML Automatic Cluster Serving Compute Environment K8s Cluster Cloud Python Libraries (Numpy/Pandas/sklearn/…) DL Frameworks (TF/PyTorch/OpenVINO/…) Distributed Analytics (Spark/Flink/Ray/…) Laptop Hadoop Cluster Powered by oneAPI
  • 8. CVPR 2020 Tutorial Integrated Big Data Analytics and AI Production Data pipeline Prototype on laptop using sample data Experiment on clusters with history data Production deployment w/ distributed data pipeline • Easily prototype end-to-end pipelines that apply AI models to big data • “Zero” code change from laptop to distributed cluster • Seamlessly deployed on production Hadoop/K8s clusters • Automate the process of applying machine learning to big data Seamless Scaling from Laptop to Distributed Big Data
  • 10. CVPR 2020 Tutorial https://colab.research.google.com/drive/1Ck-rcAYiI54ot0L9lU93Wglr2SMSYq27 • Try Analytics Zoo on Google Colab • Pull Analytics Zoo Docker image sudo docker pull intelanalytics/analytics-zoo:latest • Install Analytics Zoo with pip pip install analytics-zoo Getting Started with Analytics Zoo
  • 12. CVPR 2020 Tutorial Distributed TensorFlow/PyTorch on Spark in Analytics Zoo #pyspark code train_rdd = spark.hadoopFile(…).map(…) dataset = TFDataset.from_rdd(train_rdd,…) #tensorflow code import tensorflow as tf slim = tf.contrib.slim images, labels = dataset.tensors with slim.arg_scope(lenet.lenet_arg_scope()): logits, end_points = lenet.lenet(images, …) loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy( logits=logits, labels=labels)) #distributed training on Spark optimizer = TFOptimizer.from_loss(loss, Adam(…)) optimizer.optimize(end_trigger=MaxEpoch(5)) Write TensorFlow/PyTorch inline with Spark code Analytics Zoo API in blue
  • 13. CVPR 2020 Tutorial Image Segmentation using TFPark https://github.com/intel-analytics/zoo- tutorials/blob/master/tensorflow/notebooks/image_segmentation.ipynb
  • 14. CVPR 2020 Tutorial Face Generation Using Distributed PyTorch on Analytics Zoo https://github.com/intel-analytics/analytics- zoo/blob/master/apps/pytorch/face_generation.ipynb
  • 15. CVPR 2020 Tutorial Spark Dataframe & ML Pipeline for DL #Spark dataframe code parquetfile = spark.read.parquet(…) train_df = parquetfile.withColumn(…) #Keras API model = Sequential() .add(Convolution2D(32, 3, 3)) .add(MaxPooling2D(pool_size=(2, 2))) .add(Flatten()).add(Dense(10))) #Spark ML pipeline code estimater = NNEstimater(model, CrossEntropyCriterion()) .setMaxEpoch(5) .setFeaturesCol("image") nnModel = estimater.fit(train_df) Analytics Zoo API in blue
  • 16. CVPR 2020 Tutorial Image Similarity using NNFrame https://github.com/intel-analytics/analytics-zoo/blob/master/apps/image- similarity/image-similarity.ipynb
  • 17. CVPR 2020 Tutorial RayOnSpark Run Ray programs directly on YARN/Spark/K8s cluster “RayOnSpark: Running Emerging AI Applications on Big Data Clusters with Ray and Analytics Zoo” https://medium.com/riselab/rayonspark-running-emerging-ai-applications-on-big-data-clusters- with-ray-and-analytics-zoo-923e0136ed6a Analytics Zoo API in blue sc = init_spark_on_yarn(...) ray_ctx = RayContext(sc=sc, ...) ray_ctx.init() #Ray code @ray.remote class TestRay(): def hostname(self): import socket return socket.gethostname() actors = [TestRay.remote() for i in range(0, 100)] print([ray.get(actor.hostname.remote()) for actor in actors]) ray_ctx.stop()
  • 18. CVPR 2020 Tutorial Sharded Parameter Server With RayOnSpark https://github.com/intel-analytics/analytics-zoo/blob/master/apps/image-similarity/image- similarity.ipynb
  • 20. CVPR 2020 Tutorial Distributed Inference Made Easy with Cluster Serving P5 P4 P3 P2 P1 R4 R3 R2 R1R5 Input Queue for requests Output Queue (or files/DB tables) for prediction results Local node or Docker container Hadoop/Yarn/K8s cluster Network connection Model Simple Python script https://software.intel.com/en -us/articles/distributed- inference-made-easy-with- analytics-zoo-cluster-serving#enqueue request input = InputQueue() img = cv2.imread(path) img = cv2.resize(img, (224, 224)) input.enqueue_image(id, img) #dequeue response output = OutputQueue() result = output.dequeue() for k in result.keys(): print(k + “: “ + json.loads(result[k])) √ Users freed from complex distributed inference solutions √ Distributed, real-time inference automatically managed by Analytics Zoo − TensorFlow, PyTorch, Caffe, BigDL, OpenVINO, … − Spark Streaming, Flink, … Analytics Zoo API in blue
  • 21. CVPR 2020 Tutorial Scalable AutoML for Time Series Prediction “Scalable AutoML for Time Series Prediction using Ray and Analytics Zoo” https://medium.com/riselab/scalable-automl-for-time-series-prediction- using-ray-and-analytics-zoo-b79a6fd08139 Automated feature selection, model selection and hyper parameter tuning using Ray tsp = TimeSequencePredictor( dt_col="datetime", target_col="value") pipeline = tsp.fit(train_df, val_df, metric="mse", recipe=RandomRecipe()) pipeline.predict(test_df) Analytics Zoo API in blue
  • 22. CVPR 2020 Tutorial FeatureTransformer Model SearchEngine Search presets Workflow implemented in TimeSequencePredictor trial trial trial trial …best model /parameters trail jobs Pipeline with tunable parameters with tunable parameters configured with best parameters/model Each trial runs a different combination of hyper parameters Ray Tune rolling, scaling, feature generation, etc. Spark + Ray AutoML Training
  • 23. CVPR 2020 Tutorial AutoML Notebook https://github.com/intel-analytics/analytics-zoo/blob/master/apps/automl/nyc_taxi_dataset.ipynb
  • 24. CVPR 2020 Tutorial Work in Progress
  • 25. CVPR 2020 Tutorial Project Zouwu: Time Series for Telco Project Zouwu • Use case - reference time series use cases for Telco (such as network traffic forecasting, etc.) • Models - built-in models for time series analysis (such as LSTM, MTNet, DeepGlo) • AutoTS - AutoML support for building E2E time series analysis pipelines (including automatic feature generation, model selection and hyperparameter tuning) Project Zouwu Built-in Models ML Workflow AutoML Workflow Integrated Analytics & AI Pipelines use-case models autots *Joint-collaborations with NPG https://github.com/intel-analytics/analytics- zoo/tree/master/pyzoo/zoo/zouwu
  • 27. CVPR 2020 Tutorial Project Orca: Easily Scaling Python AI pipeline on Analytics Zoo Seamless scale Python notebook from laptop to distributed big data • orca.data: data-parallel pre-processing for (any) Python libs • pandas, numpy, sklearn, PIL, spacy, tensorflow Dataset, pytorch dataloader, spark, etc. • orca.learn: transparently distributed training for deep learning • sklearn style estimator for TensorFlow, PyTorch, Keras, Horovod, MXNet, etc. https://github.com/intel-analytics/analytics- zoo/tree/master/pyzoo/zoo/orca
  • 29. CVPR 2020 Tutorial Migrating from GPU in SK Telecom Time Series Based Network Quality Prediction https://webinar.intel.com/AI_Monitoring_WebinarREG Data Loader DRAM Store tiering forked. Flash Store customized. Data Source APIs Spark-SQL Preproce ss SQL Queries (Web, Jupyter) LegacyDesignwithGPU Export Preprocessing AITraining/Inference GPU Servers ReduceAIInferencelatency ScalableAITraining NewArchitecture: Unified DataAnalytic+AIPlatform Preprocessing RDDofTensor AIModelCodeofTF 2nd Generation Intel® Xeon® Scalable Processors
  • 30. CVPR 2020 Tutorial Migrating from GPU in SK Telecom Time Series Based Network Quality Prediction Python Distributed Preprocessing (DASK) & Inference on GPU Intel Analytics Zoo 1 Server Xeon 6240 Intel Analytics Zoo 3 Servers Xeon 6240 Python Preprocessing (Pandas) & Inference on GPU 74.26 10.24 3.24 1.61 3X 6X Test Data: 80K Cell Tower, 8 days, 5mins period, 8 Quality Indicator TCOoptimizedAIperformance with [ 1 ] AnalyticsZoo [ 2 ] IntelOptimizedTensorflow [ 3 ] DistributedAIProcessing [ 1 ] Pre-processing& InferenceLatency Seconds 0 200 400 600 800 1000 1200 1400 1600 1800 BS 4,096 BS 8,192 BS 16,384 BS 32,768 BS 65,536 Intel Analytics Zoo - 1 Server ( Xeon 6240) GPU Intel Analytics Zoo - 3 Servers Distributed Training - Scalability case (Xeon 6240) [ 2 ] Time-To-TrainingPerformance Performance test validation @ SK Telecom Testbedhttps://webinar.intel.com/AI_Monitoring_WebinarREG For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
  • 31. CVPR 2020 Tutorial Edge to Cloud Architecture in Midea Computer Vision Based Product Defect Detection https://software.intel.com/en-us/articles/industrial-inspection-platform-in-midea-and-kuka-using-distributed-tensorflow-on-analytics
  • 32. CVPR 2020 Tutorial Product Recommendation on AWS in Office Depot https://software.intel.com/en- us/articles/real-time-product- recommendations-for-office-depot- using-apache-spark-and-analytics- zoo-on
  • 33. CVPR 2020 Tutorial Recommender Service on Cloudera in MasterCard https://software.intel.com/en-us/articles/deep-learning-with-analytic-zoo-optimizes-mastercard-recommender-ai-service Train NCF Model Features Models Model Candidates Models sampled partition Training Data … Load Parquet Train Multiple Models Train Wide & Deep Model sampled partition sampled partition Spark ML Pipeline Stages Test Data Predictions Test Spark DataFramesParquet Files Feature Selections SparkMLPipeline Neural Recommender using Spark and Analytics Zoo Estimator Transformer Model Evaluation & Fine Tune Train ALS Model
  • 34. CVPR 2020 Tutorial NLP Based Customer Service Chatbot for Microsoft Azure https://software.intel.com/en-us/articles/use-analytics-zoo-to-inject-ai-into-customer-service-platforms-on-microsoft-azure-part-1 https://www.infoq.com/articles/analytics-zoo-qa-module/
  • 35. CVPR 2020 Tutorial Technology EndUsersCloudServiceProviders *Other names and brands may be claimed as the property of others. software.intel.com/data-analytics Not a full list And Many More
  • 36. CVPR 2020 Tutorial • Github • Project repo: https://github.com/intel-analytics/analytics-zoo https://github.com/intel-analytics/BigDL • Getting started: https://analytics-zoo.github.io/master/#gettingstarted/ • Technical paper/tutorials • CVPR 2018: https://jason-dai.github.io/cvpr2018/ • AAAI 2019: https://jason-dai.github.io/aaai2019/ • SoCC 2019: https://arxiv.org/abs/1804.05839 • Use cases • Azure, CERN, MasterCard, Office Depot, Tencent, Midea, etc. • https://analytics-zoo.github.io/master/#powered-by/ Summary
  • 38. CVPR 2020 Tutorial • Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations, and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit intel.com/performance. • Intel does not control or audit the design or implementation of third-party benchmark data or websites referenced in this document. Intel encourages all of its customers to visit the referenced websites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase. • Optimization notice: Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice. • Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com/benchmarks. • Intel, the Intel logo, Intel Inside, the Intel Inside logo, Intel Atom, Intel Core, Iris, Movidius, Myriad, Intel Nervana, OpenVINO, Intel Optane, Stratix, and Xeon are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. • *Other names and brands may be claimed as the property of others. • © Intel Corporation Legal Notices and Disclaimers