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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

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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