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技術(shù)創(chuàng)新,變革未來(lái)統(tǒng)一的大數(shù)據(jù)分析及AI應(yīng)用平臺(tái)All
products,
computer
systems,
dates,
and
figures
are
preliminary
based
on
current
expectations,
and
are
subject
to
change
without
notice.2IntelGPUfutureAutomatedDrivingDedicatedMedia/VisionAcceleratio
DLnferencn I eDedicatedDLTrainingGraphics,Media&
Analytics2H’192H’19NNP-LNNP-IFlexible
IfneededDedicatededgeCloudDevIceOneSizeDoes
NotFitAll31Anopensourceversionisavailableat:01.org/openvinotoolkit*Othernamesandbrandsmaybeclaimedasthepropertyof
others.Developerpersonasshowaboverepresenttheprimaryuserbaseforeachrow,butarenot
mutually-exclusiveAllproducts,computersystems,dates,andfiguresarepreliminarybasedoncurrentexpectations,andaresubjecttochangewithout
notice.TOOLKITSAppdeveloperslibrariesDatascientistsKernelsLibrarydevelopersOpensourceplatformforbuildingE2EAnalytics&AIapplicationsonApacheSpark*withdistributedTensorFlow*,Keras*,
BigDLDeeplearninginferencedeploymentonCPU/GPU/FPGA/VPUforCaffe*,TensorFlow*,MXNet*,ONNX*,
Kaldi*Opensource,scalable,andextensibledistributeddeeplearningplatformbuiltonKubernetes
(BETA)Intel-optimized
FrameworksAndmoreframeworkoptimizationsunderwayincludingPaddlePaddle*,Chainer*,CNTK*&
othersPythonScikit-learnPandasNumPyRCartRandom
Foreste1071DistributedMlLib(on
Spark)MahoutIntel?
Distribution
for
Python*Inteldistributionoptimizedformachine
learningIntel?Data
Analytics
AccelerationLibrary
(DAAL)Highperformancemachinelearning&dataanalytics
libraryOpensourcecompilerfordeeplearningmodelcomputationsoptimizedformultipledevices(CPU,GPU,NNP)frommultipleframeworks(TF,MXNet,
ONNX)Intel?Math
Kernel
LibraryforDeep
NeuralNetworks
(MKL-DNN)OpensourceDNNfunctions
forCPU/integrated
graphicsMachine
learning Deep
learning*****SpeedUp
DevelopmentUsingOpenAI
SoftwareDistributed,
High-PerformanceDeepLearning
FrameworkforApache
Spark*/intel-analytics/bigdlAnalytics+AI
PlatformDistributedTensorFlow*,Keras*and
BigDLonApache
Spark*/intel-analytics/analytics-zooAI
onUnifyingAnalytics+AIonApache
Spark**Othernamesandbrandsmaybeclaimedasthepropertyof
others.WhyAnalytics
Zoo?Real-WorldML/DLApplicationsAreComplexDataAnalytics
Pipelines“Hidden
Technical
Debt
in
Machine
Learning
Systems”,Sculleyetal.,Google,NIPS2015
PaperLarge-ScaleImageRecognitionat
JD.com/en-us/articles/building-large-scale-image-feature-extraction-with-bigdl-at-jdcomChasmb/wDeepLearningandBigDataCommunitiesDeeplearning
expertsTheChasmReal-worldusers(bigdatausers,datascientists,analysts,
etc.)Distributed,
High-PerformanceDeepLearning
FrameworkforApache
Spark*/intel-analytics/bigdlAnalytics+AI
PlatformDistributedTensorFlow*,Keras*and
BigDLonApache
Spark*/intel-analytics/analytics-zooAI
onUnifyingAnalytics+AIonApache
Spark**Othernamesandbrandsmaybeclaimedasthepropertyof
others./en-us/videos/analytics-zoo-overviewAnalyticsZoo
VideoAnalyticsZoo:End-to-EndDLPipelineMadeEasyforBig
DataPrototypeonlaptopusingsample
dataExperimentonclusterswithhistory
dataDeploymentwithproduction,distribtued
bigdata
pipelines“Zero”codechangefromlaptoptodistributed
clusterDirectlyaccessingproductionbigdata
(Hadoop/Hive/HBase)Easilyprototypingtheend-to-end
pipelineSeamlesslydeployedonproductionbigdata
clustersWhatisAnalytics
Zoo?Analytics
ZooBERTtfpark:DistributedTF
onBigDatannframes:SparkDataframes&
MLPipelinesforDeep
LearningDistributedKerasw/autogradonBig
DataDistributedModelServing(batch,streaming&
online)Image
ClassificationObject
Detectionimage3D
imageTransformertextSeq2SeqUse
caseModelFeature
EngineeringHigh
LevelPipelinesBackend/Librarytime
seriesRecommendation Anomaly
Detection Text
Classification Text
Matching
End-to-End,
Integrated
Data
Analytics
+
AI
Platform /intel-analytics/analytics-zooKeras PyTorch BigDL NLP
Architect Apache
Spark Apache
FlinkMKLDNN OpenVINO Intel?Optane?
DCPMM DLBoost
(VNNI)TensorFlowRayAnalyticsZooUnifiedAnalytics+AIPlatformforBig
DataBuildend-to-enddeeplearningapplicationsforbig
dataDistributedTensorFlowon
SparkKerasAPI(withautograd&transferlearningsupport)on
Sparknnframes:nativeDLsupportforSparkDataFramesandML
PipelinesProductionizedeeplearningapplicationsforbigdataat
scalePlainJava/PythonmodelservingAPIs(w/OpenVINO
support)SupportWebServices,Spark,Flink,Storm,Kafka,etc.Out-of-the-box
solutionsBuilt-indeeplearningmodels,featureengineeringoperations,andreferenceusecasesDistributedTF&Kerason
SparkDatawranglingandanalysisusing
PySparkDeeplearning
modeldevelopmentusingTensorFlowor
KerasDistributedtraining
/inferenceon
Spark#pyspark
codetrain_rdd=spark.hadoopFile(…).map(…)dataset=
TFDataset.from_rdd(train_rdd,…)#tensorflow
codeimporttensorflowas
tfslim=
tf.contrib.slimimages,labels=
dataset.tensorswithslim.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))#distributedtrainingon
Sparkoptimizer=TFOptimizer.from_loss(loss,Adam(…))
\optimizer.optimize(end_trigger=MaxEpoch(5))WriteTensorFlowcodeinlineinPySpark
programSparkDataframe&MLPipelinefor
DL#Sparkdataframetransformationsparquetfile=spark.read.parquet(…)train_df=
parquetfile.withColumn(…)#Keras
APImodel=
Sequential().add(Convolution2D(32,3,3,activation='relu',input_shape=…))
\.add(MaxPooling2D(pool_size=(2,2)))
\.add(Flatten()).add(Dense(10,
activation='softmax')))#SparkML
pipelineEstimater=NNEstimater(model,CrossEntropyCriterion())
\.setLearningRate(0.003).setBatchSize(40).setMaxEpoch(5)
\.setFeaturesCol("image")nnModel=
estimater.fit(train_df)DistributedModel
ServingHDFS/S3KafkaFlumeKinesisTwitterSpoutAnalyticsZooModelSpoutBoltBoltBoltAnalyticsZooModelBoltBoltDistributedmodelservinginWebService,Flink,Kafka,Storm,
etc.PlainJavaorPythonAPI,withOpenVINOandDLBoost(VNNI)
supportAnalyticsZooUse
CasesComputerVisionBasedProductDefectDetectionin
Midea/en-us/articles/industrial-inspection-platform-in-midea-and-kuka-using-distributed-tensorflow-on-
analyticsNLPBasedCustomerServiceChatbotforMicrosoft
Azure/en-us/articles
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