MATLAB and NVIDIA Docker- A Complete AI Solution MATLAB 和 NVIDIA Docker：完整的 AI 解決方案

MATLABandNVIDIAACompleteAISolution,WhereYouNeedIt,InanInstantJosSeniorEngineeringManager–Parallel1?2019TheMathWorks,1MotivatingMotivatingPAGEPAGE2Buildascalableweb-applicationthatcanclassifythegenreofashortsampleofmusicBuildamodeltodetecttumoursin3-DMRIbrainDeepLearningDeepLearningPAGEPAGE3AccessAccessSelectProblem:MusicProblem:MusicGenreREF:REF:MusicPopcorn–Avisualizationofthemusicgenrespace.PAGE4Canweclassifya(short)portionofaudiointooneanumberof(Selfimposed)Trytoassumeaslittleaspossibleabouthowtodooptimizehyper-parameters,networkarchitecture,Tryoutvariousdifferentpre-processingProblem:MusicProblem:MusicGenrePAGEPAGE5SomefundamentalConvertaudiotoimage-likeWanttouseCNN-likeNotgoingtouserecurrentnetworkWehaveaclassificationThisisnotanobviouschoiceandmightbeworthWorkona5sEverythingStartsEverythingStartswithGTZANGenreCollectionLabelledsetof1000~30slongin10REF:"Musicalgenreclassificationofaudiosignals"byG.TzanetakisandP.CookinIEEETransactionsonAudioandSpeechProcessingDOWNLOAD: ExploringMyExploringMyNewPAGEPAGE7Sound2Sound2Image–MELPAGEPAGE8Sound2Sound2Image–WaveletPAGEPAGE9LotsofLotsofSound2LotsofLotsofSound2Image–JustWhatMightWhatMightMyNetworkLookThatIrksomeThatIrksome(Selfimposed)TrytoassumeaslittleasabouthowtodoCanIknowaprioritheNetworkTrainingREF:AugusteRodin'The LettingLettingtheAlgorithmChooseForFromBayesianoptimizationisasequentialdesignstrategyforglobaloptimizationofblack-boxfunctionsthatdoesn’trequirederivatives.SoundsgoodFromBayesianoptimizationisasequentialdesignforglobaloptimizationofblack-boxfunctionsthatdoesn’trequirederivatives.SoundsgoodWewantthebestpre-processingstrategy,networkarchitectureandhyper-parametersforgenrerecognition(noneofwhichweknowapriori)FromBayesianoptimizationisasequentialdesignforglobaloptimizationofblack-boxfunctionsthatdoesn’trequirederivatives.SoundsgoodWewantthebestpre-processingstrategy,networkarchitectureandhyper-parametersforgenrerecognition(noneofwhichweknowapriori)WehavenoideahowchanginganyofthosethingsmightaffecttheFromBayesianoptimizationisasequentialdesignforglobaloptimizationofblack-boxfunctionsthatrequireSoundsgoodWewantthebestpre-processingstrategy,networkarchitectureandhyper-parametersforgenrerecognition(noneofwhichweknowapriori)WehavenoideahowchanginganyofthosethingsmightaffecttheWewanttogettothebestsolutionasquicklyasWhatShouldWhatShouldWeLetItPooling&convNumberofchannelsinFinalpooling ReplicatethisconvolutionalblockavariablenumberofDropoutTrainingInitiallearnLearnratedropTrainingInitiallearnLearnratedropTrmmgP0”“(2E-FTrmmgP0”“(2E-F2Ol9—勹可口

imds=imageDatastore(.m.m

TrainingProgress(26-Feb-2019

l“l(fā)-ecngiarecngiarT

Validationaccuracy:Trainingfinished:emtiemtist

Reached什nal(一勹(16of。2192of。答答{)Auemps,peEIIoptions=trainps,peEII

Maximum Frequen勻；137Iterations otherHardwaeresource, Leamingrateschedule,Piecewise＼Tralnlng|寸Traln|--?--,-..de ,,_.一一一一企一一，一刁。＼Tralnlng|寸Traln|--?--,-..de ,,_.一一一一企一一，一刁。?Training(smoot心--?--trainedNlet=＼＼－JI sso

。。

Learning問Learn

WhenWillItGiveMeanHowmanytimesshouldBayesianOptimizationevaluatemycostRuleofthumb…10-15xnumberofvariablesbeingSoabout100>>>>disp(2*100*1500/3600/3.4722“We’reGonna“We’reGonnaNeedaBiggerdockerlogindockerpullrsync-ravessh/data/genres/dgx:/tmp/genresnvidia-dockerrun–it--rm–p6080:6080\--shm-size=512M-v/tmp/genres/:/dataDesktopaccessusingOrOratthedockercommandGettingStartMATLAB&CUDADevicewithFndFles心"lcompm 5Dat.Wo

NewVarlableOpeaVacable?ClearWork.ce.

?--,[$Acalenand．，乏ClmCommd,

Name:1TeslaVlOO-DGXS-l6GB1Index:1＊今由3回I戶I?homematIab?Documents,MAT

ComputeCapability:17.SupportsDouble: °NewtoMATLASupportsDouble:[10241024?CUDADemcemthName,'TeslaVlOO-DGXS-16GB'Index,1ComputeCapab盧立pportsDouble,DnverVers,on,Toolk1tVers10n:MaxhreadsPerBlock,MaxShmemPerBlock,MaxThreadBlockS,ze,[10241024MaxGridS,ze,[2.1475e+0965535SIM1dth:TotalMemory,1.Mult1processorCount:80clockeKHz,1530000GPverlapsTransfers,KernelExecutionTimeout,CanMapHostMemory,Deviceorted,Dev,ceSelected,?fx.?

[2.1475e+0965535SIMDWidth:32TotalMemory:AvailableMemory:l.6130e+l0MultiprocessorCount:80ClockRateKHz: 1DefaultGPUOverlapsTransfers:KernelExecutionTimeout:CanMapHostMemory:DeviceSupported:Deviceselected:SelectaIdeto-.iew

>>gpuD的44PuttingEverythingSitback,relax,HowDidWaveletDecompositionAndtheTime’sA-wasting–IHaven’tGotaWholedockerlogindockerpullnvcr.io/partners/matlab:r2019arsyncdockerpullnvcr.io/partners/matlab:r2019arsync-ravessh/data/genres/dgx:/tmp/genresnvidia-dockerrun–it--rm–p6080:6080--shm-size=512M-v/tmp/genres/:/data\OverallWaveletpre-processinghadaslightlylowerlossthanmel–0.044opposedtoWedid3.1daysworkin19hontheDGX(andin~9honaWeknowthebestwaytotrainoursystemiswavelet+model Tr19Pr”“',IO4l9l土TrainingProgress(04-Mar-2019

Validation

二』

＼＼

TrainingTrainingTimeStarttime:Elapsedtime:TrainingCyclenerationsperepoch:Maximumiterations:Other

Reachedfinal04-Mar-201920min5720of:;4aoof00543aAuemHardware Sin00543aAuem Learningrateschedule:Learnng l.007le-

learn。

II 22s8II 22s8｀｀卜\I\5l1｀Tralnlng\I\5l1｀0。 0。ltera廿

5。5。

--?--YalidaTraini心(smoot惱令Train!--?--

IstheIstheModelAnyDetectingTumorsDetectingTumorsin3-DBrain240-by-240-by-155-by-4BrainMRILabeleveryvoxelastumorornot-ThisisSemanticUses3-D240-by-240-by-155-by-4BrainMRILabeleveryvoxelastumorornot-ThisisSemanticUses3-DPre-processtoPre-processtoReduceDataDefineDataDefineDataDefinedataDefinedataDefineDefineTrainTrain“We’reGonnaN