dotnet / dotnet/machinelearning-modelbuilder

Bug in ML Model Builder 16.17.0.2332602 Data From csv file Text Classified scenario

Aperta
#2,863 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub
Lingua principale
Dockerfile
Stelle
285
Fork
66
Metriche di merge delle PR
Nessuna PR unita negli ultimi 30g

Descrizione

**I am developing an Application using ML C# in Console Core**
- Model Builder Version 16.17.0.2332602:
- Visual Studio Version 2019

**Details & Bud Location**
- It takes a long time to do training.
- After completing the training, an error message appears.
- src/Microsoft.ML.ModelBuilder.AutoMLService/ServiceFactory/CodeGeneratorService.cs:line 139
- src/Microsoft.ML.ModelBuilder.AutoMLService/ServiceFactory/CodeGeneratorService.cs:line 155

**Error in logfile**
DEBUG Exception has been thrown by the target of an invocation.
at System.RuntimeMethodHandle.InvokeMethod(Object target, Object[] arguments, Signature sig, Boolean constructor)
at System.Reflection.RuntimeMethodInfo.UnsafeInvokeInternal(Object obj, Object[] parameters, Object[] arguments)
at System.Reflection.RuntimeMethodInfo.Invoke(Object obj, BindingFlags invokeAttr, Binder binder, Object[] parameters, CultureInfo culture)
at Microsoft.ML.Runtime.ComponentCatalog.LoadableClassInfo.CreateInstanceCore(Object[] ctorArgs)
at Microsoft.ML.Runtime.ComponentCatalog.TryCreateInstance[TRes](IHostEnvironment env, Type signatureType, TRes& result, String name, String options, Object[] extra)
at Microsoft.ML.Runtime.ComponentCatalog.TryCreateInstance[TRes,TSig](IHostEnvironment env, TRes& result, String name, String options, Object[] extra)
at Microsoft.ML.ModelLoadContext.TryLoadModelCore[TRes,TSig](IHostEnvironment env, TRes& result, Object[] extra)
at Microsoft.ML.ModelLoadContext.TryLoadModel[TRes,TSig](IHostEnvironment env, TRes& result, RepositoryReader rep, Entry ent, String dir, Object[] extra)
at Microsoft.ML.ModelLoadContext.LoadModel[TRes,TSig](IHostEnvironment env, TRes& result, RepositoryReader rep, Entry ent, String dir, Object[] extra)
at Microsoft.ML.ModelLoadContext.LoadModelOrNull[TRes,TSig](IHostEnvironment env, TRes& result, RepositoryReader rep, String dir, Object[] extra)
at Microsoft.ML.ModelLoadContext.LoadModel[TRes,TSig](IHostEnvironment env, TRes& result, RepositoryReader rep, String dir, Object[] extra)
at Microsoft.ML.ModelOperationsCatalog.Load(Stream stream, DataViewSchema& inputSchema)
at Microsoft.ML.ModelOperationsCatalog.Load(String filePath, DataViewSchema& inputSchema)
at Microsoft.ML.ModelBuilder.AutoMLService.ServiceFactory.CodeGeneratorService.SetTorchRunTimeFolderAndLoadModel(ITrainingConfiguration configuration, String modelPath, MLContext& context, ITransformer& model, DataViewSchema& inputSchema) in /_/src/Microsoft.ML.ModelBuilder.AutoMLService/ServiceFactory/CodeGeneratorService.cs:line 139
at Microsoft.ML.ModelBuilder.AutoMLService.ServiceFactory.CodeGeneratorService.GenerateConsumptionAsync(ITrainingConfiguration configuration, String trainingConfigurationFolder, String nameSpace, String className, TargetType target, String[] labels, CancellationToken ct) in /_/src/Microsoft.ML.ModelBuilder.AutoMLService/ServiceFactory/CodeGeneratorService.cs:line 155 at StreamJsonRpc.JsonRpc.d__139`1.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task)
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at System.Runtime.CompilerServices.TaskAwaiter`1.GetResult()
at Microsoft.ML.ModelBuilder.ViewModels.TrainViewModel.d__100.MoveNext() (Microsoft.ML.ModelBuilder.Utils.Logger.Debug)

Guida per i contributori

Nessuna guida per i contributori indicizzata per questo repository

Direzione di ricerca

Inizia da src/Microsoft.ML.ModelBuilder.AutoMLService/ServiceFactory/CodeGeneratorService.cs, in particolare dalle righe 139 e 155, e riproduci lo scenario Text Classified usando dati provenienti da un file CSV. Confronta lo stack trace del logfile con i passaggi di caricamento del modello e generazione del codice; il lavoro è completato quando l'addestramento termina senza l'errore segnalato e il codice di consumo viene generato correttamente.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
csharp, machine-learning
Ambito
machine-learning, tooling
Tipo di issue
Bug
Difficoltà
4/5
Tempo stimato
3-5 giorni
Stato di attività
Ferma
Chiarezza
Da chiarire
Idoneità per principianti
32/100

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