Move to async training

Sources/ClassifierCreator.swift

@@ -1,5 +1,6 @@
 import Foundation
 import CreateML
+import Combine
 
 final class ClassifierCreator {
     
@@ -11,6 +12,8 @@ final class ClassifierCreator {
     
     let thumbnailDirectory: URL
     
+    let sessionDirectory: URL
+    
     let classifierUrl: URL
     
     let df = DateFormatter()
@@ -23,6 +26,7 @@ final class ClassifierCreator {
         self.server = try configuration.serverUrl()
         let contentDirectory = URL(fileURLWithPath: configuration.contentFolder)
         self.imageDirectory = contentDirectory.appendingPathComponent("images")
+        self.sessionDirectory = contentDirectory.appendingPathComponent("session")
         self.classifierUrl = contentDirectory.appendingPathComponent("classifier.mlmodel")
         self.thumbnailDirectory = contentDirectory.appendingPathComponent("thumbnails")
         df.dateFormat = "yy-MM-dd-HH-mm-ss"
@@ -55,8 +59,8 @@ final class ClassifierCreator {
         print("[INFO] Classes: \(classes.count)")
         print("[INFO] Iterations: \(configuration.trainingIterations)")
 
-        try trainModel()
+        try await trainAndSaveModel()
-        try await upload(version: newVersion)
+        try await uploadModel(version: newVersion)
         try await upload(classes: classes, lastUpdate: imagesSnapshotDate)
         await createThumbnails(changed: changedMainImages)
         print("[INFO] Done")
@@ -254,18 +258,80 @@ final class ClassifierCreator {
     
     // MARK: Step 4: Train classifier
     
-    func trainModel() throws {
+    func trainAndSaveModel() async throws {
-        var params = MLImageClassifier.ModelParameters(augmentation: [])
+        let model = try await trainModelAsync()
-        params.maxIterations = configuration.trainingIterations
+        //let model = try trainModelSync()
-        let model: MLImageClassifier
+        try save(model: model)
+    }
+    
+    func trainModelAsync() async throws -> MLImageClassifier {
+        let params = MLImageClassifier.ModelParameters(
+            maxIterations: configuration.trainingIterations,
+            augmentation: [])
+        
+        let sessionParameters = MLTrainingSessionParameters(
+            sessionDirectory: sessionDirectory,
+            reportInterval: 10,
+            checkpointInterval: 100,
+            iterations: 1000)
+        
+        var subscriptions = [AnyCancellable]()
+        
+        let job: MLJob<MLImageClassifier>
         do {
-            model = try MLImageClassifier(
+            job = try MLImageClassifier.train(
+                trainingData: .labeledDirectories(at: imageDirectory),
+                parameters: params,
+                sessionParameters: sessionParameters)
+        } catch {
+            throw TrainingError.failedToCreateClassifier
+        }
+        
+        job.progress
+            .publisher(for: \.fractionCompleted)
+            .sink { completed in
+                print(String(format: "  %.1f %% completed", completed * 100), terminator: "\r")
+                fflush(stdout)
+                
+                //guard let progress = MLProgress(progress: job.progress) else {
+                //    return
+                //}
+                //if let styleLoss = progress.metrics[.styleLoss] { _ = styleLoss }
+                //if let contentLoss = progress.metrics[.contentLoss] { _ = contentLoss }
+                
+            }
+            .store(in: &subscriptions)
+
+        return try await withCheckedThrowingContinuation { continuation in
+            // Register a sink to receive the resulting model.
+            job.result.sink { result in
+                print("[ERROR] \(result)")
+                continuation.resume(throwing: TrainingError.failedToCreateClassifier)
+            } receiveValue: { model in
+                // Use model
+                print("[INFO] Created model")
+                continuation.resume(returning: model)
+            }
+            .store(in: &subscriptions)
+        }
+    }
+    
+    func trainModelSync() throws -> MLImageClassifier {
+        let params = MLImageClassifier.ModelParameters(
+            maxIterations: configuration.trainingIterations,
+            augmentation: [])
+        
+        do {
+            return try MLImageClassifier(
                 trainingData: .labeledDirectories(at: imageDirectory),
                 parameters: params)
         } catch {
             print("[ERROR] Failed to create classifier: \(error)")
-            throw TrainingError.failedToCreateClassifier(error)
+            throw TrainingError.failedToCreateClassifier
         }
+    }
+    
+    private func save(model: MLImageClassifier) throws {
 
         print("[INFO] Saving classifier...")
         do {
@@ -278,7 +344,7 @@ final class ClassifierCreator {
     
     // MARK: Step 5: Upload classifier
     
-    func upload(version: Int) async throws {
+    func uploadModel(version: Int) async throws {
         print("[INFO] Uploading classifier...")
         let modelData: Data
         do {

Sources/TrainingError.swift

@@ -17,7 +17,7 @@ enum TrainingError: Error {
     case failedToGetClassifierVersion
     case invalidClassifierVersion(String)
     
-    case failedToCreateClassifier(Error)
+    case failedToCreateClassifier
     case failedToWriteClassifier(Error)
     
     case failedToGetChangedImagesList