CreateML crashes with Unexpected Error on Feature Extraction

Additionally, in case it's a lower barrier to solve, I tried to put this script together for my own training run using something similar to what I imagine createml does behind the scenes. My problem is it does not seem to save the model or checkpoints to the ~/.mlmodel file path specified. I added comments where I think my code is messed up, appreciate if anyone can take a look!

import CreateML
import Foundation
import Combine

let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: "/Users/giovannizinzi/Desktop/FoodData/Train", isDirectory: true))
let parameters = MLImageClassifier.ModelParameters(
    validation: .split(strategy: .automatic),
    augmentation: [],
    algorithm: .transferLearning(
        featureExtractor: .scenePrint(revision: 2),
        classifier: .logisticRegressor
    )
)
var cancellables = Set<AnyCancellable>()
let trainingSession = try MLImageClassifier.makeTrainingSession(
    trainingData: trainingData,
    parameters: parameters
)
print(trainingSession.iteration)
print(trainingSession.checkpoints)
print(trainingSession.phase.rawValue)

let checkpointInterval = 0.05

Task{
    do {
        let trainingJob = try MLImageClassifier.resume(trainingSession)
        let progress = trainingJob.progress
        
        progress.publisher(for: \.fractionCompleted)
            .sink { fractionCompleted in
                print("Training progress: \(fractionCompleted * 100)%")
            }
            .store(in: &cancellables)
          
        trainingJob.phase
            .sink { phase in
                print("Current phase: \(phase)")
            }
            .store(in: &cancellables)
          
        trainingJob.checkpoints
            .sink { checkpoint in
                print("Checkpoint: \(checkpoint)")
                print("Checkpoint: \(checkpoint)")
                print("Model URL: \(checkpoint.url)")
// do I need to save the model here for a given checkpoint?
            }
            .store(in: &cancellables)
          
        trainingJob.result
            .sink { completion in
                switch completion {
                case .finished:
                    print("Training finished successfully.")
                case .failure(let error):
                    print("Training failed with error: \(error)")
                }
            } receiveValue: { classifier in
                let model = classifier.model
  
                // Save the model, not working? .store(in: &cancellables) culprit?
                let modelURL = URL(fileURLWithPath: "/Users/giovannizinzi/Desktop/avofeb24.mlmodel")
                let metadata = MLModelMetadata(author: "Gio", shortDescription: "Avo", version: "1.0")
                do {
                    try classifier.write(to: modelURL)
                    print("Model saved successfully at the end of training.")
                } catch {
                    print("Failed to save model: \(error)")
                }
            }
            .store(in: &cancellables)
          
    } catch {
        print("Error occurred: \(error)")
    }
}

print(trainingSession.checkpoints)
print(trainingSession.phase)
print(trainingSession.iteration)

Please try extracting the features separately from training. If you can't extract features for all of your images in memory, try instead to write the extracted features to disk. Then use the saved features for training. This has the added advantage that you will only need to do this once, and can then train multiple models, or one model over multiple iterations. For reference please see the WWDC session and the ImageFeaturePrint documentation..

Thanks for reporting the issue, we are looking into it.