OK, I have learnt why it was failing to do anything. I had set it up within a Task, using Task { await withTaskGroup(of: Void.self) { group in for i in 0...numberoftasks - 1 { group.addTask { autoreleasepool { self.functiontomakemodel() } } } } } If I remove the Task, and just keep the loop it works. Now I just need to learn how to adjust this to use multiple cores with the new OS, any help would be much appreciated.

Specific MacOS of Sonoma is 14.0 - 23A344. I had kept up with the updates on Ventura, so it would have been the latest version before the roll out.

I am using a dataframe. When I accidentally upgraded to Sonoma the code was using MLDataTable. I switched to a dataframe on seeing the results to see if this is what caused the issue. The speeds are the same regardless, a huge degradation of performance. Is the preferredMetalDevice meant to be (null)? All documentation I read says it should be nil.

All datasets are doubles with around 20-50 columns and around 1000 rows. I have noticed the significant slowdown across the board, whether training with just 50 rows or up to 1000. All exhibit the same degree of slowdown. The slowdown scales linearly with the number of iterations performed.

Not sure what happened to the code but it should have read print(model.model.configuration) to notice preferred Metal Device is null

I am not using tensor flow, simply training a model using .csv files and DataFrames using something simple like let params = MLBoostedTreeRegressor.ModelParameters(validation: .split(strategy: .automatic),maxIterations:5000) let model = try MLBoostedTreeRegressor(trainingData: trainingdata, targetColumn: columntopredict, parameters: params) It is almost 7x slower on Sonoma, roughly the same difference in speed that you are noticing. I have tried looking for flags to set, I have changed all deprecated code, with nothing making any difference. My M1 laptop used to run hot whilst running my code under Ventura, and now it is at a pleasant ambient temperature and not really trying under Sonoma. Something has definitely changed in the update to Sonoma, and it has made my application stupidly slow. The only advice I've had so far is to try the developer beta, but I'm just not willing to go that route yet.

Is it slower as compared to before you upgraded to Sonoma? My coreML model training/building is 7x slower after upgrading from Ventura to Sonoma. Perhaps they are related?

I have not yet, I didn't really want to update to Sonoma and only did so accidentally!! I am willing to try if there is the possibility that these issues might be fixed. It's either that or downgrading back to Ventura.

Just to note, I am using this to build the model let params_boosted = MLBoostedTreeRegressor.ModelParameters(maxIterations: 10000) let model = try MLBoostedTreeRegressor(trainingData: trainingdata, targetColumn: columntopredict, parameters: params_boosted) on an M1 MacBook Pro 16Gb. Models are literally taking 7x longer to build.