I am interested in classify binary data using a Random Forest method. My data is numeric tensor and label of each class represented by 1.0 or 0.0. The size of the training data is ~16 million examples, and the size of the test data is around 500 million examples.

rfST = Classify[traningData, 
  Method -> {"RandomForest", "TreeNumber" -> 500}, 
  TrainingProgressReporting -> "Print", TargetDevice -> {"GPU", 1}]

predictionRes = 
    Values[rfST[testData, "Probabilities", TargetDevice -> {"GPU", 1}]]][[

To speed up the training and test, I am using TargetDevice -> "GPU." When I am run the prediction I "feel" that a GPU doesn't take part in computation, however, the CPU is a computation engine. Please see the attached figures. enter image description here enter image description here

Any suggestion on what to check to speed up the calculation Note: I am using Win 10, and WL 11.3 and the GPU is GTX 1080 (I update the driver)

  • 1
    $\begingroup$ try to install CUDA drivers manually $\endgroup$ – Fortsaint Jan 11 at 16:40
  • $\begingroup$ I did it, but I don't see improvement. $\endgroup$ – Kiril Danilchenko Jan 11 at 16:49
  • 1
    $\begingroup$ TargetDevice -> {"GPU", 2} ? $\endgroup$ – Fortsaint Jan 11 at 17:05
  • $\begingroup$ similar performance, also I try 'TargetDevice -> {"GPU",{1,2}}' $\endgroup$ – Kiril Danilchenko Jan 11 at 17:52
  • 1
    $\begingroup$ @KirilDanilchenko You cannot use GPU because RandomForest method uses Intel DAAL library under the hood. To speed up training, use option PerformanceGoal -> "DirectTraining". $\endgroup$ – Alexey Golyshev Jan 12 at 12:17

Your Answer

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.

Browse other questions tagged or ask your own question.