Change Details

As reported by Marc-Andre, the `MitkDFTraining` MiniApp cannot handle the 200k tractogram with the ISMRM Challenge Dataset. Until now, following observations were done: 1. The ML-Training is very memory-consuming and there is no information about the (at least estimated) memory requirements of some instance; on some systems at least the user is informed that the application was killed because the system ran into //out-of-memory// 2. Even on big (cluster) systems with almost unlimited resources, the ML-Training stops processing without any meaningful message. The reported crash happened 10 minutes after the training was started. `MitkDFTraining[8954]: segfault at 2b8c5a25d010 ip 00000000004f9179 sp 00002b985eecd9d0 error 4 in MitkDFTraining[400000+188000]` which seems (after applying the ##addr2line## tool ) to point to a function in Vigra library _ZN5vigra6detail12contains_nanILj2EdNS_15StridedArrayTagEEEbRKNS_14MultiArrayViewIXT_ET0_T1_EE /home/coteharn/scratch/MITK-superbuild/ep/include/vigra/random_forest/rf_preprocessing.hxx:130`

As reported by Marc-Andre, the `MitkDFTraining` MiniApp cannot handle the 200k tractogram with the ISMRM Challenge Dataset. Until now, following observations were done: 1. The ML-Training is very memory-consuming and there is no information about the (at least estimated) memory requirements of some instance; on some systems at least the user is informed that the application was killed because the system ran into //out-of-memory// 2. Even on big (cluster) systems with almost unlimited resources, the ML-Training stops processing without any meaningful message. The reported crash happened 10 minutes after the training was started. `MitkDFTraining[8954]: segfault at 2b8c5a25d010 ip 00000000004f9179 sp 00002b985eecd9d0 error 4 in MitkDFTraining[400000+188000]` which seems (after applying the ##addr2line## tool ) to point to a function in Vigra library ``` _ZN5vigra6detail12contains_nanILj2EdNS_15StridedArrayTagEEEbRKNS_14MultiArrayViewIXT_ET0_T1_EE /home/coteharn/scratch/MITK-superbuild/ep/include/vigra/random_forest/rf_preprocessing.hxx:130 ```

As reported by Marc-Andre, the `MitkDFTraining` MiniApp cannot handle the 200k tractogram with the ISMRM Challenge Dataset. Until now, following observations were done: 1. The ML-Training is very memory-consuming and there is no information about the (at least estimated) memory requirements of some instance; on some systems at least the user is informed that the application was killed because the system ran into //out-of-memory// 2. Even on big (cluster) systems with almost unlimited resources, the ML-Training stops processing without any meaningful message. The reported crash happened 10 minutes after the training was started. `MitkDFTraining[8954]: segfault at 2b8c5a25d010 ip 00000000004f9179 sp 00002b985eecd9d0 error 4 in MitkDFTraining[400000+188000]` which seems (after applying the ##addr2line## tool ) to point to a function in Vigra library ``` _ZN5vigra6detail12contains_nanILj2EdNS_15StridedArrayTagEEEbRKNS_14MultiArrayViewIXT_ET0_T1_EE /home/coteharn/scratch/MITK-superbuild/ep/include/vigra/random_forest/rf_preprocessing.hxx:130` ```