[2024-06-27T13:08:25.875Z] Running test renaissance-movie-lens_0 ... [2024-06-27T13:08:25.875Z] =============================================== [2024-06-27T13:08:26.202Z] renaissance-movie-lens_0 Start Time: Thu Jun 27 13:08:25 2024 Epoch Time (ms): 1719493705896 [2024-06-27T13:08:26.202Z] variation: NoOptions [2024-06-27T13:08:26.533Z] JVM_OPTIONS: [2024-06-27T13:08:26.533Z] { \ [2024-06-27T13:08:26.533Z] echo ""; echo "TEST SETUP:"; \ [2024-06-27T13:08:26.533Z] echo "Nothing to be done for setup."; \ [2024-06-27T13:08:26.533Z] mkdir -p "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194924267308\\renaissance-movie-lens_0"; \ [2024-06-27T13:08:26.533Z] cd "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194924267308\\renaissance-movie-lens_0"; \ [2024-06-27T13:08:26.533Z] echo ""; echo "TESTING:"; \ [2024-06-27T13:08:26.533Z] "c:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/jdkbinary/j2sdk-image\\bin\\java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194924267308\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2024-06-27T13:08:26.533Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/; rm -f -r "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194924267308\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-06-27T13:08:26.533Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-06-27T13:08:26.533Z] echo "Nothing to be done for teardown."; \ [2024-06-27T13:08:26.533Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194924267308\\TestTargetResult"; [2024-06-27T13:08:26.533Z] [2024-06-27T13:08:26.533Z] TEST SETUP: [2024-06-27T13:08:26.533Z] Nothing to be done for setup. [2024-06-27T13:08:26.533Z] [2024-06-27T13:08:26.533Z] TESTING: [2024-06-27T13:08:37.224Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-06-27T13:08:38.865Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-06-27T13:08:41.855Z] Got 100004 ratings from 671 users on 9066 movies. [2024-06-27T13:08:41.855Z] Training: 60056, validation: 20285, test: 19854 [2024-06-27T13:08:41.855Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-06-27T13:08:41.855Z] GC before operation: completed in 71.869 ms, heap usage 80.793 MB -> 36.960 MB. [2024-06-27T13:08:54.954Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:09:02.151Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:09:10.960Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:09:18.137Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:09:21.916Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:09:25.667Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:09:30.436Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:09:34.189Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:09:34.190Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:09:34.659Z] The best model improves the baseline by 14.52%. [2024-06-27T13:09:34.659Z] Movies recommended for you: [2024-06-27T13:09:34.659Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:09:34.659Z] There is no way to check that no silent failure occurred. [2024-06-27T13:09:34.659Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (52621.932 ms) ====== [2024-06-27T13:09:34.659Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-06-27T13:09:34.659Z] GC before operation: completed in 101.098 ms, heap usage 279.968 MB -> 50.994 MB. [2024-06-27T13:09:41.924Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:09:49.141Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:09:56.377Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:10:03.555Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:10:07.258Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:10:10.975Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:10:14.689Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:10:19.374Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:10:19.374Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:10:19.374Z] The best model improves the baseline by 14.52%. [2024-06-27T13:10:19.374Z] Movies recommended for you: [2024-06-27T13:10:19.374Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:10:19.374Z] There is no way to check that no silent failure occurred. [2024-06-27T13:10:19.374Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (44670.713 ms) ====== [2024-06-27T13:10:19.374Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-06-27T13:10:19.374Z] GC before operation: completed in 88.067 ms, heap usage 106.189 MB -> 52.819 MB. [2024-06-27T13:10:28.165Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:10:34.034Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:10:41.230Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:10:48.408Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:10:52.132Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:10:55.842Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:11:00.518Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:11:04.251Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:11:04.251Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:11:04.251Z] The best model improves the baseline by 14.52%. [2024-06-27T13:11:04.251Z] Movies recommended for you: [2024-06-27T13:11:04.251Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:11:04.251Z] There is no way to check that no silent failure occurred. [2024-06-27T13:11:04.251Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (44846.797 ms) ====== [2024-06-27T13:11:04.251Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-06-27T13:11:04.251Z] GC before operation: completed in 83.602 ms, heap usage 100.202 MB -> 49.760 MB. [2024-06-27T13:11:11.434Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:11:18.611Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:11:25.791Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:11:31.622Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:11:36.322Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:11:40.058Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:11:43.793Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:11:47.508Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:11:47.844Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:11:47.844Z] The best model improves the baseline by 14.52%. [2024-06-27T13:11:47.844Z] Movies recommended for you: [2024-06-27T13:11:47.844Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:11:47.844Z] There is no way to check that no silent failure occurred. [2024-06-27T13:11:47.844Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (43572.841 ms) ====== [2024-06-27T13:11:47.844Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-06-27T13:11:48.173Z] GC before operation: completed in 92.718 ms, heap usage 190.315 MB -> 50.194 MB. [2024-06-27T13:11:55.375Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:12:02.579Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:12:09.790Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:12:15.604Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:12:20.303Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:12:24.084Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:12:27.815Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:12:31.591Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:12:31.914Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:12:31.914Z] The best model improves the baseline by 14.52%. [2024-06-27T13:12:32.242Z] Movies recommended for you: [2024-06-27T13:12:32.242Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:12:32.242Z] There is no way to check that no silent failure occurred. [2024-06-27T13:12:32.242Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (44106.863 ms) ====== [2024-06-27T13:12:32.242Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-06-27T13:12:32.242Z] GC before operation: completed in 85.932 ms, heap usage 96.824 MB -> 50.293 MB. [2024-06-27T13:12:39.408Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:12:45.240Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:12:52.449Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:12:59.618Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:13:03.357Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:13:07.069Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:13:10.813Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:13:15.522Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:13:15.522Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:13:15.522Z] The best model improves the baseline by 14.52%. [2024-06-27T13:13:15.522Z] Movies recommended for you: [2024-06-27T13:13:15.522Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:13:15.522Z] There is no way to check that no silent failure occurred. [2024-06-27T13:13:15.522Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (43198.802 ms) ====== [2024-06-27T13:13:15.522Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-06-27T13:13:15.522Z] GC before operation: completed in 79.790 ms, heap usage 122.411 MB -> 50.220 MB. [2024-06-27T13:13:22.700Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:13:28.520Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:13:35.684Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:13:41.490Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:13:45.215Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:13:48.986Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:13:53.663Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:13:56.565Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:13:57.264Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:13:57.264Z] The best model improves the baseline by 14.52%. [2024-06-27T13:13:57.264Z] Movies recommended for you: [2024-06-27T13:13:57.264Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:13:57.264Z] There is no way to check that no silent failure occurred. [2024-06-27T13:13:57.264Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (41845.786 ms) ====== [2024-06-27T13:13:57.264Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-06-27T13:13:57.264Z] GC before operation: completed in 85.170 ms, heap usage 190.461 MB -> 50.475 MB. [2024-06-27T13:14:04.446Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:14:11.645Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:14:17.487Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:14:24.698Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:14:28.430Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:14:32.164Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:14:35.884Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:14:39.611Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:14:39.942Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:14:39.942Z] The best model improves the baseline by 14.52%. [2024-06-27T13:14:39.942Z] Movies recommended for you: [2024-06-27T13:14:39.942Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:14:39.942Z] There is no way to check that no silent failure occurred. [2024-06-27T13:14:39.942Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (42582.346 ms) ====== [2024-06-27T13:14:39.942Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-06-27T13:14:39.942Z] GC before operation: completed in 85.113 ms, heap usage 210.425 MB -> 52.511 MB. [2024-06-27T13:14:47.118Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:14:52.940Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:15:00.117Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:15:07.284Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:15:10.214Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:15:13.932Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:15:17.731Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:15:21.467Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:15:22.180Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:15:22.180Z] The best model improves the baseline by 14.52%. [2024-06-27T13:15:22.180Z] Movies recommended for you: [2024-06-27T13:15:22.180Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:15:22.180Z] There is no way to check that no silent failure occurred. [2024-06-27T13:15:22.180Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (42083.373 ms) ====== [2024-06-27T13:15:22.180Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-06-27T13:15:22.181Z] GC before operation: completed in 86.537 ms, heap usage 170.252 MB -> 50.584 MB. [2024-06-27T13:15:29.341Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:15:35.188Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:15:42.371Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:15:49.601Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:15:52.508Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:15:56.234Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:16:00.923Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:16:04.635Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:16:04.635Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:16:04.635Z] The best model improves the baseline by 14.52%. [2024-06-27T13:16:04.635Z] Movies recommended for you: [2024-06-27T13:16:04.635Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:16:04.635Z] There is no way to check that no silent failure occurred. [2024-06-27T13:16:04.635Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (42393.960 ms) ====== [2024-06-27T13:16:04.635Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-06-27T13:16:04.635Z] GC before operation: completed in 84.837 ms, heap usage 206.069 MB -> 50.670 MB. [2024-06-27T13:16:11.797Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:16:17.670Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:16:24.834Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:16:32.002Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:16:35.704Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:16:39.413Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:16:43.154Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:16:46.871Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:16:47.231Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:16:47.231Z] The best model improves the baseline by 14.52%. [2024-06-27T13:16:47.231Z] Movies recommended for you: [2024-06-27T13:16:47.231Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:16:47.231Z] There is no way to check that no silent failure occurred. [2024-06-27T13:16:47.231Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (42603.998 ms) ====== [2024-06-27T13:16:47.231Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-06-27T13:16:47.563Z] GC before operation: completed in 82.923 ms, heap usage 200.700 MB -> 50.412 MB. [2024-06-27T13:16:54.733Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:17:00.556Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:17:07.744Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:17:13.595Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:17:18.273Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:17:21.184Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:17:25.866Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:17:28.771Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:17:29.475Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:17:29.475Z] The best model improves the baseline by 14.52%. [2024-06-27T13:17:29.475Z] Movies recommended for you: [2024-06-27T13:17:29.475Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:17:29.475Z] There is no way to check that no silent failure occurred. [2024-06-27T13:17:29.475Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (42001.088 ms) ====== [2024-06-27T13:17:29.476Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-06-27T13:17:29.476Z] GC before operation: completed in 87.088 ms, heap usage 239.917 MB -> 53.836 MB. [2024-06-27T13:17:36.653Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:17:42.542Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:17:49.753Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:17:56.933Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:17:59.910Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:18:03.650Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:18:08.345Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:18:11.244Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:18:11.991Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:18:11.991Z] The best model improves the baseline by 14.52%. [2024-06-27T13:18:11.991Z] Movies recommended for you: [2024-06-27T13:18:11.991Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:18:11.991Z] There is no way to check that no silent failure occurred. [2024-06-27T13:18:11.991Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (42471.405 ms) ====== [2024-06-27T13:18:11.991Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-06-27T13:18:11.991Z] GC before operation: completed in 84.436 ms, heap usage 100.740 MB -> 53.991 MB. [2024-06-27T13:18:19.166Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:18:26.331Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:18:33.524Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:18:39.355Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:18:43.075Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:18:46.785Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:18:50.511Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:18:53.440Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:18:53.765Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:18:53.765Z] The best model improves the baseline by 14.52%. [2024-06-27T13:18:54.127Z] Movies recommended for you: [2024-06-27T13:18:54.127Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:18:54.127Z] There is no way to check that no silent failure occurred. [2024-06-27T13:18:54.127Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (41982.874 ms) ====== [2024-06-27T13:18:54.127Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-06-27T13:18:54.127Z] GC before operation: completed in 95.588 ms, heap usage 262.506 MB -> 53.731 MB. [2024-06-27T13:19:01.319Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:19:08.508Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:19:14.349Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:19:21.558Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:19:25.293Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:19:29.018Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:19:32.779Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:19:36.495Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:19:36.495Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:19:36.495Z] The best model improves the baseline by 14.52%. [2024-06-27T13:19:36.827Z] Movies recommended for you: [2024-06-27T13:19:36.827Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:19:36.827Z] There is no way to check that no silent failure occurred. [2024-06-27T13:19:36.827Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (42551.109 ms) ====== [2024-06-27T13:19:36.827Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-06-27T13:19:36.827Z] GC before operation: completed in 85.589 ms, heap usage 232.644 MB -> 50.680 MB. [2024-06-27T13:19:44.055Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:19:49.884Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:19:57.071Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:20:04.254Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:20:07.187Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:20:10.887Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:20:14.682Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:20:18.415Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:20:19.137Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:20:19.137Z] The best model improves the baseline by 14.52%. [2024-06-27T13:20:19.137Z] Movies recommended for you: [2024-06-27T13:20:19.137Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:20:19.137Z] There is no way to check that no silent failure occurred. [2024-06-27T13:20:19.137Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (42315.563 ms) ====== [2024-06-27T13:20:19.137Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-06-27T13:20:19.137Z] GC before operation: completed in 83.070 ms, heap usage 126.669 MB -> 50.687 MB. [2024-06-27T13:20:26.322Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:20:32.155Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:20:39.342Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:20:45.181Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:20:48.916Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:20:52.697Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:20:56.418Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:21:00.156Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:21:00.507Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:21:00.507Z] The best model improves the baseline by 14.52%. [2024-06-27T13:21:00.849Z] Movies recommended for you: [2024-06-27T13:21:00.849Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:21:00.849Z] There is no way to check that no silent failure occurred. [2024-06-27T13:21:00.849Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (41470.354 ms) ====== [2024-06-27T13:21:00.849Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-06-27T13:21:00.849Z] GC before operation: completed in 91.918 ms, heap usage 96.981 MB -> 50.522 MB. [2024-06-27T13:21:08.007Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:21:15.193Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:21:21.030Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:21:28.190Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:21:31.932Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:21:35.699Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:21:40.375Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:21:43.307Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:21:44.029Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:21:44.029Z] The best model improves the baseline by 14.52%. [2024-06-27T13:21:44.029Z] Movies recommended for you: [2024-06-27T13:21:44.029Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:21:44.029Z] There is no way to check that no silent failure occurred. [2024-06-27T13:21:44.029Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (43280.248 ms) ====== [2024-06-27T13:21:44.029Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-06-27T13:21:44.029Z] GC before operation: completed in 82.846 ms, heap usage 125.473 MB -> 50.579 MB. [2024-06-27T13:21:51.245Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:22:00.064Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:22:05.907Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:22:13.123Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:22:16.050Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:22:20.720Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:22:24.443Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:22:28.182Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:22:28.182Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:22:28.182Z] The best model improves the baseline by 14.52%. [2024-06-27T13:22:28.519Z] Movies recommended for you: [2024-06-27T13:22:28.519Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:22:28.519Z] There is no way to check that no silent failure occurred. [2024-06-27T13:22:28.519Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (44241.094 ms) ====== [2024-06-27T13:22:28.519Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-06-27T13:22:28.519Z] GC before operation: completed in 91.459 ms, heap usage 138.680 MB -> 54.098 MB. [2024-06-27T13:22:35.698Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T13:22:41.561Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T13:22:48.800Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T13:22:54.669Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T13:22:58.394Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T13:23:02.112Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T13:23:06.797Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T13:23:09.701Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T13:23:10.028Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T13:23:10.028Z] The best model improves the baseline by 14.52%. [2024-06-27T13:23:10.359Z] Movies recommended for you: [2024-06-27T13:23:10.359Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T13:23:10.359Z] There is no way to check that no silent failure occurred. [2024-06-27T13:23:10.359Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (41922.200 ms) ====== [2024-06-27T13:23:10.685Z] ----------------------------------- [2024-06-27T13:23:10.685Z] renaissance-movie-lens_0_PASSED [2024-06-27T13:23:10.685Z] ----------------------------------- [2024-06-27T13:23:11.371Z] [2024-06-27T13:23:11.371Z] TEST TEARDOWN: [2024-06-27T13:23:11.371Z] Nothing to be done for teardown. [2024-06-27T13:23:11.689Z] renaissance-movie-lens_0 Finish Time: Thu Jun 27 13:23:11 2024 Epoch Time (ms): 1719494591462