[2025-12-06T17:03:32.150Z] Running test renaissance-movie-lens_0 ... [2025-12-06T17:03:32.150Z] =============================================== [2025-12-06T17:03:32.150Z] renaissance-movie-lens_0 Start Time: Sat Dec 6 17:03:31 2025 Epoch Time (ms): 1765040612000 [2025-12-06T17:03:32.150Z] variation: NoOptions [2025-12-06T17:03:32.150Z] JVM_OPTIONS: [2025-12-06T17:03:32.150Z] { \ [2025-12-06T17:03:32.150Z] echo ""; echo "TEST SETUP:"; \ [2025-12-06T17:03:32.150Z] echo "Nothing to be done for setup."; \ [2025-12-06T17:03:32.150Z] mkdir -p "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17650377762141/renaissance-movie-lens_0"; \ [2025-12-06T17:03:32.151Z] cd "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17650377762141/renaissance-movie-lens_0"; \ [2025-12-06T17:03:32.151Z] echo ""; echo "TESTING:"; \ [2025-12-06T17:03:32.151Z] "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/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 "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17650377762141/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-12-06T17:03:32.151Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17650377762141/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-12-06T17:03:32.151Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-12-06T17:03:32.151Z] echo "Nothing to be done for teardown."; \ [2025-12-06T17:03:32.151Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17650377762141/TestTargetResult"; [2025-12-06T17:03:32.151Z] [2025-12-06T17:03:32.151Z] TEST SETUP: [2025-12-06T17:03:32.151Z] Nothing to be done for setup. [2025-12-06T17:03:32.151Z] [2025-12-06T17:03:32.151Z] TESTING: [2025-12-06T17:03:35.140Z] WARNING: A terminally deprecated method in sun.misc.Unsafe has been called [2025-12-06T17:03:35.140Z] WARNING: sun.misc.Unsafe::objectFieldOffset has been called by scala.runtime.LazyVals$ (file:/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/output_17650377762141/renaissance-movie-lens_0/launcher-170332-6525977353698212923/renaissance-harness_3/lib/scala3-library_3-3.3.4.jar) [2025-12-06T17:03:35.140Z] WARNING: Please consider reporting this to the maintainers of class scala.runtime.LazyVals$ [2025-12-06T17:03:35.140Z] WARNING: sun.misc.Unsafe::objectFieldOffset will be removed in a future release [2025-12-06T17:03:58.399Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-12-06T17:04:26.246Z] 17:04:25.809 WARN [dispatcher-event-loop-0] org.apache.spark.scheduler.TaskSetManager - Stage 8 contains a task of very large size (1401 KiB). The maximum recommended task size is 1000 KiB. [2025-12-06T17:04:37.291Z] Got 100004 ratings from 671 users on 9066 movies. [2025-12-06T17:04:38.463Z] Training: 60056, validation: 20285, test: 19854 [2025-12-06T17:04:38.463Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-12-06T17:04:39.180Z] GC before operation: completed in 598.203 ms, heap usage 373.264 MB -> 76.024 MB. [2025-12-06T17:05:07.052Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:05:20.404Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:05:33.633Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:05:44.638Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:05:52.135Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:05:58.110Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:06:05.463Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:06:11.556Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:06:12.725Z] 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. [2025-12-06T17:06:12.725Z] The best model improves the baseline by 14.52%. [2025-12-06T17:06:13.899Z] Top recommended movies for user id 72: [2025-12-06T17:06:13.899Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:06:13.899Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:06:13.899Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:06:13.899Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:06:13.899Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:06:13.899Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (94879.722 ms) ====== [2025-12-06T17:06:13.899Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-12-06T17:06:15.083Z] GC before operation: completed in 867.110 ms, heap usage 302.171 MB -> 89.821 MB. [2025-12-06T17:06:26.028Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:06:36.945Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:06:45.906Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:06:54.864Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:07:00.936Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:07:05.766Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:07:11.739Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:07:17.710Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:07:17.710Z] 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. [2025-12-06T17:07:17.710Z] The best model improves the baseline by 14.52%. [2025-12-06T17:07:18.434Z] Top recommended movies for user id 72: [2025-12-06T17:07:18.434Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:07:18.434Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:07:18.434Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:07:18.434Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:07:18.434Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:07:18.434Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (63627.198 ms) ====== [2025-12-06T17:07:18.434Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-12-06T17:07:19.625Z] GC before operation: completed in 971.236 ms, heap usage 643.300 MB -> 92.460 MB. [2025-12-06T17:07:30.545Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:07:39.591Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:07:48.649Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:07:57.620Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:08:02.409Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:08:08.359Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:08:13.158Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:08:17.949Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:08:18.669Z] 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. [2025-12-06T17:08:18.669Z] The best model improves the baseline by 14.52%. [2025-12-06T17:08:19.384Z] Top recommended movies for user id 72: [2025-12-06T17:08:19.385Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:08:19.385Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:08:19.385Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:08:19.385Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:08:19.385Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:08:19.385Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (60031.057 ms) ====== [2025-12-06T17:08:19.385Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-12-06T17:08:20.585Z] GC before operation: completed in 917.967 ms, heap usage 212.825 MB -> 89.430 MB. [2025-12-06T17:08:29.543Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:08:36.950Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:08:45.904Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:08:54.930Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:08:59.731Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:09:04.533Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:09:09.322Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:09:14.120Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:09:14.842Z] 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. [2025-12-06T17:09:14.842Z] The best model improves the baseline by 14.52%. [2025-12-06T17:09:15.563Z] Top recommended movies for user id 72: [2025-12-06T17:09:15.563Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:09:15.563Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:09:15.563Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:09:15.563Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:09:15.563Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:09:15.563Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (55159.133 ms) ====== [2025-12-06T17:09:15.563Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-12-06T17:09:16.395Z] GC before operation: completed in 953.549 ms, heap usage 277.877 MB -> 89.887 MB. [2025-12-06T17:09:25.353Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:09:34.333Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:09:43.296Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:09:50.630Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:09:55.441Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:10:00.253Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:10:05.154Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:10:11.111Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:10:11.445Z] 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. [2025-12-06T17:10:11.445Z] The best model improves the baseline by 14.52%. [2025-12-06T17:10:12.165Z] Top recommended movies for user id 72: [2025-12-06T17:10:12.165Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:10:12.165Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:10:12.165Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:10:12.165Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:10:12.165Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:10:12.165Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (55679.672 ms) ====== [2025-12-06T17:10:12.165Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-12-06T17:10:13.365Z] GC before operation: completed in 1016.494 ms, heap usage 455.168 MB -> 90.146 MB. [2025-12-06T17:10:22.327Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:10:29.653Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:10:38.610Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:10:45.940Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:10:50.830Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:10:55.640Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:11:00.461Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:11:05.263Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:11:05.994Z] 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. [2025-12-06T17:11:05.994Z] The best model improves the baseline by 14.52%. [2025-12-06T17:11:06.711Z] Top recommended movies for user id 72: [2025-12-06T17:11:06.711Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:11:06.711Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:11:06.711Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:11:06.711Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:11:06.711Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:11:06.711Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (53477.854 ms) ====== [2025-12-06T17:11:06.711Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-12-06T17:11:07.894Z] GC before operation: completed in 955.425 ms, heap usage 219.173 MB -> 90.146 MB. [2025-12-06T17:11:16.858Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:11:24.200Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:11:31.552Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:11:40.702Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:11:44.524Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:11:49.324Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:11:54.121Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:11:57.947Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:11:59.112Z] 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. [2025-12-06T17:11:59.112Z] The best model improves the baseline by 14.52%. [2025-12-06T17:11:59.446Z] Top recommended movies for user id 72: [2025-12-06T17:11:59.447Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:11:59.447Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:11:59.447Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:11:59.447Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:11:59.447Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:11:59.447Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (51955.149 ms) ====== [2025-12-06T17:11:59.447Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-12-06T17:12:00.622Z] GC before operation: completed in 944.666 ms, heap usage 255.430 MB -> 90.136 MB. [2025-12-06T17:12:09.591Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:12:17.012Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-12-06T17:12:24.350Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-12-06T17:12:31.691Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-12-06T17:12:36.493Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-12-06T17:12:41.294Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-12-06T17:12:46.097Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-12-06T17:12:52.061Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-12-06T17:12:52.394Z] 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. [2025-12-06T17:12:52.395Z] The best model improves the baseline by 14.52%. [2025-12-06T17:12:53.125Z] Top recommended movies for user id 72: [2025-12-06T17:12:53.125Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-12-06T17:12:53.125Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-12-06T17:12:53.125Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-12-06T17:12:53.125Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-12-06T17:12:53.125Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-12-06T17:12:53.125Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (52717.486 ms) ====== [2025-12-06T17:12:53.125Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-12-06T17:12:54.306Z] GC before operation: completed in 875.867 ms, heap usage 124.973 MB -> 90.217 MB. [2025-12-06T17:13:03.522Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-12-06T17:13:03.522Z] 17:13:02.985 ERROR [Executor task launch worker for task 0.0 in stage 12601.0 (TID 12259)] org.apache.spark.executor.Executor - Exception in task 0.0 in stage 12601.0 (TID 12259) [2025-12-06T17:13:03.522Z] java.lang.ClassCastException: cannot assign instance of org.apache.spark.executor.InputMetrics to field org.apache.spark.executor.TaskMetrics.inputMetrics of type org.apache.spark.executor.InputMetrics in instance of org.apache.spark.executor.TaskMetrics [2025-12-06T17:13:03.522Z] at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:1966) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectStreamClass$FieldReflector.checkObjectFieldValueTypes(ObjectStreamClass.java:1930) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectStreamClass.checkObjFieldValueTypes(ObjectStreamClass.java:1223) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectInputStream$FieldValues.defaultCheckFieldValues(ObjectInputStream.java:2559) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2360) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2133) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1620) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectInputStream.readObject(ObjectInputStream.java:487) ~[?:?] [2025-12-06T17:13:03.522Z] at java.io.ObjectInputStream.readObject(ObjectInputStream.java:445) ~[?:?] [2025-12-06T17:13:03.522Z] at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:87) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:123) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.scheduler.Task.metrics$lzycompute(Task.scala:76) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.scheduler.Task.metrics(Task.scala:75) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.scheduler.Task.run(Task.scala:109) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) [spark-core_2.13-3.5.3.jar:3.5.3] [2025-12-06T17:13:03.522Z] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1090) [?:?] [2025-12-06T17:13:03.522Z] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:614) [?:?] [2025-12-06T17:13:03.522Z] at java.lang.Thread.run(Thread.java:1474) [?:?] [2025-12-06T17:13:03.522Z] Exception in thread "Executor task launch worker for task 0.0 in stage 12601.0 (TID 12259)" java.lang.ClassCastException: cannot assign instance of org.apache.spark.executor.InputMetrics to field org.apache.spark.executor.TaskMetrics.inputMetrics of type org.apache.spark.executor.InputMetrics in instance of org.apache.spark.executor.TaskMetrics [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:1966) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectStreamClass$FieldReflector.checkObjectFieldValueTypes(ObjectStreamClass.java:1930) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectStreamClass.checkObjFieldValueTypes(ObjectStreamClass.java:1223) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectInputStream$FieldValues.defaultCheckFieldValues(ObjectInputStream.java:2559) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2360) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2133) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1620) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectInputStream.readObject(ObjectInputStream.java:487) [2025-12-06T17:13:03.522Z] at java.base/java.io.ObjectInputStream.readObject(ObjectInputStream.java:445) [2025-12-06T17:13:03.522Z] at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:87) [2025-12-06T17:13:03.522Z] at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:123) [2025-12-06T17:13:03.522Z] at org.apache.spark.scheduler.Task.metrics$lzycompute(Task.scala:76) [2025-12-06T17:13:03.522Z] at org.apache.spark.scheduler.Task.metrics(Task.scala:75) [2025-12-06T17:13:03.522Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$collectAccumulatorsAndResetStatusOnFailure$1(Executor.scala:523) [2025-12-06T17:13:03.522Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$collectAccumulatorsAndResetStatusOnFailure$1$adapted(Executor.scala:522) [2025-12-06T17:13:03.522Z] at scala.Option.foreach(Option.scala:437) [2025-12-06T17:13:03.522Z] at org.apache.spark.executor.Executor$TaskRunner.collectAccumulatorsAndResetStatusOnFailure(Executor.scala:522) [2025-12-06T17:13:03.522Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:800) [2025-12-06T17:13:03.522Z] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1090) [2025-12-06T17:13:03.522Z] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:614) [2025-12-06T17:13:03.522Z] at java.base/java.lang.Thread.run(Thread.java:1474) [2025-12-06T20:08:49.402Z] test-rise-ubuntu2404-riscv64-4 seems to be removed or offline (java.lang.InterruptedException); will wait for 5 min 0 sec for it to come back online [2025-12-06T20:09:25.105Z] test-rise-ubuntu2404-riscv64-4 is back online [2025-12-06T20:09:53.092Z] test-rise-ubuntu2404-riscv64-4 seems to be removed or offline (java.lang.InterruptedException); will wait for 5 min 0 sec for it to come back online [2025-12-06T20:20:30.520Z] test-rise-ubuntu2404-riscv64-4 has been removed or offline for 5 min 0 sec; assuming it is not coming back, and terminating shell step [Pipeline] sh [Pipeline] } Creating placeholder flownodes because failed loading originals. ERROR: Cannot resume build because FlowNode 118 for FlowHead 1 could not be loaded. This is expected to happen when using the PERFORMANCE_OPTIMIZED durability setting and Jenkins is not shut down cleanly. Consider investigating to understand if Jenkins was not shut down cleanly or switching to the MAX_SURVIVABILITY durability setting which should prevent this issue in most cases. Finished: FAILURE