[2025-06-08T02:00:08.824Z] Running test renaissance-movie-lens_0 ... [2025-06-08T02:00:08.824Z] =============================================== [2025-06-08T02:00:08.824Z] renaissance-movie-lens_0 Start Time: Sun Jun 8 02:00:08 2025 Epoch Time (ms): 1749348008422 [2025-06-08T02:00:08.824Z] variation: NoOptions [2025-06-08T02:00:08.824Z] JVM_OPTIONS: [2025-06-08T02:00:08.824Z] { \ [2025-06-08T02:00:08.824Z] echo ""; echo "TEST SETUP:"; \ [2025-06-08T02:00:08.824Z] echo "Nothing to be done for setup."; \ [2025-06-08T02:00:08.824Z] mkdir -p "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/../TKG/output_17493435959871/renaissance-movie-lens_0"; \ [2025-06-08T02:00:08.824Z] cd "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/../TKG/output_17493435959871/renaissance-movie-lens_0"; \ [2025-06-08T02:00:08.824Z] echo ""; echo "TESTING:"; \ [2025-06-08T02:00:08.824Z] "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/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/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/../TKG/output_17493435959871/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-06-08T02:00:08.824Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/../TKG/output_17493435959871/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-06-08T02:00:08.824Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-06-08T02:00:08.824Z] echo "Nothing to be done for teardown."; \ [2025-06-08T02:00:08.824Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/../TKG/output_17493435959871/TestTargetResult"; [2025-06-08T02:00:08.824Z] [2025-06-08T02:00:08.824Z] TEST SETUP: [2025-06-08T02:00:08.824Z] Nothing to be done for setup. [2025-06-08T02:00:08.824Z] [2025-06-08T02:00:08.824Z] TESTING: [2025-06-08T02:00:11.167Z] WARNING: A terminally deprecated method in sun.misc.Unsafe has been called [2025-06-08T02:00:11.167Z] WARNING: sun.misc.Unsafe::objectFieldOffset has been called by scala.runtime.LazyVals$ (file:/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/output_17493435959871/renaissance-movie-lens_0/launcher-020009-8762098582942573293/renaissance-harness_3/lib/scala3-library_3-3.3.4.jar) [2025-06-08T02:00:11.167Z] WARNING: Please consider reporting this to the maintainers of class scala.runtime.LazyVals$ [2025-06-08T02:00:11.167Z] WARNING: sun.misc.Unsafe::objectFieldOffset will be removed in a future release [2025-06-08T02:00:34.201Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-06-08T02:01:07.505Z] 02:01:02.019 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. [2025-06-08T02:01:11.278Z] Got 100004 ratings from 671 users on 9066 movies. [2025-06-08T02:01:13.520Z] Training: 60056, validation: 20285, test: 19854 [2025-06-08T02:01:13.520Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-06-08T02:01:14.247Z] GC before operation: completed in 571.951 ms, heap usage 371.749 MB -> 76.137 MB. [2025-06-08T02:01:42.130Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:01:52.930Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:02:06.048Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:02:16.831Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:02:22.855Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:02:30.099Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:02:37.366Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:02:43.242Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:02:43.569Z] 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. [2025-06-08T02:02:43.569Z] The best model improves the baseline by 14.52%. [2025-06-08T02:02:44.714Z] Top recommended movies for user id 72: [2025-06-08T02:02:44.714Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:02:44.714Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:02:44.714Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:02:44.714Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:02:44.714Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:02:44.714Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (90518.173 ms) ====== [2025-06-08T02:02:44.714Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-06-08T02:02:45.893Z] GC before operation: completed in 1048.733 ms, heap usage 250.038 MB -> 89.673 MB. [2025-06-08T02:02:58.989Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:03:08.035Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:03:16.899Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:03:27.707Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:03:32.752Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:03:38.628Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:03:44.522Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:03:50.414Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:03:51.551Z] 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. [2025-06-08T02:03:51.551Z] The best model improves the baseline by 14.52%. [2025-06-08T02:03:52.259Z] Top recommended movies for user id 72: [2025-06-08T02:03:52.259Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:03:52.259Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:03:52.259Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:03:52.259Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:03:52.259Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:03:52.259Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (66621.176 ms) ====== [2025-06-08T02:03:52.259Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-06-08T02:03:53.428Z] GC before operation: completed in 905.860 ms, heap usage 185.184 MB -> 88.701 MB. [2025-06-08T02:04:04.227Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:04:13.103Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:04:24.065Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:04:32.917Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:04:38.827Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:04:44.717Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:04:50.589Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:04:56.466Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:04:56.466Z] 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. [2025-06-08T02:04:56.794Z] The best model improves the baseline by 14.52%. [2025-06-08T02:04:57.685Z] Top recommended movies for user id 72: [2025-06-08T02:04:57.685Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:04:57.685Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:04:57.685Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:04:57.685Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:04:57.685Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:04:57.685Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (64172.491 ms) ====== [2025-06-08T02:04:57.685Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-06-08T02:04:58.419Z] GC before operation: completed in 793.805 ms, heap usage 395.955 MB -> 89.693 MB. [2025-06-08T02:05:07.277Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:05:16.140Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:05:25.014Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:05:35.801Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:05:40.540Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:05:45.411Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:05:51.295Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:05:57.180Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:05:57.881Z] 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. [2025-06-08T02:05:58.206Z] The best model improves the baseline by 14.52%. [2025-06-08T02:05:58.925Z] Top recommended movies for user id 72: [2025-06-08T02:05:58.925Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:05:58.925Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:05:58.925Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:05:58.925Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:05:58.925Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:05:58.925Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (60578.475 ms) ====== [2025-06-08T02:05:58.925Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-06-08T02:05:59.646Z] GC before operation: completed in 787.556 ms, heap usage 130.758 MB -> 89.709 MB. [2025-06-08T02:06:08.499Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:06:17.370Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:06:26.364Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:06:35.219Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:06:39.954Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:06:45.830Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:06:51.706Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:06:56.456Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:06:57.155Z] 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. [2025-06-08T02:06:57.155Z] The best model improves the baseline by 14.52%. [2025-06-08T02:06:57.867Z] Top recommended movies for user id 72: [2025-06-08T02:06:57.867Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:06:57.867Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:06:57.867Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:06:57.867Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:06:57.867Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:06:57.867Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (58018.035 ms) ====== [2025-06-08T02:06:57.867Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-06-08T02:06:58.635Z] GC before operation: completed in 939.783 ms, heap usage 829.127 MB -> 93.872 MB. [2025-06-08T02:07:09.457Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:07:16.705Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:07:25.550Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:07:32.780Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:07:37.504Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:07:42.525Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:07:47.259Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:07:51.990Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:07:52.319Z] 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. [2025-06-08T02:07:52.645Z] The best model improves the baseline by 14.52%. [2025-06-08T02:07:52.972Z] Top recommended movies for user id 72: [2025-06-08T02:07:52.972Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:07:52.972Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:07:52.972Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:07:52.972Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:07:52.972Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:07:52.972Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (54492.347 ms) ====== [2025-06-08T02:07:52.972Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-06-08T02:07:54.140Z] GC before operation: completed in 869.882 ms, heap usage 1.009 GB -> 94.938 MB. [2025-06-08T02:08:03.017Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:08:11.895Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:08:20.752Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:08:27.997Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:08:33.885Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:08:38.646Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:08:44.525Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:08:49.255Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:08:50.390Z] 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. [2025-06-08T02:08:50.390Z] The best model improves the baseline by 14.52%. [2025-06-08T02:08:51.090Z] Top recommended movies for user id 72: [2025-06-08T02:08:51.090Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:08:51.090Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:08:51.090Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:08:51.090Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:08:51.090Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:08:51.090Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (57123.085 ms) ====== [2025-06-08T02:08:51.090Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-06-08T02:08:51.812Z] GC before operation: completed in 808.200 ms, heap usage 253.064 MB -> 90.008 MB. [2025-06-08T02:09:00.921Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:09:09.776Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:09:18.657Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:09:27.545Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:09:31.301Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:09:37.201Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:09:41.998Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:09:47.870Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:09:48.198Z] 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. [2025-06-08T02:09:48.541Z] The best model improves the baseline by 14.52%. [2025-06-08T02:09:49.251Z] Top recommended movies for user id 72: [2025-06-08T02:09:49.251Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:09:49.251Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:09:49.251Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:09:49.251Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:09:49.251Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:09:49.251Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (57253.133 ms) ====== [2025-06-08T02:09:49.251Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-06-08T02:09:49.971Z] GC before operation: completed in 796.218 ms, heap usage 207.776 MB -> 90.262 MB. [2025-06-08T02:09:58.826Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:10:07.703Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:10:14.955Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:10:23.959Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:10:28.697Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:10:33.429Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:10:39.303Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:10:45.187Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:10:45.187Z] 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. [2025-06-08T02:10:45.187Z] The best model improves the baseline by 14.52%. [2025-06-08T02:10:46.345Z] Top recommended movies for user id 72: [2025-06-08T02:10:46.345Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:10:46.345Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:10:46.345Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:10:46.345Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:10:46.345Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:10:46.345Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (56208.611 ms) ====== [2025-06-08T02:10:46.345Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-06-08T02:10:47.066Z] GC before operation: completed in 841.843 ms, heap usage 622.643 MB -> 93.827 MB. [2025-06-08T02:10:56.042Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:11:04.908Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:11:12.146Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:11:19.402Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:11:24.137Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:11:28.862Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:11:33.591Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:11:38.332Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:11:39.470Z] 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. [2025-06-08T02:11:39.470Z] The best model improves the baseline by 14.52%. [2025-06-08T02:11:40.173Z] Top recommended movies for user id 72: [2025-06-08T02:11:40.173Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:11:40.173Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:11:40.173Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:11:40.173Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:11:40.173Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:11:40.173Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (53151.021 ms) ====== [2025-06-08T02:11:40.173Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-06-08T02:11:40.899Z] GC before operation: completed in 848.643 ms, heap usage 235.315 MB -> 90.259 MB. [2025-06-08T02:11:49.749Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:11:57.002Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:12:04.245Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:12:11.514Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:12:16.327Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:12:21.064Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:12:26.954Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:12:31.682Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:12:32.383Z] 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. [2025-06-08T02:12:32.383Z] The best model improves the baseline by 14.52%. [2025-06-08T02:12:33.091Z] Top recommended movies for user id 72: [2025-06-08T02:12:33.091Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:12:33.091Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:12:33.092Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:12:33.092Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:12:33.092Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:12:33.092Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (52134.060 ms) ====== [2025-06-08T02:12:33.092Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-06-08T02:12:33.815Z] GC before operation: completed in 806.494 ms, heap usage 127.394 MB -> 89.893 MB. [2025-06-08T02:12:42.668Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:12:51.632Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:12:58.864Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:13:07.740Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:13:12.464Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:13:17.198Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:13:23.075Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:13:27.805Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:13:28.585Z] 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. [2025-06-08T02:13:28.585Z] The best model improves the baseline by 14.52%. [2025-06-08T02:13:29.311Z] Top recommended movies for user id 72: [2025-06-08T02:13:29.311Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:13:29.311Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:13:29.311Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:13:29.311Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:13:29.312Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:13:29.312Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (55242.672 ms) ====== [2025-06-08T02:13:29.312Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-06-08T02:13:30.025Z] GC before operation: completed in 830.972 ms, heap usage 192.126 MB -> 90.278 MB. [2025-06-08T02:13:38.887Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:13:47.761Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:13:56.608Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:14:03.867Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:14:08.602Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:14:14.469Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:14:19.200Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:14:25.113Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:14:25.440Z] 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. [2025-06-08T02:14:25.766Z] The best model improves the baseline by 14.52%. [2025-06-08T02:14:26.479Z] Top recommended movies for user id 72: [2025-06-08T02:14:26.479Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:14:26.479Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:14:26.479Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:14:26.479Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:14:26.479Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:14:26.479Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (56256.214 ms) ====== [2025-06-08T02:14:26.479Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-06-08T02:14:27.202Z] GC before operation: completed in 801.177 ms, heap usage 418.243 MB -> 90.642 MB. [2025-06-08T02:14:36.063Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:14:45.006Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:14:52.297Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:15:01.147Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:15:05.881Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:15:11.776Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:15:16.502Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:15:21.405Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:15:22.106Z] 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. [2025-06-08T02:15:22.106Z] The best model improves the baseline by 14.52%. [2025-06-08T02:15:22.812Z] Top recommended movies for user id 72: [2025-06-08T02:15:22.812Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:15:22.812Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:15:22.812Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:15:22.812Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:15:22.812Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:15:22.812Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (55825.294 ms) ====== [2025-06-08T02:15:22.812Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-06-08T02:15:23.965Z] GC before operation: completed in 817.928 ms, heap usage 189.657 MB -> 90.140 MB. [2025-06-08T02:15:32.828Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:15:41.722Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:15:48.961Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:15:57.853Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:16:01.607Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:16:07.505Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:16:12.244Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:16:18.116Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:16:18.116Z] 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. [2025-06-08T02:16:18.116Z] The best model improves the baseline by 14.52%. [2025-06-08T02:16:19.271Z] Top recommended movies for user id 72: [2025-06-08T02:16:19.271Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:16:19.271Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:16:19.271Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:16:19.271Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:16:19.271Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:16:19.271Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (55256.187 ms) ====== [2025-06-08T02:16:19.271Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-06-08T02:16:19.981Z] GC before operation: completed in 812.002 ms, heap usage 303.433 MB -> 90.671 MB. [2025-06-08T02:16:28.835Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:16:37.766Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:16:45.003Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:16:53.874Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:16:57.666Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:17:03.551Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:17:08.355Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:17:13.118Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:17:13.820Z] 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. [2025-06-08T02:17:14.146Z] The best model improves the baseline by 14.52%. [2025-06-08T02:17:14.846Z] Top recommended movies for user id 72: [2025-06-08T02:17:14.846Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:17:14.846Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:17:14.846Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:17:14.846Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:17:14.846Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:17:14.846Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (55010.244 ms) ====== [2025-06-08T02:17:14.846Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-06-08T02:17:15.561Z] GC before operation: completed in 841.350 ms, heap usage 302.958 MB -> 90.390 MB. [2025-06-08T02:17:24.419Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:17:33.274Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:17:42.172Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:17:49.469Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:17:55.354Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:18:00.095Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:18:04.836Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:18:10.722Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:18:10.722Z] 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. [2025-06-08T02:18:11.049Z] The best model improves the baseline by 14.52%. [2025-06-08T02:18:11.749Z] Top recommended movies for user id 72: [2025-06-08T02:18:11.749Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2025-06-08T02:18:11.749Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2025-06-08T02:18:11.749Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2025-06-08T02:18:11.749Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2025-06-08T02:18:11.749Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2025-06-08T02:18:11.749Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (56053.171 ms) ====== [2025-06-08T02:18:11.749Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-06-08T02:18:12.476Z] GC before operation: completed in 845.930 ms, heap usage 278.142 MB -> 90.428 MB. [2025-06-08T02:18:21.444Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-06-08T02:18:28.683Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-06-08T02:18:35.914Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-06-08T02:18:43.150Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-06-08T02:18:47.884Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-06-08T02:18:53.984Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-06-08T02:18:58.739Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-06-08T02:19:03.468Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-06-08T02:19:04.178Z] 02:19:03.875 ERROR [Executor task launch worker for task 3.0 in stage 27781.0 (TID 26970)] org.apache.spark.executor.Executor - Exception in task 3.0 in stage 27781.0 (TID 26970) [2025-06-08T02:19:04.178Z] java.lang.ClassCastException: cannot assign instance of org.apache.spark.util.CollectionAccumulator to field org.apache.spark.executor.TaskMetrics._updatedBlockStatuses of type org.apache.spark.util.CollectionAccumulator in instance of org.apache.spark.executor.TaskMetrics [2025-06-08T02:19:04.178Z] at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:1966) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectStreamClass$FieldReflector.checkObjectFieldValueTypes(ObjectStreamClass.java:1930) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectStreamClass.checkObjFieldValueTypes(ObjectStreamClass.java:1223) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream$FieldValues.defaultCheckFieldValues(ObjectInputStream.java:2559) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2360) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2133) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1620) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readObject(ObjectInputStream.java:487) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readObject(ObjectInputStream.java:445) ~[?:?] [2025-06-08T02:19:04.178Z] at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:87) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:123) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.metrics$lzycompute(Task.scala:76) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.metrics(Task.scala:75) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.run(Task.scala:109) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) [spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1090) [?:?] [2025-06-08T02:19:04.178Z] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:614) [?:?] [2025-06-08T02:19:04.178Z] at java.lang.Thread.run(Thread.java:1474) [?:?] [2025-06-08T02:19:04.178Z] 02:19:03.884 ERROR [Executor task launch worker for task 1.0 in stage 27781.0 (TID 26968)] org.apache.spark.executor.Executor - Exception in task 1.0 in stage 27781.0 (TID 26968) [2025-06-08T02:19:04.178Z] java.lang.ClassCastException: cannot assign instance of org.apache.spark.util.CollectionAccumulator to field org.apache.spark.executor.TaskMetrics._updatedBlockStatuses of type org.apache.spark.util.CollectionAccumulator in instance of org.apache.spark.executor.TaskMetrics [2025-06-08T02:19:04.178Z] at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:1966) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectStreamClass$FieldReflector.checkObjectFieldValueTypes(ObjectStreamClass.java:1930) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectStreamClass.checkObjFieldValueTypes(ObjectStreamClass.java:1223) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream$FieldValues.defaultCheckFieldValues(ObjectInputStream.java:2559) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2360) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2133) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1620) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readObject(ObjectInputStream.java:487) ~[?:?] [2025-06-08T02:19:04.178Z] at java.io.ObjectInputStream.readObject(ObjectInputStream.java:445) ~[?:?] [2025-06-08T02:19:04.178Z] at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:87) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:123) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.metrics$lzycompute(Task.scala:76) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.metrics(Task.scala:75) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.run(Task.scala:109) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) [spark-core_2.13-3.5.3.jar:3.5.3] [2025-06-08T02:19:04.178Z] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1090) [?:?] [2025-06-08T02:19:04.178Z] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:614) [?:?] [2025-06-08T02:19:04.178Z] at java.lang.Thread.run(Thread.java:1474) [?:?] [2025-06-08T02:19:04.178Z] Exception in thread "Executor task launch worker for task 1.0 in stage 27781.0 (TID 26968)" java.lang.ClassCastException: cannot assign instance of org.apache.spark.util.CollectionAccumulator to field org.apache.spark.executor.TaskMetrics._updatedBlockStatuses of type org.apache.spark.util.CollectionAccumulator in instance of org.apache.spark.executor.TaskMetrics [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:1966) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectStreamClass$FieldReflector.checkObjectFieldValueTypes(ObjectStreamClass.java:1930) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectStreamClass.checkObjFieldValueTypes(ObjectStreamClass.java:1223) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectInputStream$FieldValues.defaultCheckFieldValues(ObjectInputStream.java:2559) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2360) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2133) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1620) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectInputStream.readObject(ObjectInputStream.java:487) [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectInputStream.readObject(ObjectInputStream.java:445) [2025-06-08T02:19:04.178Z] at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:87) [2025-06-08T02:19:04.178Z] at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:123) [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.metrics$lzycompute(Task.scala:76) [2025-06-08T02:19:04.178Z] at org.apache.spark.scheduler.Task.metrics(Task.scala:75) [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$collectAccumulatorsAndResetStatusOnFailure$1(Executor.scala:523) [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$collectAccumulatorsAndResetStatusOnFailure$1$adapted(Executor.scala:522) [2025-06-08T02:19:04.178Z] at scala.Option.foreach(Option.scala:437) [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.collectAccumulatorsAndResetStatusOnFailure(Executor.scala:522) [2025-06-08T02:19:04.178Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:800) [2025-06-08T02:19:04.178Z] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1090) [2025-06-08T02:19:04.178Z] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:614) [2025-06-08T02:19:04.178Z] at java.base/java.lang.Thread.run(Thread.java:1474) [2025-06-08T02:19:04.178Z] Exception in thread "Executor task launch worker for task 3.0 in stage 27781.0 (TID 26970)" java.lang.ClassCastException: cannot assign instance of org.apache.spark.util.CollectionAccumulator to field org.apache.spark.executor.TaskMetrics._updatedBlockStatuses of type org.apache.spark.util.CollectionAccumulator in instance of org.apache.spark.executor.TaskMetrics [2025-06-08T02:19:04.178Z] at java.base/java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:1966) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectStreamClass$FieldReflector.checkObjectFieldValueTypes(ObjectStreamClass.java:1930) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectStreamClass.checkObjFieldValueTypes(ObjectStreamClass.java:1223) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectInputStream$FieldValues.defaultCheckFieldValues(ObjectInputStream.java:2559) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2360) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2133) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1620) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectInputStream.readObject(ObjectInputStream.java:487) [2025-06-08T02:19:04.179Z] at java.base/java.io.ObjectInputStream.readObject(ObjectInputStream.java:445) [2025-06-08T02:19:04.179Z] at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:87) [2025-06-08T02:19:04.179Z] at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:123) [2025-06-08T02:19:04.179Z] at org.apache.spark.scheduler.Task.metrics$lzycompute(Task.scala:76) [2025-06-08T02:19:04.179Z] at org.apache.spark.scheduler.Task.metrics(Task.scala:75) [2025-06-08T02:19:04.179Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$collectAccumulatorsAndResetStatusOnFailure$1(Executor.scala:523) [2025-06-08T02:19:04.179Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$collectAccumulatorsAndResetStatusOnFailure$1$adapted(Executor.scala:522) [2025-06-08T02:19:04.179Z] at scala.Option.foreach(Option.scala:437) [2025-06-08T02:19:04.179Z] at org.apache.spark.executor.Executor$TaskRunner.collectAccumulatorsAndResetStatusOnFailure(Executor.scala:522) [2025-06-08T02:19:04.179Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:800) [2025-06-08T02:19:04.179Z] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1090) [2025-06-08T02:19:04.179Z] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:614) [2025-06-08T02:19:04.179Z] at java.base/java.lang.Thread.run(Thread.java:1474) [2025-06-09T00:33:25.236Z] Cancelling nested steps due to timeout [2025-06-09T00:33:25.326Z] Sending interrupt signal to process [2025-06-09T00:33:26.811Z] Terminated [2025-06-09T00:33:27.974Z] make[4]: *** [autoGen.mk:256: renaissance-movie-lens_0] Error 143 [2025-06-09T00:33:27.974Z] make[4]: Leaving directory '/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/perf/renaissance' [2025-06-09T00:33:27.974Z] make[3]: *** [/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/../TKG/settings.mk:362: extended.perf-renaissance] Error 2 [2025-06-09T00:33:27.974Z] make[3]: Leaving directory '/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/perf' [2025-06-09T00:33:27.974Z] make[2]: *** [/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG/../TKG/settings.mk:362: extended.perf-perf] Error 2 [2025-06-09T00:33:27.974Z] make[2]: Leaving directory '/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests' [2025-06-09T00:33:27.974Z] make[1]: *** [settings.mk:362: extended.perf-..] Error 2 [2025-06-09T00:33:27.974Z] make[1]: Leaving directory '/home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux/aqa-tests/TKG' [2025-06-09T00:33:27.974Z] make: *** [makefile:62: _extended.perf] Error 2 [2025-06-09T00:33:28.046Z] script returned exit code 2 [Pipeline] sh [2025-06-09T00:33:29.589Z] + uname [2025-06-09T00:33:29.589Z] + [ Linux = AIX ] [2025-06-09T00:33:29.589Z] + uname [2025-06-09T00:33:29.590Z] + [ Linux = SunOS ] [2025-06-09T00:33:29.590Z] + uname [2025-06-09T00:33:29.590Z] + [ Linux = *BSD ] [2025-06-09T00:33:29.590Z] + MAKE=make [2025-06-09T00:33:29.590Z] + make -f ./aqa-tests/TKG/testEnv.mk testEnvTeardown [2025-06-09T00:33:29.590Z] make: Nothing to be done for 'testEnvTeardown'. [Pipeline] } [2025-06-09T00:33:29.655Z] Xvfb stopping [Pipeline] // wrap [Pipeline] echo [2025-06-09T00:33:29.856Z] Could not find test result, set build result to FAILURE. [Pipeline] } [Pipeline] // stage [Pipeline] stage [Pipeline] { (Post) [Pipeline] step [2025-06-09T00:33:29.969Z] TAP Reports Processing: START [2025-06-09T00:33:29.970Z] Looking for TAP results report in workspace using pattern: aqa-tests/TKG/**/*.tap [2025-06-09T00:33:30.472Z] Did not find any matching files. Setting build result to FAILURE. [Pipeline] echo [2025-06-09T00:33:30.482Z] Saving aqa-tests/testenv/testenv.properties file on jenkins. [Pipeline] archiveArtifacts [2025-06-09T00:33:30.521Z] Archiving artifacts [2025-06-09T00:33:30.623Z] Recording fingerprints [Pipeline] echo [2025-06-09T00:33:30.693Z] Saving aqa-tests/TKG/**/*.tap file on jenkins. [Pipeline] archiveArtifacts [2025-06-09T00:33:30.721Z] Archiving artifacts [Pipeline] sh [2025-06-09T00:33:31.484Z] + tar -cf benchmark_test_output.tar.gz ./aqa-tests/TKG/output_17493435959871 [Pipeline] echo [2025-06-09T00:33:34.508Z] ARTIFACTORY_SERVER is not set. Saving artifacts on jenkins. [Pipeline] archiveArtifacts [2025-06-09T00:33:34.544Z] Archiving artifacts [2025-06-09T00:34:25.241Z] Body did not finish within grace period; terminating with extreme prejudice [Pipeline] } [Pipeline] // stage [Pipeline] echo [2025-06-09T00:34:25.282Z] PROCESSCATCH: Terminating any hung/left over test processes: [Pipeline] sh [2025-06-09T00:34:25.795Z] + aqa-tests/terminateTestProcesses.sh jenkins [2025-06-09T00:34:25.795Z] Unix type machine.. [2025-06-09T00:34:25.795Z] Running on a Linux host [2025-06-09T00:34:25.795Z] Woohoo - no rogue processes detected! [Pipeline] cleanWs [2025-06-09T00:34:25.918Z] [WS-CLEANUP] Deleting project workspace... [2025-06-09T00:34:25.918Z] [WS-CLEANUP] Deferred wipeout is disabled by the job configuration... [2025-06-09T00:34:35.802Z] [WS-CLEANUP] done [Pipeline] sh [2025-06-09T00:34:36.307Z] + find /tmp -name *core* -print -exec rm -f {} ; [2025-06-09T00:34:36.307Z] + true [Pipeline] } [Pipeline] // timeout [Pipeline] echo [2025-06-09T00:34:36.407Z] Exception: org.jenkinsci.plugins.workflow.steps.FlowInterruptedException [Pipeline] timeout [2025-06-09T00:34:36.412Z] Timeout set to expire in 5 min 0 sec [Pipeline] { [Pipeline] } [Pipeline] // timeout [Pipeline] } [Pipeline] // node [Pipeline] } [Pipeline] // stage [Pipeline] echo [2025-06-09T00:34:36.559Z] SETUP_LABEL: ci.role.test [Pipeline] stage [Pipeline] { (Parallel Tests) [Pipeline] parallel [2025-06-09T00:34:36.593Z] No branches to run [Pipeline] // parallel [Pipeline] node [2025-06-09T00:34:36.648Z] Running on test-docker-alpine319-x64-4 in /home/jenkins/workspace/Test_openjdk25_hs_extended.perf_riscv64_linux [Pipeline] { [Pipeline] cleanWs [2025-06-09T00:34:36.940Z] [WS-CLEANUP] Deleting project workspace... [2025-06-09T00:34:36.940Z] [WS-CLEANUP] Deferred wipeout is disabled by the job configuration... [2025-06-09T00:34:37.083Z] [WS-CLEANUP] done [Pipeline] findFiles [Pipeline] cleanWs [2025-06-09T00:34:37.513Z] [WS-CLEANUP] Deleting project workspace... [2025-06-09T00:34:37.513Z] [WS-CLEANUP] Deferred wipeout is disabled by the job configuration... [2025-06-09T00:34:37.647Z] [WS-CLEANUP] done [Pipeline] } [Pipeline] // node [Pipeline] } [Pipeline] // stage [Pipeline] } [Pipeline] // timestamps [Pipeline] End of Pipeline Finished: ABORTED