[2025-01-11T08:51:04.297Z] Running test renaissance-movie-lens_0 ... [2025-01-11T08:51:04.297Z] =============================================== [2025-01-11T08:51:04.297Z] renaissance-movie-lens_0 Start Time: Sat Jan 11 08:51:02 2025 Epoch Time (ms): 1736585462571 [2025-01-11T08:51:04.297Z] variation: NoOptions [2025-01-11T08:51:04.297Z] JVM_OPTIONS: [2025-01-11T08:51:04.297Z] { \ [2025-01-11T08:51:04.297Z] echo ""; echo "TEST SETUP:"; \ [2025-01-11T08:51:04.297Z] echo "Nothing to be done for setup."; \ [2025-01-11T08:51:04.297Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17365824671824/renaissance-movie-lens_0"; \ [2025-01-11T08:51:04.297Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17365824671824/renaissance-movie-lens_0"; \ [2025-01-11T08:51:04.297Z] echo ""; echo "TESTING:"; \ [2025-01-11T08:51:04.297Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17365824671824/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-01-11T08:51:04.297Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17365824671824/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-01-11T08:51:04.297Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-01-11T08:51:04.297Z] echo "Nothing to be done for teardown."; \ [2025-01-11T08:51:04.297Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_1/aqa-tests/TKG/../TKG/output_17365824671824/TestTargetResult"; [2025-01-11T08:51:04.297Z] [2025-01-11T08:51:04.297Z] TEST SETUP: [2025-01-11T08:51:04.297Z] Nothing to be done for setup. [2025-01-11T08:51:04.297Z] [2025-01-11T08:51:04.297Z] TESTING: [2025-01-11T08:51:14.324Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2025-01-11T08:51:23.985Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-01-11T08:51:41.578Z] Got 100004 ratings from 671 users on 9066 movies. [2025-01-11T08:51:41.578Z] Training: 60056, validation: 20285, test: 19854 [2025-01-11T08:51:41.578Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-01-11T08:51:41.578Z] GC before operation: completed in 321.611 ms, heap usage 73.279 MB -> 39.076 MB. [2025-01-11T08:52:13.585Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:52:26.157Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:52:40.397Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:52:52.441Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:53:00.098Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:53:06.719Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T08:53:13.781Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T08:53:19.028Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T08:53:21.159Z] 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. [2025-01-11T08:53:21.159Z] The best model improves the baseline by 14.52%. [2025-01-11T08:53:22.218Z] Movies recommended for you: [2025-01-11T08:53:22.218Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T08:53:22.218Z] There is no way to check that no silent failure occurred. [2025-01-11T08:53:22.218Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (100024.818 ms) ====== [2025-01-11T08:53:22.218Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-01-11T08:53:22.218Z] GC before operation: completed in 638.703 ms, heap usage 312.481 MB -> 51.278 MB. [2025-01-11T08:53:34.018Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:53:45.222Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:53:54.687Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:54:04.091Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:54:10.271Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:54:15.712Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T08:54:20.863Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T08:54:26.055Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T08:54:27.789Z] 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. [2025-01-11T08:54:27.789Z] The best model improves the baseline by 14.52%. [2025-01-11T08:54:27.789Z] Movies recommended for you: [2025-01-11T08:54:27.789Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T08:54:27.789Z] There is no way to check that no silent failure occurred. [2025-01-11T08:54:27.789Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (65483.974 ms) ====== [2025-01-11T08:54:27.789Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-01-11T08:54:28.663Z] GC before operation: completed in 721.325 ms, heap usage 502.116 MB -> 55.109 MB. [2025-01-11T08:54:38.125Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:54:49.067Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:54:56.782Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:55:06.098Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:55:13.362Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:55:18.668Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T08:55:23.483Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T08:55:28.657Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T08:55:30.085Z] 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. [2025-01-11T08:55:30.085Z] The best model improves the baseline by 14.52%. [2025-01-11T08:55:31.000Z] Movies recommended for you: [2025-01-11T08:55:31.000Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T08:55:31.000Z] There is no way to check that no silent failure occurred. [2025-01-11T08:55:31.000Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (61846.562 ms) ====== [2025-01-11T08:55:31.000Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-01-11T08:55:31.000Z] GC before operation: completed in 712.418 ms, heap usage 691.870 MB -> 55.584 MB. [2025-01-11T08:55:40.447Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:55:48.652Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:55:57.980Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:56:05.912Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:56:10.811Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:56:17.758Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T08:56:22.118Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T08:56:27.417Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T08:56:29.202Z] 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. [2025-01-11T08:56:29.202Z] The best model improves the baseline by 14.52%. [2025-01-11T08:56:29.950Z] Movies recommended for you: [2025-01-11T08:56:29.950Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T08:56:29.950Z] There is no way to check that no silent failure occurred. [2025-01-11T08:56:29.950Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (57721.854 ms) ====== [2025-01-11T08:56:29.950Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-01-11T08:56:29.950Z] GC before operation: completed in 728.757 ms, heap usage 500.644 MB -> 55.714 MB. [2025-01-11T08:56:39.285Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:56:47.073Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:56:55.312Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:57:03.163Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:57:08.743Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:57:13.827Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T08:57:19.215Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T08:57:24.334Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T08:57:25.036Z] 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. [2025-01-11T08:57:25.036Z] The best model improves the baseline by 14.52%. [2025-01-11T08:57:25.976Z] Movies recommended for you: [2025-01-11T08:57:25.976Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T08:57:25.976Z] There is no way to check that no silent failure occurred. [2025-01-11T08:57:25.976Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (55923.112 ms) ====== [2025-01-11T08:57:25.976Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-01-11T08:57:25.976Z] GC before operation: completed in 684.093 ms, heap usage 217.442 MB -> 52.445 MB. [2025-01-11T08:57:34.188Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:57:42.216Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:57:51.107Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:57:57.878Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:58:04.594Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:58:10.094Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T08:58:14.942Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T08:58:20.290Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T08:58:21.272Z] 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. [2025-01-11T08:58:21.273Z] The best model improves the baseline by 14.52%. [2025-01-11T08:58:22.229Z] Movies recommended for you: [2025-01-11T08:58:22.229Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T08:58:22.229Z] There is no way to check that no silent failure occurred. [2025-01-11T08:58:22.229Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (55554.147 ms) ====== [2025-01-11T08:58:22.229Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-01-11T08:58:22.229Z] GC before operation: completed in 697.349 ms, heap usage 306.727 MB -> 52.502 MB. [2025-01-11T08:58:31.190Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:58:38.870Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:58:46.402Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:58:55.194Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:58:59.742Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:59:04.245Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T08:59:09.417Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T08:59:14.567Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T08:59:14.567Z] 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. [2025-01-11T08:59:14.567Z] The best model improves the baseline by 14.52%. [2025-01-11T08:59:15.871Z] Movies recommended for you: [2025-01-11T08:59:15.871Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T08:59:15.871Z] There is no way to check that no silent failure occurred. [2025-01-11T08:59:15.871Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (52784.558 ms) ====== [2025-01-11T08:59:15.871Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-01-11T08:59:15.871Z] GC before operation: completed in 729.326 ms, heap usage 429.539 MB -> 56.030 MB. [2025-01-11T08:59:24.828Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T08:59:32.378Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T08:59:41.722Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T08:59:49.225Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T08:59:53.089Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T08:59:58.107Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:00:03.338Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:00:07.870Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:00:08.605Z] 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. [2025-01-11T09:00:08.605Z] The best model improves the baseline by 14.52%. [2025-01-11T09:00:09.607Z] Movies recommended for you: [2025-01-11T09:00:09.607Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:00:09.607Z] There is no way to check that no silent failure occurred. [2025-01-11T09:00:09.607Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (53201.141 ms) ====== [2025-01-11T09:00:09.607Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-01-11T09:00:09.607Z] GC before operation: completed in 704.476 ms, heap usage 352.974 MB -> 53.002 MB. [2025-01-11T09:00:18.727Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:00:25.247Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:00:32.892Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:00:40.664Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:00:44.938Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:00:50.023Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:00:55.222Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:01:00.514Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:01:01.182Z] 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. [2025-01-11T09:01:01.182Z] The best model improves the baseline by 14.52%. [2025-01-11T09:01:02.366Z] Movies recommended for you: [2025-01-11T09:01:02.366Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:01:02.366Z] There is no way to check that no silent failure occurred. [2025-01-11T09:01:02.366Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (51814.336 ms) ====== [2025-01-11T09:01:02.366Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-01-11T09:01:02.366Z] GC before operation: completed in 717.395 ms, heap usage 286.243 MB -> 52.853 MB. [2025-01-11T09:01:11.246Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:01:19.019Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:01:27.003Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:01:33.527Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:01:38.586Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:01:43.084Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:01:47.872Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:01:52.509Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:01:53.222Z] 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. [2025-01-11T09:01:53.945Z] The best model improves the baseline by 14.52%. [2025-01-11T09:01:53.945Z] Movies recommended for you: [2025-01-11T09:01:53.945Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:01:53.945Z] There is no way to check that no silent failure occurred. [2025-01-11T09:01:53.945Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (51569.354 ms) ====== [2025-01-11T09:01:53.945Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-01-11T09:01:54.763Z] GC before operation: completed in 723.518 ms, heap usage 462.736 MB -> 56.235 MB. [2025-01-11T09:02:03.653Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:02:10.445Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:02:17.693Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:02:27.026Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:02:32.653Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:02:37.036Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:02:41.000Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:02:45.559Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:02:47.082Z] 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. [2025-01-11T09:02:47.082Z] The best model improves the baseline by 14.52%. [2025-01-11T09:02:47.082Z] Movies recommended for you: [2025-01-11T09:02:47.082Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:02:47.082Z] There is no way to check that no silent failure occurred. [2025-01-11T09:02:47.083Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (52652.743 ms) ====== [2025-01-11T09:02:47.083Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-01-11T09:02:47.989Z] GC before operation: completed in 726.280 ms, heap usage 391.724 MB -> 52.765 MB. [2025-01-11T09:02:56.812Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:03:04.087Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:03:11.505Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:03:18.986Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:03:23.337Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:03:27.400Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:03:32.599Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:03:37.814Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:03:38.424Z] 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. [2025-01-11T09:03:38.424Z] The best model improves the baseline by 14.52%. [2025-01-11T09:03:39.289Z] Movies recommended for you: [2025-01-11T09:03:39.289Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:03:39.289Z] There is no way to check that no silent failure occurred. [2025-01-11T09:03:39.289Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (50688.020 ms) ====== [2025-01-11T09:03:39.289Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-01-11T09:03:39.289Z] GC before operation: completed in 725.348 ms, heap usage 352.198 MB -> 52.954 MB. [2025-01-11T09:03:47.399Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:03:56.822Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:04:05.291Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:04:11.548Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:04:17.064Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:04:22.041Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:04:27.149Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:04:31.364Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:04:32.402Z] 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. [2025-01-11T09:04:32.402Z] The best model improves the baseline by 14.52%. [2025-01-11T09:04:33.371Z] Movies recommended for you: [2025-01-11T09:04:33.371Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:04:33.371Z] There is no way to check that no silent failure occurred. [2025-01-11T09:04:33.371Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (53410.777 ms) ====== [2025-01-11T09:04:33.371Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-01-11T09:04:33.371Z] GC before operation: completed in 705.164 ms, heap usage 349.506 MB -> 53.128 MB. [2025-01-11T09:04:41.369Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:04:49.182Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:04:57.157Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:05:05.021Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:05:09.030Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:05:14.478Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:05:20.186Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:05:25.146Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:05:26.942Z] 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. [2025-01-11T09:05:26.942Z] The best model improves the baseline by 14.52%. [2025-01-11T09:05:26.942Z] Movies recommended for you: [2025-01-11T09:05:26.942Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:05:26.942Z] There is no way to check that no silent failure occurred. [2025-01-11T09:05:26.942Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (53184.151 ms) ====== [2025-01-11T09:05:26.942Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-01-11T09:05:28.500Z] GC before operation: completed in 738.173 ms, heap usage 166.354 MB -> 52.736 MB. [2025-01-11T09:05:35.342Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:05:42.763Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:05:50.066Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:05:57.773Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:06:02.013Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:06:07.213Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:06:11.672Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:06:16.741Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:06:17.479Z] 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. [2025-01-11T09:06:17.479Z] The best model improves the baseline by 14.52%. [2025-01-11T09:06:17.479Z] Movies recommended for you: [2025-01-11T09:06:17.479Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:06:17.479Z] There is no way to check that no silent failure occurred. [2025-01-11T09:06:17.479Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (50122.666 ms) ====== [2025-01-11T09:06:17.479Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-01-11T09:06:18.491Z] GC before operation: completed in 770.974 ms, heap usage 218.132 MB -> 52.950 MB. [2025-01-11T09:06:27.578Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:06:34.598Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:06:42.014Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:06:50.654Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:06:56.010Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:07:01.265Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:07:06.413Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:07:10.824Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:07:11.478Z] 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. [2025-01-11T09:07:12.279Z] The best model improves the baseline by 14.52%. [2025-01-11T09:07:12.279Z] Movies recommended for you: [2025-01-11T09:07:12.279Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:07:12.279Z] There is no way to check that no silent failure occurred. [2025-01-11T09:07:12.279Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (53807.037 ms) ====== [2025-01-11T09:07:12.279Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-01-11T09:07:13.084Z] GC before operation: completed in 782.165 ms, heap usage 530.962 MB -> 56.393 MB. [2025-01-11T09:07:21.476Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:07:29.722Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:07:36.503Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:07:43.795Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:07:48.023Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:07:53.202Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:07:57.293Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:08:02.433Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:08:03.029Z] 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. [2025-01-11T09:08:03.029Z] The best model improves the baseline by 14.52%. [2025-01-11T09:08:03.974Z] Movies recommended for you: [2025-01-11T09:08:03.974Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:08:03.974Z] There is no way to check that no silent failure occurred. [2025-01-11T09:08:03.974Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (50509.204 ms) ====== [2025-01-11T09:08:03.974Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-01-11T09:08:03.974Z] GC before operation: completed in 744.808 ms, heap usage 209.044 MB -> 52.804 MB. [2025-01-11T09:08:13.498Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:08:21.941Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:08:29.670Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:08:36.878Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:08:41.157Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:08:45.355Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:08:50.549Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:08:54.778Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:08:55.652Z] 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. [2025-01-11T09:08:55.652Z] The best model improves the baseline by 14.52%. [2025-01-11T09:08:56.678Z] Movies recommended for you: [2025-01-11T09:08:56.678Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:08:56.678Z] There is no way to check that no silent failure occurred. [2025-01-11T09:08:56.678Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (52035.331 ms) ====== [2025-01-11T09:08:56.678Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2025-01-11T09:08:57.386Z] GC before operation: completed in 749.812 ms, heap usage 269.334 MB -> 53.003 MB. [2025-01-11T09:09:04.952Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:09:12.341Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:09:19.997Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:09:26.580Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:09:31.650Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:09:35.598Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:09:42.190Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:09:47.542Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:09:48.246Z] 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. [2025-01-11T09:09:48.246Z] The best model improves the baseline by 14.52%. [2025-01-11T09:09:49.232Z] Movies recommended for you: [2025-01-11T09:09:49.232Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:09:49.232Z] There is no way to check that no silent failure occurred. [2025-01-11T09:09:49.232Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (51679.718 ms) ====== [2025-01-11T09:09:49.232Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2025-01-11T09:09:49.232Z] GC before operation: completed in 780.633 ms, heap usage 492.269 MB -> 56.472 MB. [2025-01-11T09:09:58.197Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-01-11T09:10:04.499Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-01-11T09:10:12.680Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-01-11T09:10:20.500Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-01-11T09:10:23.842Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-01-11T09:10:28.259Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-01-11T09:10:33.159Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-01-11T09:10:38.163Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-01-11T09:10:39.482Z] 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. [2025-01-11T09:10:39.483Z] The best model improves the baseline by 14.52%. [2025-01-11T09:10:39.483Z] Movies recommended for you: [2025-01-11T09:10:39.483Z] WARNING: This benchmark provides no result that can be validated. [2025-01-11T09:10:39.483Z] There is no way to check that no silent failure occurred. [2025-01-11T09:10:39.483Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (50103.326 ms) ====== [2025-01-11T09:10:43.452Z] ----------------------------------- [2025-01-11T09:10:43.452Z] renaissance-movie-lens_0_PASSED [2025-01-11T09:10:43.452Z] ----------------------------------- [2025-01-11T09:10:43.452Z] [2025-01-11T09:10:43.452Z] TEST TEARDOWN: [2025-01-11T09:10:43.452Z] Nothing to be done for teardown. [2025-01-11T09:10:43.452Z] renaissance-movie-lens_0 Finish Time: Sat Jan 11 09:10:42 2025 Epoch Time (ms): 1736586642751