[2024-12-05T00:14:38.485Z] Running test renaissance-movie-lens_0 ... [2024-12-05T00:14:38.485Z] =============================================== [2024-12-05T00:14:38.485Z] renaissance-movie-lens_0 Start Time: Thu Dec 5 00:14:37 2024 Epoch Time (ms): 1733357677916 [2024-12-05T00:14:38.485Z] variation: NoOptions [2024-12-05T00:14:38.485Z] JVM_OPTIONS: [2024-12-05T00:14:38.485Z] { \ [2024-12-05T00:14:38.485Z] echo ""; echo "TEST SETUP:"; \ [2024-12-05T00:14:38.485Z] echo "Nothing to be done for setup."; \ [2024-12-05T00:14:38.485Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17333520776148/renaissance-movie-lens_0"; \ [2024-12-05T00:14:38.485Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17333520776148/renaissance-movie-lens_0"; \ [2024-12-05T00:14:38.485Z] echo ""; echo "TESTING:"; \ [2024-12-05T00:14:38.485Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-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_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17333520776148/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-12-05T00:14:38.485Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17333520776148/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-12-05T00:14:38.485Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-12-05T00:14:38.485Z] echo "Nothing to be done for teardown."; \ [2024-12-05T00:14:38.485Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17333520776148/TestTargetResult"; [2024-12-05T00:14:38.485Z] [2024-12-05T00:14:38.485Z] TEST SETUP: [2024-12-05T00:14:38.486Z] Nothing to be done for setup. [2024-12-05T00:14:38.486Z] [2024-12-05T00:14:38.486Z] TESTING: [2024-12-05T00:14:52.671Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-12-05T00:15:09.177Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-12-05T00:15:45.258Z] Got 100004 ratings from 671 users on 9066 movies. [2024-12-05T00:15:45.258Z] Training: 60056, validation: 20285, test: 19854 [2024-12-05T00:15:45.258Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-12-05T00:15:45.258Z] GC before operation: completed in 530.303 ms, heap usage 76.103 MB -> 37.137 MB. [2024-12-05T00:16:43.738Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:17:26.115Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:18:02.720Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:18:29.135Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:18:45.721Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:19:02.220Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:19:18.668Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:19:32.928Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:19:36.678Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:19:37.495Z] The best model improves the baseline by 14.52%. [2024-12-05T00:19:39.255Z] Movies recommended for you: [2024-12-05T00:19:39.255Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:19:39.255Z] There is no way to check that no silent failure occurred. [2024-12-05T00:19:39.255Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (234816.613 ms) ====== [2024-12-05T00:19:39.255Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-12-05T00:19:40.113Z] GC before operation: completed in 1024.573 ms, heap usage 305.309 MB -> 49.398 MB. [2024-12-05T00:20:06.280Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:20:36.792Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:21:03.571Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:21:29.086Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:21:41.216Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:21:55.238Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:22:08.161Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:22:22.235Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:22:23.832Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:22:23.832Z] The best model improves the baseline by 14.52%. [2024-12-05T00:22:24.607Z] Movies recommended for you: [2024-12-05T00:22:24.607Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:22:24.607Z] There is no way to check that no silent failure occurred. [2024-12-05T00:22:24.607Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (164751.509 ms) ====== [2024-12-05T00:22:24.607Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-12-05T00:22:25.421Z] GC before operation: completed in 653.674 ms, heap usage 317.564 MB -> 49.764 MB. [2024-12-05T00:22:51.606Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:23:11.158Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:23:37.769Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:23:57.211Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:24:11.426Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:24:21.622Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:24:35.667Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:24:46.721Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:24:48.413Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:24:48.413Z] The best model improves the baseline by 14.52%. [2024-12-05T00:24:49.229Z] Movies recommended for you: [2024-12-05T00:24:49.229Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:24:49.229Z] There is no way to check that no silent failure occurred. [2024-12-05T00:24:49.229Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (143583.016 ms) ====== [2024-12-05T00:24:49.229Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-12-05T00:24:49.229Z] GC before operation: completed in 564.770 ms, heap usage 231.121 MB -> 50.063 MB. [2024-12-05T00:25:08.645Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:25:27.929Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:25:47.290Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:26:04.422Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:26:16.212Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:26:27.949Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:26:39.912Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:26:50.142Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:26:51.832Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:26:51.832Z] The best model improves the baseline by 14.52%. [2024-12-05T00:26:52.674Z] Movies recommended for you: [2024-12-05T00:26:52.674Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:26:52.674Z] There is no way to check that no silent failure occurred. [2024-12-05T00:26:52.674Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (123206.009 ms) ====== [2024-12-05T00:26:52.674Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-12-05T00:26:53.473Z] GC before operation: completed in 662.270 ms, heap usage 84.661 MB -> 51.642 MB. [2024-12-05T00:27:13.105Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:27:32.501Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:27:54.713Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:28:11.322Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:28:25.085Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:28:35.686Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:28:47.828Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:28:58.045Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:29:00.654Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:29:00.654Z] The best model improves the baseline by 14.52%. [2024-12-05T00:29:01.505Z] Movies recommended for you: [2024-12-05T00:29:01.505Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:29:01.505Z] There is no way to check that no silent failure occurred. [2024-12-05T00:29:01.505Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (127768.085 ms) ====== [2024-12-05T00:29:01.505Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-12-05T00:29:02.310Z] GC before operation: completed in 789.659 ms, heap usage 424.850 MB -> 53.828 MB. [2024-12-05T00:29:24.812Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:29:44.871Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:30:07.264Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:30:26.562Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:30:40.716Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:30:52.924Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:31:03.924Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:31:16.182Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:31:17.917Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:31:18.766Z] The best model improves the baseline by 14.52%. [2024-12-05T00:31:18.766Z] Movies recommended for you: [2024-12-05T00:31:18.766Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:31:18.766Z] There is no way to check that no silent failure occurred. [2024-12-05T00:31:18.766Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (137182.980 ms) ====== [2024-12-05T00:31:18.766Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-12-05T00:31:19.565Z] GC before operation: completed in 627.420 ms, heap usage 195.004 MB -> 50.374 MB. [2024-12-05T00:31:39.099Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:31:58.589Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:32:18.580Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:32:35.136Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:32:47.094Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:32:57.471Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:33:09.787Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:33:20.169Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:33:21.890Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:33:21.890Z] The best model improves the baseline by 14.52%. [2024-12-05T00:33:22.753Z] Movies recommended for you: [2024-12-05T00:33:22.753Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:33:22.753Z] There is no way to check that no silent failure occurred. [2024-12-05T00:33:22.753Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (122745.990 ms) ====== [2024-12-05T00:33:22.753Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-12-05T00:33:22.753Z] GC before operation: completed in 551.443 ms, heap usage 191.916 MB -> 50.578 MB. [2024-12-05T00:33:42.704Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:34:02.261Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:34:24.711Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:34:42.886Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:34:51.944Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:34:59.141Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:35:07.847Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:35:20.025Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:35:21.662Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:35:21.662Z] The best model improves the baseline by 14.52%. [2024-12-05T00:35:22.450Z] Movies recommended for you: [2024-12-05T00:35:22.450Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:35:22.450Z] There is no way to check that no silent failure occurred. [2024-12-05T00:35:22.450Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (119505.335 ms) ====== [2024-12-05T00:35:22.450Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-12-05T00:35:23.233Z] GC before operation: completed in 631.884 ms, heap usage 62.167 MB -> 54.243 MB. [2024-12-05T00:35:42.560Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:36:02.063Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:36:21.126Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:36:32.967Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:36:44.754Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:36:56.432Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:37:05.408Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:37:13.836Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:37:14.637Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:37:14.637Z] The best model improves the baseline by 14.52%. [2024-12-05T00:37:15.429Z] Movies recommended for you: [2024-12-05T00:37:15.429Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:37:15.429Z] There is no way to check that no silent failure occurred. [2024-12-05T00:37:15.429Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (112211.300 ms) ====== [2024-12-05T00:37:15.429Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-12-05T00:37:16.215Z] GC before operation: completed in 478.760 ms, heap usage 298.597 MB -> 50.809 MB. [2024-12-05T00:37:32.432Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:37:46.312Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:38:02.707Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:38:22.584Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:38:31.330Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:38:41.510Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:38:51.468Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:39:01.632Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:39:02.441Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:39:03.271Z] The best model improves the baseline by 14.52%. [2024-12-05T00:39:03.271Z] Movies recommended for you: [2024-12-05T00:39:03.271Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:39:03.271Z] There is no way to check that no silent failure occurred. [2024-12-05T00:39:03.271Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (107625.271 ms) ====== [2024-12-05T00:39:03.271Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-12-05T00:39:04.061Z] GC before operation: completed in 583.979 ms, heap usage 212.491 MB -> 50.852 MB. [2024-12-05T00:39:20.646Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:39:34.798Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:39:51.322Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:40:07.871Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:40:19.711Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:40:28.258Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:40:38.627Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:40:48.796Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:40:49.591Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:40:49.591Z] The best model improves the baseline by 14.52%. [2024-12-05T00:40:50.416Z] Movies recommended for you: [2024-12-05T00:40:50.416Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:40:50.416Z] There is no way to check that no silent failure occurred. [2024-12-05T00:40:50.416Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (105897.922 ms) ====== [2024-12-05T00:40:50.416Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-12-05T00:40:50.416Z] GC before operation: completed in 706.352 ms, heap usage 61.468 MB -> 51.620 MB. [2024-12-05T00:41:06.830Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:41:20.821Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:41:34.861Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:41:49.508Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:41:56.552Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:42:06.576Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:42:18.329Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:42:26.617Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:42:28.265Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:42:28.265Z] The best model improves the baseline by 14.52%. [2024-12-05T00:42:28.265Z] Movies recommended for you: [2024-12-05T00:42:28.265Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:42:28.265Z] There is no way to check that no silent failure occurred. [2024-12-05T00:42:28.265Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (97854.254 ms) ====== [2024-12-05T00:42:28.265Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-12-05T00:42:29.130Z] GC before operation: completed in 543.162 ms, heap usage 424.094 MB -> 51.954 MB. [2024-12-05T00:42:45.382Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:42:59.983Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:43:16.356Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:43:30.452Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:43:40.636Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:43:50.762Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:44:01.400Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:44:11.478Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:44:13.107Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:44:13.107Z] The best model improves the baseline by 14.52%. [2024-12-05T00:44:13.881Z] Movies recommended for you: [2024-12-05T00:44:13.881Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:44:13.881Z] There is no way to check that no silent failure occurred. [2024-12-05T00:44:13.881Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (104447.923 ms) ====== [2024-12-05T00:44:13.881Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-12-05T00:44:13.881Z] GC before operation: completed in 679.816 ms, heap usage 422.261 MB -> 52.004 MB. [2024-12-05T00:44:30.275Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:44:46.863Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:45:06.525Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:45:21.642Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:45:31.872Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:45:42.248Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:45:54.625Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:46:03.740Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:46:04.635Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:46:04.635Z] The best model improves the baseline by 14.52%. [2024-12-05T00:46:05.506Z] Movies recommended for you: [2024-12-05T00:46:05.506Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:46:05.506Z] There is no way to check that no silent failure occurred. [2024-12-05T00:46:05.506Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (111147.735 ms) ====== [2024-12-05T00:46:05.506Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-12-05T00:46:06.372Z] GC before operation: completed in 545.725 ms, heap usage 273.438 MB -> 50.938 MB. [2024-12-05T00:46:23.734Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:46:40.793Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:46:58.098Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:47:10.683Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:47:19.651Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:47:28.813Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:47:38.090Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:47:47.339Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:47:50.181Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:47:50.181Z] The best model improves the baseline by 14.52%. [2024-12-05T00:47:50.181Z] Movies recommended for you: [2024-12-05T00:47:50.181Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:47:50.181Z] There is no way to check that no silent failure occurred. [2024-12-05T00:47:50.181Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (104220.823 ms) ====== [2024-12-05T00:47:50.181Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-12-05T00:47:51.048Z] GC before operation: completed in 546.925 ms, heap usage 212.394 MB -> 47.982 MB. [2024-12-05T00:48:08.250Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:48:25.529Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:48:40.377Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:48:58.119Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:49:07.231Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:49:18.130Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:49:33.149Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:49:41.988Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:49:43.786Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:49:43.786Z] The best model improves the baseline by 14.52%. [2024-12-05T00:49:44.709Z] Movies recommended for you: [2024-12-05T00:49:44.709Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:49:44.709Z] There is no way to check that no silent failure occurred. [2024-12-05T00:49:44.709Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (114049.866 ms) ====== [2024-12-05T00:49:44.709Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-12-05T00:49:45.571Z] GC before operation: completed in 680.320 ms, heap usage 94.241 MB -> 52.528 MB. [2024-12-05T00:50:03.543Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:50:21.023Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:50:41.241Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:50:56.222Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:51:08.703Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:51:21.839Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:51:36.469Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:51:45.685Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:51:48.479Z] 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. [2024-12-05T00:51:48.479Z] The best model improves the baseline by 14.52%. [2024-12-05T00:51:49.339Z] Movies recommended for you: [2024-12-05T00:51:49.339Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:51:49.339Z] There is no way to check that no silent failure occurred. [2024-12-05T00:51:49.339Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (123401.640 ms) ====== [2024-12-05T00:51:49.339Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-12-05T00:51:49.339Z] GC before operation: completed in 544.324 ms, heap usage 202.712 MB -> 48.512 MB. [2024-12-05T00:52:09.302Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:52:29.502Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:52:49.479Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:53:09.649Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:53:18.841Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:53:31.596Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:53:47.215Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:54:01.266Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:54:02.126Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:54:02.994Z] The best model improves the baseline by 14.52%. [2024-12-05T00:54:03.857Z] Movies recommended for you: [2024-12-05T00:54:03.857Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:54:03.857Z] There is no way to check that no silent failure occurred. [2024-12-05T00:54:03.857Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (134151.867 ms) ====== [2024-12-05T00:54:03.857Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-12-05T00:54:04.692Z] GC before operation: completed in 778.424 ms, heap usage 181.730 MB -> 48.561 MB. [2024-12-05T00:54:27.076Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:54:43.464Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:55:06.326Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:55:22.874Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:55:34.892Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:55:46.874Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:55:58.677Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:56:14.837Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:56:14.837Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:56:15.926Z] The best model improves the baseline by 14.52%. [2024-12-05T00:56:15.926Z] Movies recommended for you: [2024-12-05T00:56:15.926Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:56:15.926Z] There is no way to check that no silent failure occurred. [2024-12-05T00:56:15.926Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (131724.029 ms) ====== [2024-12-05T00:56:15.926Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-12-05T00:56:16.715Z] GC before operation: completed in 702.306 ms, heap usage 163.303 MB -> 48.711 MB. [2024-12-05T00:56:39.053Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T00:56:58.003Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T00:57:17.596Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T00:57:36.845Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T00:57:50.688Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T00:58:00.671Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T00:58:12.517Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T00:58:22.372Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T00:58:24.891Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-12-05T00:58:25.667Z] The best model improves the baseline by 14.52%. [2024-12-05T00:58:26.455Z] Movies recommended for you: [2024-12-05T00:58:26.455Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T00:58:26.455Z] There is no way to check that no silent failure occurred. [2024-12-05T00:58:26.455Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (129277.701 ms) ====== [2024-12-05T00:58:31.094Z] ----------------------------------- [2024-12-05T00:58:31.094Z] renaissance-movie-lens_0_PASSED [2024-12-05T00:58:31.094Z] ----------------------------------- [2024-12-05T00:58:31.094Z] [2024-12-05T00:58:31.094Z] TEST TEARDOWN: [2024-12-05T00:58:31.094Z] Nothing to be done for teardown. [2024-12-05T00:58:31.094Z] renaissance-movie-lens_0 Finish Time: Thu Dec 5 00:58:30 2024 Epoch Time (ms): 1733360310215