[2024-12-05T07:53:17.715Z] Running test renaissance-movie-lens_0 ... [2024-12-05T07:53:17.715Z] =============================================== [2024-12-05T07:53:17.715Z] renaissance-movie-lens_0 Start Time: Thu Dec 5 07:53:17 2024 Epoch Time (ms): 1733385197243 [2024-12-05T07:53:17.715Z] variation: NoOptions [2024-12-05T07:53:17.715Z] JVM_OPTIONS: [2024-12-05T07:53:17.715Z] { \ [2024-12-05T07:53:17.715Z] echo ""; echo "TEST SETUP:"; \ [2024-12-05T07:53:17.715Z] echo "Nothing to be done for setup."; \ [2024-12-05T07:53:17.715Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17333831718225/renaissance-movie-lens_0"; \ [2024-12-05T07:53:17.715Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17333831718225/renaissance-movie-lens_0"; \ [2024-12-05T07:53:17.715Z] echo ""; echo "TESTING:"; \ [2024-12-05T07:53:17.715Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_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_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17333831718225/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-12-05T07:53:17.715Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17333831718225/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-12-05T07:53:17.715Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-12-05T07:53:17.715Z] echo "Nothing to be done for teardown."; \ [2024-12-05T07:53:17.715Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17333831718225/TestTargetResult"; [2024-12-05T07:53:17.715Z] [2024-12-05T07:53:17.715Z] TEST SETUP: [2024-12-05T07:53:17.715Z] Nothing to be done for setup. [2024-12-05T07:53:17.715Z] [2024-12-05T07:53:17.715Z] TESTING: [2024-12-05T07:53:24.317Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-12-05T07:53:30.049Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads. [2024-12-05T07:53:43.786Z] Got 100004 ratings from 671 users on 9066 movies. [2024-12-05T07:53:44.487Z] Training: 60056, validation: 20285, test: 19854 [2024-12-05T07:53:44.487Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-12-05T07:53:44.487Z] GC before operation: completed in 213.706 ms, heap usage 48.201 MB -> 37.026 MB. [2024-12-05T07:54:06.456Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T07:54:20.386Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T07:54:34.189Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T07:54:43.809Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T07:54:50.831Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T07:54:58.784Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T07:55:02.905Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T07:55:10.904Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T07:55:11.790Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T07:55:11.790Z] The best model improves the baseline by 14.34%. [2024-12-05T07:55:12.517Z] Movies recommended for you: [2024-12-05T07:55:12.517Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T07:55:12.517Z] There is no way to check that no silent failure occurred. [2024-12-05T07:55:12.517Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (87923.108 ms) ====== [2024-12-05T07:55:12.517Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-12-05T07:55:12.517Z] GC before operation: completed in 314.641 ms, heap usage 194.542 MB -> 52.764 MB. [2024-12-05T07:55:22.411Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T07:55:32.923Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T07:55:44.684Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T07:55:52.597Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T07:55:58.313Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T07:56:02.806Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T07:56:12.325Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T07:56:16.500Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T07:56:17.192Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T07:56:17.192Z] The best model improves the baseline by 14.34%. [2024-12-05T07:56:17.895Z] Movies recommended for you: [2024-12-05T07:56:17.895Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T07:56:17.895Z] There is no way to check that no silent failure occurred. [2024-12-05T07:56:17.895Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (65049.347 ms) ====== [2024-12-05T07:56:17.895Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-12-05T07:56:17.895Z] GC before operation: completed in 466.295 ms, heap usage 279.113 MB -> 49.106 MB. [2024-12-05T07:56:32.563Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T07:56:40.741Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T07:56:50.465Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T07:56:56.941Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T07:57:02.250Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T07:57:06.449Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T07:57:11.779Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T07:57:17.096Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T07:57:17.821Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T07:57:17.821Z] The best model improves the baseline by 14.34%. [2024-12-05T07:57:17.821Z] Movies recommended for you: [2024-12-05T07:57:17.821Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T07:57:17.821Z] There is no way to check that no silent failure occurred. [2024-12-05T07:57:17.821Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (59906.086 ms) ====== [2024-12-05T07:57:17.821Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-12-05T07:57:18.579Z] GC before operation: completed in 441.503 ms, heap usage 117.742 MB -> 49.115 MB. [2024-12-05T07:57:26.446Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T07:57:36.337Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T07:57:45.760Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T07:57:53.614Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T07:58:00.553Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T07:58:05.023Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T07:58:10.720Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T07:58:16.046Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T07:58:16.894Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T07:58:16.894Z] The best model improves the baseline by 14.34%. [2024-12-05T07:58:16.894Z] Movies recommended for you: [2024-12-05T07:58:16.894Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T07:58:16.894Z] There is no way to check that no silent failure occurred. [2024-12-05T07:58:16.894Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (58666.253 ms) ====== [2024-12-05T07:58:16.894Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-12-05T07:58:17.598Z] GC before operation: completed in 372.163 ms, heap usage 280.891 MB -> 49.652 MB. [2024-12-05T07:58:24.070Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T07:58:35.072Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T07:58:45.233Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T07:58:51.741Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T07:58:55.770Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T07:59:01.115Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T07:59:06.433Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T07:59:12.106Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T07:59:12.904Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T07:59:12.904Z] The best model improves the baseline by 14.34%. [2024-12-05T07:59:13.638Z] Movies recommended for you: [2024-12-05T07:59:13.638Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T07:59:13.638Z] There is no way to check that no silent failure occurred. [2024-12-05T07:59:13.638Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (55920.059 ms) ====== [2024-12-05T07:59:13.638Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-12-05T07:59:13.638Z] GC before operation: completed in 173.773 ms, heap usage 159.942 MB -> 51.964 MB. [2024-12-05T07:59:22.759Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T07:59:32.388Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T07:59:43.097Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T07:59:51.373Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T07:59:55.501Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:00:00.820Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:00:07.490Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:00:10.747Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:00:11.508Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:00:11.508Z] The best model improves the baseline by 14.34%. [2024-12-05T08:00:11.508Z] Movies recommended for you: [2024-12-05T08:00:11.508Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:00:11.508Z] There is no way to check that no silent failure occurred. [2024-12-05T08:00:11.508Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (58166.332 ms) ====== [2024-12-05T08:00:11.508Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-12-05T08:00:12.209Z] GC before operation: completed in 216.278 ms, heap usage 269.304 MB -> 49.743 MB. [2024-12-05T08:00:18.910Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:00:28.629Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:00:36.541Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:00:45.308Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:00:50.766Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:00:54.838Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:01:00.119Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:01:06.835Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:01:06.835Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:01:06.835Z] The best model improves the baseline by 14.34%. [2024-12-05T08:01:07.505Z] Movies recommended for you: [2024-12-05T08:01:07.505Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:01:07.505Z] There is no way to check that no silent failure occurred. [2024-12-05T08:01:07.505Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (55244.047 ms) ====== [2024-12-05T08:01:07.505Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-12-05T08:01:07.505Z] GC before operation: completed in 164.175 ms, heap usage 153.517 MB -> 49.941 MB. [2024-12-05T08:01:19.309Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:01:27.404Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:01:35.188Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:01:44.183Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:01:49.698Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:01:54.016Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:01:59.606Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:02:04.882Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:02:05.579Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:02:05.579Z] The best model improves the baseline by 14.34%. [2024-12-05T08:02:06.293Z] Movies recommended for you: [2024-12-05T08:02:06.293Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:02:06.293Z] There is no way to check that no silent failure occurred. [2024-12-05T08:02:06.293Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (58740.724 ms) ====== [2024-12-05T08:02:06.293Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-12-05T08:02:06.293Z] GC before operation: completed in 237.358 ms, heap usage 230.812 MB -> 50.281 MB. [2024-12-05T08:02:14.449Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:02:22.636Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:02:32.096Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:02:40.096Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:02:44.738Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:02:50.207Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:02:56.786Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:03:00.889Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:03:01.647Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:03:01.647Z] The best model improves the baseline by 14.34%. [2024-12-05T08:03:01.647Z] Movies recommended for you: [2024-12-05T08:03:01.647Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:03:01.648Z] There is no way to check that no silent failure occurred. [2024-12-05T08:03:01.648Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (55126.449 ms) ====== [2024-12-05T08:03:01.648Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-12-05T08:03:02.471Z] GC before operation: completed in 521.696 ms, heap usage 169.325 MB -> 50.016 MB. [2024-12-05T08:03:10.427Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:03:18.605Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:03:25.067Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:03:34.750Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:03:39.541Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:03:45.033Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:03:49.138Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:03:53.204Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:03:53.929Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:03:53.929Z] The best model improves the baseline by 14.34%. [2024-12-05T08:03:54.769Z] Movies recommended for you: [2024-12-05T08:03:54.769Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:03:54.769Z] There is no way to check that no silent failure occurred. [2024-12-05T08:03:54.769Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (52470.852 ms) ====== [2024-12-05T08:03:54.769Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-12-05T08:03:54.769Z] GC before operation: completed in 360.004 ms, heap usage 179.020 MB -> 50.141 MB. [2024-12-05T08:04:04.408Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:04:10.774Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:04:17.487Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:04:24.015Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:04:28.253Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:04:32.533Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:04:37.991Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:04:43.236Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:04:43.236Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:04:43.236Z] The best model improves the baseline by 14.34%. [2024-12-05T08:04:43.956Z] Movies recommended for you: [2024-12-05T08:04:43.956Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:04:43.956Z] There is no way to check that no silent failure occurred. [2024-12-05T08:04:43.956Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (48780.549 ms) ====== [2024-12-05T08:04:43.956Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-12-05T08:04:43.956Z] GC before operation: completed in 179.626 ms, heap usage 64.116 MB -> 53.208 MB. [2024-12-05T08:04:52.218Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:04:58.596Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:05:06.671Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:05:14.838Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:05:18.022Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:05:22.196Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:05:26.197Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:05:31.484Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:05:32.236Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:05:32.236Z] The best model improves the baseline by 14.34%. [2024-12-05T08:05:32.236Z] Movies recommended for you: [2024-12-05T08:05:32.236Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:05:32.236Z] There is no way to check that no silent failure occurred. [2024-12-05T08:05:32.236Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (48703.705 ms) ====== [2024-12-05T08:05:32.236Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-12-05T08:05:32.926Z] GC before operation: completed in 405.175 ms, heap usage 424.658 MB -> 53.471 MB. [2024-12-05T08:05:41.393Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:05:49.436Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:05:57.346Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:06:05.391Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:06:10.998Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:06:16.377Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:06:22.996Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:06:27.062Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:06:27.748Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:06:27.748Z] The best model improves the baseline by 14.34%. [2024-12-05T08:06:27.748Z] Movies recommended for you: [2024-12-05T08:06:27.748Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:06:27.748Z] There is no way to check that no silent failure occurred. [2024-12-05T08:06:27.748Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (54887.099 ms) ====== [2024-12-05T08:06:27.748Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-12-05T08:06:27.748Z] GC before operation: completed in 125.489 ms, heap usage 199.931 MB -> 50.163 MB. [2024-12-05T08:06:35.359Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:06:42.818Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:06:50.938Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:06:57.751Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:07:01.838Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:07:06.063Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:07:11.420Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:07:15.630Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:07:15.630Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:07:15.630Z] The best model improves the baseline by 14.34%. [2024-12-05T08:07:15.630Z] Movies recommended for you: [2024-12-05T08:07:15.630Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:07:15.630Z] There is no way to check that no silent failure occurred. [2024-12-05T08:07:15.630Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (47785.351 ms) ====== [2024-12-05T08:07:15.630Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-12-05T08:07:15.630Z] GC before operation: completed in 135.240 ms, heap usage 280.826 MB -> 50.048 MB. [2024-12-05T08:07:22.146Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:07:29.797Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:07:37.759Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:07:44.570Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:07:47.808Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:07:53.079Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:07:58.889Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:08:03.027Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:08:03.826Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:08:03.826Z] The best model improves the baseline by 14.34%. [2024-12-05T08:08:03.826Z] Movies recommended for you: [2024-12-05T08:08:03.826Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:08:03.826Z] There is no way to check that no silent failure occurred. [2024-12-05T08:08:03.826Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (47784.499 ms) ====== [2024-12-05T08:08:03.826Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-12-05T08:08:03.826Z] GC before operation: completed in 247.180 ms, heap usage 64.435 MB -> 50.165 MB. [2024-12-05T08:08:11.760Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:08:17.027Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:08:23.493Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:08:30.077Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:08:34.339Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:08:39.427Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:08:43.826Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:08:48.006Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:08:48.006Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:08:48.006Z] The best model improves the baseline by 14.34%. [2024-12-05T08:08:48.840Z] Movies recommended for you: [2024-12-05T08:08:48.840Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:08:48.840Z] There is no way to check that no silent failure occurred. [2024-12-05T08:08:48.840Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (44741.253 ms) ====== [2024-12-05T08:08:48.840Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-12-05T08:08:49.560Z] GC before operation: completed in 646.483 ms, heap usage 178.162 MB -> 50.282 MB. [2024-12-05T08:08:57.226Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:09:03.641Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:09:11.656Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:09:18.027Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:09:22.244Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:09:24.602Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:09:28.883Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:09:34.811Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:09:35.601Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:09:35.601Z] The best model improves the baseline by 14.34%. [2024-12-05T08:09:35.601Z] Movies recommended for you: [2024-12-05T08:09:35.601Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:09:35.601Z] There is no way to check that no silent failure occurred. [2024-12-05T08:09:35.601Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (45796.923 ms) ====== [2024-12-05T08:09:35.601Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-12-05T08:09:35.601Z] GC before operation: completed in 202.179 ms, heap usage 226.328 MB -> 48.567 MB. [2024-12-05T08:09:42.457Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:09:48.888Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:09:55.560Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:10:02.128Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:10:05.465Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:10:09.596Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:10:14.815Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:10:18.968Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:10:18.968Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:10:18.968Z] The best model improves the baseline by 14.34%. [2024-12-05T08:10:19.962Z] Movies recommended for you: [2024-12-05T08:10:19.962Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:10:19.962Z] There is no way to check that no silent failure occurred. [2024-12-05T08:10:19.962Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (44454.279 ms) ====== [2024-12-05T08:10:19.962Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-12-05T08:10:20.628Z] GC before operation: completed in 609.997 ms, heap usage 164.526 MB -> 48.550 MB. [2024-12-05T08:10:28.408Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:10:37.559Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:10:47.053Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:10:53.436Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:10:56.565Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:11:00.816Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:11:04.997Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:11:09.013Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:11:10.830Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:11:10.830Z] The best model improves the baseline by 14.34%. [2024-12-05T08:11:11.517Z] Movies recommended for you: [2024-12-05T08:11:11.517Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:11:11.517Z] There is no way to check that no silent failure occurred. [2024-12-05T08:11:11.517Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (50964.438 ms) ====== [2024-12-05T08:11:11.517Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-12-05T08:11:11.517Z] GC before operation: completed in 273.950 ms, heap usage 173.998 MB -> 48.635 MB. [2024-12-05T08:11:18.157Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-12-05T08:11:27.921Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-12-05T08:11:38.017Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-12-05T08:11:44.316Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-12-05T08:11:51.034Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-12-05T08:11:55.248Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-12-05T08:12:00.627Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-12-05T08:12:03.777Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-12-05T08:12:05.395Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2024-12-05T08:12:05.395Z] The best model improves the baseline by 14.34%. [2024-12-05T08:12:06.098Z] Movies recommended for you: [2024-12-05T08:12:06.098Z] WARNING: This benchmark provides no result that can be validated. [2024-12-05T08:12:06.098Z] There is no way to check that no silent failure occurred. [2024-12-05T08:12:06.098Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (54226.013 ms) ====== [2024-12-05T08:12:06.848Z] ----------------------------------- [2024-12-05T08:12:06.848Z] renaissance-movie-lens_0_PASSED [2024-12-05T08:12:06.848Z] ----------------------------------- [2024-12-05T08:12:06.848Z] [2024-12-05T08:12:06.848Z] TEST TEARDOWN: [2024-12-05T08:12:06.848Z] Nothing to be done for teardown. [2024-12-05T08:12:06.848Z] renaissance-movie-lens_0 Finish Time: Thu Dec 5 08:12:06 2024 Epoch Time (ms): 1733386326601