renaissance-movie-lens_0
[2024-11-14T03:09:38.423Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T03:09:38.423Z] ===============================================
[2024-11-14T03:09:38.423Z] renaissance-movie-lens_0 Start Time: Wed Nov 13 22:09:37 2024 Epoch Time (ms): 1731553777951
[2024-11-14T03:09:38.423Z] variation: NoOptions
[2024-11-14T03:09:38.423Z] JVM_OPTIONS:
[2024-11-14T03:09:38.423Z] { \
[2024-11-14T03:09:38.423Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T03:09:38.423Z] echo "Nothing to be done for setup."; \
[2024-11-14T03:09:38.423Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17315529037184/renaissance-movie-lens_0"; \
[2024-11-14T03:09:38.423Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17315529037184/renaissance-movie-lens_0"; \
[2024-11-14T03:09:38.423Z] echo ""; echo "TESTING:"; \
[2024-11-14T03:09:38.423Z] "/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_17315529037184/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T03:09:38.423Z] 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_17315529037184/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T03:09:38.423Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T03:09:38.423Z] echo "Nothing to be done for teardown."; \
[2024-11-14T03:09:38.423Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17315529037184/TestTargetResult";
[2024-11-14T03:09:38.423Z]
[2024-11-14T03:09:38.423Z] TEST SETUP:
[2024-11-14T03:09:38.423Z] Nothing to be done for setup.
[2024-11-14T03:09:38.423Z]
[2024-11-14T03:09:38.423Z] TESTING:
[2024-11-14T03:09:40.651Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T03:09:42.872Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-11-14T03:09:46.847Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T03:09:46.847Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T03:09:46.847Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T03:09:47.490Z] GC before operation: completed in 97.519 ms, heap usage 48.444 MB -> 37.296 MB.
[2024-11-14T03:09:53.703Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:09:57.607Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:10:02.727Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:10:05.336Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:10:07.446Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:10:09.585Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:10:11.751Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:10:13.912Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:10:13.912Z] 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.
[2024-11-14T03:10:13.912Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:10:13.912Z] Movies recommended for you:
[2024-11-14T03:10:13.912Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:10:13.912Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:10:13.912Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26803.840 ms) ======
[2024-11-14T03:10:13.912Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T03:10:13.912Z] GC before operation: completed in 127.526 ms, heap usage 258.069 MB -> 54.825 MB.
[2024-11-14T03:10:16.815Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:10:19.736Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:10:23.906Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:10:26.877Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:10:28.229Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:10:30.386Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:10:32.552Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:10:34.667Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:10:34.667Z] 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.
[2024-11-14T03:10:34.667Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:10:34.667Z] Movies recommended for you:
[2024-11-14T03:10:34.668Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:10:34.668Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:10:34.668Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20643.489 ms) ======
[2024-11-14T03:10:34.668Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T03:10:34.668Z] GC before operation: completed in 110.819 ms, heap usage 115.881 MB -> 49.183 MB.
[2024-11-14T03:10:37.624Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:10:39.732Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:10:42.658Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:10:45.622Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:10:46.958Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:10:48.285Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:10:50.509Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:10:52.320Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:10:52.995Z] 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.
[2024-11-14T03:10:52.995Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:10:52.995Z] Movies recommended for you:
[2024-11-14T03:10:52.995Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:10:52.995Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:10:52.995Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17537.205 ms) ======
[2024-11-14T03:10:52.995Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T03:10:52.995Z] GC before operation: completed in 127.310 ms, heap usage 119.931 MB -> 49.403 MB.
[2024-11-14T03:10:55.085Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:10:57.993Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:11:00.959Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:11:03.133Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:11:05.307Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:11:06.677Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:11:08.036Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:11:10.189Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:11:10.189Z] 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.
[2024-11-14T03:11:10.189Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:11:10.189Z] Movies recommended for you:
[2024-11-14T03:11:10.189Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:11:10.189Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:11:10.189Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (17678.802 ms) ======
[2024-11-14T03:11:10.189Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T03:11:10.189Z] GC before operation: completed in 98.783 ms, heap usage 117.752 MB -> 49.780 MB.
[2024-11-14T03:11:13.097Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:11:16.018Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:11:18.944Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:11:21.027Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:11:22.405Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:11:23.774Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:11:25.930Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:11:27.302Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:11:27.302Z] 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.
[2024-11-14T03:11:27.302Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:11:27.302Z] Movies recommended for you:
[2024-11-14T03:11:27.302Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:11:27.302Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:11:27.302Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17077.231 ms) ======
[2024-11-14T03:11:27.302Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T03:11:27.302Z] GC before operation: completed in 83.557 ms, heap usage 286.163 MB -> 50.170 MB.
[2024-11-14T03:11:30.299Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:11:32.371Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:11:35.307Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:11:37.388Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:11:38.790Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:11:40.628Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:11:41.281Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:11:42.684Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:11:43.402Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-14T03:11:43.402Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:11:43.402Z] Movies recommended for you:
[2024-11-14T03:11:43.402Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:11:43.402Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:11:43.402Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15895.682 ms) ======
[2024-11-14T03:11:43.402Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T03:11:43.402Z] GC before operation: completed in 126.793 ms, heap usage 165.030 MB -> 49.961 MB.
[2024-11-14T03:11:46.381Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:11:48.592Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:11:51.559Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:11:54.537Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:11:55.857Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:11:57.200Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:11:59.309Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:12:01.500Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:12:02.153Z] 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.
[2024-11-14T03:12:02.153Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:12:02.153Z] Movies recommended for you:
[2024-11-14T03:12:02.153Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:12:02.153Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:12:02.153Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (18695.625 ms) ======
[2024-11-14T03:12:02.153Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T03:12:02.153Z] GC before operation: completed in 116.181 ms, heap usage 161.278 MB -> 50.164 MB.
[2024-11-14T03:12:05.150Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:12:09.132Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:12:11.352Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:12:14.413Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:12:15.778Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:12:17.154Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:12:19.348Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:12:21.688Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:12:21.688Z] 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.
[2024-11-14T03:12:21.688Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:12:22.359Z] Movies recommended for you:
[2024-11-14T03:12:22.359Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:12:22.359Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:12:22.359Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19720.195 ms) ======
[2024-11-14T03:12:22.359Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T03:12:22.359Z] GC before operation: completed in 103.952 ms, heap usage 173.944 MB -> 50.361 MB.
[2024-11-14T03:12:25.411Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:12:28.482Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:12:32.671Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:12:34.879Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:12:37.048Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:12:38.401Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:12:40.576Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:12:41.981Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:12:41.981Z] 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.
[2024-11-14T03:12:41.981Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:12:41.981Z] Movies recommended for you:
[2024-11-14T03:12:41.981Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:12:41.981Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:12:41.981Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20110.649 ms) ======
[2024-11-14T03:12:41.981Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T03:12:42.653Z] GC before operation: completed in 112.337 ms, heap usage 280.151 MB -> 50.358 MB.
[2024-11-14T03:12:44.765Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:12:47.391Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:12:51.416Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:12:54.422Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:12:55.790Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:12:57.948Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:12:59.344Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:13:01.505Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:13:01.505Z] 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.
[2024-11-14T03:13:01.505Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:13:01.505Z] Movies recommended for you:
[2024-11-14T03:13:01.505Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:13:01.505Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:13:01.505Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19214.415 ms) ======
[2024-11-14T03:13:01.505Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T03:13:01.505Z] GC before operation: completed in 132.620 ms, heap usage 287.509 MB -> 50.438 MB.
[2024-11-14T03:13:04.560Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:13:07.646Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:13:10.632Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:13:12.781Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:13:14.542Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:13:15.286Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:13:17.430Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:13:18.084Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:13:18.726Z] 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.
[2024-11-14T03:13:18.726Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:13:18.726Z] Movies recommended for you:
[2024-11-14T03:13:18.726Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:13:18.726Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:13:18.726Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17035.565 ms) ======
[2024-11-14T03:13:18.726Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T03:13:18.726Z] GC before operation: completed in 97.732 ms, heap usage 174.196 MB -> 50.112 MB.
[2024-11-14T03:13:21.285Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:13:23.451Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:13:26.521Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:13:29.527Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:13:30.899Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:13:32.316Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:13:34.429Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:13:35.809Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:13:36.463Z] 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.
[2024-11-14T03:13:36.463Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:13:36.463Z] Movies recommended for you:
[2024-11-14T03:13:36.463Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:13:36.463Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:13:36.463Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17703.223 ms) ======
[2024-11-14T03:13:36.463Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T03:13:36.463Z] GC before operation: completed in 114.579 ms, heap usage 154.763 MB -> 50.260 MB.
[2024-11-14T03:13:39.491Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:13:41.654Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:13:43.802Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:13:45.988Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:13:48.131Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:13:49.517Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:13:51.004Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:13:53.140Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:13:53.140Z] 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.
[2024-11-14T03:13:53.140Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:13:53.140Z] Movies recommended for you:
[2024-11-14T03:13:53.140Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:13:53.140Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:13:53.140Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16671.564 ms) ======
[2024-11-14T03:13:53.140Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T03:13:53.799Z] GC before operation: completed in 141.004 ms, heap usage 258.691 MB -> 50.474 MB.
[2024-11-14T03:13:56.362Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:13:58.213Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:14:01.388Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:14:04.422Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:14:05.910Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:14:07.267Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:14:08.820Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:14:10.192Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:14:10.888Z] 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.
[2024-11-14T03:14:10.888Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:14:10.888Z] Movies recommended for you:
[2024-11-14T03:14:10.889Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:14:10.889Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:14:10.889Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17224.477 ms) ======
[2024-11-14T03:14:10.889Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T03:14:10.889Z] GC before operation: completed in 135.399 ms, heap usage 66.590 MB -> 50.244 MB.
[2024-11-14T03:14:13.913Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:14:16.973Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:14:19.984Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:14:22.533Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:14:24.019Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:14:25.486Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:14:27.681Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:14:29.039Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:14:29.039Z] 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.
[2024-11-14T03:14:29.039Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:14:29.039Z] Movies recommended for you:
[2024-11-14T03:14:29.039Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:14:29.039Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:14:29.039Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (18305.602 ms) ======
[2024-11-14T03:14:29.039Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T03:14:29.039Z] GC before operation: completed in 100.861 ms, heap usage 206.125 MB -> 50.346 MB.
[2024-11-14T03:14:32.050Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:14:34.256Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:14:36.361Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:14:38.851Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:14:40.283Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:14:41.681Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:14:43.076Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:14:44.882Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:14:44.882Z] 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.
[2024-11-14T03:14:44.882Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:14:45.622Z] Movies recommended for you:
[2024-11-14T03:14:45.623Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:14:45.623Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:14:45.623Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15947.003 ms) ======
[2024-11-14T03:14:45.623Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T03:14:45.623Z] GC before operation: completed in 100.270 ms, heap usage 59.025 MB -> 50.514 MB.
[2024-11-14T03:14:47.745Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:14:49.978Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:14:53.076Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:14:55.242Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:14:55.891Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:14:58.084Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:14:58.831Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:15:00.208Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:15:00.208Z] 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.
[2024-11-14T03:15:00.838Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:15:00.838Z] Movies recommended for you:
[2024-11-14T03:15:00.838Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:15:00.838Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:15:00.838Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15303.261 ms) ======
[2024-11-14T03:15:00.838Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T03:15:00.838Z] GC before operation: completed in 110.468 ms, heap usage 343.215 MB -> 53.578 MB.
[2024-11-14T03:15:02.941Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:15:05.149Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:15:08.191Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:15:10.368Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:15:11.751Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:15:13.173Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:15:14.554Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:15:15.921Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:15:15.921Z] 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.
[2024-11-14T03:15:15.921Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:15:15.921Z] Movies recommended for you:
[2024-11-14T03:15:15.921Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:15:15.921Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:15:15.921Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15432.547 ms) ======
[2024-11-14T03:15:15.921Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T03:15:16.550Z] GC before operation: completed in 84.332 ms, heap usage 284.901 MB -> 50.509 MB.
[2024-11-14T03:15:18.656Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:15:20.813Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:15:23.038Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:15:25.163Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:15:26.524Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:15:27.963Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:15:29.311Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:15:30.693Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:15:31.366Z] 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.
[2024-11-14T03:15:31.366Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:15:31.366Z] Movies recommended for you:
[2024-11-14T03:15:31.366Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:15:31.366Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:15:31.366Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15043.474 ms) ======
[2024-11-14T03:15:31.366Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T03:15:31.366Z] GC before operation: completed in 78.585 ms, heap usage 60.703 MB -> 50.387 MB.
[2024-11-14T03:15:33.600Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T03:15:35.698Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T03:15:38.739Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T03:15:40.936Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T03:15:43.566Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T03:15:44.242Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T03:15:45.613Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T03:15:47.031Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T03:15:47.660Z] 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.
[2024-11-14T03:15:47.660Z] The best model improves the baseline by 14.34%.
[2024-11-14T03:15:47.660Z] Movies recommended for you:
[2024-11-14T03:15:47.660Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T03:15:47.660Z] There is no way to check that no silent failure occurred.
[2024-11-14T03:15:47.660Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16170.136 ms) ======
[2024-11-14T03:15:48.291Z] -----------------------------------
[2024-11-14T03:15:48.291Z] renaissance-movie-lens_0_PASSED
[2024-11-14T03:15:48.291Z] -----------------------------------
[2024-11-14T03:15:48.291Z]
[2024-11-14T03:15:48.291Z] TEST TEARDOWN:
[2024-11-14T03:15:48.291Z] Nothing to be done for teardown.
[2024-11-14T03:15:48.291Z] renaissance-movie-lens_0 Finish Time: Wed Nov 13 22:15:47 2024 Epoch Time (ms): 1731554147658