renaissance-movie-lens_0
[2024-11-09T00:36:35.248Z] Running test renaissance-movie-lens_0 ...
[2024-11-09T00:36:35.248Z] ===============================================
[2024-11-09T00:36:35.248Z] renaissance-movie-lens_0 Start Time: Sat Nov 9 00:36:34 2024 Epoch Time (ms): 1731112594611
[2024-11-09T00:36:35.248Z] variation: NoOptions
[2024-11-09T00:36:35.248Z] JVM_OPTIONS:
[2024-11-09T00:36:35.248Z] { \
[2024-11-09T00:36:35.248Z] echo ""; echo "TEST SETUP:"; \
[2024-11-09T00:36:35.248Z] echo "Nothing to be done for setup."; \
[2024-11-09T00:36:35.248Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17311125947097/renaissance-movie-lens_0"; \
[2024-11-09T00:36:35.248Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17311125947097/renaissance-movie-lens_0"; \
[2024-11-09T00:36:35.248Z] echo ""; echo "TESTING:"; \
[2024-11-09T00:36:35.248Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/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_rerun/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17311125947097/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-09T00:36:35.248Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17311125947097/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-09T00:36:35.248Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-09T00:36:35.248Z] echo "Nothing to be done for teardown."; \
[2024-11-09T00:36:35.249Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17311125947097/TestTargetResult";
[2024-11-09T00:36:35.249Z]
[2024-11-09T00:36:35.249Z] TEST SETUP:
[2024-11-09T00:36:35.249Z] Nothing to be done for setup.
[2024-11-09T00:36:35.249Z]
[2024-11-09T00:36:35.249Z] TESTING:
[2024-11-09T00:36:43.218Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-09T00:36:49.731Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-11-09T00:36:57.756Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-09T00:36:57.756Z] Training: 60056, validation: 20285, test: 19854
[2024-11-09T00:36:57.756Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-09T00:36:58.531Z] GC before operation: completed in 375.490 ms, heap usage 42.676 MB -> 37.019 MB.
[2024-11-09T00:37:11.724Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:37:25.167Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:37:34.917Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:37:44.224Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:37:48.644Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:37:54.180Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:37:58.368Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:38:00.606Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:38:01.307Z] 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.
[2024-11-09T00:38:01.307Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:38:01.307Z] Movies recommended for you:
[2024-11-09T00:38:01.307Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:38:01.307Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:38:01.307Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (63178.924 ms) ======
[2024-11-09T00:38:01.307Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-09T00:38:01.979Z] GC before operation: completed in 254.159 ms, heap usage 115.649 MB -> 52.128 MB.
[2024-11-09T00:38:09.879Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:38:16.449Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:38:22.754Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:38:27.997Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:38:30.481Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:38:35.087Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:38:39.341Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:38:42.587Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:38:43.285Z] 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.
[2024-11-09T00:38:43.285Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:38:43.285Z] Movies recommended for you:
[2024-11-09T00:38:43.285Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:38:43.285Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:38:43.285Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (41523.157 ms) ======
[2024-11-09T00:38:43.285Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-09T00:38:43.285Z] GC before operation: completed in 179.701 ms, heap usage 267.606 MB -> 48.963 MB.
[2024-11-09T00:38:52.563Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:38:58.129Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:39:05.797Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:39:12.246Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:39:16.933Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:39:19.989Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:39:25.418Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:39:28.577Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:39:30.046Z] 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.
[2024-11-09T00:39:30.046Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:39:30.972Z] Movies recommended for you:
[2024-11-09T00:39:30.972Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:39:30.972Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:39:30.972Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (47082.812 ms) ======
[2024-11-09T00:39:30.972Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-09T00:39:30.972Z] GC before operation: completed in 533.606 ms, heap usage 263.931 MB -> 49.255 MB.
[2024-11-09T00:39:37.479Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:39:42.516Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:39:49.034Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:39:54.216Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:39:58.493Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:40:02.586Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:40:06.682Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:40:11.153Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:40:11.872Z] 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.
[2024-11-09T00:40:11.872Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:40:11.872Z] Movies recommended for you:
[2024-11-09T00:40:11.872Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:40:11.872Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:40:11.872Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (40547.136 ms) ======
[2024-11-09T00:40:11.872Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-09T00:40:11.872Z] GC before operation: completed in 116.185 ms, heap usage 268.144 MB -> 49.598 MB.
[2024-11-09T00:40:18.114Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:40:22.255Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:40:29.265Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:40:35.644Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:40:38.876Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:40:41.044Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:40:44.040Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:40:47.203Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:40:47.934Z] 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.
[2024-11-09T00:40:47.934Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:40:47.934Z] Movies recommended for you:
[2024-11-09T00:40:47.934Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:40:47.934Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:40:47.934Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (36209.325 ms) ======
[2024-11-09T00:40:47.934Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-09T00:40:48.857Z] GC before operation: completed in 293.173 ms, heap usage 211.185 MB -> 49.694 MB.
[2024-11-09T00:40:54.261Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:41:00.562Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:41:06.904Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:41:12.116Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:41:13.605Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:41:17.672Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:41:19.896Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:41:22.229Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:41:22.936Z] 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.
[2024-11-09T00:41:22.936Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:41:23.622Z] Movies recommended for you:
[2024-11-09T00:41:23.623Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:41:23.623Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:41:23.623Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (35213.205 ms) ======
[2024-11-09T00:41:23.623Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-09T00:41:23.623Z] GC before operation: completed in 273.774 ms, heap usage 279.316 MB -> 49.797 MB.
[2024-11-09T00:41:28.844Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:41:35.301Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:41:41.706Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:41:46.991Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:41:48.570Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:41:51.728Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:41:54.920Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:41:58.093Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:41:58.093Z] 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.
[2024-11-09T00:41:58.093Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:41:58.794Z] Movies recommended for you:
[2024-11-09T00:41:58.794Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:41:58.794Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:41:58.794Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (34743.431 ms) ======
[2024-11-09T00:41:58.794Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-09T00:41:58.794Z] GC before operation: completed in 236.469 ms, heap usage 261.104 MB -> 50.047 MB.
[2024-11-09T00:42:03.858Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:42:08.939Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:42:14.069Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:42:18.078Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:42:20.299Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:42:23.339Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:42:26.457Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:42:28.668Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:42:29.396Z] 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.
[2024-11-09T00:42:29.396Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:42:29.396Z] Movies recommended for you:
[2024-11-09T00:42:29.396Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:42:29.396Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:42:29.396Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (30412.779 ms) ======
[2024-11-09T00:42:29.396Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-09T00:42:29.396Z] GC before operation: completed in 160.880 ms, heap usage 261.370 MB -> 50.166 MB.
[2024-11-09T00:42:34.469Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:42:39.961Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:42:45.273Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:42:50.468Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:42:53.944Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:42:57.098Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:43:00.204Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:43:03.745Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:43:03.745Z] 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.
[2024-11-09T00:43:03.745Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:43:04.455Z] Movies recommended for you:
[2024-11-09T00:43:04.455Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:43:04.455Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:43:04.455Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (34920.416 ms) ======
[2024-11-09T00:43:04.455Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-09T00:43:04.455Z] GC before operation: completed in 277.405 ms, heap usage 222.605 MB -> 50.028 MB.
[2024-11-09T00:43:10.869Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:43:16.444Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:43:21.678Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:43:28.079Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:43:31.241Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:43:34.345Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:43:37.404Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:43:40.412Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:43:40.412Z] 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.
[2024-11-09T00:43:40.412Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:43:40.412Z] Movies recommended for you:
[2024-11-09T00:43:40.412Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:43:40.412Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:43:40.412Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (35977.867 ms) ======
[2024-11-09T00:43:40.412Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-09T00:43:41.197Z] GC before operation: completed in 181.798 ms, heap usage 318.971 MB -> 50.212 MB.
[2024-11-09T00:43:46.395Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:43:50.386Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:43:55.798Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:44:00.877Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:44:03.940Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:44:06.980Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:44:09.205Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:44:12.430Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:44:13.092Z] 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.
[2024-11-09T00:44:13.092Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:44:13.092Z] Movies recommended for you:
[2024-11-09T00:44:13.092Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:44:13.093Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:44:13.093Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (32279.009 ms) ======
[2024-11-09T00:44:13.093Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-09T00:44:13.093Z] GC before operation: completed in 186.715 ms, heap usage 56.913 MB -> 49.985 MB.
[2024-11-09T00:44:17.069Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:44:22.110Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:44:26.196Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:44:30.209Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:44:32.397Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:44:34.661Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:44:37.763Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:44:39.917Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:44:39.917Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-09T00:44:39.917Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:44:40.628Z] Movies recommended for you:
[2024-11-09T00:44:40.628Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:44:40.628Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:44:40.628Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (27130.825 ms) ======
[2024-11-09T00:44:40.628Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-09T00:44:40.628Z] GC before operation: completed in 489.560 ms, heap usage 335.688 MB -> 53.348 MB.
[2024-11-09T00:44:45.610Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:44:50.020Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:44:56.343Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:45:00.490Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:45:02.708Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:45:05.921Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:45:08.229Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:45:11.310Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:45:11.980Z] 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.
[2024-11-09T00:45:11.980Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:45:11.980Z] Movies recommended for you:
[2024-11-09T00:45:11.980Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:45:11.980Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:45:11.980Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (31051.146 ms) ======
[2024-11-09T00:45:11.980Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-09T00:45:11.980Z] GC before operation: completed in 112.614 ms, heap usage 282.722 MB -> 50.292 MB.
[2024-11-09T00:45:16.665Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:45:20.967Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:45:26.288Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:45:30.456Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:45:33.615Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:45:36.633Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:45:39.706Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:45:41.865Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:45:42.617Z] 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.
[2024-11-09T00:45:42.617Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:45:42.617Z] Movies recommended for you:
[2024-11-09T00:45:42.617Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:45:42.617Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:45:42.617Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (30713.388 ms) ======
[2024-11-09T00:45:42.617Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-09T00:45:43.318Z] GC before operation: completed in 294.974 ms, heap usage 211.743 MB -> 49.957 MB.
[2024-11-09T00:45:47.376Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:45:51.575Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:45:57.925Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:46:01.936Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:46:04.550Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:46:07.817Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:46:11.361Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:46:14.599Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:46:15.295Z] 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.
[2024-11-09T00:46:15.295Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:46:15.970Z] Movies recommended for you:
[2024-11-09T00:46:15.970Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:46:15.970Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:46:15.970Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (32611.758 ms) ======
[2024-11-09T00:46:15.970Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-09T00:46:15.970Z] GC before operation: completed in 354.623 ms, heap usage 290.605 MB -> 50.137 MB.
[2024-11-09T00:46:21.786Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:46:25.892Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:46:32.449Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:46:35.696Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:46:38.929Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:46:41.923Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:46:44.968Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:46:49.108Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:46:49.108Z] 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.
[2024-11-09T00:46:49.108Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:46:49.108Z] Movies recommended for you:
[2024-11-09T00:46:49.108Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:46:49.108Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:46:49.108Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (33220.475 ms) ======
[2024-11-09T00:46:49.108Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-09T00:46:49.108Z] GC before operation: completed in 231.213 ms, heap usage 260.934 MB -> 50.240 MB.
[2024-11-09T00:46:54.083Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:47:00.511Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:47:04.435Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:47:09.574Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:47:11.762Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:47:14.716Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:47:16.908Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:47:20.191Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:47:20.191Z] 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.
[2024-11-09T00:47:20.191Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:47:20.191Z] Movies recommended for you:
[2024-11-09T00:47:20.191Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:47:20.191Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:47:20.191Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (30679.706 ms) ======
[2024-11-09T00:47:20.191Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-09T00:47:20.191Z] GC before operation: completed in 114.740 ms, heap usage 270.740 MB -> 50.071 MB.
[2024-11-09T00:47:23.312Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:47:26.372Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:47:31.475Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:47:35.429Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:47:37.599Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:47:39.779Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:47:41.931Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:47:44.135Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:47:44.791Z] 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.
[2024-11-09T00:47:44.792Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:47:44.792Z] Movies recommended for you:
[2024-11-09T00:47:44.792Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:47:44.792Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:47:44.792Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (24848.924 ms) ======
[2024-11-09T00:47:44.792Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-09T00:47:45.458Z] GC before operation: completed in 108.890 ms, heap usage 261.837 MB -> 50.195 MB.
[2024-11-09T00:47:49.479Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:47:53.800Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:47:58.935Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:48:02.017Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:48:06.163Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:48:09.491Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:48:11.761Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:48:14.859Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:48:14.859Z] 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.
[2024-11-09T00:48:14.859Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:48:15.558Z] Movies recommended for you:
[2024-11-09T00:48:15.558Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:48:15.558Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:48:15.558Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (29986.495 ms) ======
[2024-11-09T00:48:15.558Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-09T00:48:15.558Z] GC before operation: completed in 321.910 ms, heap usage 155.053 MB -> 50.154 MB.
[2024-11-09T00:48:20.788Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-09T00:48:25.038Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-09T00:48:30.327Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-09T00:48:34.459Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-09T00:48:37.573Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-09T00:48:40.636Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-09T00:48:42.793Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-09T00:48:45.068Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-09T00:48:46.229Z] 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.
[2024-11-09T00:48:46.229Z] The best model improves the baseline by 14.34%.
[2024-11-09T00:48:46.229Z] Movies recommended for you:
[2024-11-09T00:48:46.229Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-09T00:48:46.229Z] There is no way to check that no silent failure occurred.
[2024-11-09T00:48:46.229Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (30610.593 ms) ======
[2024-11-09T00:48:46.990Z] -----------------------------------
[2024-11-09T00:48:46.990Z] renaissance-movie-lens_0_PASSED
[2024-11-09T00:48:46.990Z] -----------------------------------
[2024-11-09T00:48:46.990Z]
[2024-11-09T00:48:46.990Z] TEST TEARDOWN:
[2024-11-09T00:48:46.990Z] Nothing to be done for teardown.
[2024-11-09T00:48:46.990Z] renaissance-movie-lens_0 Finish Time: Sat Nov 9 00:48:46 2024 Epoch Time (ms): 1731113326609