renaissance-movie-lens_0
[2024-09-26T05:07:27.801Z] Running test renaissance-movie-lens_0 ...
[2024-09-26T05:07:27.801Z] ===============================================
[2024-09-26T05:07:27.801Z] renaissance-movie-lens_0 Start Time: Thu Sep 26 05:07:27 2024 Epoch Time (ms): 1727327247448
[2024-09-26T05:07:27.801Z] variation: NoOptions
[2024-09-26T05:07:27.801Z] JVM_OPTIONS:
[2024-09-26T05:07:27.801Z] { \
[2024-09-26T05:07:27.801Z] echo ""; echo "TEST SETUP:"; \
[2024-09-26T05:07:27.801Z] echo "Nothing to be done for setup."; \
[2024-09-26T05:07:27.801Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273249167590/renaissance-movie-lens_0"; \
[2024-09-26T05:07:27.801Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273249167590/renaissance-movie-lens_0"; \
[2024-09-26T05:07:27.801Z] echo ""; echo "TESTING:"; \
[2024-09-26T05:07:27.801Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273249167590/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-26T05:07:27.802Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273249167590/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-26T05:07:27.802Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-26T05:07:27.802Z] echo "Nothing to be done for teardown."; \
[2024-09-26T05:07:27.802Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273249167590/TestTargetResult";
[2024-09-26T05:07:27.802Z]
[2024-09-26T05:07:27.802Z] TEST SETUP:
[2024-09-26T05:07:27.802Z] Nothing to be done for setup.
[2024-09-26T05:07:27.802Z]
[2024-09-26T05:07:27.802Z] TESTING:
[2024-09-26T05:07:35.039Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-26T05:07:40.416Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-09-26T05:07:49.063Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-26T05:07:49.707Z] Training: 60056, validation: 20285, test: 19854
[2024-09-26T05:07:49.707Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-26T05:07:50.322Z] GC before operation: completed in 229.721 ms, heap usage 50.495 MB -> 36.421 MB.
[2024-09-26T05:08:08.095Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:08:23.087Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:08:30.288Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:08:36.411Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:08:42.879Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:08:45.794Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:08:49.829Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:08:53.898Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:08:54.670Z] 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.
[2024-09-26T05:08:55.594Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:08:56.331Z] Movies recommended for you:
[2024-09-26T05:08:56.331Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:08:56.331Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:08:56.331Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (65762.385 ms) ======
[2024-09-26T05:08:56.331Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-26T05:08:56.331Z] GC before operation: completed in 343.949 ms, heap usage 210.682 MB -> 55.271 MB.
[2024-09-26T05:09:09.745Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:09:19.763Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:09:28.562Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:09:34.881Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:09:39.944Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:09:44.302Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:09:51.515Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:09:57.422Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:09:58.054Z] 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.
[2024-09-26T05:09:58.054Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:09:58.054Z] Movies recommended for you:
[2024-09-26T05:09:58.054Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:09:58.054Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:09:58.054Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (61847.056 ms) ======
[2024-09-26T05:09:58.054Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-26T05:09:58.054Z] GC before operation: completed in 216.107 ms, heap usage 275.544 MB -> 48.585 MB.
[2024-09-26T05:10:04.065Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:10:10.073Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:10:14.911Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:10:19.645Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:10:22.872Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:10:25.821Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:10:32.182Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:10:36.395Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:10:37.202Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-26T05:10:37.202Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:10:37.202Z] Movies recommended for you:
[2024-09-26T05:10:37.202Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:10:37.202Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:10:37.202Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (39008.382 ms) ======
[2024-09-26T05:10:37.202Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-26T05:10:37.861Z] GC before operation: completed in 370.974 ms, heap usage 110.871 MB -> 51.063 MB.
[2024-09-26T05:10:47.562Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:10:57.632Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:11:09.388Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:11:17.415Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:11:24.939Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:11:29.136Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:11:32.013Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:11:35.933Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:11:35.933Z] 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.
[2024-09-26T05:11:35.933Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:11:36.579Z] Movies recommended for you:
[2024-09-26T05:11:36.579Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:11:36.579Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:11:36.579Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (58619.463 ms) ======
[2024-09-26T05:11:36.579Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-26T05:11:36.579Z] GC before operation: completed in 417.574 ms, heap usage 214.853 MB -> 49.048 MB.
[2024-09-26T05:11:47.712Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:11:56.083Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:12:07.269Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:12:15.303Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:12:18.667Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:12:24.081Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:12:28.063Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:12:30.192Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:12:30.794Z] 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.
[2024-09-26T05:12:30.794Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:12:30.794Z] Movies recommended for you:
[2024-09-26T05:12:30.794Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:12:30.794Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:12:30.794Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (54058.845 ms) ======
[2024-09-26T05:12:30.794Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-26T05:12:31.448Z] GC before operation: completed in 318.282 ms, heap usage 220.744 MB -> 49.256 MB.
[2024-09-26T05:12:39.223Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:12:46.925Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:12:54.483Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:12:59.504Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:13:02.521Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:13:07.003Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:13:11.278Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:13:15.401Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:13:17.087Z] 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.
[2024-09-26T05:13:17.975Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:13:17.975Z] Movies recommended for you:
[2024-09-26T05:13:17.975Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:13:17.975Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:13:17.975Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (46868.259 ms) ======
[2024-09-26T05:13:17.975Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-26T05:13:18.681Z] GC before operation: completed in 551.362 ms, heap usage 250.491 MB -> 49.172 MB.
[2024-09-26T05:13:25.791Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:13:34.502Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:13:41.908Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:13:44.742Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:13:48.519Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:13:50.530Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:13:53.435Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:13:56.420Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:13:57.066Z] 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.
[2024-09-26T05:13:57.066Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:13:57.066Z] Movies recommended for you:
[2024-09-26T05:13:57.066Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:13:57.066Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:13:57.066Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (38802.854 ms) ======
[2024-09-26T05:13:57.066Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-26T05:13:57.714Z] GC before operation: completed in 291.739 ms, heap usage 193.650 MB -> 49.297 MB.
[2024-09-26T05:14:02.580Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:14:10.803Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:14:17.192Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:14:21.437Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:14:25.342Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:14:29.564Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:14:38.048Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:14:45.411Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:14:46.077Z] 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.
[2024-09-26T05:14:46.077Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:14:46.728Z] Movies recommended for you:
[2024-09-26T05:14:46.728Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:14:46.728Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:14:46.728Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (49312.056 ms) ======
[2024-09-26T05:14:46.728Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-26T05:14:47.352Z] GC before operation: completed in 260.047 ms, heap usage 117.775 MB -> 50.544 MB.
[2024-09-26T05:14:54.558Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:14:58.873Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:15:04.722Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:15:10.637Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:15:12.736Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:15:17.891Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:15:21.950Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:15:24.903Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:15:25.526Z] 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.
[2024-09-26T05:15:25.526Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:15:25.526Z] Movies recommended for you:
[2024-09-26T05:15:25.526Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:15:25.526Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:15:25.526Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (38249.709 ms) ======
[2024-09-26T05:15:25.526Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-26T05:15:25.526Z] GC before operation: completed in 224.833 ms, heap usage 248.882 MB -> 49.458 MB.
[2024-09-26T05:15:32.710Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:15:37.492Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:15:42.222Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:15:47.083Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:15:50.597Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:15:52.678Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:15:56.471Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:16:00.659Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:16:02.068Z] 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.
[2024-09-26T05:16:02.068Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:16:02.068Z] Movies recommended for you:
[2024-09-26T05:16:02.068Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:16:02.068Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:16:02.068Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (36643.559 ms) ======
[2024-09-26T05:16:02.068Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-26T05:16:03.416Z] GC before operation: completed in 764.119 ms, heap usage 160.026 MB -> 49.520 MB.
[2024-09-26T05:16:08.642Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:16:15.466Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:16:25.430Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:16:33.157Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:16:36.459Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:16:38.520Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:16:41.470Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:16:45.389Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:16:45.389Z] 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.
[2024-09-26T05:16:46.026Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:16:46.026Z] Movies recommended for you:
[2024-09-26T05:16:46.026Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:16:46.026Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:16:46.026Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (42737.194 ms) ======
[2024-09-26T05:16:46.026Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-26T05:16:46.026Z] GC before operation: completed in 323.649 ms, heap usage 243.640 MB -> 49.312 MB.
[2024-09-26T05:16:53.146Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:16:59.487Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:17:05.491Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:17:09.263Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:17:12.111Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:17:14.133Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:17:17.176Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:17:19.205Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:17:19.831Z] 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.
[2024-09-26T05:17:19.831Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:17:19.831Z] Movies recommended for you:
[2024-09-26T05:17:19.831Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:17:19.831Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:17:19.831Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (33905.823 ms) ======
[2024-09-26T05:17:19.831Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-26T05:17:20.522Z] GC before operation: completed in 414.137 ms, heap usage 245.248 MB -> 49.502 MB.
[2024-09-26T05:17:26.878Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:17:33.083Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:17:39.280Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:17:43.020Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:17:46.964Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:17:48.975Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:17:51.931Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:17:54.022Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:17:54.622Z] 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.
[2024-09-26T05:17:55.225Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:17:55.225Z] Movies recommended for you:
[2024-09-26T05:17:55.225Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:17:55.225Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:17:55.225Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (34609.895 ms) ======
[2024-09-26T05:17:55.225Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-26T05:17:55.225Z] GC before operation: completed in 304.715 ms, heap usage 206.973 MB -> 49.577 MB.
[2024-09-26T05:18:02.896Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:18:08.144Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:18:14.213Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:18:19.162Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:18:21.218Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:18:24.288Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:18:29.642Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:18:36.340Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:18:36.340Z] 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.
[2024-09-26T05:18:36.340Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:18:36.340Z] Movies recommended for you:
[2024-09-26T05:18:36.340Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:18:36.340Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:18:36.340Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (40997.672 ms) ======
[2024-09-26T05:18:36.340Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-26T05:18:37.028Z] GC before operation: completed in 521.901 ms, heap usage 198.183 MB -> 50.508 MB.
[2024-09-26T05:18:44.797Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:18:54.258Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:19:01.662Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:19:07.248Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:19:12.276Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:19:15.245Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:19:18.201Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:19:20.345Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:19:20.986Z] 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.
[2024-09-26T05:19:20.986Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:19:20.986Z] Movies recommended for you:
[2024-09-26T05:19:20.986Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:19:20.986Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:19:20.986Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (43967.031 ms) ======
[2024-09-26T05:19:20.986Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-26T05:19:20.986Z] GC before operation: completed in 256.989 ms, heap usage 90.935 MB -> 49.445 MB.
[2024-09-26T05:19:28.750Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:19:35.089Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:19:44.120Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:19:52.584Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:19:56.462Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:19:58.609Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:20:01.458Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:20:04.411Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:20:05.822Z] 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.
[2024-09-26T05:20:05.822Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:20:05.822Z] Movies recommended for you:
[2024-09-26T05:20:05.822Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:20:05.822Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:20:05.822Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (44806.954 ms) ======
[2024-09-26T05:20:05.822Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-26T05:20:06.440Z] GC before operation: completed in 276.325 ms, heap usage 244.153 MB -> 49.648 MB.
[2024-09-26T05:20:14.079Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:20:20.273Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:20:26.287Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:20:32.624Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:20:35.452Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:20:38.764Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:20:40.076Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:20:43.030Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:20:43.030Z] 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.
[2024-09-26T05:20:43.030Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:20:43.030Z] Movies recommended for you:
[2024-09-26T05:20:43.030Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:20:43.030Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:20:43.030Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (37060.270 ms) ======
[2024-09-26T05:20:43.030Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-26T05:20:43.653Z] GC before operation: completed in 251.367 ms, heap usage 163.961 MB -> 49.461 MB.
[2024-09-26T05:20:49.631Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:20:53.446Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:20:59.468Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:21:03.299Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:21:07.304Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:21:11.448Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:21:17.120Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:21:22.302Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:21:22.926Z] 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.
[2024-09-26T05:21:22.926Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:21:22.926Z] Movies recommended for you:
[2024-09-26T05:21:22.926Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:21:22.926Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:21:22.926Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (39674.603 ms) ======
[2024-09-26T05:21:22.926Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-26T05:21:23.640Z] GC before operation: completed in 540.644 ms, heap usage 243.019 MB -> 49.579 MB.
[2024-09-26T05:21:35.996Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:21:41.180Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:21:50.541Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:21:57.167Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:22:03.920Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:22:09.257Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:22:13.373Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:22:19.574Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:22:22.030Z] 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.
[2024-09-26T05:22:22.030Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:22:22.030Z] Movies recommended for you:
[2024-09-26T05:22:22.031Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:22:22.031Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:22:22.031Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (57143.657 ms) ======
[2024-09-26T05:22:22.031Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-26T05:22:22.031Z] GC before operation: completed in 691.372 ms, heap usage 90.880 MB -> 49.587 MB.
[2024-09-26T05:22:28.141Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:22:35.761Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:22:45.500Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:22:50.690Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:22:57.352Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:23:02.681Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:23:06.793Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:23:11.402Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:23:12.448Z] 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.
[2024-09-26T05:23:12.448Z] The best model improves the baseline by 14.34%.
[2024-09-26T05:23:15.633Z] Movies recommended for you:
[2024-09-26T05:23:15.633Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:23:15.633Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:23:15.633Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (51301.086 ms) ======
[2024-09-26T05:23:15.633Z] -----------------------------------
[2024-09-26T05:23:15.633Z] renaissance-movie-lens_0_PASSED
[2024-09-26T05:23:15.633Z] -----------------------------------
[2024-09-26T05:23:15.633Z]
[2024-09-26T05:23:15.633Z] TEST TEARDOWN:
[2024-09-26T05:23:15.633Z] Nothing to be done for teardown.
[2024-09-26T05:23:15.633Z] renaissance-movie-lens_0 Finish Time: Thu Sep 26 05:23:15 2024 Epoch Time (ms): 1727328195383