renaissance-movie-lens_0
[2024-11-14T04:08:19.675Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T04:08:19.675Z] ===============================================
[2024-11-14T04:08:19.675Z] renaissance-movie-lens_0 Start Time: Thu Nov 14 04:08:19 2024 Epoch Time (ms): 1731557299546
[2024-11-14T04:08:19.675Z] variation: NoOptions
[2024-11-14T04:08:19.675Z] JVM_OPTIONS:
[2024-11-14T04:08:19.675Z] { \
[2024-11-14T04:08:19.675Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T04:08:19.675Z] echo "Nothing to be done for setup."; \
[2024-11-14T04:08:19.675Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17315561128260/renaissance-movie-lens_0"; \
[2024-11-14T04:08:19.675Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17315561128260/renaissance-movie-lens_0"; \
[2024-11-14T04:08:19.675Z] echo ""; echo "TESTING:"; \
[2024-11-14T04:08:19.675Z] "/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_17315561128260/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T04:08:19.675Z] 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_17315561128260/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T04:08:19.675Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T04:08:19.675Z] echo "Nothing to be done for teardown."; \
[2024-11-14T04:08:19.675Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17315561128260/TestTargetResult";
[2024-11-14T04:08:19.675Z]
[2024-11-14T04:08:19.675Z] TEST SETUP:
[2024-11-14T04:08:19.675Z] Nothing to be done for setup.
[2024-11-14T04:08:19.675Z]
[2024-11-14T04:08:19.675Z] TESTING:
[2024-11-14T04:08:23.457Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T04:08:24.807Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-11-14T04:08:28.608Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T04:08:29.243Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T04:08:29.243Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T04:08:29.869Z] GC before operation: completed in 118.931 ms, heap usage 128.227 MB -> 36.384 MB.
[2024-11-14T04:08:37.584Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:08:41.394Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:08:46.198Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:08:49.981Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:08:52.030Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:08:53.415Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:08:55.513Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:08:57.574Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:08:58.211Z] 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.
[2024-11-14T04:08:58.211Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:08:58.211Z] Movies recommended for you:
[2024-11-14T04:08:58.211Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:08:58.211Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:08:58.211Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (28760.609 ms) ======
[2024-11-14T04:08:58.211Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T04:08:58.839Z] GC before operation: completed in 214.774 ms, heap usage 215.679 MB -> 48.919 MB.
[2024-11-14T04:09:02.643Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:09:05.532Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:09:09.312Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:09:12.197Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:09:14.250Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:09:16.323Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:09:18.392Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:09:19.722Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:09:19.722Z] 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.
[2024-11-14T04:09:19.722Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:09:19.722Z] Movies recommended for you:
[2024-11-14T04:09:19.722Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:09:19.722Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:09:19.722Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21276.068 ms) ======
[2024-11-14T04:09:19.722Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T04:09:20.354Z] GC before operation: completed in 165.644 ms, heap usage 118.919 MB -> 48.399 MB.
[2024-11-14T04:09:23.483Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:09:26.572Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:09:29.433Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:09:33.452Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:09:34.840Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:09:36.962Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:09:38.376Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:09:40.513Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:09:40.513Z] 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.
[2024-11-14T04:09:41.147Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:09:41.147Z] Movies recommended for you:
[2024-11-14T04:09:41.147Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:09:41.147Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:09:41.147Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20855.854 ms) ======
[2024-11-14T04:09:41.147Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T04:09:41.147Z] GC before operation: completed in 179.895 ms, heap usage 57.885 MB -> 48.513 MB.
[2024-11-14T04:09:44.926Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:09:47.774Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:09:50.668Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:09:52.903Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:09:54.983Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:09:57.045Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:09:58.356Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:10:00.414Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:10:00.414Z] 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.
[2024-11-14T04:10:00.414Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:10:01.061Z] Movies recommended for you:
[2024-11-14T04:10:01.061Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:10:01.061Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:10:01.061Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (19712.450 ms) ======
[2024-11-14T04:10:01.061Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T04:10:01.061Z] GC before operation: completed in 177.079 ms, heap usage 150.343 MB -> 48.964 MB.
[2024-11-14T04:10:04.855Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:10:07.763Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:10:11.545Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:10:14.582Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:10:15.928Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:10:17.968Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:10:20.088Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:10:21.470Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:10:22.107Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-14T04:10:22.107Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:10:22.107Z] Movies recommended for you:
[2024-11-14T04:10:22.107Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:10:22.107Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:10:22.107Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21054.205 ms) ======
[2024-11-14T04:10:22.107Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T04:10:22.107Z] GC before operation: completed in 182.913 ms, heap usage 137.075 MB -> 49.136 MB.
[2024-11-14T04:10:24.960Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:10:27.835Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:10:30.720Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:10:33.581Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:10:35.655Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:10:36.960Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:10:39.034Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:10:41.083Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:10:41.083Z] 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.
[2024-11-14T04:10:41.083Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:10:41.083Z] Movies recommended for you:
[2024-11-14T04:10:41.083Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:10:41.083Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:10:41.083Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18927.396 ms) ======
[2024-11-14T04:10:41.083Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T04:10:41.083Z] GC before operation: completed in 153.175 ms, heap usage 150.641 MB -> 49.119 MB.
[2024-11-14T04:10:43.981Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:10:46.866Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:10:50.706Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:10:52.784Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:10:54.852Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:10:56.936Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:10:58.261Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:10:59.612Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:11:00.238Z] 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.
[2024-11-14T04:11:00.238Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:11:00.238Z] Movies recommended for you:
[2024-11-14T04:11:00.238Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:11:00.238Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:11:00.238Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (19016.584 ms) ======
[2024-11-14T04:11:00.238Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T04:11:00.238Z] GC before operation: completed in 196.966 ms, heap usage 242.382 MB -> 49.418 MB.
[2024-11-14T04:11:03.626Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:11:06.598Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:11:10.468Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:11:12.545Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:11:14.639Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:11:16.696Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:11:18.037Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:11:20.159Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:11:20.159Z] 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.
[2024-11-14T04:11:20.159Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:11:20.159Z] Movies recommended for you:
[2024-11-14T04:11:20.159Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:11:20.159Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:11:20.159Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19605.179 ms) ======
[2024-11-14T04:11:20.159Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T04:11:20.159Z] GC before operation: completed in 213.838 ms, heap usage 128.240 MB -> 49.543 MB.
[2024-11-14T04:11:23.953Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:11:26.849Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:11:29.755Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:11:32.628Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:11:34.683Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:11:35.993Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:11:38.054Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:11:39.731Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:11:40.445Z] 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.
[2024-11-14T04:11:40.445Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:11:40.445Z] Movies recommended for you:
[2024-11-14T04:11:40.445Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:11:40.445Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:11:40.445Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19951.204 ms) ======
[2024-11-14T04:11:40.445Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T04:11:40.445Z] GC before operation: completed in 155.068 ms, heap usage 135.581 MB -> 49.419 MB.
[2024-11-14T04:11:43.431Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:11:46.422Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:11:49.404Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:11:51.467Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:11:53.289Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:11:55.354Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:11:56.682Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:11:58.770Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:11:58.770Z] 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.
[2024-11-14T04:11:58.770Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:11:59.418Z] Movies recommended for you:
[2024-11-14T04:11:59.418Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:11:59.418Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:11:59.418Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (18622.981 ms) ======
[2024-11-14T04:11:59.418Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T04:11:59.418Z] GC before operation: completed in 138.872 ms, heap usage 127.464 MB -> 49.540 MB.
[2024-11-14T04:12:02.293Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:12:06.107Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:12:09.020Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:12:11.909Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:12:13.971Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:12:15.287Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:12:17.364Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:12:19.413Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:12:19.413Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-14T04:12:19.413Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:12:19.413Z] Movies recommended for you:
[2024-11-14T04:12:19.413Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:12:19.413Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:12:19.413Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20380.600 ms) ======
[2024-11-14T04:12:19.413Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T04:12:20.150Z] GC before operation: completed in 128.105 ms, heap usage 156.573 MB -> 49.310 MB.
[2024-11-14T04:12:23.060Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:12:25.957Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:12:28.858Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:12:30.983Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:12:32.294Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:12:34.383Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:12:36.461Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:12:38.542Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:12:38.542Z] 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.
[2024-11-14T04:12:38.542Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:12:38.542Z] Movies recommended for you:
[2024-11-14T04:12:38.542Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:12:38.542Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:12:38.542Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (18531.703 ms) ======
[2024-11-14T04:12:38.542Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T04:12:38.542Z] GC before operation: completed in 173.591 ms, heap usage 151.846 MB -> 49.480 MB.
[2024-11-14T04:12:41.513Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:12:44.423Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:12:47.310Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:12:50.179Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:12:51.499Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:12:53.560Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:12:55.600Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:12:56.923Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:12:57.554Z] 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.
[2024-11-14T04:12:57.554Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:12:57.554Z] Movies recommended for you:
[2024-11-14T04:12:57.554Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:12:57.554Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:12:57.554Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (19070.272 ms) ======
[2024-11-14T04:12:57.554Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T04:12:57.554Z] GC before operation: completed in 191.336 ms, heap usage 141.271 MB -> 49.597 MB.
[2024-11-14T04:13:01.338Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:13:04.196Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:13:07.124Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:13:09.191Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:13:11.251Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:13:12.563Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:13:13.893Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:13:15.982Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:13:15.982Z] 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.
[2024-11-14T04:13:15.982Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:13:15.982Z] Movies recommended for you:
[2024-11-14T04:13:15.982Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:13:15.982Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:13:15.982Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (18259.787 ms) ======
[2024-11-14T04:13:15.982Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T04:13:15.982Z] GC before operation: completed in 159.397 ms, heap usage 120.368 MB -> 49.317 MB.
[2024-11-14T04:13:19.798Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:13:22.776Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:13:26.033Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:13:28.174Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:13:29.523Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:13:31.234Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:13:33.500Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:13:34.937Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:13:34.937Z] 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.
[2024-11-14T04:13:34.937Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:13:34.937Z] Movies recommended for you:
[2024-11-14T04:13:34.937Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:13:34.937Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:13:34.937Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19017.249 ms) ======
[2024-11-14T04:13:34.937Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T04:13:35.658Z] GC before operation: completed in 211.992 ms, heap usage 109.087 MB -> 49.442 MB.
[2024-11-14T04:13:38.536Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:13:41.406Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:13:44.267Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:13:46.323Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:13:48.412Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:13:49.742Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:13:51.852Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:13:53.178Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:13:53.178Z] 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.
[2024-11-14T04:13:53.178Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:13:53.178Z] Movies recommended for you:
[2024-11-14T04:13:53.178Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:13:53.178Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:13:53.178Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (18100.524 ms) ======
[2024-11-14T04:13:53.178Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T04:13:53.868Z] GC before operation: completed in 150.180 ms, heap usage 169.299 MB -> 49.599 MB.
[2024-11-14T04:13:56.726Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:13:59.758Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:14:01.911Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:14:04.792Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:14:06.096Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:14:08.209Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:14:09.552Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:14:11.611Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:14:11.611Z] 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.
[2024-11-14T04:14:11.611Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:14:12.252Z] Movies recommended for you:
[2024-11-14T04:14:12.252Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:14:12.252Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:14:12.252Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (18367.545 ms) ======
[2024-11-14T04:14:12.252Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T04:14:12.252Z] GC before operation: completed in 160.102 ms, heap usage 138.643 MB -> 49.396 MB.
[2024-11-14T04:14:14.733Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:14:17.612Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:14:21.372Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:14:23.443Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:14:24.768Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:14:26.839Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:14:28.148Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:14:30.211Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:14:30.211Z] 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.
[2024-11-14T04:14:30.211Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:14:30.211Z] Movies recommended for you:
[2024-11-14T04:14:30.211Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:14:30.211Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:14:30.211Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (18138.492 ms) ======
[2024-11-14T04:14:30.211Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T04:14:30.211Z] GC before operation: completed in 175.987 ms, heap usage 140.850 MB -> 49.526 MB.
[2024-11-14T04:14:34.017Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:14:36.887Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:14:39.760Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:14:41.834Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:14:43.886Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:14:45.214Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:14:47.282Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:14:48.627Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:14:48.627Z] 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.
[2024-11-14T04:14:48.627Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:14:48.627Z] Movies recommended for you:
[2024-11-14T04:14:48.627Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:14:48.627Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:14:48.627Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (18323.408 ms) ======
[2024-11-14T04:14:48.627Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T04:14:48.627Z] GC before operation: completed in 147.264 ms, heap usage 198.338 MB -> 49.715 MB.
[2024-11-14T04:14:52.442Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T04:14:55.323Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T04:14:58.167Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T04:15:01.436Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T04:15:02.772Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T04:15:04.085Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T04:15:06.298Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T04:15:07.614Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T04:15:08.258Z] 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.
[2024-11-14T04:15:08.258Z] The best model improves the baseline by 14.34%.
[2024-11-14T04:15:08.258Z] Movies recommended for you:
[2024-11-14T04:15:08.258Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T04:15:08.258Z] There is no way to check that no silent failure occurred.
[2024-11-14T04:15:08.258Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19182.349 ms) ======
[2024-11-14T04:15:08.258Z] -----------------------------------
[2024-11-14T04:15:08.258Z] renaissance-movie-lens_0_PASSED
[2024-11-14T04:15:08.258Z] -----------------------------------
[2024-11-14T04:15:08.258Z]
[2024-11-14T04:15:08.258Z] TEST TEARDOWN:
[2024-11-14T04:15:08.258Z] Nothing to be done for teardown.
[2024-11-14T04:15:08.905Z] renaissance-movie-lens_0 Finish Time: Thu Nov 14 04:15:08 2024 Epoch Time (ms): 1731557708216