renaissance-movie-lens_0
[2024-09-25T22:20:22.925Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T22:20:22.925Z] ===============================================
[2024-09-25T22:20:22.925Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 18:20:22 2024 Epoch Time (ms): 1727302822861
[2024-09-25T22:20:23.623Z] variation: NoOptions
[2024-09-25T22:20:23.623Z] JVM_OPTIONS:
[2024-09-25T22:20:23.623Z] { \
[2024-09-25T22:20:23.623Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T22:20:23.623Z] echo "Nothing to be done for setup."; \
[2024-09-25T22:20:23.623Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273005053256/renaissance-movie-lens_0"; \
[2024-09-25T22:20:23.623Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273005053256/renaissance-movie-lens_0"; \
[2024-09-25T22:20:23.623Z] echo ""; echo "TESTING:"; \
[2024-09-25T22:20:23.623Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273005053256/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T22:20:23.623Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273005053256/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T22:20:23.623Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T22:20:23.623Z] echo "Nothing to be done for teardown."; \
[2024-09-25T22:20:23.623Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17273005053256/TestTargetResult";
[2024-09-25T22:20:23.623Z]
[2024-09-25T22:20:23.623Z] TEST SETUP:
[2024-09-25T22:20:23.623Z] Nothing to be done for setup.
[2024-09-25T22:20:23.623Z]
[2024-09-25T22:20:23.623Z] TESTING:
[2024-09-25T22:20:39.125Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T22:20:45.375Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-09-25T22:20:54.583Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T22:20:55.237Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T22:20:55.237Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T22:20:55.875Z] GC before operation: completed in 191.734 ms, heap usage 53.019 MB -> 37.133 MB.
[2024-09-25T22:21:12.591Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:21:26.527Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:21:42.496Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:21:50.221Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:21:55.724Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:21:59.007Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:22:04.044Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:22:07.114Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:22:07.821Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-25T22:22:07.821Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:22:08.448Z] Movies recommended for you:
[2024-09-25T22:22:08.448Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:22:08.448Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:22:08.448Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (72539.645 ms) ======
[2024-09-25T22:22:08.448Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T22:22:08.448Z] GC before operation: completed in 340.732 ms, heap usage 108.867 MB -> 53.475 MB.
[2024-09-25T22:22:12.752Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:22:22.272Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:22:28.684Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:22:33.677Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:22:37.975Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:22:43.285Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:22:47.209Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:22:54.392Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:22:55.310Z] 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.
[2024-09-25T22:22:55.310Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:22:56.429Z] Movies recommended for you:
[2024-09-25T22:22:56.429Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:22:56.429Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:22:56.429Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (47610.657 ms) ======
[2024-09-25T22:22:56.429Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T22:22:57.079Z] GC before operation: completed in 594.281 ms, heap usage 280.925 MB -> 49.803 MB.
[2024-09-25T22:23:04.716Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:23:12.989Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:23:19.878Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:23:24.684Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:23:27.679Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:23:30.533Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:23:33.686Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:23:37.821Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:23:38.533Z] 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.
[2024-09-25T22:23:38.533Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:23:38.533Z] Movies recommended for you:
[2024-09-25T22:23:38.533Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:23:38.533Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:23:38.533Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (42087.651 ms) ======
[2024-09-25T22:23:38.533Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T22:23:39.229Z] GC before operation: completed in 224.089 ms, heap usage 304.713 MB -> 49.555 MB.
[2024-09-25T22:23:45.309Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:23:50.764Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:23:55.585Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:23:59.408Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:24:03.500Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:24:08.734Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:24:11.776Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:24:16.533Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:24:17.181Z] 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.
[2024-09-25T22:24:17.181Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:24:17.932Z] Movies recommended for you:
[2024-09-25T22:24:17.932Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:24:17.932Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:24:17.932Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (38485.110 ms) ======
[2024-09-25T22:24:17.932Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T22:24:17.932Z] GC before operation: completed in 230.543 ms, heap usage 193.971 MB -> 49.723 MB.
[2024-09-25T22:24:21.911Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:24:32.437Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:24:42.849Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:24:50.593Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:24:54.792Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:24:56.886Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:25:01.119Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:25:04.257Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:25:05.736Z] 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.
[2024-09-25T22:25:05.736Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:25:05.736Z] Movies recommended for you:
[2024-09-25T22:25:05.736Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:25:05.736Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:25:05.736Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (48141.837 ms) ======
[2024-09-25T22:25:05.736Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T22:25:06.525Z] GC before operation: completed in 347.100 ms, heap usage 234.328 MB -> 49.997 MB.
[2024-09-25T22:25:11.720Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:25:17.940Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:25:23.117Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:25:28.176Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:25:30.459Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:25:33.499Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:25:40.760Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:25:44.101Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:25:44.974Z] 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.
[2024-09-25T22:25:44.975Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:25:44.975Z] Movies recommended for you:
[2024-09-25T22:25:44.975Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:25:44.975Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:25:44.975Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (38860.512 ms) ======
[2024-09-25T22:25:44.975Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T22:25:44.975Z] GC before operation: completed in 156.949 ms, heap usage 227.072 MB -> 49.951 MB.
[2024-09-25T22:25:51.537Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:25:57.175Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:26:05.456Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:26:14.761Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:26:16.993Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:26:20.343Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:26:29.040Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:26:34.580Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:26:35.577Z] 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.
[2024-09-25T22:26:35.577Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:26:36.490Z] Movies recommended for you:
[2024-09-25T22:26:36.490Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:26:36.490Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:26:36.490Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (50983.947 ms) ======
[2024-09-25T22:26:36.490Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T22:26:37.225Z] GC before operation: completed in 569.932 ms, heap usage 225.265 MB -> 50.155 MB.
[2024-09-25T22:26:43.462Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:26:48.662Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:26:58.265Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:27:07.164Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:27:09.239Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:27:13.240Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:27:15.478Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:27:19.529Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:27:19.530Z] 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.
[2024-09-25T22:27:19.530Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:27:20.186Z] Movies recommended for you:
[2024-09-25T22:27:20.186Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:27:20.186Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:27:20.186Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (43018.744 ms) ======
[2024-09-25T22:27:20.186Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T22:27:20.186Z] GC before operation: completed in 231.368 ms, heap usage 125.160 MB -> 50.819 MB.
[2024-09-25T22:27:25.182Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:27:30.013Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:27:36.169Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:27:42.512Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:27:46.776Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:27:53.656Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:28:00.372Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:28:06.100Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:28:06.919Z] 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.
[2024-09-25T22:28:06.919Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:28:06.919Z] Movies recommended for you:
[2024-09-25T22:28:06.919Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:28:06.919Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:28:06.919Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (46760.782 ms) ======
[2024-09-25T22:28:06.919Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T22:28:06.919Z] GC before operation: completed in 196.714 ms, heap usage 189.695 MB -> 50.735 MB.
[2024-09-25T22:28:14.542Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:28:19.724Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:28:27.628Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:28:34.112Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:28:37.408Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:28:40.359Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:28:44.891Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:28:49.099Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:28:49.888Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-25T22:28:49.888Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:28:50.639Z] Movies recommended for you:
[2024-09-25T22:28:50.639Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:28:50.639Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:28:50.639Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (43240.301 ms) ======
[2024-09-25T22:28:50.639Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T22:28:50.639Z] GC before operation: completed in 225.720 ms, heap usage 237.812 MB -> 50.298 MB.
[2024-09-25T22:28:57.386Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:29:03.644Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:29:07.533Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:29:13.789Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:29:16.826Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:29:19.980Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:29:24.121Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:29:26.212Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:29:26.879Z] 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.
[2024-09-25T22:29:26.879Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:29:26.879Z] Movies recommended for you:
[2024-09-25T22:29:26.879Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:29:26.879Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:29:26.879Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (36462.173 ms) ======
[2024-09-25T22:29:26.879Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T22:29:26.879Z] GC before operation: completed in 97.752 ms, heap usage 268.554 MB -> 50.202 MB.
[2024-09-25T22:29:30.753Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:29:36.909Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:29:44.435Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:29:49.444Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:29:53.362Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:29:55.716Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:30:02.537Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:30:05.701Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:30:06.395Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-25T22:30:06.395Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:30:07.052Z] Movies recommended for you:
[2024-09-25T22:30:07.052Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:30:07.052Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:30:07.052Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (39794.945 ms) ======
[2024-09-25T22:30:07.052Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T22:30:07.052Z] GC before operation: completed in 251.620 ms, heap usage 88.565 MB -> 50.098 MB.
[2024-09-25T22:30:12.728Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:30:21.882Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:30:30.640Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:30:36.872Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:30:41.049Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:30:44.077Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:30:47.117Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:30:54.430Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:30:58.727Z] 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.
[2024-09-25T22:30:58.728Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:30:58.728Z] Movies recommended for you:
[2024-09-25T22:30:58.728Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:30:58.728Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:30:58.728Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (49728.253 ms) ======
[2024-09-25T22:30:58.728Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T22:30:58.728Z] GC before operation: completed in 1186.060 ms, heap usage 193.336 MB -> 50.371 MB.
[2024-09-25T22:31:05.828Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:31:16.704Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:31:28.701Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:31:37.061Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:31:41.738Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:31:44.799Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:31:48.292Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:31:55.029Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:31:55.029Z] 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.
[2024-09-25T22:31:55.029Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:31:55.661Z] Movies recommended for you:
[2024-09-25T22:31:55.661Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:31:55.661Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:31:55.661Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (57483.830 ms) ======
[2024-09-25T22:31:55.661Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T22:31:55.661Z] GC before operation: completed in 389.622 ms, heap usage 264.178 MB -> 50.266 MB.
[2024-09-25T22:32:07.620Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:32:15.898Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:32:30.772Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:32:40.653Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:32:45.054Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:32:51.845Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:32:57.171Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:33:01.241Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:33:01.241Z] 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.
[2024-09-25T22:33:01.881Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:33:01.882Z] Movies recommended for you:
[2024-09-25T22:33:01.882Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:33:01.882Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:33:01.882Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (65894.262 ms) ======
[2024-09-25T22:33:01.882Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T22:33:01.882Z] GC before operation: completed in 243.255 ms, heap usage 310.137 MB -> 50.418 MB.
[2024-09-25T22:33:08.429Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:33:17.810Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:33:23.006Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:33:28.011Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:33:31.631Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:33:34.917Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:33:42.647Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:33:46.727Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:33:47.379Z] 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.
[2024-09-25T22:33:47.379Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:33:47.379Z] Movies recommended for you:
[2024-09-25T22:33:47.379Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:33:47.379Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:33:47.379Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (45409.249 ms) ======
[2024-09-25T22:33:47.379Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T22:33:47.379Z] GC before operation: completed in 224.514 ms, heap usage 261.817 MB -> 50.531 MB.
[2024-09-25T22:33:54.020Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:34:00.742Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:34:10.216Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:34:14.328Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:34:19.563Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:34:22.697Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:34:27.940Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:34:31.913Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:34:32.641Z] 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.
[2024-09-25T22:34:32.641Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:34:32.641Z] Movies recommended for you:
[2024-09-25T22:34:32.641Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:34:32.641Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:34:33.300Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (45210.325 ms) ======
[2024-09-25T22:34:33.300Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T22:34:33.300Z] GC before operation: completed in 401.092 ms, heap usage 269.422 MB -> 50.326 MB.
[2024-09-25T22:34:39.592Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:34:49.093Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:34:57.434Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:35:05.199Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:35:10.526Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:35:15.915Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:35:20.032Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:35:24.217Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:35:24.856Z] 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.
[2024-09-25T22:35:24.856Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:35:24.856Z] Movies recommended for you:
[2024-09-25T22:35:24.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:35:24.856Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:35:24.856Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (51431.206 ms) ======
[2024-09-25T22:35:24.856Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T22:35:24.856Z] GC before operation: completed in 283.440 ms, heap usage 236.808 MB -> 50.336 MB.
[2024-09-25T22:35:32.447Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:35:38.815Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:35:47.249Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:35:53.999Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:35:58.146Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:36:01.264Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:36:06.492Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:36:15.121Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:36:15.892Z] 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.
[2024-09-25T22:36:15.892Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:36:16.638Z] Movies recommended for you:
[2024-09-25T22:36:16.638Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:36:16.638Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:36:16.638Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (51323.403 ms) ======
[2024-09-25T22:36:16.638Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T22:36:16.638Z] GC before operation: completed in 289.353 ms, heap usage 234.107 MB -> 48.720 MB.
[2024-09-25T22:36:23.291Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T22:36:31.973Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T22:36:43.560Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T22:36:49.002Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T22:36:54.643Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T22:36:58.395Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T22:37:01.537Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T22:37:05.579Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T22:37:06.248Z] 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.
[2024-09-25T22:37:06.248Z] The best model improves the baseline by 14.34%.
[2024-09-25T22:37:06.248Z] Movies recommended for you:
[2024-09-25T22:37:06.248Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T22:37:06.248Z] There is no way to check that no silent failure occurred.
[2024-09-25T22:37:06.248Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (49789.198 ms) ======
[2024-09-25T22:37:07.588Z] -----------------------------------
[2024-09-25T22:37:07.588Z] renaissance-movie-lens_0_PASSED
[2024-09-25T22:37:07.588Z] -----------------------------------
[2024-09-25T22:37:07.588Z]
[2024-09-25T22:37:07.588Z] TEST TEARDOWN:
[2024-09-25T22:37:07.588Z] Nothing to be done for teardown.
[2024-09-25T22:37:07.588Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 18:37:07 2024 Epoch Time (ms): 1727303827474