renaissance-movie-lens_0
[2024-11-23T18:08:23.879Z] Running test renaissance-movie-lens_0 ...
[2024-11-23T18:08:23.879Z] ===============================================
[2024-11-23T18:08:23.879Z] renaissance-movie-lens_0 Start Time: Sat Nov 23 13:08:23 2024 Epoch Time (ms): 1732385303194
[2024-11-23T18:08:23.879Z] variation: NoOptions
[2024-11-23T18:08:23.879Z] JVM_OPTIONS:
[2024-11-23T18:08:23.879Z] { \
[2024-11-23T18:08:23.879Z] echo ""; echo "TEST SETUP:"; \
[2024-11-23T18:08:23.879Z] echo "Nothing to be done for setup."; \
[2024-11-23T18:08:23.879Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17323853028240/renaissance-movie-lens_0"; \
[2024-11-23T18:08:23.879Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17323853028240/renaissance-movie-lens_0"; \
[2024-11-23T18:08:23.879Z] echo ""; echo "TESTING:"; \
[2024-11-23T18:08:23.879Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17323853028240/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-23T18:08:23.879Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17323853028240/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-23T18:08:23.879Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-23T18:08:23.879Z] echo "Nothing to be done for teardown."; \
[2024-11-23T18:08:23.879Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17323853028240/TestTargetResult";
[2024-11-23T18:08:23.879Z]
[2024-11-23T18:08:23.879Z] TEST SETUP:
[2024-11-23T18:08:23.879Z] Nothing to be done for setup.
[2024-11-23T18:08:23.879Z]
[2024-11-23T18:08:23.879Z] TESTING:
[2024-11-23T18:08:30.264Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-23T18:08:35.480Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-11-23T18:08:43.263Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-23T18:08:43.263Z] Training: 60056, validation: 20285, test: 19854
[2024-11-23T18:08:43.263Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-23T18:08:43.263Z] GC before operation: completed in 275.165 ms, heap usage 138.955 MB -> 37.108 MB.
[2024-11-23T18:08:58.670Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:09:10.388Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:09:18.448Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:09:26.642Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:09:31.818Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:09:36.119Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:09:40.575Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:09:46.307Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:09:47.957Z] 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.
[2024-11-23T18:09:47.957Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:09:47.957Z] Movies recommended for you:
[2024-11-23T18:09:47.957Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:09:47.957Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:09:47.957Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (64652.273 ms) ======
[2024-11-23T18:09:47.957Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-23T18:09:48.762Z] GC before operation: completed in 441.504 ms, heap usage 78.675 MB -> 60.748 MB.
[2024-11-23T18:09:56.885Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:10:02.216Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:10:09.322Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:10:15.604Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:10:19.703Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:10:23.835Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:10:29.303Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:10:33.521Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:10:33.522Z] 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.
[2024-11-23T18:10:33.522Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:10:34.250Z] Movies recommended for you:
[2024-11-23T18:10:34.250Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:10:34.250Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:10:34.250Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (45258.404 ms) ======
[2024-11-23T18:10:34.250Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-23T18:10:34.250Z] GC before operation: completed in 263.018 ms, heap usage 187.461 MB -> 49.032 MB.
[2024-11-23T18:10:40.832Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:10:47.476Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:10:57.487Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:11:03.876Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:11:08.115Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:11:11.335Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:11:14.533Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:11:19.196Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:11:19.938Z] 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.
[2024-11-23T18:11:19.938Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:11:19.938Z] Movies recommended for you:
[2024-11-23T18:11:19.938Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:11:19.938Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:11:19.938Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (45786.108 ms) ======
[2024-11-23T18:11:19.938Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-23T18:11:20.591Z] GC before operation: completed in 364.160 ms, heap usage 204.743 MB -> 51.138 MB.
[2024-11-23T18:11:27.207Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:11:35.253Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:11:43.078Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:11:48.391Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:11:51.442Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:11:54.654Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:11:59.149Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:12:03.124Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:12:04.093Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-23T18:12:04.093Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:12:04.907Z] Movies recommended for you:
[2024-11-23T18:12:04.908Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:12:04.908Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:12:04.908Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (44054.847 ms) ======
[2024-11-23T18:12:04.908Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-23T18:12:04.908Z] GC before operation: completed in 112.473 ms, heap usage 301.155 MB -> 49.944 MB.
[2024-11-23T18:12:11.527Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:12:19.502Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:12:28.813Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:12:34.036Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:12:38.083Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:12:40.366Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:12:44.638Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:12:49.003Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:12:49.700Z] 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.
[2024-11-23T18:12:49.701Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:12:50.382Z] Movies recommended for you:
[2024-11-23T18:12:50.382Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:12:50.382Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:12:50.382Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (45591.239 ms) ======
[2024-11-23T18:12:50.382Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-23T18:12:50.382Z] GC before operation: completed in 392.927 ms, heap usage 159.617 MB -> 50.562 MB.
[2024-11-23T18:12:55.581Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:13:03.763Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:13:11.706Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:13:16.999Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:13:23.291Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:13:25.829Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:13:28.963Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:13:32.503Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:13:33.192Z] 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.
[2024-11-23T18:13:33.192Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:13:34.002Z] Movies recommended for you:
[2024-11-23T18:13:34.002Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:13:34.002Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:13:34.002Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (43078.019 ms) ======
[2024-11-23T18:13:34.002Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-23T18:13:34.002Z] GC before operation: completed in 348.972 ms, heap usage 340.928 MB -> 54.993 MB.
[2024-11-23T18:13:40.619Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:13:47.003Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:13:54.843Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:14:00.068Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:14:04.342Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:14:08.515Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:14:11.721Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:14:17.013Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:14:17.013Z] 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.
[2024-11-23T18:14:17.013Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:14:17.013Z] Movies recommended for you:
[2024-11-23T18:14:17.013Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:14:17.013Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:14:17.013Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (43095.727 ms) ======
[2024-11-23T18:14:17.013Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-23T18:14:17.013Z] GC before operation: completed in 251.376 ms, heap usage 164.470 MB -> 49.991 MB.
[2024-11-23T18:14:23.425Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:14:28.914Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:14:34.235Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:14:41.422Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:14:43.666Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:14:47.914Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:14:51.054Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:14:54.473Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:14:55.202Z] 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.
[2024-11-23T18:14:55.203Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:14:55.203Z] Movies recommended for you:
[2024-11-23T18:14:55.203Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:14:55.203Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:14:55.203Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (38035.678 ms) ======
[2024-11-23T18:14:55.203Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-23T18:14:56.182Z] GC before operation: completed in 307.284 ms, heap usage 242.832 MB -> 50.322 MB.
[2024-11-23T18:15:02.744Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:15:07.945Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:15:13.132Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:15:18.373Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:15:21.601Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:15:23.039Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:15:27.077Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:15:30.374Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:15:30.374Z] 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.
[2024-11-23T18:15:30.374Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:15:30.374Z] Movies recommended for you:
[2024-11-23T18:15:30.374Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:15:30.374Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:15:30.374Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (34938.343 ms) ======
[2024-11-23T18:15:30.374Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-23T18:15:31.057Z] GC before operation: completed in 300.437 ms, heap usage 180.383 MB -> 50.110 MB.
[2024-11-23T18:15:37.418Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:15:41.542Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:15:46.733Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:15:50.926Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:15:53.246Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:15:55.524Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:15:59.843Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:16:02.152Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:16:02.873Z] 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.
[2024-11-23T18:16:02.873Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:16:02.873Z] Movies recommended for you:
[2024-11-23T18:16:02.873Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:16:02.873Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:16:02.873Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (32005.321 ms) ======
[2024-11-23T18:16:02.873Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-23T18:16:02.873Z] GC before operation: completed in 207.980 ms, heap usage 180.689 MB -> 50.237 MB.
[2024-11-23T18:16:08.166Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:16:13.406Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:16:18.608Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:16:23.901Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:16:27.972Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:16:32.049Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:16:34.215Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:16:36.493Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:16:37.261Z] 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.
[2024-11-23T18:16:37.261Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:16:37.261Z] Movies recommended for you:
[2024-11-23T18:16:37.261Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:16:37.261Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:16:37.261Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (34235.324 ms) ======
[2024-11-23T18:16:37.261Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-23T18:16:37.261Z] GC before operation: completed in 162.396 ms, heap usage 345.828 MB -> 55.158 MB.
[2024-11-23T18:16:42.414Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:16:46.535Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:16:50.715Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:16:55.845Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:16:57.789Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:17:02.115Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:17:05.381Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:17:08.725Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:17:08.725Z] 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.
[2024-11-23T18:17:08.725Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:17:09.455Z] Movies recommended for you:
[2024-11-23T18:17:09.455Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:17:09.455Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:17:09.455Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (31746.906 ms) ======
[2024-11-23T18:17:09.455Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-23T18:17:09.455Z] GC before operation: completed in 211.593 ms, heap usage 245.152 MB -> 50.198 MB.
[2024-11-23T18:17:14.495Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:17:19.681Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:17:24.864Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:17:29.041Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:17:33.143Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:17:36.208Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:17:39.278Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:17:42.581Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:17:43.343Z] 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.
[2024-11-23T18:17:43.343Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:17:43.343Z] Movies recommended for you:
[2024-11-23T18:17:43.343Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:17:43.343Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:17:43.343Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (33923.491 ms) ======
[2024-11-23T18:17:43.343Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-23T18:17:44.015Z] GC before operation: completed in 461.020 ms, heap usage 115.224 MB -> 52.537 MB.
[2024-11-23T18:17:48.021Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:17:54.487Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:18:01.038Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:18:07.504Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:18:11.158Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:18:13.413Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:18:18.581Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:18:21.780Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:18:22.668Z] 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.
[2024-11-23T18:18:22.668Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:18:22.668Z] Movies recommended for you:
[2024-11-23T18:18:22.668Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:18:22.668Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:18:22.668Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (38950.366 ms) ======
[2024-11-23T18:18:22.668Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-23T18:18:23.376Z] GC before operation: completed in 296.939 ms, heap usage 245.056 MB -> 51.901 MB.
[2024-11-23T18:18:28.682Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:18:33.952Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:18:40.374Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:18:45.874Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:18:49.206Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:18:53.326Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:18:55.503Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:18:57.844Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:18:58.591Z] 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.
[2024-11-23T18:18:58.591Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:18:58.591Z] Movies recommended for you:
[2024-11-23T18:18:58.591Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:18:58.591Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:18:58.591Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (35706.183 ms) ======
[2024-11-23T18:18:58.591Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-23T18:18:59.258Z] GC before operation: completed in 267.137 ms, heap usage 256.213 MB -> 50.247 MB.
[2024-11-23T18:19:05.548Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:19:09.955Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:19:16.446Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:19:22.914Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:19:28.424Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:19:32.858Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:19:36.100Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:19:40.345Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:19:41.033Z] 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.
[2024-11-23T18:19:41.034Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:19:41.034Z] Movies recommended for you:
[2024-11-23T18:19:41.034Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:19:41.034Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:19:41.034Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (42024.923 ms) ======
[2024-11-23T18:19:41.034Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-23T18:19:41.034Z] GC before operation: completed in 154.648 ms, heap usage 244.504 MB -> 50.350 MB.
[2024-11-23T18:19:47.552Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:19:51.653Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:19:56.962Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:20:02.325Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:20:05.515Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:20:07.745Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:20:12.146Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:20:13.529Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:20:14.218Z] 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.
[2024-11-23T18:20:14.919Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:20:14.919Z] Movies recommended for you:
[2024-11-23T18:20:14.919Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:20:14.919Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:20:14.919Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (33495.365 ms) ======
[2024-11-23T18:20:14.919Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-23T18:20:14.919Z] GC before operation: completed in 173.620 ms, heap usage 327.827 MB -> 50.296 MB.
[2024-11-23T18:20:20.137Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:20:26.663Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:20:33.039Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:20:37.805Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:20:40.881Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:20:43.130Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:20:46.332Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:20:49.653Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:20:50.363Z] 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.
[2024-11-23T18:20:50.363Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:20:50.363Z] Movies recommended for you:
[2024-11-23T18:20:50.363Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:20:50.363Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:20:50.363Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (35310.608 ms) ======
[2024-11-23T18:20:50.363Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-23T18:20:50.363Z] GC before operation: completed in 210.225 ms, heap usage 158.599 MB -> 50.253 MB.
[2024-11-23T18:20:55.807Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:21:00.928Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:21:06.234Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:21:09.366Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:21:12.397Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:21:14.607Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:21:16.865Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:21:19.158Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:21:19.848Z] 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.
[2024-11-23T18:21:19.848Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:21:19.848Z] Movies recommended for you:
[2024-11-23T18:21:19.848Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:21:19.848Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:21:19.848Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (29535.628 ms) ======
[2024-11-23T18:21:19.848Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-23T18:21:19.848Z] GC before operation: completed in 104.973 ms, heap usage 327.137 MB -> 50.612 MB.
[2024-11-23T18:21:23.929Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T18:21:27.902Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T18:21:33.185Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T18:21:36.284Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T18:21:38.620Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T18:21:41.729Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T18:21:43.935Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T18:21:46.551Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T18:21:47.238Z] 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.
[2024-11-23T18:21:47.238Z] The best model improves the baseline by 14.34%.
[2024-11-23T18:21:47.238Z] Movies recommended for you:
[2024-11-23T18:21:47.238Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T18:21:47.238Z] There is no way to check that no silent failure occurred.
[2024-11-23T18:21:47.238Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (27086.606 ms) ======
[2024-11-23T18:21:47.878Z] -----------------------------------
[2024-11-23T18:21:47.879Z] renaissance-movie-lens_0_PASSED
[2024-11-23T18:21:47.879Z] -----------------------------------
[2024-11-23T18:21:47.879Z]
[2024-11-23T18:21:47.879Z] TEST TEARDOWN:
[2024-11-23T18:21:47.879Z] Nothing to be done for teardown.
[2024-11-23T18:21:47.879Z] renaissance-movie-lens_0 Finish Time: Sat Nov 23 13:21:47 2024 Epoch Time (ms): 1732386107300