renaissance-movie-lens_0
[2024-10-29T23:33:53.334Z] Running test renaissance-movie-lens_0 ...
[2024-10-29T23:33:53.334Z] ===============================================
[2024-10-29T23:33:53.334Z] renaissance-movie-lens_0 Start Time: Tue Oct 29 19:14:22 2024 Epoch Time (ms): 1730247262454
[2024-10-29T23:33:53.334Z] variation: NoOptions
[2024-10-29T23:33:53.334Z] JVM_OPTIONS:
[2024-10-29T23:33:53.334Z] { \
[2024-10-29T23:33:53.334Z] echo ""; echo "TEST SETUP:"; \
[2024-10-29T23:33:53.334Z] echo "Nothing to be done for setup."; \
[2024-10-29T23:33:53.334Z] mkdir -p "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_1730244775953/renaissance-movie-lens_0"; \
[2024-10-29T23:33:53.334Z] cd "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_1730244775953/renaissance-movie-lens_0"; \
[2024-10-29T23:33:53.334Z] echo ""; echo "TESTING:"; \
[2024-10-29T23:33:53.335Z] "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/jdkbinary/j2sdk-image/bin/java" -jar "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_1730244775953/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-29T23:33:53.335Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/..; rm -f -r "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_1730244775953/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-29T23:33:53.335Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-29T23:33:53.335Z] echo "Nothing to be done for teardown."; \
[2024-10-29T23:33:53.335Z] } 2>&1 | tee -a "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_1730244775953/TestTargetResult";
[2024-10-29T23:33:53.335Z]
[2024-10-29T23:33:53.335Z] TEST SETUP:
[2024-10-29T23:33:53.335Z] Nothing to be done for setup.
[2024-10-29T23:33:53.335Z]
[2024-10-29T23:33:53.335Z] TESTING:
[2024-10-29T23:34:02.421Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-29T23:34:07.107Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-29T23:34:19.156Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-29T23:34:19.156Z] Training: 60056, validation: 20285, test: 19854
[2024-10-29T23:34:19.156Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-29T23:34:19.773Z] GC before operation: completed in 556.815 ms, heap usage 165.020 MB -> 27.740 MB.
[2024-10-29T23:34:36.690Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:34:49.218Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:34:59.548Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:35:06.731Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:35:11.515Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:35:16.187Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:35:21.083Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:35:25.757Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:35:25.757Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:35:26.375Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:35:26.993Z] Movies recommended for you:
[2024-10-29T23:35:26.993Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:35:26.993Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:35:26.993Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (67271.684 ms) ======
[2024-10-29T23:35:26.993Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-29T23:35:27.607Z] GC before operation: completed in 694.413 ms, heap usage 575.843 MB -> 55.241 MB.
[2024-10-29T23:35:34.707Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:35:41.754Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:35:48.805Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:35:55.851Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:35:59.536Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:36:03.223Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:36:07.939Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:36:12.611Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:36:13.225Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:36:13.225Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:36:13.858Z] Movies recommended for you:
[2024-10-29T23:36:13.858Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:36:13.858Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:36:13.858Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (46178.102 ms) ======
[2024-10-29T23:36:13.858Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-29T23:36:14.475Z] GC before operation: completed in 535.432 ms, heap usage 152.881 MB -> 42.330 MB.
[2024-10-29T23:36:21.592Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:36:27.374Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:36:34.424Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:36:41.471Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:36:45.156Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:36:48.846Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:36:53.513Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:36:57.201Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:36:57.817Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:36:57.817Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:36:57.817Z] Movies recommended for you:
[2024-10-29T23:36:57.817Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:36:57.817Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:36:57.817Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (43897.106 ms) ======
[2024-10-29T23:36:57.817Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-29T23:36:58.431Z] GC before operation: completed in 546.579 ms, heap usage 118.302 MB -> 42.554 MB.
[2024-10-29T23:37:05.517Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:37:11.303Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:37:18.363Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:37:24.510Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:37:30.297Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:37:33.986Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:37:37.799Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:37:41.486Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:37:42.102Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:37:42.102Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:37:42.719Z] Movies recommended for you:
[2024-10-29T23:37:42.719Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:37:42.719Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:37:42.719Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (43872.597 ms) ======
[2024-10-29T23:37:42.719Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-29T23:37:42.719Z] GC before operation: completed in 438.596 ms, heap usage 75.040 MB -> 42.918 MB.
[2024-10-29T23:37:49.759Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:37:55.548Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:38:02.600Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:38:08.389Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:38:13.074Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:38:16.760Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:38:20.446Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:38:24.416Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:38:25.031Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:38:25.031Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:38:25.649Z] Movies recommended for you:
[2024-10-29T23:38:25.649Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:38:25.649Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:38:25.649Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (42542.163 ms) ======
[2024-10-29T23:38:25.649Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-29T23:38:25.649Z] GC before operation: completed in 442.176 ms, heap usage 700.704 MB -> 48.231 MB.
[2024-10-29T23:38:31.439Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:38:38.489Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:38:44.272Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:38:50.057Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:38:53.743Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:38:57.464Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:39:01.149Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:39:04.836Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:39:05.451Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:39:05.451Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:39:06.076Z] Movies recommended for you:
[2024-10-29T23:39:06.077Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:39:06.077Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:39:06.077Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (39922.672 ms) ======
[2024-10-29T23:39:06.077Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-29T23:39:06.077Z] GC before operation: completed in 387.060 ms, heap usage 666.636 MB -> 48.056 MB.
[2024-10-29T23:39:11.858Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:39:17.640Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:39:24.677Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:39:29.483Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:39:33.182Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:39:36.877Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:39:40.559Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:39:44.247Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:39:44.862Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:39:44.862Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:39:44.862Z] Movies recommended for you:
[2024-10-29T23:39:44.862Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:39:44.862Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:39:44.862Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (38678.274 ms) ======
[2024-10-29T23:39:44.862Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-29T23:39:45.476Z] GC before operation: completed in 371.552 ms, heap usage 688.505 MB -> 48.366 MB.
[2024-10-29T23:39:51.261Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:39:57.045Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:40:02.842Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:40:08.638Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:40:12.328Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:40:16.013Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:40:19.703Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:40:22.504Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:40:23.122Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:40:23.737Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:40:23.737Z] Movies recommended for you:
[2024-10-29T23:40:23.737Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:40:23.737Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:40:23.737Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (38395.933 ms) ======
[2024-10-29T23:40:23.737Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-29T23:40:24.352Z] GC before operation: completed in 428.811 ms, heap usage 692.975 MB -> 48.673 MB.
[2024-10-29T23:40:30.262Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:40:36.042Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:40:41.823Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:40:47.610Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:40:51.306Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:40:54.998Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:40:58.690Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:41:02.440Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:41:02.440Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:41:02.440Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:41:03.059Z] Movies recommended for you:
[2024-10-29T23:41:03.059Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:41:03.059Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:41:03.059Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (38795.058 ms) ======
[2024-10-29T23:41:03.059Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-29T23:41:03.674Z] GC before operation: completed in 433.276 ms, heap usage 674.153 MB -> 48.343 MB.
[2024-10-29T23:41:09.456Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:41:15.236Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:41:21.029Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:41:27.087Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:41:29.890Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:41:33.598Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:41:37.287Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:41:41.015Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:41:41.632Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:41:41.633Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:41:41.633Z] Movies recommended for you:
[2024-10-29T23:41:41.633Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:41:41.633Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:41:41.633Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (38390.171 ms) ======
[2024-10-29T23:41:41.633Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-29T23:41:42.247Z] GC before operation: completed in 487.857 ms, heap usage 675.895 MB -> 48.575 MB.
[2024-10-29T23:41:48.104Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:41:53.888Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:41:59.666Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:42:05.569Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:42:09.258Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:42:12.943Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:42:16.624Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:42:19.429Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:42:20.047Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:42:20.047Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:42:20.661Z] Movies recommended for you:
[2024-10-29T23:42:20.661Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:42:20.661Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:42:20.661Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (38162.792 ms) ======
[2024-10-29T23:42:20.661Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-29T23:42:20.661Z] GC before operation: completed in 379.434 ms, heap usage 703.794 MB -> 48.239 MB.
[2024-10-29T23:42:26.586Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:42:32.371Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:42:38.280Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:42:44.064Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:42:47.745Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:42:50.556Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:42:54.245Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:42:57.946Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:42:58.563Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:42:58.563Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:42:59.179Z] Movies recommended for you:
[2024-10-29T23:42:59.179Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:42:59.179Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:42:59.179Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (38074.736 ms) ======
[2024-10-29T23:42:59.179Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-29T23:42:59.179Z] GC before operation: completed in 390.996 ms, heap usage 695.803 MB -> 48.455 MB.
[2024-10-29T23:43:04.986Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:43:10.771Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:43:17.817Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:43:22.501Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:43:26.196Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:43:29.883Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:43:33.587Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:43:38.271Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:43:38.271Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:43:38.886Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:43:38.886Z] Movies recommended for you:
[2024-10-29T23:43:38.886Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:43:38.886Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:43:38.886Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (39602.954 ms) ======
[2024-10-29T23:43:38.886Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-29T23:43:39.503Z] GC before operation: completed in 381.113 ms, heap usage 687.809 MB -> 48.667 MB.
[2024-10-29T23:43:45.299Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:43:51.098Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:43:58.144Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:44:02.820Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:44:06.581Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:44:10.266Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:44:13.959Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:44:17.648Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:44:18.264Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:44:18.264Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:44:18.264Z] Movies recommended for you:
[2024-10-29T23:44:18.264Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:44:18.264Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:44:18.264Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (38994.195 ms) ======
[2024-10-29T23:44:18.264Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-29T23:44:18.882Z] GC before operation: completed in 362.885 ms, heap usage 689.105 MB -> 48.346 MB.
[2024-10-29T23:44:24.705Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:44:30.651Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:44:36.429Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:44:42.210Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:44:45.897Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:44:48.709Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:44:52.391Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:44:56.080Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:44:56.703Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:44:56.703Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:44:56.703Z] Movies recommended for you:
[2024-10-29T23:44:56.703Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:44:56.703Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:44:56.703Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (38247.604 ms) ======
[2024-10-29T23:44:56.703Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-29T23:44:57.317Z] GC before operation: completed in 389.744 ms, heap usage 709.420 MB -> 48.732 MB.
[2024-10-29T23:45:03.106Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:45:08.891Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:45:14.676Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:45:20.457Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:45:24.149Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:45:27.086Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:45:30.796Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:45:34.483Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:45:35.099Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:45:35.099Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:45:35.715Z] Movies recommended for you:
[2024-10-29T23:45:35.715Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:45:35.715Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:45:35.715Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (38145.631 ms) ======
[2024-10-29T23:45:35.715Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-29T23:45:35.715Z] GC before operation: completed in 366.173 ms, heap usage 663.573 MB -> 48.650 MB.
[2024-10-29T23:45:41.498Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:45:47.278Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:45:53.061Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:45:58.847Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:46:01.650Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:46:05.339Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:46:09.040Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:46:11.844Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:46:12.457Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:46:13.071Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:46:13.071Z] Movies recommended for you:
[2024-10-29T23:46:13.071Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:46:13.071Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:46:13.071Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (37298.375 ms) ======
[2024-10-29T23:46:13.071Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-29T23:46:13.690Z] GC before operation: completed in 500.649 ms, heap usage 86.699 MB -> 44.724 MB.
[2024-10-29T23:46:19.477Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:46:25.271Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:46:31.074Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:46:35.748Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:46:39.438Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:46:42.246Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:46:45.936Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:46:49.623Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:46:49.623Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:46:49.623Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:46:50.240Z] Movies recommended for you:
[2024-10-29T23:46:50.240Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:46:50.240Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:46:50.240Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (36472.974 ms) ======
[2024-10-29T23:46:50.240Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-29T23:46:50.870Z] GC before operation: completed in 482.058 ms, heap usage 127.435 MB -> 43.516 MB.
[2024-10-29T23:46:55.556Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:47:01.358Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:47:07.259Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:47:13.049Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:47:15.859Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:47:19.554Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:47:23.242Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:47:26.308Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:47:26.930Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:47:26.930Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:47:26.930Z] Movies recommended for you:
[2024-10-29T23:47:26.930Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:47:26.930Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:47:26.930Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (36651.784 ms) ======
[2024-10-29T23:47:26.930Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-29T23:47:27.545Z] GC before operation: completed in 391.297 ms, heap usage 118.771 MB -> 45.972 MB.
[2024-10-29T23:47:33.325Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:47:38.128Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:47:43.911Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:47:49.696Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:47:53.377Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:47:56.180Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:47:59.867Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:48:03.552Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:48:04.168Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:48:04.168Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:48:04.168Z] Movies recommended for you:
[2024-10-29T23:48:04.168Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:48:04.168Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:48:04.168Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (36606.790 ms) ======
[2024-10-29T23:48:04.787Z] -----------------------------------
[2024-10-29T23:48:04.787Z] renaissance-movie-lens_0_PASSED
[2024-10-29T23:48:04.787Z] -----------------------------------
[2024-10-29T23:48:04.787Z]
[2024-10-29T23:48:04.787Z] TEST TEARDOWN:
[2024-10-29T23:48:04.787Z] Nothing to be done for teardown.
[2024-10-29T23:48:05.401Z] renaissance-movie-lens_0 Finish Time: Tue Oct 29 19:28:34 2024 Epoch Time (ms): 1730248114068