renaissance-movie-lens_0
[2024-09-05T22:27:30.429Z] Running test renaissance-movie-lens_0 ...
[2024-09-05T22:27:30.429Z] ===============================================
[2024-09-05T22:27:30.429Z] renaissance-movie-lens_0 Start Time: Thu Sep 5 22:27:29 2024 Epoch Time (ms): 1725575249565
[2024-09-05T22:27:30.429Z] variation: NoOptions
[2024-09-05T22:27:30.429Z] JVM_OPTIONS:
[2024-09-05T22:27:30.429Z] { \
[2024-09-05T22:27:30.429Z] echo ""; echo "TEST SETUP:"; \
[2024-09-05T22:27:30.429Z] echo "Nothing to be done for setup."; \
[2024-09-05T22:27:30.429Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17255742407341/renaissance-movie-lens_0"; \
[2024-09-05T22:27:30.429Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17255742407341/renaissance-movie-lens_0"; \
[2024-09-05T22:27:30.429Z] echo ""; echo "TESTING:"; \
[2024-09-05T22:27:30.429Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17255742407341/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-05T22:27:30.429Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17255742407341/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-05T22:27:30.429Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-05T22:27:30.429Z] echo "Nothing to be done for teardown."; \
[2024-09-05T22:27:30.429Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17255742407341/TestTargetResult";
[2024-09-05T22:27:30.429Z]
[2024-09-05T22:27:30.429Z] TEST SETUP:
[2024-09-05T22:27:30.429Z] Nothing to be done for setup.
[2024-09-05T22:27:30.429Z]
[2024-09-05T22:27:30.429Z] TESTING:
[2024-09-05T22:27:33.365Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-05T22:27:36.294Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-05T22:27:41.607Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-05T22:27:42.533Z] Training: 60056, validation: 20285, test: 19854
[2024-09-05T22:27:42.533Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-05T22:27:42.533Z] GC before operation: completed in 229.579 ms, heap usage 108.990 MB -> 25.966 MB.
[2024-09-05T22:27:49.085Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:27:52.015Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:27:56.055Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:27:58.980Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:27:59.905Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:28:01.796Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:28:03.690Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:28:05.603Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:28:05.603Z] 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.
[2024-09-05T22:28:05.603Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:28:07.439Z] Movies recommended for you:
[2024-09-05T22:28:07.439Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:28:07.439Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:28:07.439Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23481.271 ms) ======
[2024-09-05T22:28:07.439Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-05T22:28:07.439Z] GC before operation: completed in 281.139 ms, heap usage 158.897 MB -> 43.025 MB.
[2024-09-05T22:28:09.332Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:28:11.220Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:28:14.141Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:28:17.061Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:28:17.980Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:28:19.874Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:28:21.762Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:28:22.683Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:28:23.606Z] 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.
[2024-09-05T22:28:23.606Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:28:23.606Z] Movies recommended for you:
[2024-09-05T22:28:23.606Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:28:23.606Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:28:23.606Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17100.240 ms) ======
[2024-09-05T22:28:23.606Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-05T22:28:23.606Z] GC before operation: completed in 221.419 ms, heap usage 292.236 MB -> 41.181 MB.
[2024-09-05T22:28:26.541Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:28:28.437Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:28:31.399Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:28:33.296Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:28:35.189Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:28:37.097Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:28:38.022Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:28:39.915Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:28:39.915Z] 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.
[2024-09-05T22:28:39.915Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:28:39.915Z] Movies recommended for you:
[2024-09-05T22:28:39.915Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:28:39.915Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:28:39.915Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16488.156 ms) ======
[2024-09-05T22:28:39.915Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-05T22:28:40.837Z] GC before operation: completed in 210.677 ms, heap usage 93.141 MB -> 41.024 MB.
[2024-09-05T22:28:42.746Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:28:45.677Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:28:47.573Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:28:50.504Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:28:51.425Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:28:53.317Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:28:54.239Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:28:56.159Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:28:56.159Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-09-05T22:28:56.159Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:28:56.159Z] Movies recommended for you:
[2024-09-05T22:28:56.159Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:28:56.159Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:28:56.159Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15825.633 ms) ======
[2024-09-05T22:28:56.159Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-05T22:28:56.159Z] GC before operation: completed in 163.173 ms, heap usage 62.421 MB -> 41.367 MB.
[2024-09-05T22:28:59.092Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:29:00.985Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:29:02.894Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:29:05.836Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:29:06.756Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:29:08.658Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:29:09.583Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:29:11.898Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:29:11.898Z] 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.
[2024-09-05T22:29:11.898Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:29:11.898Z] Movies recommended for you:
[2024-09-05T22:29:11.898Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:29:11.898Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:29:11.898Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14945.279 ms) ======
[2024-09-05T22:29:11.898Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-05T22:29:11.898Z] GC before operation: completed in 158.966 ms, heap usage 365.911 MB -> 42.352 MB.
[2024-09-05T22:29:13.877Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:29:15.770Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:29:17.661Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:29:19.557Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:29:21.449Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:29:22.370Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:29:24.264Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:29:25.188Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:29:26.186Z] 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.
[2024-09-05T22:29:26.186Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:29:26.186Z] Movies recommended for you:
[2024-09-05T22:29:26.186Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:29:26.186Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:29:26.186Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14267.394 ms) ======
[2024-09-05T22:29:26.186Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-05T22:29:26.186Z] GC before operation: completed in 153.105 ms, heap usage 347.528 MB -> 42.218 MB.
[2024-09-05T22:29:28.081Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:29:29.969Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:29:32.895Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:29:34.787Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:29:35.709Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:29:37.657Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:29:38.594Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:29:39.514Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:29:40.439Z] 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.
[2024-09-05T22:29:40.439Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:29:40.439Z] Movies recommended for you:
[2024-09-05T22:29:40.439Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:29:40.439Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:29:40.439Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14340.177 ms) ======
[2024-09-05T22:29:40.439Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-05T22:29:40.439Z] GC before operation: completed in 193.231 ms, heap usage 344.090 MB -> 42.437 MB.
[2024-09-05T22:29:42.331Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:29:44.218Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:29:47.145Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:29:49.053Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:29:49.974Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:29:51.869Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:29:52.795Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:29:54.690Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:29:54.690Z] 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.
[2024-09-05T22:29:54.690Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:29:54.690Z] Movies recommended for you:
[2024-09-05T22:29:54.690Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:29:54.690Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:29:54.690Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14390.228 ms) ======
[2024-09-05T22:29:54.690Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-05T22:29:54.690Z] GC before operation: completed in 199.007 ms, heap usage 115.371 MB -> 45.367 MB.
[2024-09-05T22:29:57.617Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:29:59.529Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:30:01.421Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:30:03.313Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:30:05.206Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:30:06.126Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:30:07.051Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:30:08.943Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:30:08.943Z] 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.
[2024-09-05T22:30:08.943Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:30:08.943Z] Movies recommended for you:
[2024-09-05T22:30:08.943Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:30:08.943Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:30:08.943Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14146.939 ms) ======
[2024-09-05T22:30:08.943Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-05T22:30:09.864Z] GC before operation: completed in 195.343 ms, heap usage 203.731 MB -> 54.563 MB.
[2024-09-05T22:30:11.753Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:30:14.687Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:30:15.607Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:30:17.523Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:30:19.425Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:30:20.346Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:30:21.272Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:30:23.167Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:30:23.167Z] 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.
[2024-09-05T22:30:23.167Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:30:24.099Z] Movies recommended for you:
[2024-09-05T22:30:24.099Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:30:24.100Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:30:24.100Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14224.124 ms) ======
[2024-09-05T22:30:24.100Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-05T22:30:24.100Z] GC before operation: completed in 211.106 ms, heap usage 294.393 MB -> 45.960 MB.
[2024-09-05T22:30:26.007Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:30:27.928Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:30:29.817Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:30:32.758Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:30:33.678Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:30:34.599Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:30:36.488Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:30:37.414Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:30:37.414Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-09-05T22:30:37.414Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:30:37.414Z] Movies recommended for you:
[2024-09-05T22:30:37.414Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:30:37.414Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:30:37.414Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13999.996 ms) ======
[2024-09-05T22:30:37.414Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-05T22:30:38.336Z] GC before operation: completed in 204.562 ms, heap usage 150.061 MB -> 70.008 MB.
[2024-09-05T22:30:40.232Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:30:42.124Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:30:44.185Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:30:46.087Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:30:47.977Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:30:48.897Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:30:50.794Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:30:51.720Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:30:52.638Z] 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.
[2024-09-05T22:30:52.638Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:30:52.638Z] Movies recommended for you:
[2024-09-05T22:30:52.638Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:30:52.638Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:30:52.638Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14308.781 ms) ======
[2024-09-05T22:30:52.638Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-05T22:30:52.638Z] GC before operation: completed in 151.338 ms, heap usage 140.178 MB -> 52.438 MB.
[2024-09-05T22:30:54.525Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:30:56.415Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:30:59.391Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:31:01.285Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:31:02.205Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:31:04.091Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:31:05.010Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:31:05.931Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:31:06.850Z] 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.
[2024-09-05T22:31:06.851Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:31:06.851Z] Movies recommended for you:
[2024-09-05T22:31:06.851Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:31:06.851Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:31:06.851Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14100.685 ms) ======
[2024-09-05T22:31:06.851Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-05T22:31:06.851Z] GC before operation: completed in 202.388 ms, heap usage 138.288 MB -> 70.720 MB.
[2024-09-05T22:31:08.741Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:31:10.629Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:31:13.558Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:31:15.453Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:31:17.331Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:31:18.251Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:31:19.171Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:31:21.070Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:31:21.070Z] 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.
[2024-09-05T22:31:21.070Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:31:21.070Z] Movies recommended for you:
[2024-09-05T22:31:21.070Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:31:21.070Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:31:21.070Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14372.413 ms) ======
[2024-09-05T22:31:21.070Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-05T22:31:21.070Z] GC before operation: completed in 152.798 ms, heap usage 409.015 MB -> 56.643 MB.
[2024-09-05T22:31:22.969Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:31:25.893Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:31:27.924Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:31:29.813Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:31:30.822Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:31:32.716Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:31:33.646Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:31:34.566Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:31:35.487Z] 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.
[2024-09-05T22:31:35.487Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:31:35.487Z] Movies recommended for you:
[2024-09-05T22:31:35.487Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:31:35.487Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:31:35.487Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13990.001 ms) ======
[2024-09-05T22:31:35.487Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-05T22:31:35.487Z] GC before operation: completed in 201.130 ms, heap usage 145.097 MB -> 70.586 MB.
[2024-09-05T22:31:37.403Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:31:39.296Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:31:42.216Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:31:44.124Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:31:45.049Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:31:45.969Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:31:47.860Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:31:48.781Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:31:49.701Z] 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.
[2024-09-05T22:31:49.701Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:31:49.701Z] Movies recommended for you:
[2024-09-05T22:31:49.701Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:31:49.701Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:31:49.701Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13869.333 ms) ======
[2024-09-05T22:31:49.701Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-05T22:31:49.701Z] GC before operation: completed in 208.901 ms, heap usage 163.032 MB -> 70.620 MB.
[2024-09-05T22:31:51.588Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:31:53.474Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:31:55.362Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:31:57.294Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:31:59.183Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:32:00.101Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:32:01.988Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:32:02.906Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:32:03.824Z] 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.
[2024-09-05T22:32:03.824Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:32:03.824Z] Movies recommended for you:
[2024-09-05T22:32:03.824Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:32:03.824Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:32:03.824Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14006.513 ms) ======
[2024-09-05T22:32:03.824Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-05T22:32:03.824Z] GC before operation: completed in 185.473 ms, heap usage 184.313 MB -> 45.532 MB.
[2024-09-05T22:32:05.712Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:32:07.600Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:32:10.523Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:32:12.414Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:32:13.335Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:32:15.228Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:32:16.162Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:32:17.088Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:32:18.010Z] 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.
[2024-09-05T22:32:18.010Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:32:18.010Z] Movies recommended for you:
[2024-09-05T22:32:18.010Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:32:18.010Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:32:18.011Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14000.460 ms) ======
[2024-09-05T22:32:18.011Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-05T22:32:18.011Z] GC before operation: completed in 178.007 ms, heap usage 157.819 MB -> 70.504 MB.
[2024-09-05T22:32:21.074Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:32:21.996Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:32:24.921Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:32:26.815Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:32:27.735Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:32:28.658Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:32:30.551Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:32:31.475Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:32:31.475Z] 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.
[2024-09-05T22:32:31.475Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:32:32.396Z] Movies recommended for you:
[2024-09-05T22:32:32.396Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:32:32.396Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:32:32.396Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14049.444 ms) ======
[2024-09-05T22:32:32.396Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-05T22:32:32.396Z] GC before operation: completed in 187.950 ms, heap usage 169.045 MB -> 70.681 MB.
[2024-09-05T22:32:34.287Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T22:32:36.182Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T22:32:38.084Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T22:32:41.014Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T22:32:41.935Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T22:32:42.857Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T22:32:44.751Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T22:32:45.670Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T22:32:45.670Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-09-05T22:32:45.670Z] The best model improves the baseline by 14.52%.
[2024-09-05T22:32:45.670Z] Movies recommended for you:
[2024-09-05T22:32:45.670Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T22:32:45.670Z] There is no way to check that no silent failure occurred.
[2024-09-05T22:32:45.670Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13770.699 ms) ======
[2024-09-05T22:32:46.590Z] -----------------------------------
[2024-09-05T22:32:46.590Z] renaissance-movie-lens_0_PASSED
[2024-09-05T22:32:46.590Z] -----------------------------------
[2024-09-05T22:32:46.590Z]
[2024-09-05T22:32:46.590Z] TEST TEARDOWN:
[2024-09-05T22:32:46.590Z] Nothing to be done for teardown.
[2024-09-05T22:32:46.590Z] renaissance-movie-lens_0 Finish Time: Thu Sep 5 22:32:46 2024 Epoch Time (ms): 1725575566100