renaissance-movie-lens_0
[2024-11-28T22:34:45.650Z] Running test renaissance-movie-lens_0 ...
[2024-11-28T22:34:45.650Z] ===============================================
[2024-11-28T22:34:45.650Z] renaissance-movie-lens_0 Start Time: Thu Nov 28 22:34:44 2024 Epoch Time (ms): 1732833284746
[2024-11-28T22:34:45.650Z] variation: NoOptions
[2024-11-28T22:34:45.650Z] JVM_OPTIONS:
[2024-11-28T22:34:45.650Z] { \
[2024-11-28T22:34:45.650Z] echo ""; echo "TEST SETUP:"; \
[2024-11-28T22:34:45.650Z] echo "Nothing to be done for setup."; \
[2024-11-28T22:34:45.650Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_1732832259821/renaissance-movie-lens_0"; \
[2024-11-28T22:34:45.650Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_1732832259821/renaissance-movie-lens_0"; \
[2024-11-28T22:34:45.650Z] echo ""; echo "TESTING:"; \
[2024-11-28T22:34:45.650Z] "/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_1732832259821/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-28T22:34:45.650Z] 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_1732832259821/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-28T22:34:45.650Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-28T22:34:45.650Z] echo "Nothing to be done for teardown."; \
[2024-11-28T22:34:45.650Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_1732832259821/TestTargetResult";
[2024-11-28T22:34:45.650Z]
[2024-11-28T22:34:45.650Z] TEST SETUP:
[2024-11-28T22:34:45.650Z] Nothing to be done for setup.
[2024-11-28T22:34:45.650Z]
[2024-11-28T22:34:45.650Z] TESTING:
[2024-11-28T22:34:48.661Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-28T22:34:51.660Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-28T22:34:56.872Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-28T22:34:56.872Z] Training: 60056, validation: 20285, test: 19854
[2024-11-28T22:34:56.872Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-28T22:34:56.872Z] GC before operation: completed in 257.935 ms, heap usage 83.424 MB -> 25.828 MB.
[2024-11-28T22:35:03.567Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:35:07.686Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:35:10.719Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:35:13.737Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:35:15.690Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:35:16.642Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:35:18.586Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:35:20.555Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:35:20.555Z] 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.
[2024-11-28T22:35:20.555Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:35:21.497Z] Movies recommended for you:
[2024-11-28T22:35:21.498Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:35:21.498Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:35:21.498Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24091.250 ms) ======
[2024-11-28T22:35:21.498Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-28T22:35:21.498Z] GC before operation: completed in 302.832 ms, heap usage 342.461 MB -> 43.425 MB.
[2024-11-28T22:35:24.518Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:35:26.469Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:35:29.485Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:35:32.511Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:35:33.458Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:35:35.422Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:35:37.394Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:35:38.338Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:35:39.282Z] 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.
[2024-11-28T22:35:39.282Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:35:39.282Z] Movies recommended for you:
[2024-11-28T22:35:39.282Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:35:39.282Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:35:39.282Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17823.491 ms) ======
[2024-11-28T22:35:39.282Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-28T22:35:39.282Z] GC before operation: completed in 216.819 ms, heap usage 245.698 MB -> 41.069 MB.
[2024-11-28T22:35:42.274Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:35:44.214Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:35:47.203Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:35:49.145Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:35:51.095Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:35:52.055Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:35:54.022Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:35:57.004Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:35:57.004Z] 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.
[2024-11-28T22:35:57.004Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:35:57.004Z] Movies recommended for you:
[2024-11-28T22:35:57.004Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:35:57.004Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:35:57.004Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16447.959 ms) ======
[2024-11-28T22:35:57.004Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-28T22:35:57.004Z] GC before operation: completed in 221.449 ms, heap usage 338.270 MB -> 41.601 MB.
[2024-11-28T22:35:58.950Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:36:00.901Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:36:02.837Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:36:05.848Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:36:06.791Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:36:08.729Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:36:09.671Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:36:11.604Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:36:11.604Z] 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.
[2024-11-28T22:36:11.604Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:36:11.604Z] Movies recommended for you:
[2024-11-28T22:36:11.604Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:36:11.604Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:36:11.605Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15483.265 ms) ======
[2024-11-28T22:36:11.605Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-28T22:36:11.605Z] GC before operation: completed in 167.540 ms, heap usage 107.136 MB -> 41.429 MB.
[2024-11-28T22:36:13.570Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:36:16.561Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:36:18.522Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:36:20.472Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:36:22.426Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:36:23.379Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:36:25.329Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:36:26.274Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:36:26.274Z] 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.
[2024-11-28T22:36:26.274Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:36:27.219Z] Movies recommended for you:
[2024-11-28T22:36:27.219Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:36:27.219Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:36:27.219Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15061.130 ms) ======
[2024-11-28T22:36:27.219Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-28T22:36:27.219Z] GC before operation: completed in 157.633 ms, heap usage 167.375 MB -> 43.648 MB.
[2024-11-28T22:36:29.154Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:36:31.097Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:36:33.033Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:36:36.028Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:36:36.979Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:36:37.928Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:36:39.882Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:36:41.830Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:36:41.830Z] 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.
[2024-11-28T22:36:41.830Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:36:41.830Z] Movies recommended for you:
[2024-11-28T22:36:41.830Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:36:41.830Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:36:41.830Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14760.262 ms) ======
[2024-11-28T22:36:41.830Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-28T22:36:41.830Z] GC before operation: completed in 185.688 ms, heap usage 126.093 MB -> 44.911 MB.
[2024-11-28T22:36:43.778Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:36:46.802Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:36:48.750Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:36:51.748Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:36:52.712Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:36:54.674Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:36:55.618Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:36:58.818Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:36:58.818Z] 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.
[2024-11-28T22:36:58.818Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:36:58.818Z] Movies recommended for you:
[2024-11-28T22:36:58.818Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:36:58.818Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:36:58.818Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15622.787 ms) ======
[2024-11-28T22:36:58.818Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-28T22:36:58.818Z] GC before operation: completed in 213.933 ms, heap usage 128.319 MB -> 61.264 MB.
[2024-11-28T22:36:59.760Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:37:01.715Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:37:04.725Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:37:06.672Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:37:07.615Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:37:09.570Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:37:10.520Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:37:12.474Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:37:12.475Z] 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.
[2024-11-28T22:37:12.475Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:37:12.475Z] Movies recommended for you:
[2024-11-28T22:37:12.475Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:37:12.475Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:37:12.475Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14798.961 ms) ======
[2024-11-28T22:37:12.475Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-28T22:37:12.475Z] GC before operation: completed in 154.623 ms, heap usage 103.024 MB -> 41.947 MB.
[2024-11-28T22:37:15.485Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:37:17.455Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:37:19.407Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:37:21.354Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:37:23.290Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:37:24.235Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:37:25.178Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:37:27.133Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:37:27.133Z] 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.
[2024-11-28T22:37:27.133Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:37:27.133Z] Movies recommended for you:
[2024-11-28T22:37:27.133Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:37:27.133Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:37:27.133Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14557.962 ms) ======
[2024-11-28T22:37:27.133Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-28T22:37:27.133Z] GC before operation: completed in 152.730 ms, heap usage 340.594 MB -> 50.625 MB.
[2024-11-28T22:37:30.143Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:37:32.083Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:37:34.026Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:37:35.984Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:37:37.000Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:37:38.941Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:37:39.911Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:37:41.879Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:37:41.879Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-11-28T22:37:41.879Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:37:41.879Z] Movies recommended for you:
[2024-11-28T22:37:41.879Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:37:41.879Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:37:41.879Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14308.403 ms) ======
[2024-11-28T22:37:41.879Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-28T22:37:41.879Z] GC before operation: completed in 207.184 ms, heap usage 375.133 MB -> 61.124 MB.
[2024-11-28T22:37:44.008Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:37:46.100Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:37:48.053Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:37:50.006Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:37:51.941Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:37:52.884Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:37:55.874Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:37:55.874Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:37:55.874Z] 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.
[2024-11-28T22:37:55.874Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:37:55.874Z] Movies recommended for you:
[2024-11-28T22:37:55.874Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:37:55.874Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:37:55.874Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14093.794 ms) ======
[2024-11-28T22:37:55.874Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-28T22:37:55.874Z] GC before operation: completed in 146.956 ms, heap usage 87.277 MB -> 43.854 MB.
[2024-11-28T22:37:58.870Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:38:00.813Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:38:02.754Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:38:04.700Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:38:06.661Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:38:07.615Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:38:09.572Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:38:11.550Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:38:11.550Z] 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.
[2024-11-28T22:38:11.550Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:38:11.550Z] Movies recommended for you:
[2024-11-28T22:38:11.550Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:38:11.550Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:38:11.550Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15266.006 ms) ======
[2024-11-28T22:38:11.550Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-28T22:38:11.550Z] GC before operation: completed in 149.376 ms, heap usage 353.162 MB -> 47.505 MB.
[2024-11-28T22:38:13.491Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:38:16.524Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:38:18.463Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:38:20.409Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:38:22.373Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:38:23.316Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:38:24.274Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:38:26.222Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:38:26.222Z] 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.
[2024-11-28T22:38:26.222Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:38:26.222Z] Movies recommended for you:
[2024-11-28T22:38:26.222Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:38:26.222Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:38:26.222Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14874.275 ms) ======
[2024-11-28T22:38:26.222Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-28T22:38:26.222Z] GC before operation: completed in 173.967 ms, heap usage 301.661 MB -> 46.074 MB.
[2024-11-28T22:38:29.231Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:38:31.193Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:38:33.142Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:38:35.095Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:38:37.048Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:38:39.003Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:38:40.115Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:38:42.069Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:38:42.069Z] 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.
[2024-11-28T22:38:42.069Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:38:42.069Z] Movies recommended for you:
[2024-11-28T22:38:42.069Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:38:42.069Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:38:42.069Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15342.694 ms) ======
[2024-11-28T22:38:42.069Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-28T22:38:42.069Z] GC before operation: completed in 235.170 ms, heap usage 137.822 MB -> 70.237 MB.
[2024-11-28T22:38:45.106Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:38:47.049Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:38:48.986Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:38:50.926Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:38:52.872Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:38:53.819Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:38:55.754Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:38:56.700Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:38:56.700Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-11-28T22:38:56.700Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:38:56.700Z] Movies recommended for you:
[2024-11-28T22:38:56.700Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:38:56.700Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:38:56.700Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14892.765 ms) ======
[2024-11-28T22:38:56.700Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-28T22:38:58.531Z] GC before operation: completed in 170.121 ms, heap usage 150.616 MB -> 52.450 MB.
[2024-11-28T22:38:59.475Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:39:01.425Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:39:04.440Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:39:06.402Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:39:08.353Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:39:09.310Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:39:11.304Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:39:12.258Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:39:12.258Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-11-28T22:39:12.258Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:39:12.258Z] Movies recommended for you:
[2024-11-28T22:39:12.258Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:39:12.258Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:39:12.259Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15256.978 ms) ======
[2024-11-28T22:39:12.259Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-28T22:39:13.217Z] GC before operation: completed in 235.643 ms, heap usage 148.141 MB -> 70.590 MB.
[2024-11-28T22:39:15.161Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:39:17.113Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:39:19.055Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:39:22.053Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:39:22.999Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:39:23.942Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:39:25.884Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:39:27.823Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:39:27.823Z] 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.
[2024-11-28T22:39:27.823Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:39:27.823Z] Movies recommended for you:
[2024-11-28T22:39:27.823Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:39:27.823Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:39:27.823Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14951.366 ms) ======
[2024-11-28T22:39:27.823Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-28T22:39:27.823Z] GC before operation: completed in 206.937 ms, heap usage 156.526 MB -> 45.420 MB.
[2024-11-28T22:39:29.772Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:39:32.791Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:39:34.732Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:39:36.685Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:39:38.663Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:39:39.614Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:39:41.552Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:39:42.495Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:39:42.495Z] 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.
[2024-11-28T22:39:43.440Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:39:43.440Z] Movies recommended for you:
[2024-11-28T22:39:43.440Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:39:43.440Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:39:43.440Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15115.419 ms) ======
[2024-11-28T22:39:43.440Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-28T22:39:43.440Z] GC before operation: completed in 192.642 ms, heap usage 151.971 MB -> 70.499 MB.
[2024-11-28T22:39:45.374Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:39:47.312Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:39:49.245Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:39:51.188Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:39:53.126Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:39:54.071Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:39:56.216Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:39:58.026Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:39:58.026Z] 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.
[2024-11-28T22:39:58.026Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:39:58.026Z] Movies recommended for you:
[2024-11-28T22:39:58.026Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:39:58.026Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:39:58.026Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14127.348 ms) ======
[2024-11-28T22:39:58.026Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-28T22:39:58.026Z] GC before operation: completed in 235.896 ms, heap usage 158.880 MB -> 70.549 MB.
[2024-11-28T22:39:59.987Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T22:40:03.004Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T22:40:04.936Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T22:40:06.899Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T22:40:08.838Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T22:40:09.800Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T22:40:11.761Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T22:40:12.703Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T22:40:12.703Z] 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.
[2024-11-28T22:40:12.703Z] The best model improves the baseline by 14.52%.
[2024-11-28T22:40:12.703Z] Movies recommended for you:
[2024-11-28T22:40:12.703Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T22:40:12.704Z] There is no way to check that no silent failure occurred.
[2024-11-28T22:40:12.704Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (15322.423 ms) ======
[2024-11-28T22:40:13.647Z] -----------------------------------
[2024-11-28T22:40:13.647Z] renaissance-movie-lens_0_PASSED
[2024-11-28T22:40:13.647Z] -----------------------------------
[2024-11-28T22:40:13.647Z]
[2024-11-28T22:40:13.647Z] TEST TEARDOWN:
[2024-11-28T22:40:13.647Z] Nothing to be done for teardown.
[2024-11-28T22:40:13.647Z] renaissance-movie-lens_0 Finish Time: Thu Nov 28 22:40:13 2024 Epoch Time (ms): 1732833613043