renaissance-movie-lens_0
[2023-04-20T02:06:09.695Z] Running test renaissance-movie-lens_0 ...
[2023-04-20T02:06:09.695Z] ===============================================
[2023-04-20T02:06:09.695Z] renaissance-movie-lens_0 Start Time: Thu Apr 20 02:16:57 2023 Epoch Time (ms): 1681957017202
[2023-04-20T02:06:09.695Z] variation: NoOptions
[2023-04-20T02:06:09.695Z] JVM_OPTIONS:
[2023-04-20T02:06:09.695Z] { \
[2023-04-20T02:06:09.695Z] echo ""; echo "TEST SETUP:"; \
[2023-04-20T02:06:09.695Z] echo "Nothing to be done for setup."; \
[2023-04-20T02:06:09.695Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16819559301834/renaissance-movie-lens_0"; \
[2023-04-20T02:06:09.695Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16819559301834/renaissance-movie-lens_0"; \
[2023-04-20T02:06:09.695Z] echo ""; echo "TESTING:"; \
[2023-04-20T02:06:09.695Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/openjdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16819559301834/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-20T02:06:09.696Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16819559301834/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-20T02:06:09.696Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-20T02:06:09.696Z] echo "Nothing to be done for teardown."; \
[2023-04-20T02:06:09.696Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16819559301834/TestTargetResult";
[2023-04-20T02:06:09.696Z]
[2023-04-20T02:06:09.696Z] TEST SETUP:
[2023-04-20T02:06:09.696Z] Nothing to be done for setup.
[2023-04-20T02:06:09.696Z]
[2023-04-20T02:06:09.696Z] TESTING:
[2023-04-20T02:06:15.275Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-20T02:06:18.680Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2023-04-20T02:06:24.245Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-20T02:06:25.006Z] Training: 60056, validation: 20285, test: 19854
[2023-04-20T02:06:25.006Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-20T02:06:25.006Z] GC before operation: completed in 258.104 ms, heap usage 214.863 MB -> 26.593 MB.
[2023-04-20T02:06:33.263Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:06:38.825Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:06:44.408Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:06:48.843Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:06:51.303Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:06:53.756Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:06:57.160Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:06:59.651Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:06:59.651Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:06:59.651Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:06:59.651Z] Movies recommended for you:
[2023-04-20T02:06:59.651Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:06:59.652Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:06:59.652Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (34922.992 ms) ======
[2023-04-20T02:06:59.652Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-20T02:07:00.420Z] GC before operation: completed in 331.353 ms, heap usage 295.303 MB -> 51.652 MB.
[2023-04-20T02:07:04.853Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:07:09.277Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:07:13.710Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:07:18.129Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:07:20.578Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:07:23.023Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:07:25.478Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:07:27.932Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:07:28.694Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:07:28.694Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:07:28.694Z] Movies recommended for you:
[2023-04-20T02:07:28.694Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:07:28.694Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:07:28.694Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (28372.250 ms) ======
[2023-04-20T02:07:28.694Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-20T02:07:28.694Z] GC before operation: completed in 271.999 ms, heap usage 372.351 MB -> 44.629 MB.
[2023-04-20T02:07:33.136Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:07:37.561Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:07:41.988Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:07:46.440Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:07:48.019Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:07:50.477Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:07:52.933Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:07:55.410Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:07:56.178Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:07:56.178Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:07:56.178Z] Movies recommended for you:
[2023-04-20T02:07:56.178Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:07:56.178Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:07:56.178Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (27348.479 ms) ======
[2023-04-20T02:07:56.178Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-20T02:07:56.178Z] GC before operation: completed in 171.826 ms, heap usage 124.246 MB -> 42.388 MB.
[2023-04-20T02:08:00.621Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:08:05.062Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:08:09.666Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:08:13.062Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:08:15.521Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:08:17.973Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:08:20.429Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:08:22.885Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:08:22.885Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:08:22.885Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:08:22.885Z] Movies recommended for you:
[2023-04-20T02:08:22.885Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:08:22.885Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:08:22.885Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (26886.867 ms) ======
[2023-04-20T02:08:22.885Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-20T02:08:23.647Z] GC before operation: completed in 200.983 ms, heap usage 354.181 MB -> 45.495 MB.
[2023-04-20T02:08:28.077Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:08:31.481Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:08:35.933Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:08:40.354Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:08:42.827Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:08:45.286Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:08:47.739Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:08:50.207Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:08:50.207Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:08:50.207Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:08:50.207Z] Movies recommended for you:
[2023-04-20T02:08:50.207Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:08:50.207Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:08:50.207Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (26899.249 ms) ======
[2023-04-20T02:08:50.207Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-20T02:08:50.974Z] GC before operation: completed in 251.300 ms, heap usage 196.411 MB -> 44.227 MB.
[2023-04-20T02:08:54.384Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:08:58.810Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:09:03.243Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:09:07.681Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:09:09.267Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:09:11.736Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:09:14.195Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:09:16.646Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:09:17.424Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:09:17.424Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:09:17.424Z] Movies recommended for you:
[2023-04-20T02:09:17.424Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:09:17.424Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:09:17.424Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (26616.370 ms) ======
[2023-04-20T02:09:17.424Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-20T02:09:17.424Z] GC before operation: completed in 177.749 ms, heap usage 418.102 MB -> 43.748 MB.
[2023-04-20T02:09:21.847Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:09:26.275Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:09:30.705Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:09:34.116Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:09:36.567Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:09:39.029Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:09:41.511Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:09:43.966Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:09:43.966Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:09:43.966Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:09:43.966Z] Movies recommended for you:
[2023-04-20T02:09:43.966Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:09:43.966Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:09:43.966Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (26779.814 ms) ======
[2023-04-20T02:09:43.966Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-20T02:09:44.730Z] GC before operation: completed in 175.080 ms, heap usage 356.999 MB -> 45.088 MB.
[2023-04-20T02:09:48.135Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:09:52.577Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:09:57.022Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:10:01.475Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:10:03.049Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:10:05.504Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:10:07.948Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:10:10.400Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:10:11.164Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:10:11.164Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:10:11.164Z] Movies recommended for you:
[2023-04-20T02:10:11.164Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:10:11.164Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:10:11.164Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (26598.354 ms) ======
[2023-04-20T02:10:11.164Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-20T02:10:11.164Z] GC before operation: completed in 181.006 ms, heap usage 202.007 MB -> 49.169 MB.
[2023-04-20T02:10:15.594Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:10:19.018Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:10:24.600Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:10:28.015Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:10:30.572Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:10:33.814Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:10:35.392Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:10:37.856Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:10:37.856Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:10:38.638Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:10:38.638Z] Movies recommended for you:
[2023-04-20T02:10:38.638Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:10:38.638Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:10:38.638Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (27235.813 ms) ======
[2023-04-20T02:10:38.638Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-20T02:10:38.638Z] GC before operation: completed in 199.601 ms, heap usage 399.577 MB -> 54.194 MB.
[2023-04-20T02:10:43.092Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:10:47.531Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:10:51.994Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:10:55.397Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:10:57.861Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:11:00.317Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:11:02.764Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:11:05.214Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:11:05.976Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:11:05.976Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:11:05.976Z] Movies recommended for you:
[2023-04-20T02:11:05.976Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:11:05.976Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:11:05.976Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (27189.496 ms) ======
[2023-04-20T02:11:05.976Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-20T02:11:05.976Z] GC before operation: completed in 215.491 ms, heap usage 428.642 MB -> 67.098 MB.
[2023-04-20T02:11:10.430Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:11:14.870Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:11:19.331Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:11:22.769Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:11:25.223Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:11:27.673Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:11:30.133Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:11:32.593Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:11:33.356Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:11:33.356Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:11:33.356Z] Movies recommended for you:
[2023-04-20T02:11:33.356Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:11:33.356Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:11:33.356Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (27282.333 ms) ======
[2023-04-20T02:11:33.356Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-20T02:11:33.356Z] GC before operation: completed in 191.839 ms, heap usage 419.476 MB -> 58.722 MB.
[2023-04-20T02:11:37.783Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:11:42.212Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:11:46.662Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:11:51.098Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:11:52.681Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:11:55.133Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:11:57.588Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:12:00.036Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:12:00.801Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:12:00.801Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:12:00.801Z] Movies recommended for you:
[2023-04-20T02:12:00.801Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:12:00.801Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:12:00.801Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (27210.456 ms) ======
[2023-04-20T02:12:00.801Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-20T02:12:00.801Z] GC before operation: completed in 171.727 ms, heap usage 267.046 MB -> 74.180 MB.
[2023-04-20T02:12:05.242Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:12:09.672Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:12:14.108Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:12:17.508Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:12:19.998Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:12:22.459Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:12:24.905Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:12:27.358Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:12:28.121Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:12:28.121Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:12:28.121Z] Movies recommended for you:
[2023-04-20T02:12:28.121Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:12:28.121Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:12:28.121Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (27230.306 ms) ======
[2023-04-20T02:12:28.121Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-20T02:12:28.121Z] GC before operation: completed in 171.049 ms, heap usage 280.180 MB -> 58.932 MB.
[2023-04-20T02:12:32.563Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:12:37.003Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:12:41.438Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:12:44.838Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:12:48.237Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:12:49.815Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:12:53.210Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:12:54.791Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:12:55.556Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:12:55.556Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:12:55.556Z] Movies recommended for you:
[2023-04-20T02:12:55.556Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:12:55.556Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:12:55.556Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (27419.642 ms) ======
[2023-04-20T02:12:55.556Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-20T02:12:55.556Z] GC before operation: completed in 182.015 ms, heap usage 188.279 MB -> 58.297 MB.
[2023-04-20T02:12:59.982Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:13:04.398Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:13:08.993Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:13:12.392Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:13:14.871Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:13:17.325Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:13:19.777Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:13:22.239Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:13:23.006Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:13:23.007Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:13:23.007Z] Movies recommended for you:
[2023-04-20T02:13:23.007Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:13:23.007Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:13:23.007Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (27125.498 ms) ======
[2023-04-20T02:13:23.007Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-20T02:13:23.007Z] GC before operation: completed in 230.828 ms, heap usage 377.003 MB -> 74.690 MB.
[2023-04-20T02:13:27.438Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:13:31.889Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:13:36.315Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:13:40.737Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:13:42.322Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:13:44.775Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:13:47.229Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:13:49.686Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:13:50.449Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:13:50.449Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:13:50.449Z] Movies recommended for you:
[2023-04-20T02:13:50.449Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:13:50.449Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:13:50.449Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (27088.379 ms) ======
[2023-04-20T02:13:50.449Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-20T02:13:50.449Z] GC before operation: completed in 193.891 ms, heap usage 216.749 MB -> 75.344 MB.
[2023-04-20T02:13:54.893Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:13:59.347Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:14:03.790Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:14:08.222Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:14:09.795Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:14:12.235Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:14:14.689Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:14:17.148Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:14:17.910Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-20T02:14:17.910Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:14:17.910Z] Movies recommended for you:
[2023-04-20T02:14:17.910Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:14:17.910Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:14:17.910Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (27385.073 ms) ======
[2023-04-20T02:14:17.911Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-20T02:14:17.911Z] GC before operation: completed in 202.464 ms, heap usage 250.036 MB -> 74.225 MB.
[2023-04-20T02:14:22.356Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:14:26.795Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:14:31.224Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:14:35.665Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:14:37.243Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:14:39.703Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:14:42.171Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:14:44.634Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:14:45.398Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:14:45.398Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:14:45.398Z] Movies recommended for you:
[2023-04-20T02:14:45.398Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:14:45.398Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:14:45.398Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (27219.124 ms) ======
[2023-04-20T02:14:45.398Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-20T02:14:45.398Z] GC before operation: completed in 198.409 ms, heap usage 389.386 MB -> 74.638 MB.
[2023-04-20T02:14:49.835Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:14:54.265Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:14:58.713Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:15:03.133Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:15:04.710Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:15:07.158Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:15:10.544Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:15:12.125Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:15:12.907Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:15:12.907Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:15:12.907Z] Movies recommended for you:
[2023-04-20T02:15:12.907Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:15:12.907Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:15:12.907Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (27376.744 ms) ======
[2023-04-20T02:15:12.907Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-20T02:15:12.907Z] GC before operation: completed in 269.256 ms, heap usage 447.563 MB -> 75.269 MB.
[2023-04-20T02:15:18.439Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-20T02:15:21.472Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-20T02:15:25.916Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-20T02:15:30.355Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-20T02:15:32.817Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-20T02:15:35.293Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-20T02:15:37.747Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-20T02:15:40.199Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-20T02:15:40.962Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-20T02:15:40.962Z] The best model improves the baseline by 14.43%.
[2023-04-20T02:15:40.962Z] Movies recommended for you:
[2023-04-20T02:15:40.962Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-20T02:15:40.962Z] There is no way to check that no silent failure occurred.
[2023-04-20T02:15:40.962Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (27689.458 ms) ======
[2023-04-20T02:15:41.724Z] -----------------------------------
[2023-04-20T02:15:41.724Z] renaissance-movie-lens_0_PASSED
[2023-04-20T02:15:41.724Z] -----------------------------------
[2023-04-20T02:15:41.724Z]
[2023-04-20T02:15:41.724Z] TEST TEARDOWN:
[2023-04-20T02:15:41.724Z] Nothing to be done for teardown.
[2023-04-20T02:15:41.724Z] renaissance-movie-lens_0 Finish Time: Thu Apr 20 02:26:29 2023 Epoch Time (ms): 1681957589652