renaissance-movie-lens_0
[2023-04-19T09:56:11.118Z] Running test renaissance-movie-lens_0 ...
[2023-04-19T09:56:11.118Z] ===============================================
[2023-04-19T09:56:11.118Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 10:56:11 2023 Epoch Time (ms): 1681898171080
[2023-04-19T09:56:11.118Z] variation: NoOptions
[2023-04-19T09:56:11.118Z] JVM_OPTIONS:
[2023-04-19T09:56:11.118Z] { \
[2023-04-19T09:56:11.118Z] echo ""; echo "TEST SETUP:"; \
[2023-04-19T09:56:11.118Z] echo "Nothing to be done for setup."; \
[2023-04-19T09:56:11.118Z] mkdir -p "/export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_16818976478287/renaissance-movie-lens_0"; \
[2023-04-19T09:56:11.118Z] cd "/export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_16818976478287/renaissance-movie-lens_0"; \
[2023-04-19T09:56:11.118Z] echo ""; echo "TESTING:"; \
[2023-04-19T09:56:11.119Z] "/export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/openjdkbinary/j2sdk-image/bin/java" -jar "/export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_16818976478287/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-19T09:56:11.119Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/..; rm -f -r "/export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_16818976478287/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-19T09:56:11.119Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-19T09:56:11.119Z] echo "Nothing to be done for teardown."; \
[2023-04-19T09:56:11.119Z] } 2>&1 | tee -a "/export/home/jenkins/sshagent/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_16818976478287/TestTargetResult";
[2023-04-19T09:56:11.119Z]
[2023-04-19T09:56:11.119Z] TEST SETUP:
[2023-04-19T09:56:11.119Z] Nothing to be done for setup.
[2023-04-19T09:56:11.119Z]
[2023-04-19T09:56:11.119Z] TESTING:
[2023-04-19T09:56:14.892Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-19T09:56:16.550Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2023-04-19T09:56:19.495Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-19T09:56:19.823Z] Training: 60056, validation: 20285, test: 19854
[2023-04-19T09:56:19.823Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-19T09:56:20.155Z] GC before operation: completed in 168.021 ms, heap usage 202.874 MB -> 29.525 MB.
[2023-04-19T09:56:23.930Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:56:26.578Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:56:29.532Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:56:31.181Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:56:32.325Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:56:33.464Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:56:34.612Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:56:36.263Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:56:36.263Z] 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.
[2023-04-19T09:56:36.263Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:56:36.263Z] Movies recommended for you:
[2023-04-19T09:56:36.263Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:56:36.263Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:56:36.263Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (16303.697 ms) ======
[2023-04-19T09:56:36.263Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-19T09:56:36.590Z] GC before operation: completed in 336.896 ms, heap usage 911.805 MB -> 67.512 MB.
[2023-04-19T09:56:38.836Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:56:40.491Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:56:42.740Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:56:44.402Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:56:45.555Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:56:47.212Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:56:48.354Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:56:49.490Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:56:49.820Z] 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.
[2023-04-19T09:56:49.820Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:56:49.820Z] Movies recommended for you:
[2023-04-19T09:56:49.820Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:56:49.820Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:56:49.820Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (13216.901 ms) ======
[2023-04-19T09:56:49.820Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-19T09:56:50.147Z] GC before operation: completed in 184.523 ms, heap usage 1.186 GB -> 56.379 MB.
[2023-04-19T09:56:51.980Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:56:53.650Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:56:55.305Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:56:56.970Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:56:58.633Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:56:59.774Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:00.915Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:02.063Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:02.063Z] 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.
[2023-04-19T09:57:02.063Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:02.063Z] Movies recommended for you:
[2023-04-19T09:57:02.063Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:02.063Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:02.063Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (12079.633 ms) ======
[2023-04-19T09:57:02.063Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-19T09:57:02.391Z] GC before operation: completed in 162.197 ms, heap usage 1.120 GB -> 52.790 MB.
[2023-04-19T09:57:04.037Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:05.689Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:07.340Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:08.997Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:10.143Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:11.291Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:12.435Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:13.582Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:13.582Z] 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.
[2023-04-19T09:57:13.582Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:13.582Z] Movies recommended for you:
[2023-04-19T09:57:13.582Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:13.582Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:13.582Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (11366.539 ms) ======
[2023-04-19T09:57:13.582Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-19T09:57:13.910Z] GC before operation: completed in 140.269 ms, heap usage 1.074 GB -> 53.229 MB.
[2023-04-19T09:57:15.575Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:17.242Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:18.900Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:20.559Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:21.701Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:22.402Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:23.544Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:24.686Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:24.686Z] 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.
[2023-04-19T09:57:24.686Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:24.686Z] Movies recommended for you:
[2023-04-19T09:57:24.686Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:24.686Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:24.686Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (10862.047 ms) ======
[2023-04-19T09:57:24.686Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-19T09:57:24.686Z] GC before operation: completed in 103.789 ms, heap usage 1.062 GB -> 58.951 MB.
[2023-04-19T09:57:26.331Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:27.985Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:29.666Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:31.318Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:32.025Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:32.728Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:33.865Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:34.573Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:34.901Z] 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.
[2023-04-19T09:57:34.901Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:34.901Z] Movies recommended for you:
[2023-04-19T09:57:34.901Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:34.901Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:34.901Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (10076.034 ms) ======
[2023-04-19T09:57:34.901Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-19T09:57:34.901Z] GC before operation: completed in 114.691 ms, heap usage 1.096 GB -> 53.497 MB.
[2023-04-19T09:57:36.558Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:38.207Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:40.618Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:41.760Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:42.901Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:43.614Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:44.763Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:45.905Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:45.905Z] 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.
[2023-04-19T09:57:45.905Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:45.905Z] Movies recommended for you:
[2023-04-19T09:57:45.905Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:45.905Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:45.905Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (10889.246 ms) ======
[2023-04-19T09:57:45.905Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-19T09:57:45.905Z] GC before operation: completed in 101.521 ms, heap usage 1.046 GB -> 53.118 MB.
[2023-04-19T09:57:47.565Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:49.227Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:50.879Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:52.551Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:53.258Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:54.403Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:55.110Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:56.257Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:56.257Z] 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.
[2023-04-19T09:57:56.257Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:56.257Z] Movies recommended for you:
[2023-04-19T09:57:56.257Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:56.257Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:56.257Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (10426.410 ms) ======
[2023-04-19T09:57:56.257Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-19T09:57:56.585Z] GC before operation: completed in 101.881 ms, heap usage 1.032 GB -> 55.934 MB.
[2023-04-19T09:57:58.265Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:59.412Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:58:01.072Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:58:02.223Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:58:03.511Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:58:04.219Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:58:05.373Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:58:06.520Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:58:06.520Z] 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.
[2023-04-19T09:58:06.520Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:58:06.520Z] Movies recommended for you:
[2023-04-19T09:58:06.520Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:58:06.520Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:58:06.520Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (10067.276 ms) ======
[2023-04-19T09:58:06.520Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-19T09:58:06.520Z] GC before operation: completed in 95.952 ms, heap usage 1.030 GB -> 53.255 MB.
[2023-04-19T09:58:08.785Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:58:10.450Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:58:12.106Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:58:13.761Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:58:14.463Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:58:15.607Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:58:16.749Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:58:17.890Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:58:17.890Z] 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.
[2023-04-19T09:58:17.890Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:58:17.890Z] Movies recommended for you:
[2023-04-19T09:58:17.890Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:58:17.890Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:58:17.890Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (11297.859 ms) ======
[2023-04-19T09:58:17.890Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-19T09:58:18.226Z] GC before operation: completed in 167.494 ms, heap usage 1002.166 MB -> 53.062 MB.
[2023-04-19T09:58:19.872Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:58:21.525Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:58:23.773Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:58:24.925Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:58:26.066Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:58:27.388Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:58:28.535Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:58:29.241Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:58:29.241Z] 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.
[2023-04-19T09:58:29.241Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:58:29.570Z] Movies recommended for you:
[2023-04-19T09:58:29.570Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:58:29.570Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:58:29.570Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (11308.489 ms) ======
[2023-04-19T09:58:29.570Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-19T09:58:29.570Z] GC before operation: completed in 109.416 ms, heap usage 1.106 GB -> 53.497 MB.
[2023-04-19T09:58:31.832Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:58:32.988Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:58:34.646Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:58:36.305Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:58:37.016Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:58:38.165Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:58:39.312Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:58:40.464Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:58:40.791Z] 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.
[2023-04-19T09:58:40.791Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:58:40.791Z] Movies recommended for you:
[2023-04-19T09:58:40.791Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:58:40.791Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:58:40.791Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (11251.528 ms) ======
[2023-04-19T09:58:40.791Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-19T09:58:40.791Z] GC before operation: completed in 111.310 ms, heap usage 1.032 GB -> 53.316 MB.
[2023-04-19T09:58:42.440Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:58:44.092Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:58:45.749Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:58:47.409Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:58:48.556Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:58:49.298Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:58:50.439Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:58:51.747Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:58:51.747Z] 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.
[2023-04-19T09:58:51.747Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:58:51.747Z] Movies recommended for you:
[2023-04-19T09:58:51.747Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:58:51.747Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:58:51.747Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (10740.083 ms) ======
[2023-04-19T09:58:51.747Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-19T09:58:51.747Z] GC before operation: completed in 98.499 ms, heap usage 1.011 GB -> 53.480 MB.
[2023-04-19T09:58:53.408Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:58:55.074Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:58:56.729Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:58:58.383Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:58:59.091Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:59:00.231Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:59:00.937Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:59:02.081Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:59:02.081Z] 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.
[2023-04-19T09:59:02.081Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:59:02.408Z] Movies recommended for you:
[2023-04-19T09:59:02.408Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:59:02.408Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:59:02.408Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (10550.458 ms) ======
[2023-04-19T09:59:02.408Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-19T09:59:02.408Z] GC before operation: completed in 100.707 ms, heap usage 1.046 GB -> 53.286 MB.
[2023-04-19T09:59:04.671Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:59:07.648Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:59:08.792Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:59:10.448Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:59:11.154Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:59:12.299Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:59:13.437Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:59:15.092Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:59:15.092Z] 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.
[2023-04-19T09:59:15.092Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:59:15.420Z] Movies recommended for you:
[2023-04-19T09:59:15.420Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:59:15.420Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:59:15.420Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12859.941 ms) ======
[2023-04-19T09:59:15.420Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-19T09:59:15.420Z] GC before operation: completed in 98.142 ms, heap usage 1.030 GB -> 53.518 MB.
[2023-04-19T09:59:17.216Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:59:18.869Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:59:20.517Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:59:22.164Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:59:23.312Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:59:24.460Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:59:25.609Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:59:26.317Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:59:26.644Z] 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.
[2023-04-19T09:59:26.644Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:59:26.644Z] Movies recommended for you:
[2023-04-19T09:59:26.644Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:59:26.644Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:59:26.644Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (11230.688 ms) ======
[2023-04-19T09:59:26.644Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-19T09:59:26.644Z] GC before operation: completed in 98.364 ms, heap usage 979.994 MB -> 55.522 MB.
[2023-04-19T09:59:28.317Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:59:29.963Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:59:31.616Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:59:33.268Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:59:34.401Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:59:35.112Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:59:36.256Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:59:37.395Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:59:37.395Z] 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.
[2023-04-19T09:59:37.395Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:59:37.395Z] Movies recommended for you:
[2023-04-19T09:59:37.395Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:59:37.396Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:59:37.396Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (10780.569 ms) ======
[2023-04-19T09:59:37.396Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-19T09:59:37.721Z] GC before operation: completed in 89.081 ms, heap usage 1.072 GB -> 53.726 MB.
[2023-04-19T09:59:39.379Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:59:40.701Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:59:42.348Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:59:44.608Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:59:45.312Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:59:46.456Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:59:47.166Z] RMSE (validation) = 0.9275717388537592 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:59:48.337Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:59:48.337Z] 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.
[2023-04-19T09:59:48.337Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:59:48.337Z] Movies recommended for you:
[2023-04-19T09:59:48.337Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:59:48.337Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:59:48.337Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (10807.836 ms) ======
[2023-04-19T09:59:48.337Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-19T09:59:48.337Z] GC before operation: completed in 96.610 ms, heap usage 1019.596 MB -> 55.117 MB.
[2023-04-19T09:59:49.993Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:59:51.657Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:59:53.307Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:59:54.954Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:59:56.097Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:59:57.240Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:59:57.937Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:59:59.088Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:59:59.088Z] 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.
[2023-04-19T09:59:59.088Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:59:59.088Z] Movies recommended for you:
[2023-04-19T09:59:59.088Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:59:59.088Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:59:59.088Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (10750.199 ms) ======
[2023-04-19T09:59:59.088Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-19T09:59:59.418Z] GC before operation: completed in 115.610 ms, heap usage 1.041 GB -> 53.752 MB.
[2023-04-19T10:00:01.065Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T10:00:02.719Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T10:00:04.360Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T10:00:06.607Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T10:00:08.261Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T10:00:08.960Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T10:00:10.102Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T10:00:10.810Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T10:00:10.810Z] 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.
[2023-04-19T10:00:11.138Z] The best model improves the baseline by 14.43%.
[2023-04-19T10:00:11.138Z] Movies recommended for you:
[2023-04-19T10:00:11.138Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T10:00:11.138Z] There is no way to check that no silent failure occurred.
[2023-04-19T10:00:11.138Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (11709.945 ms) ======
[2023-04-19T10:00:11.465Z] -----------------------------------
[2023-04-19T10:00:11.465Z] renaissance-movie-lens_0_PASSED
[2023-04-19T10:00:11.465Z] -----------------------------------
[2023-04-19T10:00:11.465Z]
[2023-04-19T10:00:11.466Z] TEST TEARDOWN:
[2023-04-19T10:00:11.466Z] Nothing to be done for teardown.
[2023-04-19T10:00:11.466Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 11:00:11 2023 Epoch Time (ms): 1681898411429