renaissance-movie-lens_0
[2023-04-19T09:30:36.506Z] Running test renaissance-movie-lens_0 ...
[2023-04-19T09:30:36.506Z] ===============================================
[2023-04-19T09:30:36.506Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 09:30:36 2023 Epoch Time (ms): 1681896636267
[2023-04-19T09:30:36.506Z] variation: NoOptions
[2023-04-19T09:30:36.506Z] JVM_OPTIONS:
[2023-04-19T09:30:36.506Z] { \
[2023-04-19T09:30:36.506Z] echo ""; echo "TEST SETUP:"; \
[2023-04-19T09:30:36.506Z] echo "Nothing to be done for setup."; \
[2023-04-19T09:30:36.506Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_16818954038537/renaissance-movie-lens_0"; \
[2023-04-19T09:30:36.506Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_16818954038537/renaissance-movie-lens_0"; \
[2023-04-19T09:30:36.506Z] echo ""; echo "TESTING:"; \
[2023-04-19T09:30:36.506Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/openjdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_16818954038537/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-19T09:30:36.506Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_16818954038537/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-19T09:30:36.506Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-19T09:30:36.506Z] echo "Nothing to be done for teardown."; \
[2023-04-19T09:30:36.506Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_16818954038537/TestTargetResult";
[2023-04-19T09:30:36.506Z]
[2023-04-19T09:30:36.506Z] TEST SETUP:
[2023-04-19T09:30:36.506Z] Nothing to be done for setup.
[2023-04-19T09:30:36.506Z]
[2023-04-19T09:30:36.506Z] TESTING:
[2023-04-19T09:30:40.603Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-19T09:30:43.808Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2023-04-19T09:30:48.979Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-19T09:30:48.979Z] Training: 60056, validation: 20285, test: 19854
[2023-04-19T09:30:48.979Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-19T09:30:49.356Z] GC before operation: completed in 245.705 ms, heap usage 129.429 MB -> 26.018 MB.
[2023-04-19T09:30:57.168Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:31:02.324Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:31:06.603Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:31:11.793Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:31:14.336Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:31:16.889Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:31:19.479Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:31:22.129Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:31:22.539Z] 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.
[2023-04-19T09:31:22.539Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:31:22.539Z] Movies recommended for you:
[2023-04-19T09:31:22.539Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:31:22.539Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:31:22.539Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (33450.121 ms) ======
[2023-04-19T09:31:22.539Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-19T09:31:22.924Z] GC before operation: completed in 402.860 ms, heap usage 257.708 MB -> 42.830 MB.
[2023-04-19T09:31:27.119Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:31:31.369Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:31:36.590Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:31:39.956Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:31:42.451Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:31:44.998Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:31:47.569Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:31:50.084Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:31:50.084Z] 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.
[2023-04-19T09:31:50.084Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:31:50.459Z] Movies recommended for you:
[2023-04-19T09:31:50.460Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:31:50.460Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:31:50.460Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (27325.830 ms) ======
[2023-04-19T09:31:50.460Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-19T09:31:50.460Z] GC before operation: completed in 223.217 ms, heap usage 296.606 MB -> 41.187 MB.
[2023-04-19T09:31:53.740Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:31:57.000Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:32:00.245Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:32:02.768Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:32:04.628Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:32:06.512Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:32:08.356Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:32:10.247Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:32:10.248Z] 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.
[2023-04-19T09:32:10.625Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:32:10.625Z] Movies recommended for you:
[2023-04-19T09:32:10.625Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:32:10.625Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:32:10.625Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (19951.089 ms) ======
[2023-04-19T09:32:10.625Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-19T09:32:10.625Z] GC before operation: completed in 208.370 ms, heap usage 491.646 MB -> 45.264 MB.
[2023-04-19T09:32:13.852Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:32:17.121Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:32:20.375Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:32:23.651Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:32:25.537Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:32:28.088Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:32:29.961Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:32:31.867Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:32:31.867Z] 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.
[2023-04-19T09:32:31.867Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:32:31.867Z] Movies recommended for you:
[2023-04-19T09:32:31.867Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:32:31.867Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:32:31.867Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (21192.113 ms) ======
[2023-04-19T09:32:31.867Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-19T09:32:32.239Z] GC before operation: completed in 179.687 ms, heap usage 430.698 MB -> 45.596 MB.
[2023-04-19T09:32:35.467Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:32:37.965Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:32:41.430Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:32:44.711Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:32:46.598Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:32:48.532Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:32:50.417Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:32:52.264Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:32:52.264Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:32:52.264Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:32:52.641Z] Movies recommended for you:
[2023-04-19T09:32:52.641Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:32:52.641Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:32:52.641Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20386.043 ms) ======
[2023-04-19T09:32:52.641Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-19T09:32:52.641Z] GC before operation: completed in 195.310 ms, heap usage 427.413 MB -> 45.800 MB.
[2023-04-19T09:32:55.901Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:32:58.402Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:33:00.978Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:33:04.280Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:33:05.582Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:33:07.582Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:33:09.472Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:33:10.845Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:33:11.254Z] 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.
[2023-04-19T09:33:11.254Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:33:11.254Z] Movies recommended for you:
[2023-04-19T09:33:11.254Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:33:11.254Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:33:11.254Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18548.037 ms) ======
[2023-04-19T09:33:11.254Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-19T09:33:11.631Z] GC before operation: completed in 195.792 ms, heap usage 434.720 MB -> 45.711 MB.
[2023-04-19T09:33:14.125Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:33:17.370Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:33:19.909Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:33:22.410Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:33:24.262Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:33:26.172Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:33:27.480Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:33:29.389Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:33:29.779Z] 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.
[2023-04-19T09:33:29.779Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:33:29.779Z] Movies recommended for you:
[2023-04-19T09:33:29.780Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:33:29.780Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:33:29.780Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (18286.526 ms) ======
[2023-04-19T09:33:29.780Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-19T09:33:29.780Z] GC before operation: completed in 173.744 ms, heap usage 414.077 MB -> 45.876 MB.
[2023-04-19T09:33:33.131Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:33:35.660Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:33:38.296Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:33:40.800Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:33:42.655Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:33:44.550Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:33:46.404Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:33:47.699Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:33:48.095Z] 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.
[2023-04-19T09:33:48.095Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:33:48.095Z] Movies recommended for you:
[2023-04-19T09:33:48.095Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:33:48.095Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:33:48.095Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (18205.478 ms) ======
[2023-04-19T09:33:48.095Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-19T09:33:48.465Z] GC before operation: completed in 152.052 ms, heap usage 430.168 MB -> 46.254 MB.
[2023-04-19T09:33:50.957Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:33:54.279Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:33:56.826Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:33:59.362Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:34:01.233Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:34:02.531Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:34:04.414Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:34:06.267Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:34:06.267Z] 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.
[2023-04-19T09:34:06.267Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:34:06.646Z] Movies recommended for you:
[2023-04-19T09:34:06.646Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:34:06.646Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:34:06.646Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (18181.071 ms) ======
[2023-04-19T09:34:06.646Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-19T09:34:06.646Z] GC before operation: completed in 180.773 ms, heap usage 554.601 MB -> 50.678 MB.
[2023-04-19T09:34:09.191Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:34:12.439Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:34:14.939Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:34:18.263Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:34:20.101Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:34:21.413Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:34:23.279Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:34:25.220Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:34:25.220Z] 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.
[2023-04-19T09:34:25.220Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:34:25.220Z] Movies recommended for you:
[2023-04-19T09:34:25.220Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:34:25.220Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:34:25.220Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (18709.207 ms) ======
[2023-04-19T09:34:25.220Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-19T09:34:25.591Z] GC before operation: completed in 208.299 ms, heap usage 604.036 MB -> 46.646 MB.
[2023-04-19T09:34:28.110Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:34:31.367Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:34:33.900Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:34:36.534Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:34:38.440Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:34:40.317Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:34:41.626Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:34:43.479Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:34:43.860Z] 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.
[2023-04-19T09:34:43.860Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:34:43.860Z] Movies recommended for you:
[2023-04-19T09:34:43.860Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:34:43.860Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:34:43.860Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (18259.361 ms) ======
[2023-04-19T09:34:43.860Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-19T09:34:43.860Z] GC before operation: completed in 180.158 ms, heap usage 446.506 MB -> 45.822 MB.
[2023-04-19T09:34:47.125Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:34:49.595Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:34:52.108Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:34:55.423Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:34:56.721Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:34:58.560Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:35:00.451Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:35:01.739Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:35:02.264Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:35:02.264Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:35:02.264Z] Movies recommended for you:
[2023-04-19T09:35:02.264Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:35:02.264Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:35:02.264Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (18184.776 ms) ======
[2023-04-19T09:35:02.264Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-19T09:35:02.264Z] GC before operation: completed in 158.737 ms, heap usage 424.115 MB -> 46.032 MB.
[2023-04-19T09:35:04.840Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:35:08.154Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:35:10.669Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:35:13.948Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:35:15.320Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:35:17.160Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:35:18.459Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:35:20.359Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:35:20.739Z] 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.
[2023-04-19T09:35:20.739Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:35:21.137Z] Movies recommended for you:
[2023-04-19T09:35:21.137Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:35:21.137Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:35:21.137Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (18570.382 ms) ======
[2023-04-19T09:35:21.137Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-19T09:35:21.137Z] GC before operation: completed in 152.359 ms, heap usage 418.102 MB -> 46.231 MB.
[2023-04-19T09:35:23.627Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:35:26.928Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:35:29.439Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:35:31.970Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:35:34.014Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:35:35.329Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:35:37.211Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:35:39.087Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:35:39.087Z] 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.
[2023-04-19T09:35:39.456Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:35:39.456Z] Movies recommended for you:
[2023-04-19T09:35:39.456Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:35:39.456Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:35:39.456Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (18314.340 ms) ======
[2023-04-19T09:35:39.456Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-19T09:35:39.456Z] GC before operation: completed in 155.811 ms, heap usage 432.528 MB -> 45.905 MB.
[2023-04-19T09:35:42.720Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:35:45.220Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:35:47.714Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:35:50.979Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:35:52.291Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:35:53.649Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:35:55.495Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:35:57.361Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:35:57.361Z] 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.
[2023-04-19T09:35:57.361Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:35:57.732Z] Movies recommended for you:
[2023-04-19T09:35:57.733Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:35:57.733Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:35:57.733Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (18058.011 ms) ======
[2023-04-19T09:35:57.733Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-19T09:35:57.733Z] GC before operation: completed in 157.636 ms, heap usage 435.821 MB -> 46.175 MB.
[2023-04-19T09:36:00.311Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:36:03.749Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:36:06.259Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:36:08.758Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:36:10.617Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:36:11.939Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:36:13.804Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:36:15.716Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:36:15.716Z] 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.
[2023-04-19T09:36:15.716Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:36:16.088Z] Movies recommended for you:
[2023-04-19T09:36:16.088Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:36:16.088Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:36:16.088Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (18206.988 ms) ======
[2023-04-19T09:36:16.088Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-19T09:36:16.088Z] GC before operation: completed in 186.428 ms, heap usage 429.663 MB -> 46.256 MB.
[2023-04-19T09:36:19.340Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:36:21.882Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:36:25.134Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:36:27.697Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:36:28.974Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:36:30.821Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:36:32.282Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:36:34.178Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:36:34.178Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:36:34.551Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:36:34.551Z] Movies recommended for you:
[2023-04-19T09:36:34.551Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:36:34.551Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:36:34.551Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (18342.406 ms) ======
[2023-04-19T09:36:34.551Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-19T09:36:34.551Z] GC before operation: completed in 178.388 ms, heap usage 418.730 MB -> 46.099 MB.
[2023-04-19T09:36:37.856Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:36:40.387Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:36:42.910Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:36:46.191Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:36:47.528Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:36:48.809Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:36:50.643Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:36:52.519Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:36:52.908Z] 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.
[2023-04-19T09:36:52.908Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:36:52.908Z] Movies recommended for you:
[2023-04-19T09:36:52.908Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:36:52.908Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:36:52.908Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (18197.855 ms) ======
[2023-04-19T09:36:52.908Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-19T09:36:52.908Z] GC before operation: completed in 142.757 ms, heap usage 426.587 MB -> 46.115 MB.
[2023-04-19T09:36:56.199Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:36:58.767Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:37:01.386Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:37:03.869Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:37:05.739Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:37:07.074Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:37:08.947Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:37:10.814Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:37:10.814Z] 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.
[2023-04-19T09:37:10.814Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:37:11.192Z] Movies recommended for you:
[2023-04-19T09:37:11.192Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:37:11.192Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:37:11.192Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (18076.891 ms) ======
[2023-04-19T09:37:11.192Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-19T09:37:11.192Z] GC before operation: completed in 144.582 ms, heap usage 421.855 MB -> 46.314 MB.
[2023-04-19T09:37:13.730Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:37:17.011Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:37:19.538Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:37:22.021Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:37:23.869Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:37:25.763Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:37:27.100Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:37:29.009Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:37:29.411Z] 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.
[2023-04-19T09:37:29.411Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:37:29.411Z] Movies recommended for you:
[2023-04-19T09:37:29.411Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:37:29.411Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:37:29.411Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (18154.785 ms) ======
[2023-04-19T09:37:30.214Z] -----------------------------------
[2023-04-19T09:37:30.214Z] renaissance-movie-lens_0_PASSED
[2023-04-19T09:37:30.214Z] -----------------------------------
[2023-04-19T09:37:30.214Z]
[2023-04-19T09:37:30.214Z] TEST TEARDOWN:
[2023-04-19T09:37:30.214Z] Nothing to be done for teardown.
[2023-04-19T09:37:30.214Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 09:37:29 2023 Epoch Time (ms): 1681897049948