[2023-04-19T00:11:44.390Z] Running test renaissance-movie-lens_0 ... [2023-04-19T00:11:44.390Z] =============================================== [2023-04-19T00:11:44.390Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 00:11:43 2023 Epoch Time (ms): 1681863103490 [2023-04-19T00:11:44.390Z] variation: NoOptions [2023-04-19T00:11:44.390Z] JVM_OPTIONS: [2023-04-19T00:11:44.390Z] { \ [2023-04-19T00:11:44.390Z] echo ""; echo "TEST SETUP:"; \ [2023-04-19T00:11:44.390Z] echo "Nothing to be done for setup."; \ [2023-04-19T00:11:44.390Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818622254084/renaissance-movie-lens_0"; \ [2023-04-19T00:11:44.390Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818622254084/renaissance-movie-lens_0"; \ [2023-04-19T00:11:44.390Z] echo ""; echo "TESTING:"; \ [2023-04-19T00:11:44.390Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/openjdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818622254084/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2023-04-19T00:11:44.390Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818622254084/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2023-04-19T00:11:44.390Z] echo ""; echo "TEST TEARDOWN:"; \ [2023-04-19T00:11:44.390Z] echo "Nothing to be done for teardown."; \ [2023-04-19T00:11:44.390Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818622254084/TestTargetResult"; [2023-04-19T00:11:44.390Z] [2023-04-19T00:11:44.390Z] TEST SETUP: [2023-04-19T00:11:44.390Z] Nothing to be done for setup. [2023-04-19T00:11:44.390Z] [2023-04-19T00:11:44.390Z] TESTING: [2023-04-19T00:11:47.724Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2023-04-19T00:11:49.023Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2023-04-19T00:11:52.692Z] Got 100004 ratings from 671 users on 9066 movies. [2023-04-19T00:11:52.692Z] Training: 60056, validation: 20285, test: 19854 [2023-04-19T00:11:52.692Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2023-04-19T00:11:52.692Z] GC before operation: completed in 70.155 ms, heap usage 121.868 MB -> 37.319 MB. [2023-04-19T00:11:58.453Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:12:00.364Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:12:03.691Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:12:05.605Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:12:08.065Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:12:08.994Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:12:11.073Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:12:12.002Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:12:12.932Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:12:12.932Z] The best model improves the baseline by 14.52%. [2023-04-19T00:12:12.932Z] Movies recommended for you: [2023-04-19T00:12:12.932Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:12:12.932Z] There is no way to check that no silent failure occurred. [2023-04-19T00:12:12.932Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20766.562 ms) ====== [2023-04-19T00:12:12.932Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2023-04-19T00:12:12.932Z] GC before operation: completed in 75.287 ms, heap usage 396.952 MB -> 53.292 MB. [2023-04-19T00:12:15.581Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:12:18.300Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:12:21.998Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:12:24.632Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:12:25.933Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:12:26.865Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:11.951Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:11.951Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:11.951Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:13:11.951Z] The best model improves the baseline by 14.52%. [2023-04-19T00:13:11.951Z] Movies recommended for you: [2023-04-19T00:13:11.951Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:11.951Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:11.951Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17727.382 ms) ====== [2023-04-19T00:13:11.951Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2023-04-19T00:13:11.951Z] GC before operation: completed in 64.155 ms, heap usage 206.902 MB -> 49.901 MB. [2023-04-19T00:13:11.951Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:11.951Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:11.951Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:11.951Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:11.951Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:11.951Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:11.951Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:11.951Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:11.951Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:13:11.951Z] The best model improves the baseline by 14.52%. [2023-04-19T00:13:11.951Z] Movies recommended for you: [2023-04-19T00:13:11.952Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:11.952Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:11.952Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (15215.539 ms) ====== [2023-04-19T00:13:11.952Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2023-04-19T00:13:11.952Z] GC before operation: completed in 67.355 ms, heap usage 334.829 MB -> 50.267 MB. [2023-04-19T00:13:11.952Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:11.952Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:11.952Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:11.952Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:11.952Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:13:11.952Z] The best model improves the baseline by 14.52%. [2023-04-19T00:13:11.952Z] Movies recommended for you: [2023-04-19T00:13:11.952Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:11.952Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:11.952Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14832.776 ms) ====== [2023-04-19T00:13:11.952Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2023-04-19T00:13:11.952Z] GC before operation: completed in 63.540 ms, heap usage 60.351 MB -> 50.296 MB. [2023-04-19T00:13:11.952Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:11.952Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:11.952Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:14.047Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:14.980Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:14.980Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:13:14.980Z] The best model improves the baseline by 14.52%. [2023-04-19T00:13:14.980Z] Movies recommended for you: [2023-04-19T00:13:14.980Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:14.980Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:14.980Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14330.285 ms) ====== [2023-04-19T00:13:14.980Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2023-04-19T00:13:14.980Z] GC before operation: completed in 70.237 ms, heap usage 192.648 MB -> 50.685 MB. [2023-04-19T00:13:18.949Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:20.773Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:22.089Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:24.538Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:25.470Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:38.715Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:38.715Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:38.715Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:38.715Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:13:38.715Z] The best model improves the baseline by 14.52%. [2023-04-19T00:13:38.715Z] Movies recommended for you: [2023-04-19T00:13:38.715Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:38.715Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:38.715Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14122.122 ms) ====== [2023-04-19T00:13:38.715Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2023-04-19T00:13:38.715Z] GC before operation: completed in 69.232 ms, heap usage 90.374 MB -> 50.508 MB. [2023-04-19T00:13:38.715Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:38.715Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:38.715Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:38.715Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:38.715Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:41.215Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:43.434Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:43.434Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:43.434Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:13:43.434Z] The best model improves the baseline by 14.52%. [2023-04-19T00:13:43.434Z] Movies recommended for you: [2023-04-19T00:13:43.434Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:43.434Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:43.434Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13836.166 ms) ====== [2023-04-19T00:13:43.434Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2023-04-19T00:13:43.434Z] GC before operation: completed in 76.968 ms, heap usage 87.161 MB -> 50.682 MB. [2023-04-19T00:13:45.358Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:47.862Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:49.778Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:52.425Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:53.358Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:54.291Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:55.963Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:58.012Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:58.012Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:13:58.012Z] The best model improves the baseline by 14.52%. [2023-04-19T00:13:58.012Z] Movies recommended for you: [2023-04-19T00:13:58.012Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:58.012Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:58.012Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13723.861 ms) ====== [2023-04-19T00:13:58.012Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2023-04-19T00:13:58.012Z] GC before operation: completed in 91.305 ms, heap usage 60.235 MB -> 54.449 MB. [2023-04-19T00:13:59.313Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:14:01.231Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:14:03.514Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:14:05.434Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:14:07.932Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:14:09.244Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:14:09.244Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:14:10.173Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:14:11.103Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:14:11.103Z] The best model improves the baseline by 14.52%. [2023-04-19T00:14:11.103Z] Movies recommended for you: [2023-04-19T00:14:11.103Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:14:11.103Z] There is no way to check that no silent failure occurred. [2023-04-19T00:14:11.103Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13715.724 ms) ====== [2023-04-19T00:14:11.103Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2023-04-19T00:14:11.103Z] GC before operation: completed in 82.915 ms, heap usage 344.564 MB -> 51.082 MB. [2023-04-19T00:14:13.371Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:14:14.672Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:14:17.322Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:14:19.627Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:14:21.163Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:14:21.163Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:14:24.183Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:14:24.183Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:14:24.183Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:14:24.183Z] The best model improves the baseline by 14.52%. [2023-04-19T00:14:24.183Z] Movies recommended for you: [2023-04-19T00:14:24.183Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:14:24.183Z] There is no way to check that no silent failure occurred. [2023-04-19T00:14:24.183Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13486.734 ms) ====== [2023-04-19T00:14:24.183Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2023-04-19T00:14:25.114Z] GC before operation: completed in 82.889 ms, heap usage 261.652 MB -> 51.114 MB. [2023-04-19T00:14:27.027Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:14:30.045Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:14:30.976Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:14:33.184Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:14:34.116Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:14:35.413Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:14:36.349Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:14:37.651Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:14:37.651Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:14:37.651Z] The best model improves the baseline by 14.52%. [2023-04-19T00:14:37.651Z] Movies recommended for you: [2023-04-19T00:14:37.651Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:14:37.651Z] There is no way to check that no silent failure occurred. [2023-04-19T00:14:37.651Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13311.420 ms) ====== [2023-04-19T00:14:37.651Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2023-04-19T00:14:37.651Z] GC before operation: completed in 80.624 ms, heap usage 109.469 MB -> 50.664 MB. [2023-04-19T00:14:40.533Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:14:41.632Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:14:45.023Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:14:45.955Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:14:47.264Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:14:49.175Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:14:50.474Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:14:51.403Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:14:51.403Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:14:51.403Z] The best model improves the baseline by 14.52%. [2023-04-19T00:14:51.403Z] Movies recommended for you: [2023-04-19T00:14:51.403Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:14:51.403Z] There is no way to check that no silent failure occurred. [2023-04-19T00:14:51.403Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13650.220 ms) ====== [2023-04-19T00:14:51.403Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2023-04-19T00:14:51.403Z] GC before operation: completed in 81.242 ms, heap usage 114.919 MB -> 50.884 MB. [2023-04-19T00:14:53.314Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:14:56.863Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:14:57.794Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:15:00.439Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:15:01.374Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:15:03.587Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:15:03.587Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:15:05.498Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:15:05.499Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:15:05.499Z] The best model improves the baseline by 14.52%. [2023-04-19T00:15:05.499Z] Movies recommended for you: [2023-04-19T00:15:05.499Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:15:05.499Z] There is no way to check that no silent failure occurred. [2023-04-19T00:15:05.499Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13793.441 ms) ====== [2023-04-19T00:15:05.499Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2023-04-19T00:15:05.499Z] GC before operation: completed in 68.497 ms, heap usage 96.251 MB -> 51.090 MB. [2023-04-19T00:15:07.991Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:15:10.622Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:02.386Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:02.386Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:02.386Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:02.386Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:02.386Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:02.386Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:02.386Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:02.386Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:02.386Z] Movies recommended for you: [2023-04-19T00:16:02.386Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:02.386Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:02.386Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13828.151 ms) ====== [2023-04-19T00:16:02.386Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2023-04-19T00:16:02.386Z] GC before operation: completed in 69.874 ms, heap usage 86.024 MB -> 50.770 MB. [2023-04-19T00:16:02.386Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:02.386Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:02.386Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:02.386Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:02.386Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:02.386Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:02.386Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:10.482Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:10.482Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:10.482Z] Movies recommended for you: [2023-04-19T00:16:10.482Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:10.482Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:10.482Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13828.151 ms) ====== [2023-04-19T00:16:10.482Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2023-04-19T00:16:10.482Z] GC before operation: completed in 69.874 ms, heap usage 86.024 MB -> 50.770 MB. [2023-04-19T00:16:10.482Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:10.482Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:10.482Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:10.482Z] Movies recommended for you: [2023-04-19T00:16:10.482Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:10.482Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:10.482Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13547.742 ms) ====== [2023-04-19T00:16:10.482Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2023-04-19T00:16:10.482Z] GC before operation: completed in 66.474 ms, heap usage 86.773 MB -> 50.961 MB. [2023-04-19T00:16:10.482Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:10.482Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:10.482Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:10.482Z] Movies recommended for you: [2023-04-19T00:16:10.482Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:10.482Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:10.482Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13768.533 ms) ====== [2023-04-19T00:16:10.482Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2023-04-19T00:16:10.482Z] GC before operation: completed in 82.358 ms, heap usage 86.306 MB -> 51.006 MB. [2023-04-19T00:16:10.482Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:10.482Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:10.483Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:10.483Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:10.483Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:10.483Z] Movies recommended for you: [2023-04-19T00:16:10.483Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:10.483Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:10.483Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13256.562 ms) ====== [2023-04-19T00:16:10.483Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2023-04-19T00:16:10.483Z] GC before operation: completed in 74.077 ms, heap usage 88.974 MB -> 50.854 MB. [2023-04-19T00:16:10.483Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:10.483Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:10.483Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:10.483Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:10.483Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:11.416Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:12.347Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:13.278Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:14.763Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:14.763Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:14.763Z] Movies recommended for you: [2023-04-19T00:16:14.763Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:14.763Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:14.763Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13553.550 ms) ====== [2023-04-19T00:16:14.763Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2023-04-19T00:16:14.763Z] GC before operation: completed in 66.227 ms, heap usage 86.582 MB -> 50.916 MB. [2023-04-19T00:16:16.535Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:18.452Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:20.082Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:21.995Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:23.311Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:25.902Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:25.902Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:27.205Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:27.205Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:27.205Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:27.205Z] Movies recommended for you: [2023-04-19T00:16:27.205Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:27.205Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:27.205Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (13537.018 ms) ====== [2023-04-19T00:16:27.205Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2023-04-19T00:16:27.205Z] GC before operation: completed in 81.408 ms, heap usage 443.746 MB -> 54.696 MB. [2023-04-19T00:16:29.115Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:32.070Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:34.473Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:35.402Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:36.697Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:38.353Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:39.653Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:40.589Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:40.589Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2023-04-19T00:16:40.589Z] The best model improves the baseline by 14.52%. [2023-04-19T00:16:42.051Z] Movies recommended for you: [2023-04-19T00:16:42.051Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:42.051Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:42.051Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13464.126 ms) ====== [2023-04-19T00:16:42.051Z] ----------------------------------- [2023-04-19T00:16:42.051Z] renaissance-movie-lens_0_PASSED [2023-04-19T00:16:42.051Z] ----------------------------------- [2023-04-19T00:16:42.051Z] [2023-04-19T00:16:42.051Z] TEST TEARDOWN: [2023-04-19T00:16:42.051Z] Nothing to be done for teardown. [2023-04-19T00:16:42.051Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 00:16:41 2023 Epoch Time (ms): 1681863401072