renaissance-movie-lens_0
[2024-06-27T03:14:31.009Z] Running test renaissance-movie-lens_0 ...
[2024-06-27T03:14:31.009Z] ===============================================
[2024-06-27T03:14:31.009Z] renaissance-movie-lens_0 Start Time: Thu Jun 27 03:14:30 2024 Epoch Time (ms): 1719458070154
[2024-06-27T03:14:31.009Z] variation: NoOptions
[2024-06-27T03:14:31.009Z] JVM_OPTIONS:
[2024-06-27T03:14:31.009Z] { \
[2024-06-27T03:14:31.009Z] echo ""; echo "TEST SETUP:"; \
[2024-06-27T03:14:31.009Z] echo "Nothing to be done for setup."; \
[2024-06-27T03:14:31.009Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/aqa-tests/TKG/../TKG/output_1719457098935/renaissance-movie-lens_0"; \
[2024-06-27T03:14:31.009Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/aqa-tests/TKG/../TKG/output_1719457098935/renaissance-movie-lens_0"; \
[2024-06-27T03:14:31.009Z] echo ""; echo "TESTING:"; \
[2024-06-27T03:14:31.009Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/jdkbinary/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_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/aqa-tests/TKG/../TKG/output_1719457098935/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-27T03:14:31.009Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/aqa-tests/TKG/../TKG/output_1719457098935/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-27T03:14:31.009Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-27T03:14:31.009Z] echo "Nothing to be done for teardown."; \
[2024-06-27T03:14:31.009Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux_testList_0/aqa-tests/TKG/../TKG/output_1719457098935/TestTargetResult";
[2024-06-27T03:14:31.009Z]
[2024-06-27T03:14:31.009Z] TEST SETUP:
[2024-06-27T03:14:31.009Z] Nothing to be done for setup.
[2024-06-27T03:14:31.009Z]
[2024-06-27T03:14:31.009Z] TESTING:
[2024-06-27T03:14:33.963Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-27T03:14:35.876Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-06-27T03:14:39.957Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-27T03:14:39.957Z] Training: 60056, validation: 20285, test: 19854
[2024-06-27T03:14:39.957Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-27T03:14:39.957Z] GC before operation: completed in 66.237 ms, heap usage 50.313 MB -> 36.560 MB.
[2024-06-27T03:14:46.554Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:14:49.511Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:14:52.471Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:14:55.437Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:14:57.348Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:14:58.297Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:15:00.215Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:15:02.128Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:15:02.128Z] 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.
[2024-06-27T03:15:02.128Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:15:03.058Z] Movies recommended for you:
[2024-06-27T03:15:03.058Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:15:03.058Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:15:03.058Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (22589.345 ms) ======
[2024-06-27T03:15:03.058Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-27T03:15:03.058Z] GC before operation: completed in 98.006 ms, heap usage 177.546 MB -> 48.198 MB.
[2024-06-27T03:15:06.013Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:15:07.926Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:15:10.881Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:15:12.792Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:15:14.728Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:15:16.741Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:15:17.673Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:15:19.588Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:15:19.588Z] 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.
[2024-06-27T03:15:19.588Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:15:19.588Z] Movies recommended for you:
[2024-06-27T03:15:19.588Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:15:19.588Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:15:19.588Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17038.084 ms) ======
[2024-06-27T03:15:19.588Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-27T03:15:19.588Z] GC before operation: completed in 101.654 ms, heap usage 218.750 MB -> 49.093 MB.
[2024-06-27T03:15:22.545Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:15:25.501Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:15:27.415Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:15:29.339Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:15:30.878Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:15:32.853Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:15:33.796Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:15:35.714Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:15:35.714Z] 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.
[2024-06-27T03:15:35.714Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:15:35.714Z] Movies recommended for you:
[2024-06-27T03:15:35.714Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:15:35.714Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:15:35.714Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16070.346 ms) ======
[2024-06-27T03:15:35.714Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-27T03:15:35.714Z] GC before operation: completed in 103.185 ms, heap usage 349.353 MB -> 49.562 MB.
[2024-06-27T03:15:38.679Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:15:40.590Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:15:43.544Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:15:45.458Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:15:47.379Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:15:48.311Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:15:50.225Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:15:51.158Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:15:51.158Z] 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.
[2024-06-27T03:15:51.158Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:15:51.158Z] Movies recommended for you:
[2024-06-27T03:15:51.158Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:15:51.158Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:15:51.158Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15561.442 ms) ======
[2024-06-27T03:15:51.158Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-27T03:15:52.093Z] GC before operation: completed in 92.857 ms, heap usage 184.204 MB -> 49.706 MB.
[2024-06-27T03:15:54.008Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:15:56.968Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:15:58.975Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:16:01.932Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:16:02.863Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:16:04.783Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:16:05.716Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:16:07.631Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:16:07.631Z] 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.
[2024-06-27T03:16:07.631Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:16:07.631Z] Movies recommended for you:
[2024-06-27T03:16:07.631Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:16:07.631Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:16:07.631Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15952.850 ms) ======
[2024-06-27T03:16:07.631Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-27T03:16:07.631Z] GC before operation: completed in 91.126 ms, heap usage 183.771 MB -> 49.934 MB.
[2024-06-27T03:16:09.547Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:16:12.511Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:16:14.430Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:16:16.412Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:16:17.349Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:16:19.270Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:16:20.202Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:16:22.119Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:16:22.119Z] 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.
[2024-06-27T03:16:22.119Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:16:22.119Z] Movies recommended for you:
[2024-06-27T03:16:22.119Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:16:22.119Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:16:22.119Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14545.888 ms) ======
[2024-06-27T03:16:22.119Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-27T03:16:22.119Z] GC before operation: completed in 92.937 ms, heap usage 132.923 MB -> 49.759 MB.
[2024-06-27T03:16:24.035Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:16:26.990Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:16:28.904Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:16:30.820Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:16:32.479Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:16:33.420Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:16:35.347Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:16:36.277Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:16:36.277Z] 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.
[2024-06-27T03:16:36.277Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:16:36.277Z] Movies recommended for you:
[2024-06-27T03:16:36.277Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:16:36.277Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:16:36.277Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14277.739 ms) ======
[2024-06-27T03:16:36.277Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-27T03:16:37.209Z] GC before operation: completed in 95.710 ms, heap usage 349.469 MB -> 50.180 MB.
[2024-06-27T03:16:39.127Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:16:41.035Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:16:42.946Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:16:45.899Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:16:46.841Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:16:47.775Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:16:49.691Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:16:50.625Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:16:50.625Z] 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.
[2024-06-27T03:16:51.558Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:16:51.558Z] Movies recommended for you:
[2024-06-27T03:16:51.558Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:16:51.558Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:16:51.558Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14443.846 ms) ======
[2024-06-27T03:16:51.558Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-27T03:16:51.558Z] GC before operation: completed in 94.366 ms, heap usage 336.825 MB -> 50.398 MB.
[2024-06-27T03:16:53.470Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:16:55.381Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:16:57.296Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:17:00.255Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:17:01.188Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:17:02.121Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:17:04.140Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:17:05.072Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:17:05.072Z] 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.
[2024-06-27T03:17:05.072Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:17:06.005Z] Movies recommended for you:
[2024-06-27T03:17:06.005Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:17:06.005Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:17:06.005Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14267.784 ms) ======
[2024-06-27T03:17:06.005Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-27T03:17:06.005Z] GC before operation: completed in 87.281 ms, heap usage 194.421 MB -> 50.114 MB.
[2024-06-27T03:17:07.919Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:17:09.836Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:17:12.791Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:17:14.702Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:17:15.766Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:17:17.686Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:17:18.619Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:17:20.535Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:17:20.535Z] 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.
[2024-06-27T03:17:20.535Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:17:20.535Z] Movies recommended for you:
[2024-06-27T03:17:20.535Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:17:20.535Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:17:20.536Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14849.975 ms) ======
[2024-06-27T03:17:20.536Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-27T03:17:20.536Z] GC before operation: completed in 92.243 ms, heap usage 277.057 MB -> 50.235 MB.
[2024-06-27T03:17:23.498Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:17:25.411Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:17:27.322Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:17:29.238Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:17:30.171Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:17:32.942Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:17:33.923Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:17:34.855Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:17:34.855Z] 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.
[2024-06-27T03:17:34.855Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:17:34.855Z] Movies recommended for you:
[2024-06-27T03:17:34.855Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:17:34.855Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:17:34.855Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14454.015 ms) ======
[2024-06-27T03:17:34.855Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-27T03:17:34.855Z] GC before operation: completed in 92.673 ms, heap usage 213.240 MB -> 49.952 MB.
[2024-06-27T03:17:37.813Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:17:39.730Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:17:41.644Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:17:43.563Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:17:45.477Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:17:46.410Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:17:47.341Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:17:49.256Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:17:49.256Z] 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.
[2024-06-27T03:17:49.256Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:17:49.256Z] Movies recommended for you:
[2024-06-27T03:17:49.256Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:17:49.256Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:17:49.256Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14364.011 ms) ======
[2024-06-27T03:17:49.256Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-27T03:17:49.256Z] GC before operation: completed in 94.001 ms, heap usage 242.512 MB -> 50.153 MB.
[2024-06-27T03:17:52.212Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:17:54.123Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:17:56.034Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:17:57.944Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:17:59.855Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:18:00.786Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:18:02.700Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:18:03.631Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:18:04.568Z] 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.
[2024-06-27T03:18:04.568Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:18:04.568Z] Movies recommended for you:
[2024-06-27T03:18:04.568Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:18:04.568Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:18:04.568Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14833.355 ms) ======
[2024-06-27T03:18:04.568Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-27T03:18:04.568Z] GC before operation: completed in 101.320 ms, heap usage 105.683 MB -> 50.210 MB.
[2024-06-27T03:18:06.479Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:18:08.401Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:18:11.353Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:18:13.267Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:18:14.196Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:18:16.107Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:18:17.036Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:18:18.950Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:18:18.950Z] 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.
[2024-06-27T03:18:18.950Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:18:18.950Z] Movies recommended for you:
[2024-06-27T03:18:18.950Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:18:18.950Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:18:18.950Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14443.034 ms) ======
[2024-06-27T03:18:18.950Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-27T03:18:18.950Z] GC before operation: completed in 92.576 ms, heap usage 143.120 MB -> 49.935 MB.
[2024-06-27T03:18:20.864Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:18:22.954Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:18:25.907Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:18:27.817Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:18:28.747Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:18:30.659Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:18:31.589Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:18:33.537Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:18:34.469Z] 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.
[2024-06-27T03:18:34.469Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:18:34.469Z] Movies recommended for you:
[2024-06-27T03:18:34.469Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:18:34.469Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:18:34.469Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14228.492 ms) ======
[2024-06-27T03:18:34.469Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-27T03:18:34.469Z] GC before operation: completed in 98.299 ms, heap usage 288.638 MB -> 50.276 MB.
[2024-06-27T03:18:35.402Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:18:38.356Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:18:40.268Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:18:42.181Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:18:43.112Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:18:45.022Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:18:45.953Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:18:47.866Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:18:47.866Z] 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.
[2024-06-27T03:18:47.866Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:18:47.866Z] Movies recommended for you:
[2024-06-27T03:18:47.866Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:18:47.866Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:18:47.866Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14570.673 ms) ======
[2024-06-27T03:18:47.866Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-27T03:18:47.866Z] GC before operation: completed in 93.841 ms, heap usage 204.159 MB -> 50.331 MB.
[2024-06-27T03:18:50.817Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:18:52.726Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:18:54.634Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:18:56.549Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:18:58.458Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:18:59.387Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:19:00.316Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:19:02.225Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:19:02.225Z] 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.
[2024-06-27T03:19:02.225Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:19:02.225Z] Movies recommended for you:
[2024-06-27T03:19:02.225Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:19:02.225Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:19:02.225Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14438.769 ms) ======
[2024-06-27T03:19:02.225Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-27T03:19:02.225Z] GC before operation: completed in 107.595 ms, heap usage 326.004 MB -> 50.253 MB.
[2024-06-27T03:19:05.184Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:19:07.097Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:19:09.008Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:19:11.964Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:19:12.894Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:19:13.825Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:19:15.766Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:19:16.696Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:19:17.625Z] 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.
[2024-06-27T03:19:17.625Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:19:17.625Z] Movies recommended for you:
[2024-06-27T03:19:17.625Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:19:17.625Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:19:17.625Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14707.236 ms) ======
[2024-06-27T03:19:17.625Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-27T03:19:17.625Z] GC before operation: completed in 91.744 ms, heap usage 139.216 MB -> 50.162 MB.
[2024-06-27T03:19:19.533Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:19:21.446Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:19:24.395Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:19:26.305Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:19:27.234Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:19:28.165Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:19:30.076Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:19:31.853Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:19:31.853Z] 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.
[2024-06-27T03:19:31.853Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:19:31.853Z] Movies recommended for you:
[2024-06-27T03:19:31.853Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:19:31.853Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:19:31.853Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14230.499 ms) ======
[2024-06-27T03:19:31.853Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-27T03:19:31.853Z] GC before operation: completed in 91.343 ms, heap usage 213.251 MB -> 50.361 MB.
[2024-06-27T03:19:33.923Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T03:19:36.876Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T03:19:38.789Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T03:19:40.704Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T03:19:41.637Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T03:19:43.551Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T03:19:44.487Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T03:19:46.400Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T03:19:46.400Z] 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.
[2024-06-27T03:19:46.400Z] The best model improves the baseline by 14.52%.
[2024-06-27T03:19:46.400Z] Movies recommended for you:
[2024-06-27T03:19:46.400Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T03:19:46.400Z] There is no way to check that no silent failure occurred.
[2024-06-27T03:19:46.400Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14593.446 ms) ======
[2024-06-27T03:19:47.333Z] -----------------------------------
[2024-06-27T03:19:47.333Z] renaissance-movie-lens_0_PASSED
[2024-06-27T03:19:47.333Z] -----------------------------------
[2024-06-27T03:19:47.333Z]
[2024-06-27T03:19:47.333Z] TEST TEARDOWN:
[2024-06-27T03:19:47.333Z] Nothing to be done for teardown.
[2024-06-27T03:19:47.333Z] renaissance-movie-lens_0 Finish Time: Thu Jun 27 03:19:46 2024 Epoch Time (ms): 1719458386387