renaissance-movie-lens_0
[2025-03-05T22:41:35.778Z] Running test renaissance-movie-lens_0 ...
[2025-03-05T22:41:35.778Z] ===============================================
[2025-03-05T22:41:35.778Z] renaissance-movie-lens_0 Start Time: Wed Mar 5 22:41:35 2025 Epoch Time (ms): 1741214495593
[2025-03-05T22:41:35.778Z] variation: NoOptions
[2025-03-05T22:41:35.778Z] JVM_OPTIONS:
[2025-03-05T22:41:35.778Z] { \
[2025-03-05T22:41:35.778Z] echo ""; echo "TEST SETUP:"; \
[2025-03-05T22:41:35.778Z] echo "Nothing to be done for setup."; \
[2025-03-05T22:41:35.778Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17412135471852/renaissance-movie-lens_0"; \
[2025-03-05T22:41:35.778Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17412135471852/renaissance-movie-lens_0"; \
[2025-03-05T22:41:35.778Z] echo ""; echo "TESTING:"; \
[2025-03-05T22:41:35.778Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/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/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17412135471852/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-03-05T22:41:35.778Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17412135471852/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-03-05T22:41:35.778Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-03-05T22:41:35.778Z] echo "Nothing to be done for teardown."; \
[2025-03-05T22:41:35.778Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17412135471852/TestTargetResult";
[2025-03-05T22:41:35.778Z]
[2025-03-05T22:41:35.778Z] TEST SETUP:
[2025-03-05T22:41:35.778Z] Nothing to be done for setup.
[2025-03-05T22:41:35.778Z]
[2025-03-05T22:41:35.778Z] TESTING:
[2025-03-05T22:41:39.914Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-03-05T22:41:41.873Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-03-05T22:41:44.905Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-03-05T22:41:44.905Z] Training: 60056, validation: 20285, test: 19854
[2025-03-05T22:41:44.905Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-03-05T22:41:44.905Z] GC before operation: completed in 68.639 ms, heap usage 129.979 MB -> 36.440 MB.
[2025-03-05T22:41:51.592Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:41:54.605Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:41:57.613Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:42:00.642Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:42:02.635Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:42:03.585Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:42:05.535Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:42:07.485Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:42:07.485Z] 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.
[2025-03-05T22:42:07.485Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:42:08.448Z] Movies recommended for you:
[2025-03-05T22:42:08.448Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:42:08.448Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:42:08.448Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (22685.003 ms) ======
[2025-03-05T22:42:08.448Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-03-05T22:42:08.448Z] GC before operation: completed in 99.160 ms, heap usage 296.537 MB -> 49.128 MB.
[2025-03-05T22:42:10.399Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:42:13.407Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:42:16.466Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:42:18.827Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:42:19.779Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:42:20.729Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:42:22.774Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:42:24.748Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:42:24.748Z] 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.
[2025-03-05T22:42:24.748Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:42:24.748Z] Movies recommended for you:
[2025-03-05T22:42:24.748Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:42:24.748Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:42:24.748Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16736.297 ms) ======
[2025-03-05T22:42:24.748Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-03-05T22:42:24.748Z] GC before operation: completed in 90.759 ms, heap usage 181.518 MB -> 49.053 MB.
[2025-03-05T22:42:27.823Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:42:29.822Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:42:32.834Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:42:34.789Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:42:35.738Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:42:37.685Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:42:39.667Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:42:40.623Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:42:40.623Z] 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.
[2025-03-05T22:42:40.623Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:42:40.623Z] Movies recommended for you:
[2025-03-05T22:42:40.623Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:42:40.623Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:42:40.623Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16093.518 ms) ======
[2025-03-05T22:42:40.623Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-03-05T22:42:41.571Z] GC before operation: completed in 102.953 ms, heap usage 285.631 MB -> 49.440 MB.
[2025-03-05T22:42:43.531Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:42:46.583Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:42:48.553Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:42:50.504Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:42:52.455Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:42:53.405Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:42:55.357Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:42:56.308Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:42:57.259Z] 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.
[2025-03-05T22:42:57.259Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:42:57.259Z] Movies recommended for you:
[2025-03-05T22:42:57.259Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:42:57.259Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:42:57.259Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15995.938 ms) ======
[2025-03-05T22:42:57.259Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-03-05T22:42:57.259Z] GC before operation: completed in 95.805 ms, heap usage 116.023 MB -> 49.597 MB.
[2025-03-05T22:42:59.212Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:43:02.227Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:43:04.179Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:43:06.132Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:43:08.082Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:43:09.045Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:43:10.995Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:43:11.947Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:43:12.901Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-03-05T22:43:12.901Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:43:12.901Z] Movies recommended for you:
[2025-03-05T22:43:12.901Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:43:12.901Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:43:12.901Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15412.202 ms) ======
[2025-03-05T22:43:12.901Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-03-05T22:43:12.901Z] GC before operation: completed in 96.694 ms, heap usage 228.674 MB -> 49.829 MB.
[2025-03-05T22:43:14.855Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:43:16.806Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:43:19.825Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:43:21.775Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:43:22.724Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:43:26.233Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:43:26.233Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:43:27.186Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:43:27.186Z] 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.
[2025-03-05T22:43:27.186Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:43:27.186Z] Movies recommended for you:
[2025-03-05T22:43:27.186Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:43:27.186Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:43:27.186Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14738.609 ms) ======
[2025-03-05T22:43:27.186Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-03-05T22:43:27.186Z] GC before operation: completed in 96.134 ms, heap usage 201.594 MB -> 49.804 MB.
[2025-03-05T22:43:30.193Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:43:32.142Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:43:34.090Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:43:36.039Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:43:37.986Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:43:38.941Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:43:41.034Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:43:41.990Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:43:41.990Z] 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.
[2025-03-05T22:43:41.990Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:43:41.990Z] Movies recommended for you:
[2025-03-05T22:43:41.990Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:43:41.990Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:43:41.990Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14871.469 ms) ======
[2025-03-05T22:43:41.990Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-03-05T22:43:42.946Z] GC before operation: completed in 96.252 ms, heap usage 156.512 MB -> 49.894 MB.
[2025-03-05T22:43:44.905Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:43:46.856Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:43:48.803Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:43:51.816Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:43:52.768Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:43:53.726Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:43:55.693Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:43:56.642Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:43:56.642Z] 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.
[2025-03-05T22:43:56.642Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:43:56.642Z] Movies recommended for you:
[2025-03-05T22:43:56.642Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:43:56.642Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:43:56.642Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14522.050 ms) ======
[2025-03-05T22:43:56.642Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-03-05T22:43:57.596Z] GC before operation: completed in 96.605 ms, heap usage 291.347 MB -> 50.289 MB.
[2025-03-05T22:43:59.564Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:44:01.513Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:44:04.526Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:44:06.480Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:44:07.431Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:44:08.380Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:44:10.329Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:44:11.281Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:44:11.281Z] 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.
[2025-03-05T22:44:11.281Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:44:12.239Z] Movies recommended for you:
[2025-03-05T22:44:12.239Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:44:12.239Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:44:12.239Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14720.709 ms) ======
[2025-03-05T22:44:12.239Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-03-05T22:44:12.239Z] GC before operation: completed in 93.203 ms, heap usage 106.111 MB -> 49.931 MB.
[2025-03-05T22:44:14.191Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:44:16.145Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:44:19.157Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:44:21.114Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:44:22.062Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:44:24.015Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:44:24.974Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:44:25.926Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:44:25.926Z] 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.
[2025-03-05T22:44:26.876Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:44:26.877Z] Movies recommended for you:
[2025-03-05T22:44:26.877Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:44:26.877Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:44:26.877Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14572.969 ms) ======
[2025-03-05T22:44:26.877Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-03-05T22:44:26.877Z] GC before operation: completed in 96.044 ms, heap usage 162.079 MB -> 50.132 MB.
[2025-03-05T22:44:28.825Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:44:31.834Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:44:33.967Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:44:35.919Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:44:36.869Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:44:37.819Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:44:39.799Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:44:40.753Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:44:40.753Z] 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.
[2025-03-05T22:44:40.753Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:44:41.706Z] Movies recommended for you:
[2025-03-05T22:44:41.706Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:44:41.706Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:44:41.706Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14657.458 ms) ======
[2025-03-05T22:44:41.706Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-03-05T22:44:41.706Z] GC before operation: completed in 92.917 ms, heap usage 322.422 MB -> 50.061 MB.
[2025-03-05T22:44:43.662Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:44:45.655Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:44:47.610Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:44:49.563Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:44:51.513Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:44:52.465Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:44:54.435Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:44:55.389Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:44:55.389Z] 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.
[2025-03-05T22:44:55.389Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:44:55.389Z] Movies recommended for you:
[2025-03-05T22:44:55.389Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:44:55.389Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:44:55.389Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14422.925 ms) ======
[2025-03-05T22:44:55.389Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-03-05T22:44:56.339Z] GC before operation: completed in 100.784 ms, heap usage 295.238 MB -> 50.134 MB.
[2025-03-05T22:44:58.290Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:45:00.238Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:45:02.196Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:45:05.228Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:45:06.178Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:45:07.131Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:45:09.079Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:45:10.048Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:45:10.996Z] 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.
[2025-03-05T22:45:10.996Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:45:10.996Z] Movies recommended for you:
[2025-03-05T22:45:10.996Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:45:10.996Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:45:10.996Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14879.520 ms) ======
[2025-03-05T22:45:10.996Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-03-05T22:45:10.996Z] GC before operation: completed in 93.823 ms, heap usage 103.425 MB -> 50.187 MB.
[2025-03-05T22:45:12.944Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:45:15.958Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:45:17.911Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:45:19.863Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:45:20.818Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:45:22.765Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:45:23.723Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:45:24.673Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:45:25.627Z] 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.
[2025-03-05T22:45:25.627Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:45:25.627Z] Movies recommended for you:
[2025-03-05T22:45:25.627Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:45:25.627Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:45:25.627Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14677.441 ms) ======
[2025-03-05T22:45:25.627Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-03-05T22:45:25.627Z] GC before operation: completed in 95.753 ms, heap usage 204.234 MB -> 49.979 MB.
[2025-03-05T22:45:27.581Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:45:29.535Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:45:32.545Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:45:34.706Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:45:35.842Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:45:36.793Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:45:38.744Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:45:39.695Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:45:39.695Z] 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.
[2025-03-05T22:45:39.695Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:45:39.695Z] Movies recommended for you:
[2025-03-05T22:45:39.695Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:45:39.695Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:45:39.695Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14319.923 ms) ======
[2025-03-05T22:45:39.695Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-03-05T22:45:39.695Z] GC before operation: completed in 99.890 ms, heap usage 208.256 MB -> 50.189 MB.
[2025-03-05T22:45:42.738Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:45:44.687Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:45:46.641Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:45:48.594Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:45:50.545Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:45:51.669Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:45:52.625Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:45:54.574Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:45:54.574Z] 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.
[2025-03-05T22:45:54.574Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:45:54.574Z] Movies recommended for you:
[2025-03-05T22:45:54.574Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:45:54.574Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:45:54.574Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14713.327 ms) ======
[2025-03-05T22:45:54.574Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-03-05T22:45:54.574Z] GC before operation: completed in 92.999 ms, heap usage 169.976 MB -> 50.230 MB.
[2025-03-05T22:45:57.584Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:45:59.538Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:46:01.641Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:46:03.590Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:46:05.540Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:46:06.493Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:46:07.442Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:46:09.390Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:46:09.390Z] 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.
[2025-03-05T22:46:09.390Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:46:09.390Z] Movies recommended for you:
[2025-03-05T22:46:09.390Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:46:09.390Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:46:09.390Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14667.690 ms) ======
[2025-03-05T22:46:09.390Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-03-05T22:46:09.390Z] GC before operation: completed in 93.507 ms, heap usage 261.510 MB -> 50.174 MB.
[2025-03-05T22:46:12.395Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:46:14.496Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:46:16.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:46:18.391Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:46:20.341Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:46:21.293Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:46:23.242Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:46:24.192Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:46:24.192Z] 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.
[2025-03-05T22:46:24.192Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:46:24.192Z] Movies recommended for you:
[2025-03-05T22:46:24.192Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:46:24.192Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:46:24.192Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14787.461 ms) ======
[2025-03-05T22:46:24.192Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-03-05T22:46:24.192Z] GC before operation: completed in 93.795 ms, heap usage 311.637 MB -> 50.257 MB.
[2025-03-05T22:46:27.199Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:46:29.147Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:46:31.101Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:46:33.049Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:46:34.004Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:46:35.991Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:46:36.940Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:46:38.907Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:46:38.907Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-03-05T22:46:38.907Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:46:38.907Z] Movies recommended for you:
[2025-03-05T22:46:38.907Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:46:38.907Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:46:38.907Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14295.614 ms) ======
[2025-03-05T22:46:38.907Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-03-05T22:46:38.907Z] GC before operation: completed in 96.397 ms, heap usage 231.207 MB -> 50.352 MB.
[2025-03-05T22:46:41.104Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T22:46:43.057Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T22:46:46.074Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T22:46:48.027Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T22:46:48.978Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T22:46:49.928Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T22:46:51.938Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T22:46:52.891Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T22:46:52.891Z] 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.
[2025-03-05T22:46:52.891Z] The best model improves the baseline by 14.52%.
[2025-03-05T22:46:52.891Z] Movies recommended for you:
[2025-03-05T22:46:52.891Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T22:46:52.891Z] There is no way to check that no silent failure occurred.
[2025-03-05T22:46:52.891Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14378.683 ms) ======
[2025-03-05T22:46:53.845Z] -----------------------------------
[2025-03-05T22:46:53.845Z] renaissance-movie-lens_0_PASSED
[2025-03-05T22:46:53.845Z] -----------------------------------
[2025-03-05T22:46:53.845Z]
[2025-03-05T22:46:53.845Z] TEST TEARDOWN:
[2025-03-05T22:46:53.845Z] Nothing to be done for teardown.
[2025-03-05T22:46:53.845Z] renaissance-movie-lens_0 Finish Time: Wed Mar 5 22:46:53 2025 Epoch Time (ms): 1741214813356