renaissance-movie-lens_0
[2025-01-10T18:58:52.335Z] Running test renaissance-movie-lens_0 ...
[2025-01-10T18:58:52.335Z] ===============================================
[2025-01-10T18:58:52.335Z] renaissance-movie-lens_0 Start Time: Fri Jan 10 18:58:51 2025 Epoch Time (ms): 1736535531082
[2025-01-10T18:58:52.335Z] variation: NoOptions
[2025-01-10T18:58:52.335Z] JVM_OPTIONS:
[2025-01-10T18:58:52.335Z] { \
[2025-01-10T18:58:52.335Z] echo ""; echo "TEST SETUP:"; \
[2025-01-10T18:58:52.335Z] echo "Nothing to be done for setup."; \
[2025-01-10T18:58:52.335Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17365343793589/renaissance-movie-lens_0"; \
[2025-01-10T18:58:52.335Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17365343793589/renaissance-movie-lens_0"; \
[2025-01-10T18:58:52.335Z] echo ""; echo "TESTING:"; \
[2025-01-10T18:58:52.335Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_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_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17365343793589/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-10T18:58:52.335Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17365343793589/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-10T18:58:52.335Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-10T18:58:52.335Z] echo "Nothing to be done for teardown."; \
[2025-01-10T18:58:52.335Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17365343793589/TestTargetResult";
[2025-01-10T18:58:52.335Z]
[2025-01-10T18:58:52.335Z] TEST SETUP:
[2025-01-10T18:58:52.335Z] Nothing to be done for setup.
[2025-01-10T18:58:52.335Z]
[2025-01-10T18:58:52.335Z] TESTING:
[2025-01-10T18:58:55.507Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-10T18:58:58.959Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-01-10T18:59:04.079Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-10T18:59:04.079Z] Training: 60056, validation: 20285, test: 19854
[2025-01-10T18:59:04.079Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-10T18:59:04.079Z] GC before operation: completed in 74.223 ms, heap usage 71.679 MB -> 36.430 MB.
[2025-01-10T18:59:15.578Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:59:19.730Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:59:24.918Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:59:29.037Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:59:31.631Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:59:33.472Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:59:35.976Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:59:38.519Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:59:38.519Z] 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.
[2025-01-10T18:59:38.897Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:59:38.897Z] Movies recommended for you:
[2025-01-10T18:59:38.897Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:59:38.897Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:59:38.897Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (34875.006 ms) ======
[2025-01-10T18:59:38.898Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-10T18:59:39.280Z] GC before operation: completed in 133.443 ms, heap usage 188.993 MB -> 48.149 MB.
[2025-01-10T18:59:43.438Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:59:47.570Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:59:50.830Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:59:54.049Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:59:56.593Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:59:58.430Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:00:00.328Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:00:02.833Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:00:02.833Z] 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.
[2025-01-10T19:00:02.833Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:00:02.833Z] Movies recommended for you:
[2025-01-10T19:00:02.833Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:00:02.833Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:00:02.833Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (23812.894 ms) ======
[2025-01-10T19:00:02.833Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-10T19:00:02.833Z] GC before operation: completed in 102.794 ms, heap usage 188.160 MB -> 49.037 MB.
[2025-01-10T19:00:07.089Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:00:10.379Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:00:13.621Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:00:16.857Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:00:18.716Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:00:21.235Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:00:23.067Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:00:24.919Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:00:25.302Z] 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.
[2025-01-10T19:00:25.302Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:00:25.302Z] Movies recommended for you:
[2025-01-10T19:00:25.302Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:00:25.302Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:00:25.302Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (22355.167 ms) ======
[2025-01-10T19:00:25.302Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-10T19:00:25.302Z] GC before operation: completed in 103.815 ms, heap usage 96.793 MB -> 49.232 MB.
[2025-01-10T19:00:29.370Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:00:31.881Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:00:35.138Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:00:38.389Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:00:40.218Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:00:42.048Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:00:43.907Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:00:45.764Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:00:46.129Z] 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.
[2025-01-10T19:00:46.129Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:00:46.495Z] Movies recommended for you:
[2025-01-10T19:00:46.495Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:00:46.495Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:00:46.495Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20954.517 ms) ======
[2025-01-10T19:00:46.495Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-10T19:00:46.495Z] GC before operation: completed in 95.983 ms, heap usage 218.468 MB -> 49.660 MB.
[2025-01-10T19:00:49.747Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:00:52.980Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:00:57.141Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:00:59.596Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:01:01.416Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:01:03.278Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:01:06.003Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:01:07.913Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:01:07.913Z] 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.
[2025-01-10T19:01:07.913Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:01:08.308Z] Movies recommended for you:
[2025-01-10T19:01:08.308Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:01:08.308Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:01:08.308Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21708.902 ms) ======
[2025-01-10T19:01:08.308Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-10T19:01:08.308Z] GC before operation: completed in 111.796 ms, heap usage 187.683 MB -> 49.845 MB.
[2025-01-10T19:01:11.531Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:01:14.761Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:01:18.074Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:01:20.538Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:01:22.371Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:01:24.245Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:01:26.711Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:01:28.559Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:01:28.559Z] 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.
[2025-01-10T19:01:28.559Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:01:28.936Z] Movies recommended for you:
[2025-01-10T19:01:28.936Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:01:28.936Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:01:28.936Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20418.437 ms) ======
[2025-01-10T19:01:28.936Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-10T19:01:28.936Z] GC before operation: completed in 106.430 ms, heap usage 240.675 MB -> 49.789 MB.
[2025-01-10T19:01:32.165Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:01:35.364Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:01:37.864Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:01:41.098Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:01:42.937Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:01:44.776Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:01:46.603Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:01:48.470Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:01:48.849Z] 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.
[2025-01-10T19:01:48.849Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:01:48.849Z] Movies recommended for you:
[2025-01-10T19:01:48.849Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:01:48.849Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:01:48.849Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20145.424 ms) ======
[2025-01-10T19:01:48.849Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-10T19:01:49.228Z] GC before operation: completed in 94.570 ms, heap usage 192.538 MB -> 49.960 MB.
[2025-01-10T19:01:52.446Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:01:54.920Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:01:58.136Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:00.624Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:02:03.314Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:02:05.147Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:02:06.457Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:02:08.368Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:02:08.762Z] 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.
[2025-01-10T19:02:08.762Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:02:08.762Z] Movies recommended for you:
[2025-01-10T19:02:08.762Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:02:08.762Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:02:08.762Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19727.374 ms) ======
[2025-01-10T19:02:08.762Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-10T19:02:08.762Z] GC before operation: completed in 93.505 ms, heap usage 191.747 MB -> 50.227 MB.
[2025-01-10T19:02:11.976Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:02:15.253Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:02:18.506Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:20.983Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:02:22.807Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:02:24.683Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:02:25.988Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:02:27.830Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:02:28.197Z] 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.
[2025-01-10T19:02:28.197Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:02:28.585Z] Movies recommended for you:
[2025-01-10T19:02:28.585Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:02:28.585Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:02:28.585Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19484.020 ms) ======
[2025-01-10T19:02:28.585Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-10T19:02:28.585Z] GC before operation: completed in 94.679 ms, heap usage 262.953 MB -> 50.122 MB.
[2025-01-10T19:02:31.872Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:02:34.400Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:02:37.585Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:40.058Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:02:41.900Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:02:43.722Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:02:45.559Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:02:47.427Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:02:47.796Z] 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.
[2025-01-10T19:02:47.796Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:02:47.796Z] Movies recommended for you:
[2025-01-10T19:02:47.796Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:02:47.796Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:02:47.796Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19342.716 ms) ======
[2025-01-10T19:02:47.796Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-10T19:02:47.796Z] GC before operation: completed in 91.038 ms, heap usage 233.212 MB -> 50.162 MB.
[2025-01-10T19:02:51.017Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:02:54.236Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:02:56.759Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:59.409Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:03:01.261Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:03:03.087Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:03:04.962Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:03:06.777Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:03:07.143Z] 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.
[2025-01-10T19:03:07.143Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:03:07.510Z] Movies recommended for you:
[2025-01-10T19:03:07.510Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:03:07.510Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:03:07.510Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19459.627 ms) ======
[2025-01-10T19:03:07.510Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-10T19:03:07.510Z] GC before operation: completed in 91.746 ms, heap usage 139.511 MB -> 49.830 MB.
[2025-01-10T19:03:10.788Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:03:14.040Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:03:16.503Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:03:19.698Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:03:20.980Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:03:22.795Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:03:24.656Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:03:26.527Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:03:26.527Z] 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.
[2025-01-10T19:03:26.527Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:03:26.895Z] Movies recommended for you:
[2025-01-10T19:03:26.895Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:03:26.895Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:03:26.895Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19246.556 ms) ======
[2025-01-10T19:03:26.895Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-10T19:03:26.895Z] GC before operation: completed in 94.026 ms, heap usage 171.878 MB -> 50.077 MB.
[2025-01-10T19:03:30.144Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:03:33.335Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:03:35.870Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:03:39.325Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:03:40.593Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:03:42.416Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:03:44.286Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:03:46.829Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:03:46.829Z] 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.
[2025-01-10T19:03:46.829Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:03:46.829Z] Movies recommended for you:
[2025-01-10T19:03:46.829Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:03:46.829Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:03:46.829Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20148.042 ms) ======
[2025-01-10T19:03:46.829Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-10T19:03:47.212Z] GC before operation: completed in 95.314 ms, heap usage 213.621 MB -> 50.217 MB.
[2025-01-10T19:03:50.485Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:03:52.978Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:03:56.191Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:03:59.374Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:04:00.697Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:04:02.529Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:04:04.518Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:04:06.442Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:04:06.812Z] 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.
[2025-01-10T19:04:06.812Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:04:06.812Z] Movies recommended for you:
[2025-01-10T19:04:06.812Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:04:06.812Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:04:06.812Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19765.953 ms) ======
[2025-01-10T19:04:06.812Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-10T19:04:06.812Z] GC before operation: completed in 92.431 ms, heap usage 190.219 MB -> 49.975 MB.
[2025-01-10T19:04:10.924Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:04:13.423Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:04:16.703Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:04:19.152Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:04:21.056Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:04:22.908Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:04:24.768Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:04:26.057Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:04:26.421Z] 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.
[2025-01-10T19:04:26.421Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:04:26.798Z] Movies recommended for you:
[2025-01-10T19:04:26.798Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:04:26.798Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:04:26.798Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19770.904 ms) ======
[2025-01-10T19:04:26.798Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-10T19:04:26.798Z] GC before operation: completed in 95.047 ms, heap usage 201.545 MB -> 50.153 MB.
[2025-01-10T19:04:30.030Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:04:33.297Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:04:35.805Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:04:39.050Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:04:40.902Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:04:42.185Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:04:44.689Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:04:46.507Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:04:46.507Z] 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.
[2025-01-10T19:04:46.507Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:04:46.878Z] Movies recommended for you:
[2025-01-10T19:04:46.878Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:04:46.878Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:04:46.878Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19969.469 ms) ======
[2025-01-10T19:04:46.878Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-10T19:04:46.878Z] GC before operation: completed in 94.129 ms, heap usage 192.098 MB -> 50.231 MB.
[2025-01-10T19:04:50.088Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:04:53.301Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:04:55.842Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:04:58.297Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:05:00.118Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:05:01.936Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:05:04.402Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:05:05.788Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:05:06.154Z] 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.
[2025-01-10T19:05:06.154Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:05:06.154Z] Movies recommended for you:
[2025-01-10T19:05:06.154Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:05:06.154Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:05:06.154Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19351.407 ms) ======
[2025-01-10T19:05:06.154Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-10T19:05:06.154Z] GC before operation: completed in 107.625 ms, heap usage 205.239 MB -> 50.063 MB.
[2025-01-10T19:05:09.401Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:05:12.659Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:05:15.899Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:05:18.395Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:05:20.260Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:05:22.087Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:05:23.975Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:05:25.257Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:05:25.621Z] 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.
[2025-01-10T19:05:25.621Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:05:26.051Z] Movies recommended for you:
[2025-01-10T19:05:26.051Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:05:26.051Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:05:26.051Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19579.635 ms) ======
[2025-01-10T19:05:26.051Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-10T19:05:26.051Z] GC before operation: completed in 98.232 ms, heap usage 202.637 MB -> 50.123 MB.
[2025-01-10T19:05:29.259Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:05:32.472Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:05:35.059Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:05:38.309Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:05:39.581Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:05:41.408Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:05:43.245Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:05:45.089Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:05:45.455Z] 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.
[2025-01-10T19:05:45.455Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:05:45.455Z] Movies recommended for you:
[2025-01-10T19:05:45.455Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:05:45.455Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:05:45.455Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19471.931 ms) ======
[2025-01-10T19:05:45.455Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-10T19:05:45.455Z] GC before operation: completed in 95.525 ms, heap usage 257.571 MB -> 50.364 MB.
[2025-01-10T19:05:48.688Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:05:51.886Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:05:54.391Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:05:57.606Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:05:58.929Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:06:00.786Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:06:02.652Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:06:04.499Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:06:04.877Z] 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.
[2025-01-10T19:06:04.877Z] The best model improves the baseline by 14.52%.
[2025-01-10T19:06:05.242Z] Movies recommended for you:
[2025-01-10T19:06:05.242Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:06:05.242Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:06:05.242Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19490.445 ms) ======
[2025-01-10T19:06:05.614Z] -----------------------------------
[2025-01-10T19:06:05.614Z] renaissance-movie-lens_0_PASSED
[2025-01-10T19:06:05.614Z] -----------------------------------
[2025-01-10T19:06:05.614Z]
[2025-01-10T19:06:05.614Z] TEST TEARDOWN:
[2025-01-10T19:06:05.614Z] Nothing to be done for teardown.
[2025-01-10T19:06:05.614Z] renaissance-movie-lens_0 Finish Time: Fri Jan 10 19:06:05 2025 Epoch Time (ms): 1736535965581