renaissance-movie-lens_0
[2024-11-08T15:08:16.916Z] Running test renaissance-movie-lens_0 ...
[2024-11-08T15:08:16.916Z] ===============================================
[2024-11-08T15:08:16.916Z] renaissance-movie-lens_0 Start Time: Fri Nov 8 15:08:16 2024 Epoch Time (ms): 1731078496468
[2024-11-08T15:08:16.916Z] variation: NoOptions
[2024-11-08T15:08:16.916Z] JVM_OPTIONS:
[2024-11-08T15:08:16.916Z] { \
[2024-11-08T15:08:16.916Z] echo ""; echo "TEST SETUP:"; \
[2024-11-08T15:08:16.916Z] echo "Nothing to be done for setup."; \
[2024-11-08T15:08:16.916Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17310776571078/renaissance-movie-lens_0"; \
[2024-11-08T15:08:16.916Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17310776571078/renaissance-movie-lens_0"; \
[2024-11-08T15:08:16.916Z] echo ""; echo "TESTING:"; \
[2024-11-08T15:08:16.916Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17310776571078/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-08T15:08:16.916Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17310776571078/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-08T15:08:16.916Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-08T15:08:16.916Z] echo "Nothing to be done for teardown."; \
[2024-11-08T15:08:16.916Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17310776571078/TestTargetResult";
[2024-11-08T15:08:16.916Z]
[2024-11-08T15:08:16.916Z] TEST SETUP:
[2024-11-08T15:08:16.916Z] Nothing to be done for setup.
[2024-11-08T15:08:16.916Z]
[2024-11-08T15:08:16.916Z] TESTING:
[2024-11-08T15:08:19.884Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-08T15:08:21.815Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-08T15:08:24.788Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-08T15:08:24.788Z] Training: 60056, validation: 20285, test: 19854
[2024-11-08T15:08:24.788Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-08T15:08:24.788Z] GC before operation: completed in 68.611 ms, heap usage 95.266 MB -> 37.286 MB.
[2024-11-08T15:08:30.091Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:08:33.058Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:08:36.029Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:08:38.998Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:08:39.934Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:08:41.859Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:08:42.801Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:08:44.724Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:08:44.724Z] 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.
[2024-11-08T15:08:44.724Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:08:44.724Z] Movies recommended for you:
[2024-11-08T15:08:44.724Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:08:44.724Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:08:44.724Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20515.335 ms) ======
[2024-11-08T15:08:44.724Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-08T15:08:45.660Z] GC before operation: completed in 72.715 ms, heap usage 298.613 MB -> 51.761 MB.
[2024-11-08T15:08:47.585Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:08:50.558Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:08:52.481Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:08:54.406Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:08:56.327Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:08:57.262Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:08:59.209Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:09:00.147Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:09:01.083Z] 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.
[2024-11-08T15:09:01.083Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:09:01.083Z] Movies recommended for you:
[2024-11-08T15:09:01.083Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:09:01.083Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:09:01.083Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15701.376 ms) ======
[2024-11-08T15:09:01.083Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-08T15:09:01.083Z] GC before operation: completed in 77.731 ms, heap usage 251.109 MB -> 49.854 MB.
[2024-11-08T15:09:03.008Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:09:04.930Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:09:07.900Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:09:10.172Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:09:11.652Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:09:12.590Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:09:13.527Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:09:15.515Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:09:15.515Z] 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.
[2024-11-08T15:09:15.515Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:09:15.515Z] Movies recommended for you:
[2024-11-08T15:09:15.515Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:09:15.515Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:09:15.515Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14527.275 ms) ======
[2024-11-08T15:09:15.515Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-08T15:09:15.515Z] GC before operation: completed in 65.368 ms, heap usage 486.844 MB -> 53.585 MB.
[2024-11-08T15:09:17.578Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:09:19.669Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:09:21.618Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:09:23.540Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:09:25.461Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:09:26.396Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:09:28.317Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:09:29.259Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:09:29.259Z] 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.
[2024-11-08T15:09:29.259Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:09:29.259Z] Movies recommended for you:
[2024-11-08T15:09:29.259Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:09:29.259Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:09:29.259Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13819.962 ms) ======
[2024-11-08T15:09:29.259Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-08T15:09:29.259Z] GC before operation: completed in 83.670 ms, heap usage 110.934 MB -> 50.365 MB.
[2024-11-08T15:09:31.182Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:09:34.153Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:09:36.078Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:09:38.002Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:09:38.938Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:09:40.861Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:09:41.798Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:09:42.735Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:09:43.670Z] 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.
[2024-11-08T15:09:43.670Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:09:43.670Z] Movies recommended for you:
[2024-11-08T15:09:43.670Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:09:43.670Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:09:43.670Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13922.675 ms) ======
[2024-11-08T15:09:43.670Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-08T15:09:43.670Z] GC before operation: completed in 85.006 ms, heap usage 104.122 MB -> 50.577 MB.
[2024-11-08T15:09:45.592Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:09:47.515Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:09:49.442Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:09:51.365Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:09:52.303Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:09:54.250Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:09:55.187Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:09:56.147Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:09:56.147Z] 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.
[2024-11-08T15:09:56.147Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:09:57.086Z] Movies recommended for you:
[2024-11-08T15:09:57.086Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:09:57.086Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:09:57.086Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13121.946 ms) ======
[2024-11-08T15:09:57.086Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-08T15:09:57.086Z] GC before operation: completed in 67.061 ms, heap usage 71.998 MB -> 50.519 MB.
[2024-11-08T15:09:59.008Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:10:00.930Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:10:02.858Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:10:04.782Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:10:05.722Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:10:06.661Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:10:08.582Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:10:09.524Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:10:09.524Z] 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.
[2024-11-08T15:10:09.524Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:10:09.524Z] Movies recommended for you:
[2024-11-08T15:10:09.524Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:10:09.524Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:10:09.524Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13014.132 ms) ======
[2024-11-08T15:10:09.524Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-08T15:10:09.524Z] GC before operation: completed in 67.780 ms, heap usage 220.094 MB -> 50.801 MB.
[2024-11-08T15:10:11.505Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:10:13.425Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:10:15.351Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:10:17.399Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:10:19.324Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:10:20.259Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:10:21.194Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:10:22.129Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:10:23.064Z] 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.
[2024-11-08T15:10:23.064Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:10:23.064Z] Movies recommended for you:
[2024-11-08T15:10:23.064Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:10:23.064Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:10:23.064Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12922.059 ms) ======
[2024-11-08T15:10:23.064Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-08T15:10:23.064Z] GC before operation: completed in 80.100 ms, heap usage 142.369 MB -> 51.016 MB.
[2024-11-08T15:10:24.988Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:10:26.921Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:10:28.842Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:10:30.762Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:10:31.718Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:10:32.658Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:10:33.605Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:10:35.527Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:10:35.527Z] 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.
[2024-11-08T15:10:35.527Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:10:35.527Z] Movies recommended for you:
[2024-11-08T15:10:35.527Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:10:35.527Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:10:35.527Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12776.091 ms) ======
[2024-11-08T15:10:35.527Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-08T15:10:35.527Z] GC before operation: completed in 73.839 ms, heap usage 87.628 MB -> 50.811 MB.
[2024-11-08T15:10:37.467Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:10:39.393Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:10:41.322Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:10:43.246Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:10:44.257Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:10:45.288Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:10:47.327Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:10:48.266Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:10:48.266Z] 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.
[2024-11-08T15:10:48.266Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:10:48.266Z] Movies recommended for you:
[2024-11-08T15:10:48.266Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:10:48.266Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:10:48.266Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12719.413 ms) ======
[2024-11-08T15:10:48.266Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-08T15:10:48.266Z] GC before operation: completed in 97.540 ms, heap usage 89.754 MB -> 50.834 MB.
[2024-11-08T15:10:50.193Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:10:52.116Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:10:54.061Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:10:55.986Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:10:56.923Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:10:58.850Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:10:59.788Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:11:00.738Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:11:00.738Z] 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.
[2024-11-08T15:11:00.738Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:11:01.676Z] Movies recommended for you:
[2024-11-08T15:11:01.676Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:11:01.676Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:11:01.676Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12767.574 ms) ======
[2024-11-08T15:11:01.676Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-08T15:11:01.676Z] GC before operation: completed in 67.514 ms, heap usage 125.535 MB -> 50.730 MB.
[2024-11-08T15:11:03.601Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:11:05.527Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:11:07.450Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:11:08.388Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:11:10.317Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:11:11.253Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:11:12.190Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:11:13.748Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:11:13.748Z] 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.
[2024-11-08T15:11:13.748Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:11:13.748Z] Movies recommended for you:
[2024-11-08T15:11:13.748Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:11:13.748Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:11:13.748Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12765.841 ms) ======
[2024-11-08T15:11:13.748Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-08T15:11:13.748Z] GC before operation: completed in 66.248 ms, heap usage 78.499 MB -> 50.876 MB.
[2024-11-08T15:11:15.722Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:11:17.648Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:11:19.572Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:11:21.500Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:11:23.428Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:11:24.368Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:11:25.305Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:11:27.232Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:11:27.232Z] 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.
[2024-11-08T15:11:27.232Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:11:27.232Z] Movies recommended for you:
[2024-11-08T15:11:27.232Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:11:27.232Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:11:27.232Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13112.572 ms) ======
[2024-11-08T15:11:27.232Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-08T15:11:27.232Z] GC before operation: completed in 70.471 ms, heap usage 96.156 MB -> 51.098 MB.
[2024-11-08T15:11:29.157Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:11:31.081Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:11:33.042Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:11:34.970Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:11:35.917Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:11:36.854Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:11:38.821Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:11:39.758Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:11:39.759Z] 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.
[2024-11-08T15:11:39.759Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:11:39.759Z] Movies recommended for you:
[2024-11-08T15:11:39.759Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:11:39.759Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:11:39.759Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12742.080 ms) ======
[2024-11-08T15:11:39.759Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-08T15:11:39.759Z] GC before operation: completed in 72.386 ms, heap usage 89.292 MB -> 50.772 MB.
[2024-11-08T15:11:41.689Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:11:43.615Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:11:45.540Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:11:47.521Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:11:48.457Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:11:50.381Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:11:51.318Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:11:52.260Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:11:52.260Z] 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.
[2024-11-08T15:11:52.260Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:11:53.199Z] Movies recommended for you:
[2024-11-08T15:11:53.199Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:11:53.199Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:11:53.199Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12669.667 ms) ======
[2024-11-08T15:11:53.199Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-08T15:11:53.199Z] GC before operation: completed in 68.864 ms, heap usage 88.120 MB -> 50.955 MB.
[2024-11-08T15:11:55.127Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:11:57.052Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:11:58.978Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:12:00.905Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:12:01.845Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:12:02.782Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:12:03.720Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:12:05.644Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:12:05.644Z] 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.
[2024-11-08T15:12:05.644Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:12:05.644Z] Movies recommended for you:
[2024-11-08T15:12:05.644Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:12:05.644Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:12:05.644Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12793.771 ms) ======
[2024-11-08T15:12:05.644Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-08T15:12:05.644Z] GC before operation: completed in 77.387 ms, heap usage 88.107 MB -> 51.084 MB.
[2024-11-08T15:12:07.571Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:12:09.498Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:12:11.428Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:12:13.357Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:12:15.082Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:12:16.057Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:12:17.003Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:12:17.939Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:12:17.939Z] 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.
[2024-11-08T15:12:17.939Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:12:18.875Z] Movies recommended for you:
[2024-11-08T15:12:18.875Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:12:18.875Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:12:18.875Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12763.231 ms) ======
[2024-11-08T15:12:18.875Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-08T15:12:18.875Z] GC before operation: completed in 75.480 ms, heap usage 89.862 MB -> 50.842 MB.
[2024-11-08T15:12:20.815Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:12:22.740Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:12:24.664Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:12:26.589Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:12:27.527Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:12:28.482Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:12:29.420Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:12:30.374Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:12:31.311Z] 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.
[2024-11-08T15:12:31.311Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:12:31.311Z] Movies recommended for you:
[2024-11-08T15:12:31.311Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:12:31.311Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:12:31.311Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12607.562 ms) ======
[2024-11-08T15:12:31.311Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-08T15:12:31.311Z] GC before operation: completed in 78.548 ms, heap usage 125.053 MB -> 50.982 MB.
[2024-11-08T15:12:33.242Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:12:35.166Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:12:37.106Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:12:39.033Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:12:39.970Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:12:40.908Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:12:41.845Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:12:43.770Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:12:43.770Z] 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.
[2024-11-08T15:12:43.770Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:12:43.770Z] Movies recommended for you:
[2024-11-08T15:12:43.770Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:12:43.770Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:12:43.770Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12473.257 ms) ======
[2024-11-08T15:12:43.770Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-08T15:12:43.770Z] GC before operation: completed in 72.216 ms, heap usage 89.513 MB -> 51.166 MB.
[2024-11-08T15:12:45.694Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:12:47.618Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:12:49.543Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:12:51.472Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:12:52.409Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:12:53.348Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:12:55.275Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:12:56.214Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:12:56.214Z] 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.
[2024-11-08T15:12:56.214Z] The best model improves the baseline by 14.52%.
[2024-11-08T15:12:56.214Z] Movies recommended for you:
[2024-11-08T15:12:56.214Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:12:56.214Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:12:56.214Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12468.057 ms) ======
[2024-11-08T15:12:57.152Z] -----------------------------------
[2024-11-08T15:12:57.152Z] renaissance-movie-lens_0_PASSED
[2024-11-08T15:12:57.152Z] -----------------------------------
[2024-11-08T15:12:57.152Z]
[2024-11-08T15:12:57.152Z] TEST TEARDOWN:
[2024-11-08T15:12:57.152Z] Nothing to be done for teardown.
[2024-11-08T15:12:57.152Z] renaissance-movie-lens_0 Finish Time: Fri Nov 8 15:12:56 2024 Epoch Time (ms): 1731078776292