renaissance-movie-lens_0
[2024-08-28T20:57:28.811Z] Running test renaissance-movie-lens_0 ...
[2024-08-28T20:57:28.811Z] ===============================================
[2024-08-28T20:57:28.811Z] renaissance-movie-lens_0 Start Time: Wed Aug 28 20:57:28 2024 Epoch Time (ms): 1724878648656
[2024-08-28T20:57:28.811Z] variation: NoOptions
[2024-08-28T20:57:28.811Z] JVM_OPTIONS:
[2024-08-28T20:57:28.811Z] { \
[2024-08-28T20:57:28.811Z] echo ""; echo "TEST SETUP:"; \
[2024-08-28T20:57:28.811Z] echo "Nothing to be done for setup."; \
[2024-08-28T20:57:28.811Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17248776991470/renaissance-movie-lens_0"; \
[2024-08-28T20:57:28.811Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17248776991470/renaissance-movie-lens_0"; \
[2024-08-28T20:57:28.811Z] echo ""; echo "TESTING:"; \
[2024-08-28T20:57:28.811Z] "/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_17248776991470/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-28T20:57:28.811Z] 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_17248776991470/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-28T20:57:28.811Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-28T20:57:28.811Z] echo "Nothing to be done for teardown."; \
[2024-08-28T20:57:28.811Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17248776991470/TestTargetResult";
[2024-08-28T20:57:28.811Z]
[2024-08-28T20:57:28.811Z] TEST SETUP:
[2024-08-28T20:57:28.811Z] Nothing to be done for setup.
[2024-08-28T20:57:28.811Z]
[2024-08-28T20:57:28.811Z] TESTING:
[2024-08-28T20:57:32.948Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-28T20:57:34.857Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-08-28T20:57:37.822Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-28T20:57:37.822Z] Training: 60056, validation: 20285, test: 19854
[2024-08-28T20:57:37.822Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-28T20:57:38.757Z] GC before operation: completed in 76.182 ms, heap usage 78.027 MB -> 36.426 MB.
[2024-08-28T20:57:44.020Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:57:46.974Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:57:49.926Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:57:52.899Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:57:54.808Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:57:55.739Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:57:57.648Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:57:59.569Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:57:59.569Z] 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.
[2024-08-28T20:57:59.569Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:57:59.569Z] Movies recommended for you:
[2024-08-28T20:57:59.569Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:57:59.569Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:57:59.569Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21398.284 ms) ======
[2024-08-28T20:57:59.569Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-28T20:57:59.569Z] GC before operation: completed in 93.382 ms, heap usage 153.064 MB -> 48.199 MB.
[2024-08-28T20:58:02.520Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:58:04.434Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:58:07.386Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:58:09.295Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:58:11.209Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:58:13.117Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:58:14.045Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:58:15.957Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:58:15.957Z] 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.
[2024-08-28T20:58:15.957Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:58:15.957Z] Movies recommended for you:
[2024-08-28T20:58:15.957Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:58:15.957Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:58:15.957Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16089.915 ms) ======
[2024-08-28T20:58:15.957Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-28T20:58:15.957Z] GC before operation: completed in 76.211 ms, heap usage 210.989 MB -> 49.096 MB.
[2024-08-28T20:58:18.911Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:58:20.821Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:58:23.079Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:58:24.893Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:58:26.806Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:58:27.737Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:58:28.665Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:58:30.630Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:58:30.630Z] 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.
[2024-08-28T20:58:30.630Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:58:30.630Z] Movies recommended for you:
[2024-08-28T20:58:30.630Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:58:30.630Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:58:30.630Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14699.216 ms) ======
[2024-08-28T20:58:30.630Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-28T20:58:30.630Z] GC before operation: completed in 88.856 ms, heap usage 212.714 MB -> 49.411 MB.
[2024-08-28T20:58:33.585Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:58:35.497Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:58:37.416Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:58:39.329Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:58:41.243Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:58:42.174Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:58:44.083Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:58:45.017Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:58:45.017Z] 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.
[2024-08-28T20:58:45.017Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:58:45.017Z] Movies recommended for you:
[2024-08-28T20:58:45.017Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:58:45.017Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:58:45.017Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14607.535 ms) ======
[2024-08-28T20:58:45.017Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-28T20:58:45.946Z] GC before operation: completed in 81.878 ms, heap usage 209.464 MB -> 49.696 MB.
[2024-08-28T20:58:47.854Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:58:49.767Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:58:52.761Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:58:54.681Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:58:55.609Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:58:57.522Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:58:58.451Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:59:00.363Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:59:00.363Z] 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.
[2024-08-28T20:59:00.363Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:59:00.363Z] Movies recommended for you:
[2024-08-28T20:59:00.363Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:59:00.363Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:59:00.363Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14899.879 ms) ======
[2024-08-28T20:59:00.363Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-28T20:59:00.363Z] GC before operation: completed in 96.159 ms, heap usage 275.439 MB -> 49.984 MB.
[2024-08-28T20:59:02.273Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:59:05.224Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:59:07.137Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:59:09.047Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:59:10.956Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:59:11.885Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:59:13.793Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:59:14.727Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:59:14.727Z] 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.
[2024-08-28T20:59:14.727Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:59:14.727Z] Movies recommended for you:
[2024-08-28T20:59:14.727Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:59:14.727Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:59:14.727Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14544.163 ms) ======
[2024-08-28T20:59:14.727Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-28T20:59:14.727Z] GC before operation: completed in 85.085 ms, heap usage 99.725 MB -> 49.717 MB.
[2024-08-28T20:59:17.691Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:59:19.602Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:59:22.592Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:59:23.525Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:59:25.444Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:59:26.377Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:59:27.306Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:59:29.234Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:59:29.234Z] 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.
[2024-08-28T20:59:29.234Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:59:29.234Z] Movies recommended for you:
[2024-08-28T20:59:29.234Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:59:29.234Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:59:29.234Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14001.387 ms) ======
[2024-08-28T20:59:29.234Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-28T20:59:29.234Z] GC before operation: completed in 84.064 ms, heap usage 315.458 MB -> 50.135 MB.
[2024-08-28T20:59:31.151Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:59:33.058Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:59:36.010Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:59:37.917Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:59:38.846Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:59:39.777Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:59:41.696Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:59:42.625Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:59:42.626Z] 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.
[2024-08-28T20:59:42.626Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:59:42.626Z] Movies recommended for you:
[2024-08-28T20:59:42.626Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:59:42.626Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:59:42.626Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13616.772 ms) ======
[2024-08-28T20:59:42.626Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-28T20:59:42.626Z] GC before operation: completed in 82.224 ms, heap usage 212.354 MB -> 50.215 MB.
[2024-08-28T20:59:45.586Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:59:47.511Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:59:49.422Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:59:51.331Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:59:52.267Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:59:54.176Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:59:55.106Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:59:56.035Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:59:56.964Z] 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.
[2024-08-28T20:59:56.964Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:59:56.964Z] Movies recommended for you:
[2024-08-28T20:59:56.964Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:59:56.964Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:59:56.964Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13719.174 ms) ======
[2024-08-28T20:59:56.964Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-28T20:59:56.964Z] GC before operation: completed in 82.104 ms, heap usage 201.750 MB -> 50.083 MB.
[2024-08-28T20:59:58.947Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:00.858Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:02.771Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:00:04.681Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:00:06.590Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:00:07.520Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:00:08.450Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:00:10.373Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:00:10.373Z] 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.
[2024-08-28T21:00:10.373Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:00:10.373Z] Movies recommended for you:
[2024-08-28T21:00:10.373Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:00:10.373Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:00:10.373Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13628.799 ms) ======
[2024-08-28T21:00:10.373Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-28T21:00:10.373Z] GC before operation: completed in 82.902 ms, heap usage 125.779 MB -> 50.141 MB.
[2024-08-28T21:00:12.285Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:14.197Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:16.108Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:00:18.905Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:00:19.834Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:00:20.762Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:00:22.679Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:00:23.608Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:00:23.608Z] 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.
[2024-08-28T21:00:23.608Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:00:23.608Z] Movies recommended for you:
[2024-08-28T21:00:23.608Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:00:23.608Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:00:23.608Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13361.201 ms) ======
[2024-08-28T21:00:23.608Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-28T21:00:23.608Z] GC before operation: completed in 89.145 ms, heap usage 183.418 MB -> 49.904 MB.
[2024-08-28T21:00:26.561Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:28.472Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:30.382Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:00:32.376Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:00:33.329Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:00:35.238Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:00:36.175Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:00:37.106Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:00:37.106Z] 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.
[2024-08-28T21:00:37.106Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:00:38.035Z] Movies recommended for you:
[2024-08-28T21:00:38.035Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:00:38.035Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:00:38.035Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13752.787 ms) ======
[2024-08-28T21:00:38.035Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-28T21:00:38.035Z] GC before operation: completed in 81.012 ms, heap usage 309.781 MB -> 50.239 MB.
[2024-08-28T21:00:39.951Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:41.869Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:43.777Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:00:46.730Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:00:47.662Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:00:48.614Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:00:50.527Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:00:51.457Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:00:51.457Z] 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.
[2024-08-28T21:00:51.457Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:00:52.387Z] Movies recommended for you:
[2024-08-28T21:00:52.387Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:00:52.387Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:00:52.387Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14243.134 ms) ======
[2024-08-28T21:00:52.387Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-28T21:00:52.387Z] GC before operation: completed in 90.232 ms, heap usage 259.271 MB -> 50.384 MB.
[2024-08-28T21:00:54.297Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:56.205Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:59.154Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:01.067Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:01.997Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:02.926Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:04.836Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:05.765Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:05.765Z] 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.
[2024-08-28T21:01:05.765Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:01:05.765Z] Movies recommended for you:
[2024-08-28T21:01:05.765Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:05.765Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:05.765Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14068.113 ms) ======
[2024-08-28T21:01:05.765Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-28T21:01:05.765Z] GC before operation: completed in 83.373 ms, heap usage 184.204 MB -> 50.024 MB.
[2024-08-28T21:01:08.725Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:10.636Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:12.551Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:14.459Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:16.324Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:17.298Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:18.228Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:19.160Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:19.160Z] 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.
[2024-08-28T21:01:19.160Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:01:19.160Z] Movies recommended for you:
[2024-08-28T21:01:19.160Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:19.160Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:19.160Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13376.924 ms) ======
[2024-08-28T21:01:19.160Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-28T21:01:20.130Z] GC before operation: completed in 84.009 ms, heap usage 201.625 MB -> 50.233 MB.
[2024-08-28T21:01:22.043Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:23.956Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:25.878Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:27.791Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:29.704Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:30.800Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:31.742Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:33.652Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:33.652Z] 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.
[2024-08-28T21:01:33.652Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:01:33.652Z] Movies recommended for you:
[2024-08-28T21:01:33.652Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:33.652Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:33.652Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13971.591 ms) ======
[2024-08-28T21:01:33.652Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-28T21:01:33.652Z] GC before operation: completed in 94.631 ms, heap usage 207.342 MB -> 50.256 MB.
[2024-08-28T21:01:36.621Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:38.533Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:40.462Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:42.562Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:43.494Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:44.425Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:45.366Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:47.279Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:47.279Z] 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.
[2024-08-28T21:01:47.279Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:01:47.279Z] Movies recommended for you:
[2024-08-28T21:01:47.279Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:47.279Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:47.279Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13641.171 ms) ======
[2024-08-28T21:01:47.279Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-28T21:01:47.279Z] GC before operation: completed in 111.238 ms, heap usage 314.960 MB -> 50.258 MB.
[2024-08-28T21:01:50.248Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:52.167Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:54.081Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:55.991Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:56.921Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:58.831Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:59.775Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:02:00.711Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:02:01.640Z] 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.
[2024-08-28T21:02:01.640Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:02:01.640Z] Movies recommended for you:
[2024-08-28T21:02:01.640Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:02:01.640Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:02:01.640Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13854.460 ms) ======
[2024-08-28T21:02:01.640Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-28T21:02:01.640Z] GC before operation: completed in 81.717 ms, heap usage 106.699 MB -> 50.142 MB.
[2024-08-28T21:02:03.552Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:02:05.462Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:02:07.377Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:02:09.303Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:02:10.236Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:02:13.024Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:02:13.024Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:02:13.954Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:02:13.954Z] 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.
[2024-08-28T21:02:14.884Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:02:14.884Z] Movies recommended for you:
[2024-08-28T21:02:14.884Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:02:14.884Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:02:14.884Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (13094.072 ms) ======
[2024-08-28T21:02:14.884Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-28T21:02:14.884Z] GC before operation: completed in 84.640 ms, heap usage 244.756 MB -> 50.410 MB.
[2024-08-28T21:02:16.795Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:02:18.737Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:02:20.654Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:02:22.569Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:02:23.507Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:02:25.416Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:02:26.348Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:02:27.279Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:02:27.279Z] 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.
[2024-08-28T21:02:28.207Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:02:28.207Z] Movies recommended for you:
[2024-08-28T21:02:28.207Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:02:28.207Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:02:28.207Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13279.023 ms) ======
[2024-08-28T21:02:28.207Z] -----------------------------------
[2024-08-28T21:02:28.207Z] renaissance-movie-lens_0_PASSED
[2024-08-28T21:02:28.207Z] -----------------------------------
[2024-08-28T21:02:28.207Z]
[2024-08-28T21:02:28.207Z] TEST TEARDOWN:
[2024-08-28T21:02:28.207Z] Nothing to be done for teardown.
[2024-08-28T21:02:28.207Z] renaissance-movie-lens_0 Finish Time: Wed Aug 28 21:02:27 2024 Epoch Time (ms): 1724878947806