renaissance-movie-lens_0
[2024-10-02T21:07:53.468Z] Running test renaissance-movie-lens_0 ...
[2024-10-02T21:07:53.468Z] ===============================================
[2024-10-02T21:07:53.468Z] renaissance-movie-lens_0 Start Time: Wed Oct 2 21:07:52 2024 Epoch Time (ms): 1727903272653
[2024-10-02T21:07:53.468Z] variation: NoOptions
[2024-10-02T21:07:53.468Z] JVM_OPTIONS:
[2024-10-02T21:07:53.468Z] { \
[2024-10-02T21:07:53.468Z] echo ""; echo "TEST SETUP:"; \
[2024-10-02T21:07:53.468Z] echo "Nothing to be done for setup."; \
[2024-10-02T21:07:53.468Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279023323367/renaissance-movie-lens_0"; \
[2024-10-02T21:07:53.468Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279023323367/renaissance-movie-lens_0"; \
[2024-10-02T21:07:53.468Z] echo ""; echo "TESTING:"; \
[2024-10-02T21:07:53.468Z] "/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_17279023323367/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-02T21:07:53.468Z] 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_17279023323367/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-02T21:07:53.468Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-02T21:07:53.468Z] echo "Nothing to be done for teardown."; \
[2024-10-02T21:07:53.468Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279023323367/TestTargetResult";
[2024-10-02T21:07:53.468Z]
[2024-10-02T21:07:53.468Z] TEST SETUP:
[2024-10-02T21:07:53.468Z] Nothing to be done for setup.
[2024-10-02T21:07:53.468Z]
[2024-10-02T21:07:53.468Z] TESTING:
[2024-10-02T21:07:56.455Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-02T21:07:58.386Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-02T21:08:02.501Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-02T21:08:02.501Z] Training: 60056, validation: 20285, test: 19854
[2024-10-02T21:08:02.501Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-02T21:08:02.501Z] GC before operation: completed in 76.042 ms, heap usage 101.579 MB -> 36.431 MB.
[2024-10-02T21:08:09.158Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:08:12.164Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:08:16.272Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:08:18.216Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:08:20.150Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:08:22.088Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:08:23.039Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:08:24.977Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:08:24.977Z] 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.
[2024-10-02T21:08:24.977Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:08:24.977Z] Movies recommended for you:
[2024-10-02T21:08:24.977Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:08:24.977Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:08:24.977Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23115.400 ms) ======
[2024-10-02T21:08:24.977Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-02T21:08:25.919Z] GC before operation: completed in 87.931 ms, heap usage 287.829 MB -> 49.137 MB.
[2024-10-02T21:08:27.855Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:08:30.844Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:08:32.779Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:08:34.721Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:08:36.660Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:08:37.602Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:08:39.536Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:08:40.479Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:08:41.420Z] 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.
[2024-10-02T21:08:41.420Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:08:41.420Z] Movies recommended for you:
[2024-10-02T21:08:41.420Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:08:41.420Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:08:41.420Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15835.390 ms) ======
[2024-10-02T21:08:41.420Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-02T21:08:41.420Z] GC before operation: completed in 88.732 ms, heap usage 134.117 MB -> 49.048 MB.
[2024-10-02T21:08:43.355Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:08:46.340Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:08:48.276Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:08:50.210Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:08:52.152Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:08:53.094Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:08:54.035Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:08:55.984Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:08:55.984Z] 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.
[2024-10-02T21:08:55.984Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:08:55.984Z] Movies recommended for you:
[2024-10-02T21:08:55.984Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:08:55.984Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:08:55.984Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14764.057 ms) ======
[2024-10-02T21:08:55.984Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-02T21:08:55.984Z] GC before operation: completed in 79.771 ms, heap usage 169.523 MB -> 49.356 MB.
[2024-10-02T21:08:57.979Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:00.965Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:02.900Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:09:04.837Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:09:05.779Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:07.712Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:08.655Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:10.591Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:10.591Z] 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.
[2024-10-02T21:09:10.591Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:10.591Z] Movies recommended for you:
[2024-10-02T21:09:10.591Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:10.591Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:10.591Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14284.285 ms) ======
[2024-10-02T21:09:10.591Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-02T21:09:10.591Z] GC before operation: completed in 79.443 ms, heap usage 182.204 MB -> 49.723 MB.
[2024-10-02T21:09:12.542Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:15.652Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:17.593Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:09:19.527Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:09:21.459Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:22.401Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:23.342Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:25.275Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:25.275Z] 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.
[2024-10-02T21:09:25.275Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:25.275Z] Movies recommended for you:
[2024-10-02T21:09:25.275Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:25.275Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:25.275Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14685.003 ms) ======
[2024-10-02T21:09:25.275Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-02T21:09:25.275Z] GC before operation: completed in 76.530 ms, heap usage 153.971 MB -> 49.825 MB.
[2024-10-02T21:09:27.211Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:29.144Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:31.076Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:09:33.011Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:09:34.961Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:35.907Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:38.704Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:40.169Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:40.169Z] 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.
[2024-10-02T21:09:40.169Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:40.169Z] Movies recommended for you:
[2024-10-02T21:09:40.169Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:40.169Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:40.169Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13646.007 ms) ======
[2024-10-02T21:09:40.169Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-02T21:09:40.169Z] GC before operation: completed in 89.983 ms, heap usage 286.504 MB -> 49.895 MB.
[2024-10-02T21:09:41.191Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:43.122Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:46.115Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:09:48.049Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:09:48.989Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:49.942Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:51.881Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:52.846Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:52.846Z] 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.
[2024-10-02T21:09:52.846Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:52.846Z] Movies recommended for you:
[2024-10-02T21:09:52.846Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:52.846Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:52.846Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13993.808 ms) ======
[2024-10-02T21:09:52.846Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-02T21:09:52.846Z] GC before operation: completed in 85.372 ms, heap usage 186.074 MB -> 50.037 MB.
[2024-10-02T21:09:54.781Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:57.867Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:59.814Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:01.749Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:02.696Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:10:03.645Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:10:05.589Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:10:06.532Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:10:06.532Z] 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.
[2024-10-02T21:10:06.532Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:10:06.532Z] Movies recommended for you:
[2024-10-02T21:10:06.532Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:10:06.532Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:10:06.532Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13619.632 ms) ======
[2024-10-02T21:10:06.532Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-02T21:10:06.532Z] GC before operation: completed in 81.806 ms, heap usage 181.772 MB -> 50.244 MB.
[2024-10-02T21:10:09.519Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:11.454Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:13.388Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:15.321Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:16.262Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:10:17.212Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:10:19.150Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:10:20.091Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:10:20.091Z] 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.
[2024-10-02T21:10:20.091Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:10:21.077Z] Movies recommended for you:
[2024-10-02T21:10:21.077Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:10:21.077Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:10:21.077Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13763.773 ms) ======
[2024-10-02T21:10:21.077Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-02T21:10:21.077Z] GC before operation: completed in 87.436 ms, heap usage 191.568 MB -> 50.115 MB.
[2024-10-02T21:10:23.010Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:24.946Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:27.934Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:29.871Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:30.818Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:10:31.758Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:10:33.691Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:10:34.631Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:10:34.631Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:10:34.631Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:10:34.631Z] Movies recommended for you:
[2024-10-02T21:10:34.631Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:10:34.631Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:10:34.631Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14311.998 ms) ======
[2024-10-02T21:10:34.631Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-02T21:10:35.574Z] GC before operation: completed in 90.027 ms, heap usage 253.517 MB -> 50.228 MB.
[2024-10-02T21:10:37.513Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:39.451Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:41.385Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:43.319Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:45.254Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:10:46.196Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:10:47.141Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:10:49.087Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:10:49.087Z] 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.
[2024-10-02T21:10:49.087Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:10:49.087Z] Movies recommended for you:
[2024-10-02T21:10:49.087Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:10:49.087Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:10:49.087Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13990.423 ms) ======
[2024-10-02T21:10:49.087Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-02T21:10:49.087Z] GC before operation: completed in 80.792 ms, heap usage 328.752 MB -> 50.047 MB.
[2024-10-02T21:10:52.088Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:54.025Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:55.982Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:57.993Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:59.932Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:00.876Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:01.829Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:03.766Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:03.766Z] 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.
[2024-10-02T21:11:03.766Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:03.766Z] Movies recommended for you:
[2024-10-02T21:11:03.766Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:03.766Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:03.766Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14672.078 ms) ======
[2024-10-02T21:11:03.766Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-02T21:11:03.766Z] GC before operation: completed in 84.419 ms, heap usage 164.148 MB -> 50.024 MB.
[2024-10-02T21:11:06.760Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:11:08.704Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:11:10.641Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:11:12.573Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:11:13.516Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:15.459Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:16.404Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:17.345Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:18.287Z] 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.
[2024-10-02T21:11:18.287Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:18.287Z] Movies recommended for you:
[2024-10-02T21:11:18.287Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:18.287Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:18.287Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14050.232 ms) ======
[2024-10-02T21:11:18.287Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-02T21:11:18.287Z] GC before operation: completed in 81.336 ms, heap usage 126.055 MB -> 50.176 MB.
[2024-10-02T21:11:20.219Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:11:22.158Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:11:24.096Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:11:26.033Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:11:27.975Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:28.916Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:29.857Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:30.811Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:31.752Z] 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.
[2024-10-02T21:11:31.752Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:31.752Z] Movies recommended for you:
[2024-10-02T21:11:31.752Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:31.752Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:31.752Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13408.965 ms) ======
[2024-10-02T21:11:31.752Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-02T21:11:31.752Z] GC before operation: completed in 78.241 ms, heap usage 64.160 MB -> 49.894 MB.
[2024-10-02T21:11:33.688Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:11:35.625Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:11:37.572Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:11:39.505Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:11:41.437Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:42.378Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:43.319Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:45.257Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:45.257Z] 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.
[2024-10-02T21:11:45.257Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:45.257Z] Movies recommended for you:
[2024-10-02T21:11:45.257Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:45.257Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:45.257Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13568.138 ms) ======
[2024-10-02T21:11:45.257Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-02T21:11:45.257Z] GC before operation: completed in 80.101 ms, heap usage 325.157 MB -> 50.283 MB.
[2024-10-02T21:11:47.190Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:11:49.132Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:11:52.149Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:11:54.109Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:11:55.065Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:56.005Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:56.985Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:58.919Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:58.919Z] 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.
[2024-10-02T21:11:58.919Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:58.919Z] Movies recommended for you:
[2024-10-02T21:11:58.919Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:58.919Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:58.919Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13830.022 ms) ======
[2024-10-02T21:11:58.919Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-02T21:11:58.919Z] GC before operation: completed in 89.886 ms, heap usage 131.642 MB -> 50.206 MB.
[2024-10-02T21:12:01.916Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:12:03.863Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:12:05.795Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:12:07.739Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:12:08.684Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:12:09.626Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:12:12.599Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:12:12.599Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:12:12.599Z] 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.
[2024-10-02T21:12:12.599Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:12:12.599Z] Movies recommended for you:
[2024-10-02T21:12:12.599Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:12:12.599Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:12:12.599Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13541.162 ms) ======
[2024-10-02T21:12:12.599Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-02T21:12:14.077Z] GC before operation: completed in 100.075 ms, heap usage 194.489 MB -> 50.154 MB.
[2024-10-02T21:12:15.258Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:12:17.192Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:12:19.123Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:12:21.063Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:12:22.010Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:12:23.949Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:12:24.892Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:12:25.883Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:12:25.883Z] 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.
[2024-10-02T21:12:25.883Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:12:26.825Z] Movies recommended for you:
[2024-10-02T21:12:26.825Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:12:26.825Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:12:26.825Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13567.765 ms) ======
[2024-10-02T21:12:26.825Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-02T21:12:26.825Z] GC before operation: completed in 82.044 ms, heap usage 178.588 MB -> 50.161 MB.
[2024-10-02T21:12:28.763Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:12:30.694Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:12:32.666Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:12:34.600Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:12:36.550Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:12:37.502Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:12:38.448Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:12:39.410Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:12:40.354Z] 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.
[2024-10-02T21:12:40.354Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:12:40.354Z] Movies recommended for you:
[2024-10-02T21:12:40.354Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:12:40.354Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:12:40.354Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (13668.444 ms) ======
[2024-10-02T21:12:40.354Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-02T21:12:40.354Z] GC before operation: completed in 83.321 ms, heap usage 151.623 MB -> 50.312 MB.
[2024-10-02T21:12:42.287Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:12:44.221Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:12:46.163Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:12:48.098Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:12:50.031Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:12:50.977Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:12:51.928Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:12:53.867Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:12:53.867Z] 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.
[2024-10-02T21:12:53.867Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:12:53.867Z] Movies recommended for you:
[2024-10-02T21:12:53.867Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:12:53.867Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:12:53.867Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13540.159 ms) ======
[2024-10-02T21:12:53.867Z] -----------------------------------
[2024-10-02T21:12:53.867Z] renaissance-movie-lens_0_PASSED
[2024-10-02T21:12:53.867Z] -----------------------------------
[2024-10-02T21:12:53.867Z]
[2024-10-02T21:12:53.867Z] TEST TEARDOWN:
[2024-10-02T21:12:53.867Z] Nothing to be done for teardown.
[2024-10-02T21:12:53.867Z] renaissance-movie-lens_0 Finish Time: Wed Oct 2 21:12:53 2024 Epoch Time (ms): 1727903573738