renaissance-movie-lens_0
[2024-10-03T01:41:58.867Z] Running test renaissance-movie-lens_0 ...
[2024-10-03T01:41:58.867Z] ===============================================
[2024-10-03T01:41:58.867Z] renaissance-movie-lens_0 Start Time: Thu Oct 3 01:41:58 2024 Epoch Time (ms): 1727919718170
[2024-10-03T01:41:58.867Z] variation: NoOptions
[2024-10-03T01:41:58.867Z] JVM_OPTIONS:
[2024-10-03T01:41:58.867Z] { \
[2024-10-03T01:41:58.867Z] echo ""; echo "TEST SETUP:"; \
[2024-10-03T01:41:58.867Z] echo "Nothing to be done for setup."; \
[2024-10-03T01:41:58.867Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17279186985186/renaissance-movie-lens_0"; \
[2024-10-03T01:41:58.867Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17279186985186/renaissance-movie-lens_0"; \
[2024-10-03T01:41:58.867Z] echo ""; echo "TESTING:"; \
[2024-10-03T01:41:58.867Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/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_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17279186985186/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-03T01:41:58.867Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17279186985186/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-03T01:41:58.867Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-03T01:41:58.867Z] echo "Nothing to be done for teardown."; \
[2024-10-03T01:41:58.867Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17279186985186/TestTargetResult";
[2024-10-03T01:41:58.867Z]
[2024-10-03T01:41:58.867Z] TEST SETUP:
[2024-10-03T01:41:58.867Z] Nothing to be done for setup.
[2024-10-03T01:41:58.867Z]
[2024-10-03T01:41:58.867Z] TESTING:
[2024-10-03T01:42:03.407Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-03T01:42:06.842Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-10-03T01:42:10.292Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-03T01:42:11.064Z] Training: 60056, validation: 20285, test: 19854
[2024-10-03T01:42:11.065Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-03T01:42:11.065Z] GC before operation: completed in 59.041 ms, heap usage 144.398 MB -> 38.084 MB.
[2024-10-03T01:42:16.687Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:42:21.175Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:42:24.611Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:42:28.046Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:42:30.530Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:42:32.125Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:42:34.609Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:42:36.209Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:42:36.982Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:42:36.982Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:42:36.982Z] Movies recommended for you:
[2024-10-03T01:42:36.982Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:42:36.982Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:42:36.982Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26155.527 ms) ======
[2024-10-03T01:42:36.982Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-03T01:42:36.982Z] GC before operation: completed in 104.035 ms, heap usage 187.745 MB -> 51.258 MB.
[2024-10-03T01:42:40.436Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:42:44.596Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:42:48.141Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:42:49.948Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:42:52.434Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:42:54.037Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:42:56.533Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:42:58.134Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:42:58.134Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:42:58.913Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:42:58.913Z] Movies recommended for you:
[2024-10-03T01:42:58.913Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:42:58.913Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:42:58.913Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21533.877 ms) ======
[2024-10-03T01:42:58.913Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-03T01:42:58.913Z] GC before operation: completed in 115.798 ms, heap usage 365.338 MB -> 51.846 MB.
[2024-10-03T01:43:02.359Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:43:04.840Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:43:08.272Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:43:11.700Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:43:13.307Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:43:15.792Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:43:17.383Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:43:18.986Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:43:19.758Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:43:19.758Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:43:19.758Z] Movies recommended for you:
[2024-10-03T01:43:19.758Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:43:19.758Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:43:19.758Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20916.788 ms) ======
[2024-10-03T01:43:19.758Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-03T01:43:19.758Z] GC before operation: completed in 113.779 ms, heap usage 598.021 MB -> 55.684 MB.
[2024-10-03T01:43:23.203Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:43:25.689Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:43:29.122Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:43:32.563Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:43:34.159Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:43:36.654Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:43:38.247Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:43:39.846Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:43:40.619Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:43:40.619Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:43:40.619Z] Movies recommended for you:
[2024-10-03T01:43:40.619Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:43:40.619Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:43:40.619Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20760.102 ms) ======
[2024-10-03T01:43:40.619Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-03T01:43:40.619Z] GC before operation: completed in 120.304 ms, heap usage 299.702 MB -> 52.525 MB.
[2024-10-03T01:43:44.088Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:43:46.565Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:43:49.996Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:43:53.429Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:43:55.023Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:43:56.625Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:43:59.131Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:44:00.745Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:44:01.673Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:44:01.673Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:44:01.673Z] Movies recommended for you:
[2024-10-03T01:44:01.673Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:44:01.673Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:44:01.673Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20590.045 ms) ======
[2024-10-03T01:44:01.673Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-03T01:44:01.673Z] GC before operation: completed in 113.329 ms, heap usage 494.187 MB -> 56.076 MB.
[2024-10-03T01:44:04.151Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:44:07.596Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:44:11.033Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:44:13.516Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:44:16.001Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:44:17.609Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:44:19.199Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:44:21.682Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:44:21.682Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:44:21.682Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:44:21.682Z] Movies recommended for you:
[2024-10-03T01:44:21.682Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:44:21.682Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:44:21.682Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20360.749 ms) ======
[2024-10-03T01:44:21.682Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-03T01:44:21.682Z] GC before operation: completed in 107.545 ms, heap usage 472.203 MB -> 52.734 MB.
[2024-10-03T01:44:25.108Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:44:28.535Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:44:31.027Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:44:34.471Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:44:36.068Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:44:38.548Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:44:40.141Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:44:41.743Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:44:42.513Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:44:42.514Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:44:42.514Z] Movies recommended for you:
[2024-10-03T01:44:42.514Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:44:42.514Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:44:42.514Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20575.234 ms) ======
[2024-10-03T01:44:42.514Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-03T01:44:42.514Z] GC before operation: completed in 104.841 ms, heap usage 443.746 MB -> 52.870 MB.
[2024-10-03T01:44:45.955Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:44:48.437Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:44:51.877Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:44:55.306Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:44:56.907Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:44:58.511Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:45:00.281Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:45:02.769Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:45:02.769Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:45:02.769Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:45:02.769Z] Movies recommended for you:
[2024-10-03T01:45:02.769Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:45:02.769Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:45:02.769Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20250.205 ms) ======
[2024-10-03T01:45:02.769Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-03T01:45:02.769Z] GC before operation: completed in 108.846 ms, heap usage 532.811 MB -> 56.454 MB.
[2024-10-03T01:45:06.221Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:45:08.714Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:45:12.157Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:45:15.591Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:45:17.187Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:45:18.778Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:45:21.282Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:45:22.893Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:45:22.893Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:45:22.893Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:45:22.893Z] Movies recommended for you:
[2024-10-03T01:45:22.893Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:45:22.893Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:45:22.893Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20284.648 ms) ======
[2024-10-03T01:45:22.893Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-03T01:45:23.666Z] GC before operation: completed in 117.827 ms, heap usage 388.698 MB -> 52.942 MB.
[2024-10-03T01:45:26.152Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:45:29.601Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:45:33.056Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:45:35.533Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:45:37.134Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:45:39.625Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:45:41.219Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:45:42.823Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:45:43.594Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:45:43.594Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:45:43.594Z] Movies recommended for you:
[2024-10-03T01:45:43.594Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:45:43.594Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:45:43.594Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20265.374 ms) ======
[2024-10-03T01:45:43.594Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-03T01:45:43.594Z] GC before operation: completed in 116.663 ms, heap usage 298.584 MB -> 53.007 MB.
[2024-10-03T01:45:47.021Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:45:49.504Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:45:52.932Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:45:56.376Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:45:57.976Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:45:59.566Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:46:02.050Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:46:03.650Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:46:03.650Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:46:03.650Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:46:03.650Z] Movies recommended for you:
[2024-10-03T01:46:03.650Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:46:03.650Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:46:03.650Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20324.000 ms) ======
[2024-10-03T01:46:03.650Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-03T01:46:04.423Z] GC before operation: completed in 117.000 ms, heap usage 240.596 MB -> 52.663 MB.
[2024-10-03T01:46:06.903Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:46:10.358Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:46:13.808Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:46:16.286Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:46:17.883Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:46:20.359Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:46:21.962Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:46:23.560Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:46:24.332Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:46:24.332Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:46:24.332Z] Movies recommended for you:
[2024-10-03T01:46:24.332Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:46:24.332Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:46:24.332Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20188.992 ms) ======
[2024-10-03T01:46:24.332Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-03T01:46:24.332Z] GC before operation: completed in 112.906 ms, heap usage 242.095 MB -> 52.863 MB.
[2024-10-03T01:46:27.791Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:46:30.267Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:46:33.706Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:46:37.170Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:46:38.767Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:46:40.359Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:46:42.843Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:46:44.449Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:46:44.450Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:46:44.450Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:46:44.450Z] Movies recommended for you:
[2024-10-03T01:46:44.450Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:46:44.450Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:46:44.450Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20311.752 ms) ======
[2024-10-03T01:46:44.450Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-03T01:46:45.219Z] GC before operation: completed in 116.457 ms, heap usage 300.353 MB -> 53.114 MB.
[2024-10-03T01:46:47.705Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:46:51.156Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:46:54.601Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:46:57.082Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:46:58.680Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:47:01.176Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:47:02.773Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:47:04.371Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:47:05.146Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:47:05.146Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:47:05.146Z] Movies recommended for you:
[2024-10-03T01:47:05.146Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:47:05.146Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:47:05.146Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (20287.786 ms) ======
[2024-10-03T01:47:05.146Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-03T01:47:05.146Z] GC before operation: completed in 136.695 ms, heap usage 197.567 MB -> 52.740 MB.
[2024-10-03T01:47:08.592Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:47:11.085Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:47:14.519Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:47:17.953Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:47:19.573Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:47:21.172Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:47:22.770Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:47:25.243Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:47:25.243Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:47:25.243Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:47:25.243Z] Movies recommended for you:
[2024-10-03T01:47:25.243Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:47:25.243Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:47:25.243Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20221.971 ms) ======
[2024-10-03T01:47:25.243Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-03T01:47:25.243Z] GC before operation: completed in 116.767 ms, heap usage 498.467 MB -> 56.417 MB.
[2024-10-03T01:47:28.689Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:47:32.127Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:47:34.610Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:47:38.069Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:47:39.672Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:47:41.260Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:47:43.742Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:47:45.344Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:47:45.344Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:47:45.344Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:47:45.344Z] Movies recommended for you:
[2024-10-03T01:47:45.344Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:47:45.344Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:47:45.344Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (20068.288 ms) ======
[2024-10-03T01:47:45.344Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-03T01:47:46.118Z] GC before operation: completed in 125.489 ms, heap usage 854.845 MB -> 56.991 MB.
[2024-10-03T01:47:48.593Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:47:52.034Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:47:55.485Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:47:57.960Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:47:59.564Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:48:02.044Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:48:03.655Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:48:05.257Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:48:06.030Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:48:06.030Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:48:06.030Z] Movies recommended for you:
[2024-10-03T01:48:06.030Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:48:06.030Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:48:06.030Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (20005.295 ms) ======
[2024-10-03T01:48:06.030Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-03T01:48:06.030Z] GC before operation: completed in 114.685 ms, heap usage 278.086 MB -> 52.889 MB.
[2024-10-03T01:48:08.516Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:48:11.948Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:48:15.398Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:48:17.875Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:48:20.352Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:48:21.958Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:48:23.569Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:48:25.175Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:48:25.947Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:48:25.948Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:48:25.948Z] Movies recommended for you:
[2024-10-03T01:48:25.948Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:48:25.948Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:48:25.948Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (20025.320 ms) ======
[2024-10-03T01:48:25.948Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-03T01:48:25.948Z] GC before operation: completed in 115.663 ms, heap usage 496.130 MB -> 56.382 MB.
[2024-10-03T01:48:29.377Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:48:31.865Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:48:35.290Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:48:38.736Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:48:40.331Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:48:41.931Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:48:44.406Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:48:46.009Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:48:46.009Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:48:46.009Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:48:46.009Z] Movies recommended for you:
[2024-10-03T01:48:46.009Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:48:46.009Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:48:46.009Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (20153.570 ms) ======
[2024-10-03T01:48:46.009Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-03T01:48:46.782Z] GC before operation: completed in 155.777 ms, heap usage 227.346 MB -> 53.139 MB.
[2024-10-03T01:48:49.255Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T01:48:52.693Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T01:48:56.129Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T01:48:58.611Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T01:49:00.206Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T01:49:01.903Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T01:49:04.399Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T01:49:05.996Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T01:49:05.996Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T01:49:06.773Z] The best model improves the baseline by 14.43%.
[2024-10-03T01:49:06.773Z] Movies recommended for you:
[2024-10-03T01:49:06.773Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T01:49:06.773Z] There is no way to check that no silent failure occurred.
[2024-10-03T01:49:06.773Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (20106.588 ms) ======
[2024-10-03T01:49:06.773Z] -----------------------------------
[2024-10-03T01:49:06.773Z] renaissance-movie-lens_0_PASSED
[2024-10-03T01:49:06.773Z] -----------------------------------
[2024-10-03T01:49:06.773Z]
[2024-10-03T01:49:06.773Z] TEST TEARDOWN:
[2024-10-03T01:49:06.773Z] Nothing to be done for teardown.
[2024-10-03T01:49:07.544Z] renaissance-movie-lens_0 Finish Time: Thu Oct 3 01:49:06 2024 Epoch Time (ms): 1727920146693