renaissance-movie-lens_0
[2025-01-22T00:39:04.517Z] Running test renaissance-movie-lens_0 ...
[2025-01-22T00:39:04.517Z] ===============================================
[2025-01-22T00:39:04.517Z] renaissance-movie-lens_0 Start Time: Wed Jan 22 00:39:03 2025 Epoch Time (ms): 1737506343462
[2025-01-22T00:39:04.517Z] variation: NoOptions
[2025-01-22T00:39:04.517Z] JVM_OPTIONS:
[2025-01-22T00:39:04.517Z] { \
[2025-01-22T00:39:04.517Z] echo ""; echo "TEST SETUP:"; \
[2025-01-22T00:39:04.517Z] echo "Nothing to be done for setup."; \
[2025-01-22T00:39:04.517Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375054946497/renaissance-movie-lens_0"; \
[2025-01-22T00:39:04.517Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375054946497/renaissance-movie-lens_0"; \
[2025-01-22T00:39:04.517Z] echo ""; echo "TESTING:"; \
[2025-01-22T00:39:04.517Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375054946497/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-22T00:39:04.517Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375054946497/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-22T00:39:04.517Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-22T00:39:04.517Z] echo "Nothing to be done for teardown."; \
[2025-01-22T00:39:04.517Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375054946497/TestTargetResult";
[2025-01-22T00:39:04.517Z]
[2025-01-22T00:39:04.517Z] TEST SETUP:
[2025-01-22T00:39:04.517Z] Nothing to be done for setup.
[2025-01-22T00:39:04.517Z]
[2025-01-22T00:39:04.517Z] TESTING:
[2025-01-22T00:39:06.469Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-22T00:39:08.421Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-01-22T00:39:11.433Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-22T00:39:11.433Z] Training: 60056, validation: 20285, test: 19854
[2025-01-22T00:39:11.433Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-22T00:39:11.433Z] GC before operation: completed in 54.062 ms, heap usage 118.071 MB -> 37.045 MB.
[2025-01-22T00:39:17.029Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:39:19.096Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:39:22.288Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:39:25.512Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:39:26.523Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:39:28.594Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:39:29.604Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:39:31.670Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:39:31.670Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:39:31.670Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:39:31.670Z] Movies recommended for you:
[2025-01-22T00:39:31.670Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:39:31.670Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:39:31.670Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20044.285 ms) ======
[2025-01-22T00:39:31.670Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-22T00:39:31.670Z] GC before operation: completed in 72.624 ms, heap usage 194.360 MB -> 55.067 MB.
[2025-01-22T00:39:34.859Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:39:36.928Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:39:38.996Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:39:41.064Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:39:43.135Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:39:44.176Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:39:46.243Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:39:47.253Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:39:47.253Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:39:47.253Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:39:47.253Z] Movies recommended for you:
[2025-01-22T00:39:47.253Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:39:47.253Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:39:47.253Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15658.848 ms) ======
[2025-01-22T00:39:47.253Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-22T00:39:47.253Z] GC before operation: completed in 66.475 ms, heap usage 223.302 MB -> 49.620 MB.
[2025-01-22T00:39:49.321Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:39:52.508Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:39:54.576Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:39:57.297Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:39:57.297Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:39:59.376Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:40:00.386Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:40:01.395Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:40:01.395Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:40:01.395Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:40:01.395Z] Movies recommended for you:
[2025-01-22T00:40:01.395Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:40:01.395Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:40:01.395Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14276.735 ms) ======
[2025-01-22T00:40:01.395Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-22T00:40:02.413Z] GC before operation: completed in 90.885 ms, heap usage 103.761 MB -> 49.934 MB.
[2025-01-22T00:40:03.435Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:40:05.504Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:40:07.573Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:40:10.764Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:40:11.776Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:40:12.786Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:40:13.799Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:40:15.879Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:40:15.879Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:40:15.879Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:40:15.879Z] Movies recommended for you:
[2025-01-22T00:40:15.879Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:40:15.879Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:40:15.879Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13852.221 ms) ======
[2025-01-22T00:40:15.879Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-22T00:40:15.879Z] GC before operation: completed in 63.380 ms, heap usage 123.096 MB -> 50.253 MB.
[2025-01-22T00:40:17.947Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:40:20.017Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:40:22.089Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:40:24.160Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:40:25.171Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:40:27.243Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:40:28.260Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:40:29.271Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:40:29.271Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:40:29.271Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:40:29.271Z] Movies recommended for you:
[2025-01-22T00:40:29.271Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:40:29.271Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:40:29.271Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13924.370 ms) ======
[2025-01-22T00:40:29.271Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-22T00:40:30.280Z] GC before operation: completed in 67.680 ms, heap usage 121.845 MB -> 50.449 MB.
[2025-01-22T00:40:31.290Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:40:33.363Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:40:35.432Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:40:37.503Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:40:38.512Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:40:40.582Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:40:41.592Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:40:42.602Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:40:42.602Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:40:42.602Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:40:42.602Z] Movies recommended for you:
[2025-01-22T00:40:42.602Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:40:42.602Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:40:42.602Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13213.927 ms) ======
[2025-01-22T00:40:42.602Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-22T00:40:43.613Z] GC before operation: completed in 74.407 ms, heap usage 101.012 MB -> 53.680 MB.
[2025-01-22T00:40:44.623Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:40:46.693Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:40:48.761Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:40:50.833Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:40:51.842Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:40:53.911Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:40:54.922Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:40:55.933Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:40:55.933Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:40:55.933Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:40:55.933Z] Movies recommended for you:
[2025-01-22T00:40:55.933Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:40:55.933Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:40:55.933Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13019.791 ms) ======
[2025-01-22T00:40:55.933Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-22T00:40:55.933Z] GC before operation: completed in 67.872 ms, heap usage 86.444 MB -> 50.525 MB.
[2025-01-22T00:40:58.004Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:40:59.520Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:41:01.601Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:41:03.672Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:41:04.683Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:41:06.755Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:41:07.765Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:41:08.776Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:41:08.776Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:41:08.776Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:41:08.776Z] Movies recommended for you:
[2025-01-22T00:41:08.776Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:41:08.776Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:41:08.776Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12905.839 ms) ======
[2025-01-22T00:41:08.776Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-22T00:41:08.776Z] GC before operation: completed in 75.867 ms, heap usage 123.005 MB -> 50.863 MB.
[2025-01-22T00:41:10.845Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:41:12.927Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:41:15.001Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:41:17.076Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:41:18.085Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:41:19.094Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:41:20.104Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:41:22.174Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:41:22.174Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:41:22.174Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:41:22.174Z] Movies recommended for you:
[2025-01-22T00:41:22.174Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:41:22.174Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:41:22.174Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12919.380 ms) ======
[2025-01-22T00:41:22.174Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-22T00:41:22.174Z] GC before operation: completed in 74.445 ms, heap usage 439.908 MB -> 54.218 MB.
[2025-01-22T00:41:24.245Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:41:26.315Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:41:28.390Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:41:29.399Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:41:31.469Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:41:32.482Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:41:33.492Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:41:34.502Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:41:34.502Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:41:34.502Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:41:35.514Z] Movies recommended for you:
[2025-01-22T00:41:35.514Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:41:35.514Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:41:35.514Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12847.402 ms) ======
[2025-01-22T00:41:35.514Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-22T00:41:35.514Z] GC before operation: completed in 67.719 ms, heap usage 177.275 MB -> 50.887 MB.
[2025-01-22T00:41:36.525Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:41:38.591Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:41:40.658Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:41:42.725Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:41:43.737Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:41:44.745Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:41:46.814Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:41:47.825Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:41:47.825Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:41:47.825Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:41:47.825Z] Movies recommended for you:
[2025-01-22T00:41:47.825Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:41:47.825Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:41:47.825Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12889.894 ms) ======
[2025-01-22T00:41:47.825Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-22T00:41:47.825Z] GC before operation: completed in 66.271 ms, heap usage 106.199 MB -> 50.592 MB.
[2025-01-22T00:41:49.892Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:41:51.959Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:41:54.026Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:41:56.099Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:41:57.117Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:41:58.131Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:41:59.140Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:42:00.148Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:42:01.158Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:42:01.158Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:42:01.158Z] Movies recommended for you:
[2025-01-22T00:42:01.158Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:42:01.158Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:42:01.158Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12797.594 ms) ======
[2025-01-22T00:42:01.158Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-22T00:42:01.158Z] GC before operation: completed in 68.316 ms, heap usage 86.928 MB -> 50.755 MB.
[2025-01-22T00:42:02.939Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:42:05.008Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:42:07.077Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:42:09.147Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:42:10.157Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:42:11.165Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:42:12.173Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:42:14.240Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:42:14.240Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:42:14.240Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:42:14.240Z] Movies recommended for you:
[2025-01-22T00:42:14.240Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:42:14.240Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:42:14.240Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13126.746 ms) ======
[2025-01-22T00:42:14.240Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-22T00:42:14.240Z] GC before operation: completed in 70.108 ms, heap usage 443.468 MB -> 54.373 MB.
[2025-01-22T00:42:16.306Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:42:18.373Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:42:20.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:42:21.453Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:42:23.519Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:42:24.529Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:42:25.537Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:42:26.546Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:42:26.546Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:42:26.546Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:42:26.546Z] Movies recommended for you:
[2025-01-22T00:42:26.546Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:42:26.546Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:42:26.546Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12839.005 ms) ======
[2025-01-22T00:42:26.546Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-22T00:42:26.546Z] GC before operation: completed in 64.529 ms, heap usage 100.904 MB -> 50.620 MB.
[2025-01-22T00:42:28.617Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:42:30.687Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:42:32.758Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:42:34.830Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:42:35.840Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:42:36.850Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:42:37.858Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:42:38.866Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:42:38.867Z] 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.
[2025-01-22T00:42:38.867Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:42:38.867Z] Movies recommended for you:
[2025-01-22T00:42:38.867Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:42:38.867Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:42:38.867Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12097.339 ms) ======
[2025-01-22T00:42:38.867Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-22T00:42:38.867Z] GC before operation: completed in 59.310 ms, heap usage 87.789 MB -> 50.756 MB.
[2025-01-22T00:42:40.932Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:42:43.000Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:42:45.069Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:42:46.079Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:42:47.088Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:42:49.163Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:42:50.173Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:42:51.189Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:42:51.189Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:42:51.189Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:42:51.189Z] Movies recommended for you:
[2025-01-22T00:42:51.189Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:42:51.189Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:42:51.189Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12209.362 ms) ======
[2025-01-22T00:42:51.189Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-22T00:42:51.189Z] GC before operation: completed in 62.094 ms, heap usage 115.969 MB -> 50.915 MB.
[2025-01-22T00:42:53.261Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:42:55.333Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:42:57.402Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:42:58.415Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:42:59.425Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:43:02.305Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:43:02.305Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:43:03.315Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:43:03.316Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:43:03.316Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:43:03.316Z] Movies recommended for you:
[2025-01-22T00:43:03.316Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:43:03.316Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:43:03.316Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12262.597 ms) ======
[2025-01-22T00:43:03.316Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-22T00:43:03.316Z] GC before operation: completed in 66.171 ms, heap usage 85.699 MB -> 50.646 MB.
[2025-01-22T00:43:05.384Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:43:07.455Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:43:09.527Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:43:10.535Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:43:12.611Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:43:13.620Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:43:14.635Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:43:15.648Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:43:15.648Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:43:15.648Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:43:15.648Z] Movies recommended for you:
[2025-01-22T00:43:15.648Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:43:15.648Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:43:15.648Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12059.913 ms) ======
[2025-01-22T00:43:15.648Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-22T00:43:15.648Z] GC before operation: completed in 71.260 ms, heap usage 122.245 MB -> 50.813 MB.
[2025-01-22T00:43:17.717Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:43:19.796Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:43:21.865Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:43:22.878Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:43:23.887Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:43:25.957Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:43:26.968Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:43:27.983Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:43:27.983Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:43:27.983Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:43:27.983Z] Movies recommended for you:
[2025-01-22T00:43:27.983Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:43:27.983Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:43:27.983Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12098.603 ms) ======
[2025-01-22T00:43:27.983Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-22T00:43:27.983Z] GC before operation: completed in 81.684 ms, heap usage 84.993 MB -> 51.004 MB.
[2025-01-22T00:43:30.063Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:43:32.132Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:43:33.144Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:43:35.218Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:43:36.228Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:43:37.239Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:43:38.251Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:43:39.260Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:43:40.269Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T00:43:40.269Z] The best model improves the baseline by 14.52%.
[2025-01-22T00:43:40.269Z] Movies recommended for you:
[2025-01-22T00:43:40.269Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:43:40.269Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:43:40.269Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (11949.777 ms) ======
[2025-01-22T00:43:40.269Z] -----------------------------------
[2025-01-22T00:43:40.269Z] renaissance-movie-lens_0_PASSED
[2025-01-22T00:43:40.269Z] -----------------------------------
[2025-01-22T00:43:40.269Z]
[2025-01-22T00:43:40.269Z] TEST TEARDOWN:
[2025-01-22T00:43:40.269Z] Nothing to be done for teardown.
[2025-01-22T00:43:40.269Z] renaissance-movie-lens_0 Finish Time: Wed Jan 22 00:43:39 2025 Epoch Time (ms): 1737506619971