renaissance-movie-lens_0
[2024-11-15T23:49:50.798Z] Running test renaissance-movie-lens_0 ...
[2024-11-15T23:49:50.798Z] ===============================================
[2024-11-15T23:49:50.799Z] renaissance-movie-lens_0 Start Time: Fri Nov 15 23:49:49 2024 Epoch Time (ms): 1731714589769
[2024-11-15T23:49:50.799Z] variation: NoOptions
[2024-11-15T23:49:50.799Z] JVM_OPTIONS:
[2024-11-15T23:49:50.799Z] { \
[2024-11-15T23:49:50.799Z] echo ""; echo "TEST SETUP:"; \
[2024-11-15T23:49:50.799Z] echo "Nothing to be done for setup."; \
[2024-11-15T23:49:50.799Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17317136185048/renaissance-movie-lens_0"; \
[2024-11-15T23:49:50.799Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17317136185048/renaissance-movie-lens_0"; \
[2024-11-15T23:49:50.799Z] echo ""; echo "TESTING:"; \
[2024-11-15T23:49:50.799Z] "/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_17317136185048/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-15T23:49:50.799Z] 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_17317136185048/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-15T23:49:50.799Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-15T23:49:50.799Z] echo "Nothing to be done for teardown."; \
[2024-11-15T23:49:50.799Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17317136185048/TestTargetResult";
[2024-11-15T23:49:50.799Z]
[2024-11-15T23:49:50.799Z] TEST SETUP:
[2024-11-15T23:49:50.799Z] Nothing to be done for setup.
[2024-11-15T23:49:50.799Z]
[2024-11-15T23:49:50.799Z] TESTING:
[2024-11-15T23:49:53.778Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-15T23:49:55.704Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-15T23:49:58.709Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-15T23:49:59.645Z] Training: 60056, validation: 20285, test: 19854
[2024-11-15T23:49:59.645Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-15T23:49:59.645Z] GC before operation: completed in 102.924 ms, heap usage 46.109 MB -> 36.468 MB.
[2024-11-15T23:50:06.239Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:50:09.210Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:50:12.182Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:50:15.152Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:50:17.591Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:50:19.208Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:50:20.143Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:50:22.077Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:50:22.077Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:50:22.077Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:50:23.011Z] Movies recommended for you:
[2024-11-15T23:50:23.011Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:50:23.011Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:50:23.011Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23253.514 ms) ======
[2024-11-15T23:50:23.011Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-15T23:50:23.011Z] GC before operation: completed in 110.147 ms, heap usage 269.291 MB -> 49.154 MB.
[2024-11-15T23:50:25.980Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:50:27.900Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:50:30.869Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:50:33.840Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:50:34.779Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:50:36.704Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:50:38.628Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:50:40.551Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:50:40.551Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:50:40.551Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:50:40.551Z] Movies recommended for you:
[2024-11-15T23:50:40.551Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:50:40.551Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:50:40.551Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17841.696 ms) ======
[2024-11-15T23:50:40.551Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-15T23:50:40.551Z] GC before operation: completed in 105.099 ms, heap usage 184.412 MB -> 49.076 MB.
[2024-11-15T23:50:43.520Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:50:45.444Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:50:48.410Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:50:50.335Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:50:52.258Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:50:54.184Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:50:55.120Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:50:57.048Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:50:57.048Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:50:57.048Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:50:57.048Z] Movies recommended for you:
[2024-11-15T23:50:57.048Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:50:57.048Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:50:57.048Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16420.916 ms) ======
[2024-11-15T23:50:57.048Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-15T23:50:57.048Z] GC before operation: completed in 111.123 ms, heap usage 127.921 MB -> 49.315 MB.
[2024-11-15T23:51:00.041Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:01.963Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:04.937Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:06.856Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:08.889Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:09.824Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:11.751Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:51:12.687Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:51:12.687Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:51:12.687Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:51:12.687Z] Movies recommended for you:
[2024-11-15T23:51:12.687Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:51:12.687Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:51:12.687Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15942.030 ms) ======
[2024-11-15T23:51:12.687Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-15T23:51:13.641Z] GC before operation: completed in 98.870 ms, heap usage 81.451 MB -> 49.549 MB.
[2024-11-15T23:51:15.576Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:19.936Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:20.871Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:23.852Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:24.788Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:26.736Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:27.673Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:51:29.594Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:51:29.594Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:51:29.594Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:51:29.594Z] Movies recommended for you:
[2024-11-15T23:51:29.594Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:51:29.594Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:51:29.594Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16556.558 ms) ======
[2024-11-15T23:51:29.594Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-15T23:51:29.594Z] GC before operation: completed in 100.389 ms, heap usage 434.960 MB -> 53.242 MB.
[2024-11-15T23:51:32.637Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:34.581Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:36.509Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:39.476Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:40.412Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:42.338Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:43.281Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:51:45.205Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:51:45.205Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:51:45.205Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:51:45.205Z] Movies recommended for you:
[2024-11-15T23:51:45.205Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:51:45.205Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:51:45.205Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15342.128 ms) ======
[2024-11-15T23:51:45.205Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-15T23:51:45.205Z] GC before operation: completed in 142.245 ms, heap usage 253.577 MB -> 49.902 MB.
[2024-11-15T23:51:48.172Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:50.110Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:53.088Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:55.012Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:56.938Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:57.875Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:59.804Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:52:00.741Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:52:00.741Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:52:00.741Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:52:00.741Z] Movies recommended for you:
[2024-11-15T23:52:00.741Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:52:00.741Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:52:00.741Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15726.344 ms) ======
[2024-11-15T23:52:00.741Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-15T23:52:01.684Z] GC before operation: completed in 95.138 ms, heap usage 140.778 MB -> 49.908 MB.
[2024-11-15T23:52:03.607Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:52:05.556Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:52:08.532Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:52:10.458Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:52:11.406Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:52:13.368Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:52:14.316Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:52:16.240Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:52:16.240Z] 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.
[2024-11-15T23:52:16.240Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:52:16.240Z] Movies recommended for you:
[2024-11-15T23:52:16.240Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:52:16.240Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:52:16.240Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15297.155 ms) ======
[2024-11-15T23:52:16.240Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-15T23:52:16.240Z] GC before operation: completed in 95.510 ms, heap usage 181.544 MB -> 50.284 MB.
[2024-11-15T23:52:19.942Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:52:20.878Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:52:23.887Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:52:25.819Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:52:26.778Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:52:28.702Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:52:29.638Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:52:30.609Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:52:31.546Z] 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.
[2024-11-15T23:52:31.546Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:52:31.546Z] Movies recommended for you:
[2024-11-15T23:52:31.546Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:52:31.546Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:52:31.546Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14759.283 ms) ======
[2024-11-15T23:52:31.546Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-15T23:52:31.546Z] GC before operation: completed in 93.772 ms, heap usage 199.623 MB -> 50.076 MB.
[2024-11-15T23:52:34.522Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:52:36.458Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:52:38.381Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:52:40.308Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:52:42.242Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:52:43.181Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:52:45.115Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:52:46.053Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:52:46.993Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:52:46.993Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:52:46.993Z] Movies recommended for you:
[2024-11-15T23:52:46.993Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:52:46.993Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:52:46.993Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15200.530 ms) ======
[2024-11-15T23:52:46.993Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-15T23:52:46.993Z] GC before operation: completed in 94.470 ms, heap usage 198.983 MB -> 50.221 MB.
[2024-11-15T23:52:48.917Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:52:50.848Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:52:53.827Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:52:55.760Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:52:56.697Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:52:58.626Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:52:59.570Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:53:00.507Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:53:01.447Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:53:01.447Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:53:01.447Z] Movies recommended for you:
[2024-11-15T23:53:01.447Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:53:01.447Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:53:01.447Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14645.309 ms) ======
[2024-11-15T23:53:01.447Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-15T23:53:01.447Z] GC before operation: completed in 97.351 ms, heap usage 198.776 MB -> 49.897 MB.
[2024-11-15T23:53:04.425Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:53:06.346Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:53:08.272Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:53:10.211Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:53:12.144Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:53:13.091Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:53:15.016Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:53:15.950Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:53:16.887Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:53:16.887Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:53:16.887Z] Movies recommended for you:
[2024-11-15T23:53:16.887Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:53:16.887Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:53:16.887Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15069.438 ms) ======
[2024-11-15T23:53:16.887Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-15T23:53:16.887Z] GC before operation: completed in 96.133 ms, heap usage 208.683 MB -> 50.117 MB.
[2024-11-15T23:53:20.116Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:53:21.056Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:53:24.029Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:53:25.953Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:53:26.889Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:53:28.814Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:53:29.750Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:53:31.673Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:53:31.673Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:53:31.673Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:53:31.673Z] Movies recommended for you:
[2024-11-15T23:53:31.673Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:53:31.673Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:53:31.673Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15085.401 ms) ======
[2024-11-15T23:53:31.673Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-15T23:53:31.673Z] GC before operation: completed in 88.105 ms, heap usage 334.273 MB -> 50.367 MB.
[2024-11-15T23:53:33.592Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:53:36.564Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:53:38.493Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:53:40.416Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:53:42.513Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:53:43.466Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:53:44.404Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:53:46.326Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:53:46.326Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:53:46.326Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:53:46.326Z] Movies recommended for you:
[2024-11-15T23:53:46.326Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:53:46.326Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:53:46.326Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14653.817 ms) ======
[2024-11-15T23:53:46.327Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-15T23:53:46.327Z] GC before operation: completed in 94.572 ms, heap usage 310.538 MB -> 50.155 MB.
[2024-11-15T23:53:49.306Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:53:51.231Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:53:53.164Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:53:55.090Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:53:57.025Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:53:57.960Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:53:59.893Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:54:00.832Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:54:01.776Z] 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.
[2024-11-15T23:54:01.776Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:54:01.776Z] Movies recommended for you:
[2024-11-15T23:54:01.776Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:54:01.776Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:54:01.776Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14928.676 ms) ======
[2024-11-15T23:54:01.776Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-15T23:54:01.776Z] GC before operation: completed in 94.842 ms, heap usage 69.725 MB -> 50.010 MB.
[2024-11-15T23:54:03.727Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:54:05.668Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:54:08.638Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:54:10.568Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:54:11.508Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:54:13.433Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:54:15.426Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:54:17.140Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:54:17.140Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:54:17.140Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:54:17.140Z] Movies recommended for you:
[2024-11-15T23:54:17.140Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:54:17.140Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:54:17.140Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15070.712 ms) ======
[2024-11-15T23:54:17.140Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-15T23:54:17.140Z] GC before operation: completed in 93.939 ms, heap usage 207.961 MB -> 50.283 MB.
[2024-11-15T23:54:19.064Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:54:20.988Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:54:23.967Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:54:25.899Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:54:26.848Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:54:28.899Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:54:29.836Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:54:30.776Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:54:31.712Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:54:31.712Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:54:31.712Z] Movies recommended for you:
[2024-11-15T23:54:31.712Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:54:31.712Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:54:31.712Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14792.456 ms) ======
[2024-11-15T23:54:31.712Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-15T23:54:31.712Z] GC before operation: completed in 112.509 ms, heap usage 204.994 MB -> 50.086 MB.
[2024-11-15T23:54:33.639Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:54:36.614Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:54:38.541Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:54:40.469Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:54:42.411Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:54:43.347Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:54:44.284Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:54:46.221Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:54:46.221Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:54:46.221Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:54:46.221Z] Movies recommended for you:
[2024-11-15T23:54:46.221Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:54:46.221Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:54:46.221Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14756.524 ms) ======
[2024-11-15T23:54:46.221Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-15T23:54:46.221Z] GC before operation: completed in 97.373 ms, heap usage 70.060 MB -> 50.042 MB.
[2024-11-15T23:54:49.189Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:54:51.118Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:54:53.042Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:54:56.009Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:54:56.946Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:54:57.882Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:54:59.808Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:55:00.746Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:55:01.691Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:55:01.691Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:55:01.691Z] Movies recommended for you:
[2024-11-15T23:55:01.691Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:55:01.691Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:55:01.691Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14851.159 ms) ======
[2024-11-15T23:55:01.691Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-15T23:55:01.691Z] GC before operation: completed in 87.160 ms, heap usage 186.695 MB -> 50.330 MB.
[2024-11-15T23:55:03.614Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:55:05.539Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:55:08.506Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:55:10.432Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:55:11.379Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:55:13.305Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:55:14.239Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:55:17.196Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:55:17.196Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-15T23:55:17.196Z] The best model improves the baseline by 14.52%.
[2024-11-15T23:55:17.196Z] Movies recommended for you:
[2024-11-15T23:55:17.196Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:55:17.196Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:55:17.196Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14702.798 ms) ======
[2024-11-15T23:55:18.144Z] -----------------------------------
[2024-11-15T23:55:18.144Z] renaissance-movie-lens_0_PASSED
[2024-11-15T23:55:18.144Z] -----------------------------------
[2024-11-15T23:55:18.144Z]
[2024-11-15T23:55:18.144Z] TEST TEARDOWN:
[2024-11-15T23:55:18.144Z] Nothing to be done for teardown.
[2024-11-15T23:55:18.144Z] renaissance-movie-lens_0 Finish Time: Fri Nov 15 23:55:16 2024 Epoch Time (ms): 1731714916185