renaissance-movie-lens_0
[2024-11-14T12:57:39.924Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T12:57:39.924Z] ===============================================
[2024-11-14T12:57:39.924Z] renaissance-movie-lens_0 Start Time: Thu Nov 14 12:57:39 2024 Epoch Time (ms): 1731589059280
[2024-11-14T12:57:39.924Z] variation: NoOptions
[2024-11-14T12:57:39.924Z] JVM_OPTIONS:
[2024-11-14T12:57:39.924Z] { \
[2024-11-14T12:57:39.924Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T12:57:39.924Z] echo "Nothing to be done for setup."; \
[2024-11-14T12:57:39.924Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17315876662546/renaissance-movie-lens_0"; \
[2024-11-14T12:57:39.924Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17315876662546/renaissance-movie-lens_0"; \
[2024-11-14T12:57:39.924Z] echo ""; echo "TESTING:"; \
[2024-11-14T12:57:39.924Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-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_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17315876662546/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T12:57:39.925Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17315876662546/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T12:57:39.925Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T12:57:39.925Z] echo "Nothing to be done for teardown."; \
[2024-11-14T12:57:39.925Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17315876662546/TestTargetResult";
[2024-11-14T12:57:39.925Z]
[2024-11-14T12:57:39.925Z] TEST SETUP:
[2024-11-14T12:57:39.925Z] Nothing to be done for setup.
[2024-11-14T12:57:39.925Z]
[2024-11-14T12:57:39.925Z] TESTING:
[2024-11-14T12:57:46.937Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T12:57:52.609Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-14T12:58:04.403Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T12:58:06.024Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T12:58:06.024Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T12:58:06.024Z] GC before operation: completed in 161.040 ms, heap usage 49.896 MB -> 37.134 MB.
[2024-11-14T12:58:31.873Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T12:58:48.136Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T12:58:59.935Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T12:59:10.556Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T12:59:16.238Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T12:59:23.442Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T12:59:32.016Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T12:59:40.395Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T12:59:41.179Z] 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.
[2024-11-14T12:59:42.793Z] The best model improves the baseline by 14.52%.
[2024-11-14T12:59:42.793Z] Movies recommended for you:
[2024-11-14T12:59:42.793Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T12:59:42.793Z] There is no way to check that no silent failure occurred.
[2024-11-14T12:59:43.587Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (97203.970 ms) ======
[2024-11-14T12:59:43.587Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T12:59:43.587Z] GC before operation: completed in 431.791 ms, heap usage 239.160 MB -> 52.478 MB.
[2024-11-14T12:59:59.895Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:00:19.029Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:00:41.917Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:00:50.319Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:00:56.034Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:01:07.909Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:01:15.019Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:01:22.074Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:01:23.796Z] 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.
[2024-11-14T13:01:23.796Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:01:24.621Z] Movies recommended for you:
[2024-11-14T13:01:24.621Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:01:24.621Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:01:24.621Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (100905.676 ms) ======
[2024-11-14T13:01:24.621Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T13:01:25.412Z] GC before operation: completed in 498.310 ms, heap usage 284.728 MB -> 49.845 MB.
[2024-11-14T13:01:42.565Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:01:51.055Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:02:02.939Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:02:21.990Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:02:26.501Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:02:32.161Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:02:37.798Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:02:43.441Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:02:45.981Z] 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.
[2024-11-14T13:02:45.981Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:02:45.981Z] Movies recommended for you:
[2024-11-14T13:02:45.981Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:02:45.981Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:02:45.981Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (81267.860 ms) ======
[2024-11-14T13:02:45.981Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T13:02:46.784Z] GC before operation: completed in 413.799 ms, heap usage 436.192 MB -> 53.344 MB.
[2024-11-14T13:03:00.671Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:03:11.484Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:03:19.954Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:03:29.360Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:03:36.371Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:03:44.815Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:03:49.366Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:03:55.079Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:03:55.865Z] 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.
[2024-11-14T13:03:55.865Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:03:56.658Z] Movies recommended for you:
[2024-11-14T13:03:56.658Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:03:56.658Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:03:56.658Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (69926.122 ms) ======
[2024-11-14T13:03:56.658Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T13:03:57.472Z] GC before operation: completed in 418.040 ms, heap usage 107.532 MB -> 50.262 MB.
[2024-11-14T13:04:06.376Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:04:20.334Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:04:37.319Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:04:47.387Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:04:54.437Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:05:00.127Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:05:10.185Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:05:17.316Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:05:18.963Z] 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.
[2024-11-14T13:05:18.963Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:05:19.766Z] Movies recommended for you:
[2024-11-14T13:05:19.766Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:05:19.766Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:05:19.766Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (82698.872 ms) ======
[2024-11-14T13:05:19.766Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T13:05:19.766Z] GC before operation: completed in 352.479 ms, heap usage 104.315 MB -> 50.406 MB.
[2024-11-14T13:05:34.368Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:05:46.729Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:06:01.200Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:06:13.509Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:06:23.933Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:06:31.186Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:06:40.517Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:06:53.303Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:06:55.003Z] 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.
[2024-11-14T13:06:55.003Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:06:55.823Z] Movies recommended for you:
[2024-11-14T13:06:55.823Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:06:55.823Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:06:55.823Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (95292.071 ms) ======
[2024-11-14T13:06:55.823Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T13:06:55.823Z] GC before operation: completed in 430.154 ms, heap usage 196.444 MB -> 50.411 MB.
[2024-11-14T13:07:11.970Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:07:28.718Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:07:43.061Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:07:57.473Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:08:04.008Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:08:12.604Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:08:24.898Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:08:32.053Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:08:33.737Z] 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.
[2024-11-14T13:08:33.737Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:08:34.574Z] Movies recommended for you:
[2024-11-14T13:08:34.574Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:08:34.574Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:08:34.574Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (98701.812 ms) ======
[2024-11-14T13:08:34.574Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T13:08:35.395Z] GC before operation: completed in 458.279 ms, heap usage 121.586 MB -> 51.097 MB.
[2024-11-14T13:08:47.644Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:09:04.448Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:09:17.746Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:09:30.146Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:09:38.927Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:09:46.166Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:09:53.330Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:10:02.099Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:10:03.787Z] 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.
[2024-11-14T13:10:03.788Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:10:03.788Z] Movies recommended for you:
[2024-11-14T13:10:03.788Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:10:03.788Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:10:03.788Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (88862.780 ms) ======
[2024-11-14T13:10:03.788Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T13:10:04.609Z] GC before operation: completed in 636.483 ms, heap usage 430.228 MB -> 54.580 MB.
[2024-11-14T13:10:18.994Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:10:33.994Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:10:50.778Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:11:03.024Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:11:11.628Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:11:20.246Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:11:28.944Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:11:37.580Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:11:39.313Z] 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.
[2024-11-14T13:11:39.313Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:11:40.135Z] Movies recommended for you:
[2024-11-14T13:11:40.135Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:11:40.135Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:11:40.135Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (95281.874 ms) ======
[2024-11-14T13:11:40.135Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T13:11:40.135Z] GC before operation: completed in 560.800 ms, heap usage 424.588 MB -> 54.098 MB.
[2024-11-14T13:11:57.284Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:12:11.393Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:12:25.556Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:12:39.782Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:12:48.300Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:12:55.407Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:13:04.752Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:13:14.915Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:13:15.708Z] 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.
[2024-11-14T13:13:15.708Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:13:16.509Z] Movies recommended for you:
[2024-11-14T13:13:16.509Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:13:16.509Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:13:16.509Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (96111.232 ms) ======
[2024-11-14T13:13:16.509Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T13:13:17.304Z] GC before operation: completed in 556.632 ms, heap usage 299.253 MB -> 50.864 MB.
[2024-11-14T13:13:31.604Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:13:45.913Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:14:00.310Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:14:14.525Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:14:23.799Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:14:32.637Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:14:42.872Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:14:49.986Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:14:51.687Z] 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.
[2024-11-14T13:14:52.483Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:14:52.483Z] Movies recommended for you:
[2024-11-14T13:14:52.483Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:14:52.483Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:14:52.483Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (95742.593 ms) ======
[2024-11-14T13:14:52.483Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T13:14:53.274Z] GC before operation: completed in 549.230 ms, heap usage 148.508 MB -> 50.542 MB.
[2024-11-14T13:15:11.239Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:15:25.436Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:15:42.317Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:15:59.046Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:16:07.804Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:16:15.016Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:16:26.957Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:16:35.814Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:16:36.657Z] 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.
[2024-11-14T13:16:36.657Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:16:37.540Z] Movies recommended for you:
[2024-11-14T13:16:37.540Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:16:37.540Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:16:37.540Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (103976.331 ms) ======
[2024-11-14T13:16:37.540Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T13:16:38.373Z] GC before operation: completed in 641.493 ms, heap usage 179.184 MB -> 50.825 MB.
[2024-11-14T13:16:52.879Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:17:07.353Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:17:24.401Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:17:39.148Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:17:46.527Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:17:55.374Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:18:04.258Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:18:14.612Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:18:16.301Z] 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.
[2024-11-14T13:18:16.301Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:18:17.126Z] Movies recommended for you:
[2024-11-14T13:18:17.126Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:18:17.126Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:18:17.126Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (99174.372 ms) ======
[2024-11-14T13:18:17.126Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T13:18:17.933Z] GC before operation: completed in 517.836 ms, heap usage 216.704 MB -> 50.919 MB.
[2024-11-14T13:18:32.224Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:18:46.887Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:19:03.694Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:19:16.019Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:19:26.747Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:19:35.553Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:19:46.548Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:19:53.838Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:19:54.672Z] 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.
[2024-11-14T13:19:54.672Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:19:55.487Z] Movies recommended for you:
[2024-11-14T13:19:55.487Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:19:55.487Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:19:55.487Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (97648.270 ms) ======
[2024-11-14T13:19:55.487Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T13:19:55.487Z] GC before operation: completed in 464.572 ms, heap usage 105.886 MB -> 50.687 MB.
[2024-11-14T13:20:09.766Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:20:24.076Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:20:40.978Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:20:55.208Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:21:03.210Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:21:12.024Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:21:20.760Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:21:29.725Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:21:30.566Z] 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.
[2024-11-14T13:21:30.567Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:21:31.432Z] Movies recommended for you:
[2024-11-14T13:21:31.432Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:21:31.432Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:21:31.432Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (95769.675 ms) ======
[2024-11-14T13:21:31.432Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T13:21:32.267Z] GC before operation: completed in 534.435 ms, heap usage 260.158 MB -> 51.053 MB.
[2024-11-14T13:21:46.644Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:22:04.056Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:22:18.415Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:22:30.659Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:22:38.001Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:22:46.667Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:22:56.963Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:23:05.638Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:23:06.486Z] 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.
[2024-11-14T13:23:06.486Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:23:07.874Z] Movies recommended for you:
[2024-11-14T13:23:07.874Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:23:07.874Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:23:07.874Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (95455.836 ms) ======
[2024-11-14T13:23:07.874Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T13:23:07.874Z] GC before operation: completed in 693.724 ms, heap usage 148.059 MB -> 48.577 MB.
[2024-11-14T13:23:24.729Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:23:41.454Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:23:53.961Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:24:06.274Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:24:12.289Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:24:18.822Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:24:27.652Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:24:32.581Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:24:34.337Z] 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.
[2024-11-14T13:24:34.337Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:24:34.337Z] Movies recommended for you:
[2024-11-14T13:24:34.337Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:24:34.337Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:24:34.337Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (86222.501 ms) ======
[2024-11-14T13:24:34.338Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T13:24:35.183Z] GC before operation: completed in 374.548 ms, heap usage 193.852 MB -> 49.039 MB.
[2024-11-14T13:24:47.615Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:24:56.228Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:25:06.793Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:25:17.455Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:25:22.903Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:25:29.024Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:25:36.511Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:25:42.651Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:25:43.502Z] 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.
[2024-11-14T13:25:43.502Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:25:43.502Z] Movies recommended for you:
[2024-11-14T13:25:43.502Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:25:43.502Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:25:43.502Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (68890.189 ms) ======
[2024-11-14T13:25:43.502Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T13:25:44.442Z] GC before operation: completed in 415.412 ms, heap usage 192.635 MB -> 48.884 MB.
[2024-11-14T13:25:55.073Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:26:05.780Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:26:16.406Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:26:26.702Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:26:32.468Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:26:38.270Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:26:45.460Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:26:52.546Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:26:54.175Z] 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.
[2024-11-14T13:26:54.175Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:26:54.967Z] Movies recommended for you:
[2024-11-14T13:26:54.967Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:26:54.967Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:26:54.967Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (70495.437 ms) ======
[2024-11-14T13:26:54.967Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T13:26:54.967Z] GC before operation: completed in 418.796 ms, heap usage 263.244 MB -> 48.897 MB.
[2024-11-14T13:27:06.957Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T13:27:19.093Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T13:27:31.617Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T13:27:41.860Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T13:27:49.138Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T13:27:56.342Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T13:28:03.553Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T13:28:10.682Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T13:28:12.369Z] 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.
[2024-11-14T13:28:12.369Z] The best model improves the baseline by 14.52%.
[2024-11-14T13:28:12.369Z] Movies recommended for you:
[2024-11-14T13:28:12.369Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T13:28:12.369Z] There is no way to check that no silent failure occurred.
[2024-11-14T13:28:12.369Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (77442.850 ms) ======
[2024-11-14T13:28:14.922Z] -----------------------------------
[2024-11-14T13:28:14.922Z] renaissance-movie-lens_0_PASSED
[2024-11-14T13:28:14.922Z] -----------------------------------
[2024-11-14T13:28:14.922Z]
[2024-11-14T13:28:14.922Z] TEST TEARDOWN:
[2024-11-14T13:28:14.922Z] Nothing to be done for teardown.
[2024-11-14T13:28:14.922Z] renaissance-movie-lens_0 Finish Time: Thu Nov 14 13:28:14 2024 Epoch Time (ms): 1731590894353