renaissance-movie-lens_0
[2025-02-26T22:43:37.158Z] Running test renaissance-movie-lens_0 ...
[2025-02-26T22:43:37.158Z] ===============================================
[2025-02-26T22:43:37.158Z] renaissance-movie-lens_0 Start Time: Wed Feb 26 22:43:36 2025 Epoch Time (ms): 1740609816986
[2025-02-26T22:43:37.158Z] variation: NoOptions
[2025-02-26T22:43:37.158Z] JVM_OPTIONS:
[2025-02-26T22:43:37.158Z] { \
[2025-02-26T22:43:37.158Z] echo ""; echo "TEST SETUP:"; \
[2025-02-26T22:43:37.158Z] echo "Nothing to be done for setup."; \
[2025-02-26T22:43:37.158Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406088324162/renaissance-movie-lens_0"; \
[2025-02-26T22:43:37.158Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406088324162/renaissance-movie-lens_0"; \
[2025-02-26T22:43:37.158Z] echo ""; echo "TESTING:"; \
[2025-02-26T22:43:37.158Z] "/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_17406088324162/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-26T22:43:37.158Z] 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_17406088324162/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-26T22:43:37.158Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-26T22:43:37.158Z] echo "Nothing to be done for teardown."; \
[2025-02-26T22:43:37.158Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406088324162/TestTargetResult";
[2025-02-26T22:43:37.158Z]
[2025-02-26T22:43:37.158Z] TEST SETUP:
[2025-02-26T22:43:37.158Z] Nothing to be done for setup.
[2025-02-26T22:43:37.158Z]
[2025-02-26T22:43:37.158Z] TESTING:
[2025-02-26T22:43:41.306Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-26T22:43:43.266Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-26T22:43:46.290Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-26T22:43:46.290Z] Training: 60056, validation: 20285, test: 19854
[2025-02-26T22:43:46.290Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-26T22:43:46.290Z] GC before operation: completed in 80.911 ms, heap usage 119.033 MB -> 36.450 MB.
[2025-02-26T22:43:51.719Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:43:54.726Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:43:57.764Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:44:00.775Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:44:02.814Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:44:03.771Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:44:05.735Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:44:06.685Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:44:07.638Z] 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.
[2025-02-26T22:44:07.638Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:44:07.638Z] Movies recommended for you:
[2025-02-26T22:44:07.638Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:44:07.638Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:44:07.638Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20984.367 ms) ======
[2025-02-26T22:44:07.638Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-26T22:44:07.638Z] GC before operation: completed in 88.790 ms, heap usage 216.395 MB -> 48.189 MB.
[2025-02-26T22:44:09.685Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:44:12.738Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:44:14.698Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:44:16.649Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:44:18.602Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:44:19.641Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:44:21.590Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:44:22.543Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:44:22.543Z] 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.
[2025-02-26T22:44:23.497Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:44:23.497Z] Movies recommended for you:
[2025-02-26T22:44:23.497Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:44:23.497Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:44:23.497Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15583.754 ms) ======
[2025-02-26T22:44:23.497Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-26T22:44:23.497Z] GC before operation: completed in 86.287 ms, heap usage 181.242 MB -> 49.068 MB.
[2025-02-26T22:44:25.461Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:44:27.428Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:44:30.821Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:44:31.780Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:44:33.735Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:44:34.686Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:44:35.640Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:44:37.595Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:44:37.595Z] 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.
[2025-02-26T22:44:37.595Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:44:37.595Z] Movies recommended for you:
[2025-02-26T22:44:37.595Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:44:37.595Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:44:37.595Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14522.192 ms) ======
[2025-02-26T22:44:37.595Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-26T22:44:37.595Z] GC before operation: completed in 85.963 ms, heap usage 133.741 MB -> 49.305 MB.
[2025-02-26T22:44:40.609Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:44:42.581Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:44:44.539Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:44:46.552Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:44:48.500Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:44:49.453Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:44:50.402Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:44:52.352Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:44:52.352Z] 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.
[2025-02-26T22:44:52.352Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:44:52.352Z] Movies recommended for you:
[2025-02-26T22:44:52.352Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:44:52.352Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:44:52.352Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14508.515 ms) ======
[2025-02-26T22:44:52.352Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-26T22:44:52.352Z] GC before operation: completed in 78.920 ms, heap usage 131.842 MB -> 49.591 MB.
[2025-02-26T22:44:54.307Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:44:56.263Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:44:59.276Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:45:01.236Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:45:02.185Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:45:03.135Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:45:05.087Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:45:06.037Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:45:06.037Z] 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.
[2025-02-26T22:45:06.037Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:45:06.987Z] Movies recommended for you:
[2025-02-26T22:45:06.987Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:45:06.987Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:45:06.987Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14133.305 ms) ======
[2025-02-26T22:45:06.987Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-26T22:45:06.987Z] GC before operation: completed in 83.808 ms, heap usage 88.117 MB -> 49.727 MB.
[2025-02-26T22:45:08.960Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:45:10.909Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:45:12.859Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:45:14.811Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:45:15.767Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:45:17.717Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:45:18.667Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:45:19.620Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:45:19.620Z] 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.
[2025-02-26T22:45:19.620Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:45:20.568Z] Movies recommended for you:
[2025-02-26T22:45:20.569Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:45:20.569Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:45:20.569Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13534.788 ms) ======
[2025-02-26T22:45:20.569Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-26T22:45:20.569Z] GC before operation: completed in 86.833 ms, heap usage 215.377 MB -> 49.855 MB.
[2025-02-26T22:45:22.518Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:45:24.507Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:45:26.531Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:45:28.481Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:45:29.448Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:45:31.404Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:45:32.357Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:45:33.316Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:45:34.269Z] 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.
[2025-02-26T22:45:34.269Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:45:34.269Z] Movies recommended for you:
[2025-02-26T22:45:34.269Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:45:34.269Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:45:34.269Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13689.149 ms) ======
[2025-02-26T22:45:34.269Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-26T22:45:34.269Z] GC before operation: completed in 83.445 ms, heap usage 104.523 MB -> 49.874 MB.
[2025-02-26T22:45:36.218Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:45:38.166Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:45:40.119Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:45:42.200Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:45:43.150Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:45:44.100Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:45:46.052Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:45:47.005Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:45:47.005Z] 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.
[2025-02-26T22:45:47.005Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:45:47.005Z] Movies recommended for you:
[2025-02-26T22:45:47.005Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:45:47.005Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:45:47.005Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13188.095 ms) ======
[2025-02-26T22:45:47.005Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-26T22:45:47.005Z] GC before operation: completed in 78.480 ms, heap usage 123.239 MB -> 50.172 MB.
[2025-02-26T22:45:48.958Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:45:50.907Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:45:52.874Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:45:54.826Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:45:56.776Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:45:57.726Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:45:58.680Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:45:59.630Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:46:00.578Z] 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.
[2025-02-26T22:46:00.578Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:46:00.578Z] Movies recommended for you:
[2025-02-26T22:46:00.578Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:46:00.578Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:46:00.578Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13035.447 ms) ======
[2025-02-26T22:46:00.578Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-26T22:46:00.578Z] GC before operation: completed in 74.325 ms, heap usage 123.921 MB -> 50.035 MB.
[2025-02-26T22:46:02.535Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:46:04.495Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:46:06.456Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:46:08.403Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:46:09.517Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:46:11.467Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:46:12.422Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:46:13.373Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:46:13.373Z] 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.
[2025-02-26T22:46:14.323Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:46:14.323Z] Movies recommended for you:
[2025-02-26T22:46:14.323Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:46:14.323Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:46:14.323Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13570.789 ms) ======
[2025-02-26T22:46:14.323Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-26T22:46:14.323Z] GC before operation: completed in 77.803 ms, heap usage 133.744 MB -> 50.092 MB.
[2025-02-26T22:46:16.274Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:46:18.226Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:46:20.178Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:46:22.146Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:46:23.095Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:46:24.047Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:46:24.997Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:46:26.947Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:46:26.947Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T22:46:26.947Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:46:26.947Z] Movies recommended for you:
[2025-02-26T22:46:26.947Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:46:26.947Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:46:26.947Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12855.797 ms) ======
[2025-02-26T22:46:26.947Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-26T22:46:26.947Z] GC before operation: completed in 78.566 ms, heap usage 216.014 MB -> 49.932 MB.
[2025-02-26T22:46:28.935Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:46:30.889Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:46:32.839Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:46:34.787Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:46:35.736Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:46:37.721Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:46:38.671Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:46:39.627Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:46:39.627Z] 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.
[2025-02-26T22:46:39.627Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:46:40.577Z] Movies recommended for you:
[2025-02-26T22:46:40.577Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:46:40.577Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:46:40.577Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13152.398 ms) ======
[2025-02-26T22:46:40.577Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-26T22:46:40.577Z] GC before operation: completed in 76.168 ms, heap usage 144.029 MB -> 50.085 MB.
[2025-02-26T22:46:42.526Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:46:44.533Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:46:46.481Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:46:48.438Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:46:49.390Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:46:50.340Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:46:52.293Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:46:53.243Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:46:53.243Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T22:46:53.243Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:46:54.192Z] Movies recommended for you:
[2025-02-26T22:46:54.192Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:46:54.192Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:46:54.192Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13509.672 ms) ======
[2025-02-26T22:46:54.192Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-26T22:46:54.192Z] GC before operation: completed in 79.345 ms, heap usage 124.536 MB -> 50.164 MB.
[2025-02-26T22:46:56.140Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:46:58.089Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:47:00.036Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:47:01.949Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:47:02.897Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:47:03.848Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:47:05.797Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:47:06.748Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:47:06.748Z] 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.
[2025-02-26T22:47:06.748Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:47:06.748Z] Movies recommended for you:
[2025-02-26T22:47:06.748Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:47:06.748Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:47:06.748Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13090.154 ms) ======
[2025-02-26T22:47:06.748Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-26T22:47:06.748Z] GC before operation: completed in 81.398 ms, heap usage 185.555 MB -> 50.026 MB.
[2025-02-26T22:47:08.702Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:47:10.693Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:47:12.641Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:47:14.598Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:47:15.556Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:47:17.680Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:47:18.630Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:47:19.580Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:47:19.580Z] 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.
[2025-02-26T22:47:19.580Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:47:20.529Z] Movies recommended for you:
[2025-02-26T22:47:20.529Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:47:20.529Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:47:20.529Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13083.426 ms) ======
[2025-02-26T22:47:20.529Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-26T22:47:20.529Z] GC before operation: completed in 89.784 ms, heap usage 316.713 MB -> 50.346 MB.
[2025-02-26T22:47:22.485Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:47:24.435Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:47:26.386Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:47:28.337Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:47:29.287Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:47:31.256Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:47:32.206Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:47:33.161Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:47:33.161Z] 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.
[2025-02-26T22:47:33.161Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:47:34.117Z] Movies recommended for you:
[2025-02-26T22:47:34.117Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:47:34.117Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:47:34.117Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13608.995 ms) ======
[2025-02-26T22:47:34.117Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-26T22:47:34.117Z] GC before operation: completed in 83.854 ms, heap usage 107.668 MB -> 50.155 MB.
[2025-02-26T22:47:36.066Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:47:38.047Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:47:39.998Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:47:41.949Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:47:42.899Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:47:43.854Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:47:44.805Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:47:46.753Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:47:46.753Z] 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.
[2025-02-26T22:47:46.753Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:47:46.753Z] Movies recommended for you:
[2025-02-26T22:47:46.753Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:47:46.753Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:47:46.754Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12952.356 ms) ======
[2025-02-26T22:47:46.754Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-26T22:47:46.754Z] GC before operation: completed in 75.862 ms, heap usage 252.193 MB -> 50.179 MB.
[2025-02-26T22:47:48.744Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:47:50.697Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:47:52.646Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:47:54.598Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:47:55.547Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:47:57.505Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:47:58.455Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:47:59.412Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:47:59.412Z] 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.
[2025-02-26T22:47:59.412Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:48:00.362Z] Movies recommended for you:
[2025-02-26T22:48:00.362Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:48:00.362Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:48:00.362Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13045.756 ms) ======
[2025-02-26T22:48:00.362Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-26T22:48:00.362Z] GC before operation: completed in 74.204 ms, heap usage 121.827 MB -> 50.131 MB.
[2025-02-26T22:48:02.320Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:48:04.272Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:48:06.222Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:48:08.181Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:48:09.150Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:48:10.151Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:48:12.106Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:48:13.055Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:48:13.055Z] 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.
[2025-02-26T22:48:13.055Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:48:13.055Z] Movies recommended for you:
[2025-02-26T22:48:13.055Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:48:13.055Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:48:13.055Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (13252.738 ms) ======
[2025-02-26T22:48:13.055Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-26T22:48:13.055Z] GC before operation: completed in 80.153 ms, heap usage 116.833 MB -> 50.247 MB.
[2025-02-26T22:48:17.148Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:48:18.270Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:48:20.219Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:48:22.173Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:48:23.124Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:48:24.081Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:48:26.032Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:48:26.985Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:48:26.985Z] 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.
[2025-02-26T22:48:26.985Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:48:26.985Z] Movies recommended for you:
[2025-02-26T22:48:26.985Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:48:26.985Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:48:26.985Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13786.202 ms) ======
[2025-02-26T22:48:27.947Z] -----------------------------------
[2025-02-26T22:48:27.947Z] renaissance-movie-lens_0_PASSED
[2025-02-26T22:48:27.947Z] -----------------------------------
[2025-02-26T22:48:27.947Z]
[2025-02-26T22:48:27.947Z] TEST TEARDOWN:
[2025-02-26T22:48:27.947Z] Nothing to be done for teardown.
[2025-02-26T22:48:27.947Z] renaissance-movie-lens_0 Finish Time: Wed Feb 26 22:48:27 2025 Epoch Time (ms): 1740610107104