renaissance-movie-lens_0
[2024-10-02T21:05:17.264Z] Running test renaissance-movie-lens_0 ...
[2024-10-02T21:05:17.264Z] ===============================================
[2024-10-02T21:05:17.264Z] renaissance-movie-lens_0 Start Time: Wed Oct 2 21:05:17 2024 Epoch Time (ms): 1727903117063
[2024-10-02T21:05:17.264Z] variation: NoOptions
[2024-10-02T21:05:17.264Z] JVM_OPTIONS:
[2024-10-02T21:05:17.264Z] { \
[2024-10-02T21:05:17.264Z] echo ""; echo "TEST SETUP:"; \
[2024-10-02T21:05:17.264Z] echo "Nothing to be done for setup."; \
[2024-10-02T21:05:17.264Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17279021011768/renaissance-movie-lens_0"; \
[2024-10-02T21:05:17.264Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17279021011768/renaissance-movie-lens_0"; \
[2024-10-02T21:05:17.264Z] echo ""; echo "TESTING:"; \
[2024-10-02T21:05:17.264Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_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_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17279021011768/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-02T21:05:17.264Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17279021011768/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-02T21:05:17.264Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-02T21:05:17.264Z] echo "Nothing to be done for teardown."; \
[2024-10-02T21:05:17.264Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17279021011768/TestTargetResult";
[2024-10-02T21:05:17.264Z]
[2024-10-02T21:05:17.264Z] TEST SETUP:
[2024-10-02T21:05:17.264Z] Nothing to be done for setup.
[2024-10-02T21:05:17.264Z]
[2024-10-02T21:05:17.264Z] TESTING:
[2024-10-02T21:05:21.490Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-02T21:05:24.008Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-02T21:05:29.309Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-02T21:05:29.309Z] Training: 60056, validation: 20285, test: 19854
[2024-10-02T21:05:29.309Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-02T21:05:29.309Z] GC before operation: completed in 62.278 ms, heap usage 100.705 MB -> 37.095 MB.
[2024-10-02T21:05:38.991Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:05:44.288Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:05:48.528Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:05:51.932Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:05:54.482Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:05:56.384Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:05:58.281Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:06:00.819Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:06:00.819Z] 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.
[2024-10-02T21:06:00.819Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:06:01.215Z] Movies recommended for you:
[2024-10-02T21:06:01.215Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:06:01.215Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:06:01.215Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (32039.336 ms) ======
[2024-10-02T21:06:01.215Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-02T21:06:01.215Z] GC before operation: completed in 88.241 ms, heap usage 284.135 MB -> 53.728 MB.
[2024-10-02T21:06:04.530Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:06:07.858Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:06:11.148Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:06:13.723Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:06:15.630Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:06:17.522Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:06:19.414Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:06:21.370Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:06:21.750Z] 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.
[2024-10-02T21:06:21.750Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:06:21.750Z] Movies recommended for you:
[2024-10-02T21:06:21.750Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:06:21.750Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:06:21.750Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20592.630 ms) ======
[2024-10-02T21:06:21.750Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-02T21:06:21.750Z] GC before operation: completed in 72.006 ms, heap usage 206.257 MB -> 49.632 MB.
[2024-10-02T21:06:25.103Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:06:27.788Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:06:31.058Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:06:33.591Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:06:36.181Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:06:37.522Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:06:40.112Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:06:41.465Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:06:41.842Z] 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.
[2024-10-02T21:06:41.842Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:06:41.842Z] Movies recommended for you:
[2024-10-02T21:06:41.842Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:06:41.842Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:06:41.842Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20066.371 ms) ======
[2024-10-02T21:06:41.842Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-02T21:06:41.842Z] GC before operation: completed in 71.800 ms, heap usage 249.662 MB -> 50.059 MB.
[2024-10-02T21:06:45.123Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:06:47.729Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:06:50.298Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:06:53.606Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:06:54.907Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:06:56.793Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:06:58.256Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:07:00.203Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:07:00.203Z] 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.
[2024-10-02T21:07:00.203Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:07:00.615Z] Movies recommended for you:
[2024-10-02T21:07:00.615Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:07:00.615Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:07:00.615Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18474.562 ms) ======
[2024-10-02T21:07:00.615Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-02T21:07:00.615Z] GC before operation: completed in 70.775 ms, heap usage 311.177 MB -> 50.362 MB.
[2024-10-02T21:07:03.160Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:07:06.454Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:07:09.018Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:07:11.559Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:07:13.455Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:07:14.771Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:07:16.684Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:07:18.636Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:07:19.043Z] 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.
[2024-10-02T21:07:19.043Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:07:19.043Z] Movies recommended for you:
[2024-10-02T21:07:19.043Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:07:19.043Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:07:19.043Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (18618.373 ms) ======
[2024-10-02T21:07:19.043Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-02T21:07:19.043Z] GC before operation: completed in 81.351 ms, heap usage 77.411 MB -> 53.018 MB.
[2024-10-02T21:07:22.348Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:07:24.894Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:07:27.476Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:07:29.989Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:07:32.008Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:07:33.330Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:07:35.226Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:07:37.136Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:07:37.136Z] 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.
[2024-10-02T21:07:37.136Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:07:37.136Z] Movies recommended for you:
[2024-10-02T21:07:37.136Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:07:37.136Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:07:37.136Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18029.464 ms) ======
[2024-10-02T21:07:37.136Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-02T21:07:37.136Z] GC before operation: completed in 79.533 ms, heap usage 403.596 MB -> 53.812 MB.
[2024-10-02T21:07:40.422Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:07:43.011Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:07:45.559Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:07:48.101Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:07:49.989Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:07:51.323Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:07:53.201Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:07:54.568Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:07:54.975Z] 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.
[2024-10-02T21:07:54.975Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:07:54.975Z] Movies recommended for you:
[2024-10-02T21:07:54.975Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:07:54.975Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:07:54.975Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17750.979 ms) ======
[2024-10-02T21:07:54.975Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-02T21:07:54.975Z] GC before operation: completed in 74.651 ms, heap usage 196.685 MB -> 50.632 MB.
[2024-10-02T21:07:58.236Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:08:00.235Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:08:03.536Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:08:05.461Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:08:07.356Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:08:08.679Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:08:10.603Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:08:12.473Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:08:12.473Z] 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.
[2024-10-02T21:08:12.473Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:08:12.856Z] Movies recommended for you:
[2024-10-02T21:08:12.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:08:12.856Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:08:12.856Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17594.495 ms) ======
[2024-10-02T21:08:12.856Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-02T21:08:12.856Z] GC before operation: completed in 80.730 ms, heap usage 433.527 MB -> 54.334 MB.
[2024-10-02T21:08:15.364Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:08:17.911Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:08:20.410Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:08:23.004Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:08:24.932Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:08:26.268Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:08:28.143Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:08:29.498Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:08:29.874Z] 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.
[2024-10-02T21:08:29.874Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:08:29.874Z] Movies recommended for you:
[2024-10-02T21:08:29.874Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:08:29.874Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:08:29.874Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17058.826 ms) ======
[2024-10-02T21:08:29.874Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-02T21:08:29.874Z] GC before operation: completed in 76.150 ms, heap usage 322.819 MB -> 50.896 MB.
[2024-10-02T21:08:32.508Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:08:35.024Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:08:37.530Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:08:40.073Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:08:41.946Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:08:43.291Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:08:45.185Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:08:46.492Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:08:46.872Z] 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.
[2024-10-02T21:08:46.872Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:08:47.259Z] Movies recommended for you:
[2024-10-02T21:08:47.259Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:08:47.259Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:08:47.259Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17149.370 ms) ======
[2024-10-02T21:08:47.259Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-02T21:08:47.259Z] GC before operation: completed in 72.119 ms, heap usage 258.969 MB -> 50.876 MB.
[2024-10-02T21:08:49.828Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:08:52.369Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:08:54.881Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:08:57.380Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:08:59.318Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:00.662Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:02.091Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:04.054Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:04.055Z] 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.
[2024-10-02T21:09:04.055Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:04.055Z] Movies recommended for you:
[2024-10-02T21:09:04.055Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:04.055Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:04.055Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17041.797 ms) ======
[2024-10-02T21:09:04.055Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-02T21:09:04.429Z] GC before operation: completed in 72.367 ms, heap usage 226.314 MB -> 50.599 MB.
[2024-10-02T21:09:06.960Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:10.548Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:13.090Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:09:15.623Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:09:17.537Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:18.870Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:20.727Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:22.088Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:22.469Z] 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.
[2024-10-02T21:09:22.469Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:22.469Z] Movies recommended for you:
[2024-10-02T21:09:22.469Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:22.469Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:22.469Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (18182.068 ms) ======
[2024-10-02T21:09:22.469Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-02T21:09:22.469Z] GC before operation: completed in 72.934 ms, heap usage 274.859 MB -> 50.837 MB.
[2024-10-02T21:09:24.982Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:27.521Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:30.888Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:09:32.765Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:09:34.653Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:35.975Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:37.873Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:39.808Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:39.808Z] 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.
[2024-10-02T21:09:39.808Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:39.808Z] Movies recommended for you:
[2024-10-02T21:09:39.808Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:39.808Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:39.808Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17417.470 ms) ======
[2024-10-02T21:09:39.808Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-02T21:09:40.184Z] GC before operation: completed in 83.231 ms, heap usage 410.983 MB -> 54.309 MB.
[2024-10-02T21:09:42.722Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:09:45.271Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:09:47.824Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:09:50.340Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:09:52.222Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:09:53.552Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:09:55.434Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:09:56.748Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:09:57.149Z] 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.
[2024-10-02T21:09:57.149Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:09:57.149Z] Movies recommended for you:
[2024-10-02T21:09:57.149Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:09:57.149Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:09:57.149Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17265.704 ms) ======
[2024-10-02T21:09:57.149Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-02T21:09:57.524Z] GC before operation: completed in 72.443 ms, heap usage 221.774 MB -> 50.716 MB.
[2024-10-02T21:10:00.041Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:02.572Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:05.277Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:07.829Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:09.153Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:10:10.481Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:10:12.346Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:10:13.648Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:10:14.029Z] 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.
[2024-10-02T21:10:14.029Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:10:14.413Z] Movies recommended for you:
[2024-10-02T21:10:14.413Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:10:14.413Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:10:14.413Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16843.320 ms) ======
[2024-10-02T21:10:14.413Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-02T21:10:14.413Z] GC before operation: completed in 72.785 ms, heap usage 199.927 MB -> 50.953 MB.
[2024-10-02T21:10:16.925Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:19.430Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:21.991Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:24.550Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:25.879Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:10:27.784Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:10:29.096Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:10:31.024Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:10:31.024Z] 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.
[2024-10-02T21:10:31.024Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:10:31.024Z] Movies recommended for you:
[2024-10-02T21:10:31.024Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:10:31.024Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:10:31.024Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16854.987 ms) ======
[2024-10-02T21:10:31.024Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-02T21:10:31.398Z] GC before operation: completed in 76.581 ms, heap usage 287.412 MB -> 51.022 MB.
[2024-10-02T21:10:33.905Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:36.588Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:39.113Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:41.726Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:43.061Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:10:44.396Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:10:46.245Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:10:47.588Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:10:47.980Z] 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.
[2024-10-02T21:10:47.980Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:10:47.980Z] Movies recommended for you:
[2024-10-02T21:10:47.980Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:10:47.980Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:10:47.980Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16873.518 ms) ======
[2024-10-02T21:10:47.980Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-02T21:10:48.355Z] GC before operation: completed in 74.490 ms, heap usage 125.519 MB -> 50.740 MB.
[2024-10-02T21:10:50.921Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:10:53.449Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:10:56.031Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:10:58.618Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:10:59.952Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:01.279Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:03.186Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:04.703Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:05.079Z] 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.
[2024-10-02T21:11:05.079Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:05.079Z] Movies recommended for you:
[2024-10-02T21:11:05.079Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:05.079Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:05.079Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16890.089 ms) ======
[2024-10-02T21:11:05.079Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-02T21:11:05.079Z] GC before operation: completed in 79.817 ms, heap usage 166.612 MB -> 50.870 MB.
[2024-10-02T21:11:07.605Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:11:10.128Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:11:12.641Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:11:15.188Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:11:17.100Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:18.404Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:19.729Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:21.614Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:21.614Z] 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.
[2024-10-02T21:11:21.614Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:21.989Z] Movies recommended for you:
[2024-10-02T21:11:21.989Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:21.989Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:21.989Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16754.571 ms) ======
[2024-10-02T21:11:21.989Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-02T21:11:21.989Z] GC before operation: completed in 75.960 ms, heap usage 178.010 MB -> 51.071 MB.
[2024-10-02T21:11:24.513Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:11:27.035Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:11:29.580Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:11:32.109Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:11:33.431Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:11:35.353Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:11:36.775Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:11:38.087Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:11:38.469Z] 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.
[2024-10-02T21:11:38.469Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:11:38.905Z] Movies recommended for you:
[2024-10-02T21:11:38.905Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:11:38.905Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:11:38.905Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16697.026 ms) ======
[2024-10-02T21:11:39.279Z] -----------------------------------
[2024-10-02T21:11:39.279Z] renaissance-movie-lens_0_PASSED
[2024-10-02T21:11:39.279Z] -----------------------------------
[2024-10-02T21:11:39.279Z]
[2024-10-02T21:11:39.279Z] TEST TEARDOWN:
[2024-10-02T21:11:39.279Z] Nothing to be done for teardown.
[2024-10-02T21:11:39.279Z] renaissance-movie-lens_0 Finish Time: Wed Oct 2 21:11:39 2024 Epoch Time (ms): 1727903499162