renaissance-movie-lens_0
[2024-08-28T20:37:37.569Z] Running test renaissance-movie-lens_0 ...
[2024-08-28T20:37:37.569Z] ===============================================
[2024-08-28T20:37:37.569Z] renaissance-movie-lens_0 Start Time: Wed Aug 28 20:37:37 2024 Epoch Time (ms): 1724877457196
[2024-08-28T20:37:37.569Z] variation: NoOptions
[2024-08-28T20:37:37.569Z] JVM_OPTIONS:
[2024-08-28T20:37:37.569Z] { \
[2024-08-28T20:37:37.569Z] echo ""; echo "TEST SETUP:"; \
[2024-08-28T20:37:37.569Z] echo "Nothing to be done for setup."; \
[2024-08-28T20:37:37.569Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17248754842896/renaissance-movie-lens_0"; \
[2024-08-28T20:37:37.569Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17248754842896/renaissance-movie-lens_0"; \
[2024-08-28T20:37:37.569Z] echo ""; echo "TESTING:"; \
[2024-08-28T20:37:37.569Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-17.0.13+5/bin/..//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_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17248754842896/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-28T20:37:37.569Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17248754842896/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-28T20:37:37.569Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-28T20:37:37.569Z] echo "Nothing to be done for teardown."; \
[2024-08-28T20:37:37.569Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17248754842896/TestTargetResult";
[2024-08-28T20:37:37.569Z]
[2024-08-28T20:37:37.569Z] TEST SETUP:
[2024-08-28T20:37:37.569Z] Nothing to be done for setup.
[2024-08-28T20:37:37.569Z]
[2024-08-28T20:37:37.569Z] TESTING:
[2024-08-28T20:37:47.183Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-28T20:37:55.304Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-08-28T20:38:11.275Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-28T20:38:12.866Z] Training: 60056, validation: 20285, test: 19854
[2024-08-28T20:38:12.866Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-28T20:38:12.866Z] GC before operation: completed in 218.824 ms, heap usage 49.309 MB -> 37.326 MB.
[2024-08-28T20:38:42.858Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:39:04.932Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:39:21.557Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:39:37.762Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:39:47.614Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:39:59.456Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:40:09.313Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:40:19.077Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:40:20.712Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-08-28T20:40:20.712Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:40:21.516Z] Movies recommended for you:
[2024-08-28T20:40:21.516Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:40:21.516Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:40:21.516Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (128448.853 ms) ======
[2024-08-28T20:40:21.516Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-28T20:40:22.308Z] GC before operation: completed in 648.845 ms, heap usage 156.886 MB -> 54.704 MB.
[2024-08-28T20:40:35.993Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:40:49.828Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:41:01.370Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:41:11.104Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:41:17.860Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:41:24.640Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:41:32.789Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:41:38.314Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:41:39.065Z] 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.
[2024-08-28T20:41:39.065Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:41:39.065Z] Movies recommended for you:
[2024-08-28T20:41:39.065Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:41:39.065Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:41:39.065Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (77296.162 ms) ======
[2024-08-28T20:41:39.065Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-28T20:41:39.821Z] GC before operation: completed in 351.503 ms, heap usage 591.425 MB -> 53.360 MB.
[2024-08-28T20:41:51.424Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:42:03.166Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:42:15.405Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:42:25.211Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:42:30.736Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:42:36.253Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:42:43.070Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:42:48.746Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:42:50.289Z] 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.
[2024-08-28T20:42:50.289Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:42:51.056Z] Movies recommended for you:
[2024-08-28T20:42:51.056Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:42:51.057Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:42:51.057Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (70952.853 ms) ======
[2024-08-28T20:42:51.057Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-28T20:42:51.057Z] GC before operation: completed in 366.258 ms, heap usage 210.009 MB -> 50.035 MB.
[2024-08-28T20:43:01.438Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:43:11.200Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:43:22.729Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:43:30.906Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:43:37.641Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:43:44.386Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:43:49.896Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:43:55.404Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:43:56.150Z] 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.
[2024-08-28T20:43:56.150Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:43:56.982Z] Movies recommended for you:
[2024-08-28T20:43:56.982Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:43:56.982Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:43:56.982Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (65718.805 ms) ======
[2024-08-28T20:43:56.982Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-28T20:43:56.982Z] GC before operation: completed in 315.024 ms, heap usage 310.300 MB -> 50.486 MB.
[2024-08-28T20:44:06.801Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:44:16.602Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:44:26.380Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:44:36.744Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:44:42.238Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:44:48.978Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:44:54.588Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:45:01.357Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:45:02.186Z] 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.
[2024-08-28T20:45:02.186Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:45:02.951Z] Movies recommended for you:
[2024-08-28T20:45:02.951Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:45:02.951Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:45:02.951Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (65824.722 ms) ======
[2024-08-28T20:45:02.951Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-28T20:45:02.952Z] GC before operation: completed in 341.131 ms, heap usage 355.484 MB -> 50.678 MB.
[2024-08-28T20:45:14.420Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:45:24.092Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:45:36.094Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:45:46.108Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:45:51.617Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:45:58.498Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:46:05.479Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:46:12.330Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:46:12.330Z] 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.
[2024-08-28T20:46:13.075Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:46:13.075Z] Movies recommended for you:
[2024-08-28T20:46:13.075Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:46:13.075Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:46:13.075Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (69856.859 ms) ======
[2024-08-28T20:46:13.075Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-28T20:46:13.824Z] GC before operation: completed in 341.935 ms, heap usage 311.238 MB -> 50.597 MB.
[2024-08-28T20:46:23.523Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:46:35.082Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:46:44.860Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:46:56.858Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:47:05.058Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:47:09.572Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:47:16.349Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:47:23.139Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:47:23.950Z] 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.
[2024-08-28T20:47:23.950Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:47:23.950Z] Movies recommended for you:
[2024-08-28T20:47:23.950Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:47:23.950Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:47:23.950Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (70649.614 ms) ======
[2024-08-28T20:47:23.950Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-28T20:47:24.696Z] GC before operation: completed in 318.397 ms, heap usage 314.837 MB -> 50.832 MB.
[2024-08-28T20:47:34.560Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:47:46.387Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:47:54.560Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:48:04.820Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:48:09.200Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:48:14.730Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:48:20.212Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:48:25.707Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:48:27.285Z] 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.
[2024-08-28T20:48:27.285Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:48:28.061Z] Movies recommended for you:
[2024-08-28T20:48:28.061Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:48:28.061Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:48:28.061Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (63328.496 ms) ======
[2024-08-28T20:48:28.061Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-28T20:48:28.061Z] GC before operation: completed in 320.521 ms, heap usage 183.536 MB -> 50.973 MB.
[2024-08-28T20:48:37.933Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:48:46.122Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:48:55.809Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:49:02.597Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:49:09.390Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:49:13.395Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:49:18.883Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:49:24.395Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:49:25.153Z] 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.
[2024-08-28T20:49:25.153Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:49:25.153Z] Movies recommended for you:
[2024-08-28T20:49:25.153Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:49:25.153Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:49:25.153Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (57191.165 ms) ======
[2024-08-28T20:49:25.153Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-28T20:49:25.912Z] GC before operation: completed in 267.194 ms, heap usage 232.643 MB -> 50.854 MB.
[2024-08-28T20:49:34.077Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:49:40.789Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:49:50.609Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:49:57.526Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:50:03.071Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:50:07.942Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:50:13.508Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:50:19.017Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:50:19.774Z] 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.
[2024-08-28T20:50:19.774Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:50:19.774Z] Movies recommended for you:
[2024-08-28T20:50:19.774Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:50:19.774Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:50:19.774Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (54494.272 ms) ======
[2024-08-28T20:50:19.774Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-28T20:50:20.525Z] GC before operation: completed in 375.912 ms, heap usage 236.782 MB -> 51.022 MB.
[2024-08-28T20:50:30.256Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:50:38.471Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:50:48.127Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:50:54.904Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:51:00.385Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:51:05.862Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:51:11.372Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:51:16.878Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:51:17.630Z] 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.
[2024-08-28T20:51:17.630Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:51:17.630Z] Movies recommended for you:
[2024-08-28T20:51:17.630Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:51:17.630Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:51:17.630Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (57383.779 ms) ======
[2024-08-28T20:51:17.630Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-28T20:51:18.404Z] GC before operation: completed in 296.362 ms, heap usage 395.487 MB -> 54.119 MB.
[2024-08-28T20:51:30.565Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:51:38.721Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:51:46.869Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:51:56.615Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:52:02.120Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:52:06.502Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:52:12.081Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:52:18.947Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:52:19.706Z] 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.
[2024-08-28T20:52:19.706Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:52:20.468Z] Movies recommended for you:
[2024-08-28T20:52:20.468Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:52:20.468Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:52:20.468Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (61970.986 ms) ======
[2024-08-28T20:52:20.468Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-28T20:52:20.468Z] GC before operation: completed in 371.805 ms, heap usage 195.564 MB -> 50.892 MB.
[2024-08-28T20:52:30.122Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:52:40.333Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:52:50.016Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:52:59.766Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:53:04.234Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:53:09.741Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:53:15.256Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:53:22.067Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:53:22.810Z] 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.
[2024-08-28T20:53:22.810Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:53:22.810Z] Movies recommended for you:
[2024-08-28T20:53:22.810Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:53:22.810Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:53:22.810Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (62585.359 ms) ======
[2024-08-28T20:53:22.810Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-28T20:53:23.573Z] GC before operation: completed in 359.153 ms, heap usage 186.090 MB -> 50.974 MB.
[2024-08-28T20:53:33.427Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:53:40.159Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:53:51.800Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:53:59.989Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:54:06.808Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:54:12.348Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:54:20.476Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:54:25.960Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:54:26.721Z] 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.
[2024-08-28T20:54:26.721Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:54:26.721Z] Movies recommended for you:
[2024-08-28T20:54:26.721Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:54:26.721Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:54:26.721Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (63665.355 ms) ======
[2024-08-28T20:54:26.721Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-28T20:54:27.476Z] GC before operation: completed in 436.358 ms, heap usage 96.961 MB -> 50.636 MB.
[2024-08-28T20:54:39.141Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:54:47.262Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:54:57.144Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:55:07.094Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:55:12.580Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:55:18.078Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:55:24.820Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:55:30.314Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:55:31.116Z] 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.
[2024-08-28T20:55:31.116Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:55:31.116Z] Movies recommended for you:
[2024-08-28T20:55:31.116Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:55:31.116Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:55:31.116Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (63904.140 ms) ======
[2024-08-28T20:55:31.116Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-28T20:55:31.888Z] GC before operation: completed in 343.357 ms, heap usage 405.891 MB -> 54.372 MB.
[2024-08-28T20:55:41.653Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:55:51.521Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:56:03.055Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:56:12.897Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:56:19.776Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:56:25.324Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:56:32.148Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:56:38.913Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:56:39.700Z] 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.
[2024-08-28T20:56:39.700Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:56:39.700Z] Movies recommended for you:
[2024-08-28T20:56:39.701Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:56:39.701Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:56:39.701Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (68161.843 ms) ======
[2024-08-28T20:56:39.701Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-28T20:56:40.473Z] GC before operation: completed in 364.174 ms, heap usage 156.734 MB -> 51.002 MB.
[2024-08-28T20:56:54.043Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:57:05.529Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:57:15.458Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:57:25.296Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:57:31.388Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:57:35.767Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:57:41.283Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:57:46.820Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:57:48.374Z] 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.
[2024-08-28T20:57:48.374Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:57:48.374Z] Movies recommended for you:
[2024-08-28T20:57:48.374Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:57:48.374Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:57:48.374Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (68291.076 ms) ======
[2024-08-28T20:57:48.374Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-28T20:57:49.120Z] GC before operation: completed in 284.597 ms, heap usage 526.971 MB -> 54.426 MB.
[2024-08-28T20:57:58.897Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:58:10.438Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:58:21.929Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:58:31.640Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:58:37.120Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:58:41.509Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:58:47.348Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T20:58:52.894Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T20:58:54.451Z] 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.
[2024-08-28T20:58:54.451Z] The best model improves the baseline by 14.52%.
[2024-08-28T20:58:54.451Z] Movies recommended for you:
[2024-08-28T20:58:54.451Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T20:58:54.451Z] There is no way to check that no silent failure occurred.
[2024-08-28T20:58:54.451Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (65874.522 ms) ======
[2024-08-28T20:58:54.451Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-28T20:58:55.198Z] GC before operation: completed in 353.688 ms, heap usage 183.761 MB -> 50.881 MB.
[2024-08-28T20:59:05.010Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T20:59:14.796Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T20:59:26.413Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T20:59:34.606Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T20:59:41.368Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T20:59:48.216Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T20:59:55.153Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:00:01.309Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:00:02.117Z] 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.
[2024-08-28T21:00:02.117Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:00:02.117Z] Movies recommended for you:
[2024-08-28T21:00:02.117Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:00:02.117Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:00:02.117Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (67309.852 ms) ======
[2024-08-28T21:00:02.117Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-28T21:00:02.873Z] GC before operation: completed in 366.585 ms, heap usage 186.557 MB -> 51.078 MB.
[2024-08-28T21:00:12.696Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:22.496Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:32.382Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:00:42.196Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:00:47.788Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:00:54.652Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:00.166Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:05.768Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:06.518Z] 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.
[2024-08-28T21:01:06.518Z] The best model improves the baseline by 14.52%.
[2024-08-28T21:01:07.275Z] Movies recommended for you:
[2024-08-28T21:01:07.276Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:07.276Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:07.276Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (64374.063 ms) ======
[2024-08-28T21:01:08.815Z] -----------------------------------
[2024-08-28T21:01:08.815Z] renaissance-movie-lens_0_PASSED
[2024-08-28T21:01:08.815Z] -----------------------------------
[2024-08-28T21:01:08.815Z]
[2024-08-28T21:01:08.815Z] TEST TEARDOWN:
[2024-08-28T21:01:08.815Z] Nothing to be done for teardown.
[2024-08-28T21:01:08.815Z] renaissance-movie-lens_0 Finish Time: Wed Aug 28 21:01:08 2024 Epoch Time (ms): 1724878868345