renaissance-movie-lens_0
[2024-09-25T21:21:26.192Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T21:21:26.192Z] ===============================================
[2024-09-25T21:21:26.192Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 21:21:25 2024 Epoch Time (ms): 1727299285621
[2024-09-25T21:21:26.192Z] variation: NoOptions
[2024-09-25T21:21:26.192Z] JVM_OPTIONS:
[2024-09-25T21:21:26.192Z] { \
[2024-09-25T21:21:26.192Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T21:21:26.192Z] echo "Nothing to be done for setup."; \
[2024-09-25T21:21:26.192Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17272971314824/renaissance-movie-lens_0"; \
[2024-09-25T21:21:26.192Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17272971314824/renaissance-movie-lens_0"; \
[2024-09-25T21:21:26.192Z] echo ""; echo "TESTING:"; \
[2024-09-25T21:21:26.192Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17272971314824/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T21:21:26.192Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17272971314824/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T21:21:26.192Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T21:21:26.192Z] echo "Nothing to be done for teardown."; \
[2024-09-25T21:21:26.192Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17272971314824/TestTargetResult";
[2024-09-25T21:21:26.192Z]
[2024-09-25T21:21:26.192Z] TEST SETUP:
[2024-09-25T21:21:26.192Z] Nothing to be done for setup.
[2024-09-25T21:21:26.192Z]
[2024-09-25T21:21:26.192Z] TESTING:
[2024-09-25T21:21:32.782Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T21:21:42.363Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-25T21:21:55.878Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T21:21:56.607Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T21:21:56.607Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T21:21:56.607Z] GC before operation: completed in 294.824 ms, heap usage 99.019 MB -> 37.138 MB.
[2024-09-25T21:22:26.964Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:22:42.771Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:22:58.652Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:23:08.376Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:23:16.447Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:23:25.140Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:23:33.339Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:23:40.011Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:23:40.011Z] 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.
[2024-09-25T21:23:40.750Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:23:40.750Z] Movies recommended for you:
[2024-09-25T21:23:40.750Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:23:40.750Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:23:40.750Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (103922.726 ms) ======
[2024-09-25T21:23:40.750Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T21:23:41.495Z] GC before operation: completed in 303.204 ms, heap usage 328.460 MB -> 57.546 MB.
[2024-09-25T21:23:51.004Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:23:58.999Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:24:08.524Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:24:16.875Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:24:21.169Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:24:25.479Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:24:29.824Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:24:35.180Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:24:35.180Z] 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.
[2024-09-25T21:24:35.912Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:24:35.912Z] Movies recommended for you:
[2024-09-25T21:24:35.912Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:24:35.912Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:24:35.913Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (54669.987 ms) ======
[2024-09-25T21:24:35.913Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T21:24:35.913Z] GC before operation: completed in 201.527 ms, heap usage 255.495 MB -> 49.688 MB.
[2024-09-25T21:24:45.438Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:24:53.496Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:25:04.796Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:25:12.900Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:25:20.000Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:25:24.271Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:25:30.909Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:25:36.390Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:25:37.256Z] 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.
[2024-09-25T21:25:37.257Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:25:37.257Z] Movies recommended for you:
[2024-09-25T21:25:37.257Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:25:37.257Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:25:37.257Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (61261.116 ms) ======
[2024-09-25T21:25:37.257Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T21:25:38.008Z] GC before operation: completed in 330.394 ms, heap usage 223.832 MB -> 50.003 MB.
[2024-09-25T21:25:46.163Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:25:55.903Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:26:05.577Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:26:12.830Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:26:19.564Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:26:25.051Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:26:30.539Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:26:36.060Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:26:36.898Z] 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.
[2024-09-25T21:26:36.898Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:26:37.642Z] Movies recommended for you:
[2024-09-25T21:26:37.642Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:26:37.642Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:26:37.642Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (59685.984 ms) ======
[2024-09-25T21:26:37.642Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T21:26:37.642Z] GC before operation: completed in 330.548 ms, heap usage 132.777 MB -> 50.222 MB.
[2024-09-25T21:26:47.346Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:26:57.069Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:27:05.195Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:27:13.762Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:27:19.214Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:27:25.971Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:27:31.494Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:27:35.883Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:27:36.631Z] 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.
[2024-09-25T21:27:36.631Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:27:37.420Z] Movies recommended for you:
[2024-09-25T21:27:37.420Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:27:37.420Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:27:37.420Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (59548.644 ms) ======
[2024-09-25T21:27:37.420Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T21:27:37.420Z] GC before operation: completed in 275.596 ms, heap usage 79.820 MB -> 51.881 MB.
[2024-09-25T21:27:45.553Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:27:53.671Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:28:03.465Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:28:10.262Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:28:13.738Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:28:18.128Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:28:25.012Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:28:30.542Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:28:31.280Z] 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.
[2024-09-25T21:28:31.280Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:28:32.023Z] Movies recommended for you:
[2024-09-25T21:28:32.023Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:28:32.023Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:28:32.023Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (54150.647 ms) ======
[2024-09-25T21:28:32.023Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T21:28:32.023Z] GC before operation: completed in 262.240 ms, heap usage 434.490 MB -> 53.801 MB.
[2024-09-25T21:28:40.158Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:28:48.323Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:28:56.420Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:29:04.766Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:29:11.575Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:29:15.966Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:29:21.454Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:29:25.839Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:29:27.394Z] 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.
[2024-09-25T21:29:27.394Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:29:27.394Z] Movies recommended for you:
[2024-09-25T21:29:27.394Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:29:27.394Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:29:27.394Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (55504.223 ms) ======
[2024-09-25T21:29:27.394Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T21:29:27.394Z] GC before operation: completed in 307.660 ms, heap usage 259.941 MB -> 50.726 MB.
[2024-09-25T21:29:35.491Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:29:43.567Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:29:51.674Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:29:58.737Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:30:02.070Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:30:06.429Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:30:11.872Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:30:16.290Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:30:16.290Z] 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.
[2024-09-25T21:30:17.031Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:30:17.031Z] Movies recommended for you:
[2024-09-25T21:30:17.031Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:30:17.031Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:30:17.031Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (49210.037 ms) ======
[2024-09-25T21:30:17.031Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T21:30:17.031Z] GC before operation: completed in 289.250 ms, heap usage 290.386 MB -> 50.976 MB.
[2024-09-25T21:30:25.114Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:30:33.275Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:30:41.487Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:30:48.225Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:30:52.615Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:30:56.952Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:31:03.651Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:31:08.091Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:31:08.843Z] 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.
[2024-09-25T21:31:08.843Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:31:08.843Z] Movies recommended for you:
[2024-09-25T21:31:08.843Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:31:08.843Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:31:08.843Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (51842.832 ms) ======
[2024-09-25T21:31:08.843Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T21:31:09.593Z] GC before operation: completed in 259.004 ms, heap usage 69.478 MB -> 51.619 MB.
[2024-09-25T21:31:16.338Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:31:24.480Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:31:34.211Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:31:40.915Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:31:45.778Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:31:51.237Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:31:56.691Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:32:01.070Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:32:01.842Z] 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.
[2024-09-25T21:32:01.842Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:32:01.842Z] Movies recommended for you:
[2024-09-25T21:32:01.842Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:32:01.842Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:32:01.842Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (52500.086 ms) ======
[2024-09-25T21:32:01.842Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T21:32:01.842Z] GC before operation: completed in 257.973 ms, heap usage 436.096 MB -> 54.231 MB.
[2024-09-25T21:32:09.957Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:32:16.790Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:32:24.923Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:32:31.675Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:32:37.183Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:32:41.528Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:32:47.030Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:32:50.334Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:32:51.083Z] 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.
[2024-09-25T21:32:51.083Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:32:51.872Z] Movies recommended for you:
[2024-09-25T21:32:51.872Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:32:51.872Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:32:51.872Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (49486.040 ms) ======
[2024-09-25T21:32:51.872Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T21:32:51.872Z] GC before operation: completed in 220.133 ms, heap usage 178.496 MB -> 50.622 MB.
[2024-09-25T21:32:59.983Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:33:08.137Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:33:14.996Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:33:23.078Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:33:27.412Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:33:31.241Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:33:35.558Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:33:39.998Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:33:39.998Z] 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.
[2024-09-25T21:33:40.743Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:33:40.743Z] Movies recommended for you:
[2024-09-25T21:33:40.743Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:33:40.743Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:33:40.743Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (48926.106 ms) ======
[2024-09-25T21:33:40.743Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T21:33:40.743Z] GC before operation: completed in 197.518 ms, heap usage 125.136 MB -> 50.696 MB.
[2024-09-25T21:33:47.455Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:33:55.538Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:34:02.230Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:34:10.357Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:34:14.725Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:34:19.067Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:34:23.883Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:34:28.225Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:34:28.982Z] 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.
[2024-09-25T21:34:28.982Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:34:29.730Z] Movies recommended for you:
[2024-09-25T21:34:29.731Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:34:29.731Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:34:29.731Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (48519.184 ms) ======
[2024-09-25T21:34:29.731Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T21:34:29.731Z] GC before operation: completed in 283.819 ms, heap usage 191.261 MB -> 50.929 MB.
[2024-09-25T21:34:36.416Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:34:44.528Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:34:54.264Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:35:01.038Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:35:05.428Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:35:09.774Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:35:14.111Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:35:18.658Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:35:18.658Z] 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.
[2024-09-25T21:35:18.658Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:35:19.405Z] Movies recommended for you:
[2024-09-25T21:35:19.405Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:35:19.405Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:35:19.405Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (49480.856 ms) ======
[2024-09-25T21:35:19.405Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T21:35:19.405Z] GC before operation: completed in 233.979 ms, heap usage 435.126 MB -> 54.054 MB.
[2024-09-25T21:35:26.145Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:35:34.366Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:35:41.258Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:35:49.343Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:35:52.680Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:35:57.025Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:36:01.385Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:36:05.746Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:36:07.281Z] 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.
[2024-09-25T21:36:07.281Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:36:07.281Z] Movies recommended for you:
[2024-09-25T21:36:07.281Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:36:07.281Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:36:07.281Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (47790.355 ms) ======
[2024-09-25T21:36:07.281Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T21:36:07.281Z] GC before operation: completed in 338.817 ms, heap usage 75.967 MB -> 50.697 MB.
[2024-09-25T21:36:14.118Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:36:22.263Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:36:30.366Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:36:37.118Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:36:41.489Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:36:45.857Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:36:51.339Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:36:54.691Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:36:55.447Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:36:55.447Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:36:55.447Z] Movies recommended for you:
[2024-09-25T21:36:55.447Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:36:55.447Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:36:55.447Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (48032.437 ms) ======
[2024-09-25T21:36:55.447Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T21:36:55.447Z] GC before operation: completed in 183.758 ms, heap usage 78.180 MB -> 50.844 MB.
[2024-09-25T21:37:03.609Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:37:10.868Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:37:19.115Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:37:25.950Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:37:31.427Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:37:34.835Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:37:40.335Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:37:44.661Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:37:45.397Z] 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.
[2024-09-25T21:37:45.397Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:37:46.148Z] Movies recommended for you:
[2024-09-25T21:37:46.148Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:37:46.148Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:37:46.148Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (50261.697 ms) ======
[2024-09-25T21:37:46.148Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T21:37:46.148Z] GC before operation: completed in 259.044 ms, heap usage 80.255 MB -> 50.625 MB.
[2024-09-25T21:37:54.256Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:38:02.632Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:38:10.764Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:38:17.466Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:38:21.787Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:38:26.135Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:38:30.502Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:38:34.828Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:38:36.395Z] 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.
[2024-09-25T21:38:36.395Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:38:36.395Z] Movies recommended for you:
[2024-09-25T21:38:36.395Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:38:36.395Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:38:36.395Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (50068.858 ms) ======
[2024-09-25T21:38:36.395Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T21:38:36.395Z] GC before operation: completed in 268.876 ms, heap usage 217.142 MB -> 50.830 MB.
[2024-09-25T21:38:44.522Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:38:51.191Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:38:58.484Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:39:05.261Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:39:10.737Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:39:16.250Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:39:20.658Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:39:25.024Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:39:26.627Z] 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.
[2024-09-25T21:39:26.627Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:39:26.627Z] Movies recommended for you:
[2024-09-25T21:39:26.627Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:39:26.627Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:39:26.627Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (49947.819 ms) ======
[2024-09-25T21:39:26.627Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T21:39:26.627Z] GC before operation: completed in 233.196 ms, heap usage 257.944 MB -> 51.159 MB.
[2024-09-25T21:39:34.759Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:39:42.856Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:39:51.190Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:39:59.361Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:40:03.693Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:40:08.051Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:40:14.846Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:40:18.168Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:40:18.922Z] 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.
[2024-09-25T21:40:18.922Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:40:19.696Z] Movies recommended for you:
[2024-09-25T21:40:19.696Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:40:19.696Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:40:19.696Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (52502.211 ms) ======
[2024-09-25T21:40:20.435Z] -----------------------------------
[2024-09-25T21:40:20.435Z] renaissance-movie-lens_0_PASSED
[2024-09-25T21:40:20.435Z] -----------------------------------
[2024-09-25T21:40:20.435Z]
[2024-09-25T21:40:20.435Z] TEST TEARDOWN:
[2024-09-25T21:40:20.435Z] Nothing to be done for teardown.
[2024-09-25T21:40:20.435Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 21:40:20 2024 Epoch Time (ms): 1727300420343