renaissance-movie-lens_0
[2024-10-02T23:57:00.733Z] Running test renaissance-movie-lens_0 ...
[2024-10-02T23:57:00.733Z] ===============================================
[2024-10-02T23:57:00.733Z] renaissance-movie-lens_0 Start Time: Wed Oct 2 23:57:00 2024 Epoch Time (ms): 1727913420034
[2024-10-02T23:57:00.733Z] variation: NoOptions
[2024-10-02T23:57:00.733Z] JVM_OPTIONS:
[2024-10-02T23:57:00.733Z] { \
[2024-10-02T23:57:00.733Z] echo ""; echo "TEST SETUP:"; \
[2024-10-02T23:57:00.733Z] echo "Nothing to be done for setup."; \
[2024-10-02T23:57:00.733Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279065682162/renaissance-movie-lens_0"; \
[2024-10-02T23:57:00.733Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279065682162/renaissance-movie-lens_0"; \
[2024-10-02T23:57:00.733Z] echo ""; echo "TESTING:"; \
[2024-10-02T23:57:00.733Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-11.0.25+8/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_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279065682162/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-02T23:57:00.733Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279065682162/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-02T23:57:00.733Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-02T23:57:00.733Z] echo "Nothing to be done for teardown."; \
[2024-10-02T23:57:00.733Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279065682162/TestTargetResult";
[2024-10-02T23:57:00.733Z]
[2024-10-02T23:57:00.733Z] TEST SETUP:
[2024-10-02T23:57:00.733Z] Nothing to be done for setup.
[2024-10-02T23:57:00.733Z]
[2024-10-02T23:57:00.733Z] TESTING:
[2024-10-02T23:57:17.389Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-02T23:57:33.590Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-02T23:58:00.422Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-02T23:58:02.048Z] Training: 60056, validation: 20285, test: 19854
[2024-10-02T23:58:02.048Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-02T23:58:02.818Z] GC before operation: completed in 596.093 ms, heap usage 65.228 MB -> 36.646 MB.
[2024-10-02T23:59:00.389Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T23:59:30.866Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T23:59:56.927Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:00:22.948Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:00:34.754Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:00:49.220Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:01:01.125Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:01:12.899Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:01:15.470Z] 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.
[2024-10-03T00:01:15.470Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:01:16.252Z] Movies recommended for you:
[2024-10-03T00:01:16.252Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:01:16.252Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:01:16.252Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (193559.002 ms) ======
[2024-10-03T00:01:16.252Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-03T00:01:17.017Z] GC before operation: completed in 729.776 ms, heap usage 280.897 MB -> 47.600 MB.
[2024-10-03T00:01:39.384Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:01:55.686Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:02:15.350Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:02:34.533Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:02:46.278Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:02:58.157Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:03:12.002Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:03:22.136Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:03:23.053Z] 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.
[2024-10-03T00:03:23.842Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:03:24.613Z] Movies recommended for you:
[2024-10-03T00:03:24.613Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:03:24.613Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:03:24.613Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (126966.094 ms) ======
[2024-10-03T00:03:24.613Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-03T00:03:24.613Z] GC before operation: completed in 667.143 ms, heap usage 154.039 MB -> 49.222 MB.
[2024-10-03T00:03:47.476Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:04:06.543Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:04:22.886Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:04:42.038Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:04:52.018Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:05:03.818Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:05:15.478Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:05:27.877Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:05:28.637Z] 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.
[2024-10-03T00:05:29.445Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:05:30.203Z] Movies recommended for you:
[2024-10-03T00:05:30.203Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:05:30.203Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:05:30.203Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (124941.783 ms) ======
[2024-10-03T00:05:30.203Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-03T00:05:30.203Z] GC before operation: completed in 677.323 ms, heap usage 126.919 MB -> 49.398 MB.
[2024-10-03T00:05:49.401Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:06:08.472Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:06:24.584Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:06:40.963Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:06:51.449Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:07:03.285Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:07:15.137Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:07:25.047Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:07:26.654Z] 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.
[2024-10-03T00:07:26.654Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:07:27.419Z] Movies recommended for you:
[2024-10-03T00:07:27.419Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:07:27.419Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:07:27.419Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (116677.153 ms) ======
[2024-10-03T00:07:27.419Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-03T00:07:28.198Z] GC before operation: completed in 673.852 ms, heap usage 206.708 MB -> 49.947 MB.
[2024-10-03T00:07:46.998Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:08:05.931Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:08:20.249Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:08:36.544Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:08:46.520Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:08:56.617Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:09:08.384Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:09:18.317Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:09:20.800Z] 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.
[2024-10-03T00:09:20.800Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:09:21.576Z] Movies recommended for you:
[2024-10-03T00:09:21.576Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:09:21.576Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:09:21.576Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (113597.812 ms) ======
[2024-10-03T00:09:21.576Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-03T00:09:22.344Z] GC before operation: completed in 612.901 ms, heap usage 79.630 MB -> 54.238 MB.
[2024-10-03T00:09:42.240Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:09:58.288Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:10:16.956Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:10:30.954Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:10:40.997Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:10:52.864Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:11:04.685Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:11:17.091Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:11:17.878Z] 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.
[2024-10-03T00:11:17.878Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:11:18.681Z] Movies recommended for you:
[2024-10-03T00:11:18.681Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:11:18.681Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:11:18.681Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (116630.190 ms) ======
[2024-10-03T00:11:18.681Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-03T00:11:19.454Z] GC before operation: completed in 702.366 ms, heap usage 82.008 MB -> 49.786 MB.
[2024-10-03T00:11:35.710Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:11:57.910Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:12:14.223Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:12:33.137Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:12:44.881Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:12:57.236Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:13:07.412Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:13:19.416Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:13:21.882Z] 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.
[2024-10-03T00:13:21.882Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:13:22.636Z] Movies recommended for you:
[2024-10-03T00:13:22.636Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:13:22.636Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:13:22.636Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (123021.928 ms) ======
[2024-10-03T00:13:22.636Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-03T00:13:23.408Z] GC before operation: completed in 712.003 ms, heap usage 151.556 MB -> 50.066 MB.
[2024-10-03T00:13:45.584Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:14:01.695Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:14:24.328Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:14:40.635Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:14:52.220Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:15:00.450Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:15:12.053Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:15:21.970Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:15:23.608Z] 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.
[2024-10-03T00:15:23.609Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:15:23.609Z] Movies recommended for you:
[2024-10-03T00:15:23.609Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:15:23.609Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:15:23.609Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (120627.443 ms) ======
[2024-10-03T00:15:23.609Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-03T00:15:24.392Z] GC before operation: completed in 600.813 ms, heap usage 217.856 MB -> 50.413 MB.
[2024-10-03T00:15:40.696Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:15:59.681Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:16:18.584Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:16:34.889Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:16:46.836Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:17:00.746Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:17:12.660Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:17:23.017Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:17:25.547Z] 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.
[2024-10-03T00:17:25.547Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:17:25.547Z] Movies recommended for you:
[2024-10-03T00:17:25.547Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:17:25.547Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:17:25.547Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (121457.121 ms) ======
[2024-10-03T00:17:25.547Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-03T00:17:26.317Z] GC before operation: completed in 700.552 ms, heap usage 249.038 MB -> 50.405 MB.
[2024-10-03T00:17:45.257Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:18:04.234Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:18:20.325Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:18:36.696Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:18:45.030Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:18:55.119Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:19:04.972Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:19:16.745Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:19:17.528Z] 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.
[2024-10-03T00:19:17.528Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:19:18.387Z] Movies recommended for you:
[2024-10-03T00:19:18.387Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:19:18.387Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:19:18.387Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (111781.460 ms) ======
[2024-10-03T00:19:18.387Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-03T00:19:19.168Z] GC before operation: completed in 794.894 ms, heap usage 102.173 MB -> 50.211 MB.
[2024-10-03T00:19:38.085Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:19:54.191Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:20:10.474Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:20:29.900Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:20:39.722Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:20:51.471Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:21:01.350Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:21:11.217Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:21:12.799Z] 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.
[2024-10-03T00:21:13.569Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:21:13.569Z] Movies recommended for you:
[2024-10-03T00:21:13.569Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:21:13.569Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:21:13.569Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (114736.025 ms) ======
[2024-10-03T00:21:13.569Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-03T00:21:14.331Z] GC before operation: completed in 707.646 ms, heap usage 241.822 MB -> 50.157 MB.
[2024-10-03T00:21:36.416Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:21:53.179Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:22:12.057Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:22:28.431Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:22:38.353Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:22:50.079Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:23:03.853Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:23:15.671Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:23:16.429Z] 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.
[2024-10-03T00:23:16.429Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:23:17.244Z] Movies recommended for you:
[2024-10-03T00:23:17.244Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:23:17.244Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:23:17.244Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (122823.717 ms) ======
[2024-10-03T00:23:17.244Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-03T00:23:18.000Z] GC before operation: completed in 742.746 ms, heap usage 222.093 MB -> 47.971 MB.
[2024-10-03T00:23:37.527Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:23:56.403Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:24:15.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:24:31.813Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:24:41.805Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:24:54.148Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:25:04.116Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:25:15.712Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:25:17.279Z] 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.
[2024-10-03T00:25:17.279Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:25:18.043Z] Movies recommended for you:
[2024-10-03T00:25:18.043Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:25:18.043Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:25:18.043Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (119866.022 ms) ======
[2024-10-03T00:25:18.043Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-03T00:25:18.900Z] GC before operation: completed in 723.597 ms, heap usage 130.059 MB -> 48.303 MB.
[2024-10-03T00:25:35.152Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:25:48.955Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:26:07.927Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:26:22.199Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:26:34.042Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:26:44.027Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:26:53.881Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:27:02.179Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:27:03.781Z] 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.
[2024-10-03T00:27:04.546Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:27:04.546Z] Movies recommended for you:
[2024-10-03T00:27:04.546Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:27:04.546Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:27:04.546Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (106135.135 ms) ======
[2024-10-03T00:27:04.546Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-03T00:27:05.310Z] GC before operation: completed in 638.345 ms, heap usage 174.984 MB -> 47.589 MB.
[2024-10-03T00:27:24.225Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:27:41.268Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:28:00.087Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:28:16.268Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:28:26.283Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:28:36.228Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:28:46.226Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:28:54.497Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:28:56.156Z] 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.
[2024-10-03T00:28:56.909Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:28:57.684Z] Movies recommended for you:
[2024-10-03T00:28:57.685Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:28:57.685Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:28:57.685Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (111780.850 ms) ======
[2024-10-03T00:28:57.685Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-03T00:28:58.441Z] GC before operation: completed in 741.474 ms, heap usage 132.121 MB -> 47.268 MB.
[2024-10-03T00:29:14.888Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:29:30.956Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:29:49.837Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:30:01.399Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:30:11.129Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:30:20.845Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:30:32.950Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:30:42.777Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:30:43.559Z] 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.
[2024-10-03T00:30:43.559Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:30:44.337Z] Movies recommended for you:
[2024-10-03T00:30:44.338Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:30:44.338Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:30:44.338Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (106104.518 ms) ======
[2024-10-03T00:30:44.338Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-03T00:30:45.108Z] GC before operation: completed in 628.911 ms, heap usage 213.211 MB -> 47.275 MB.
[2024-10-03T00:31:01.211Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:31:17.145Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:31:33.147Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:31:46.831Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:31:56.585Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:32:06.412Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:32:18.077Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:32:26.261Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:32:27.017Z] 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.
[2024-10-03T00:32:27.784Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:32:27.784Z] Movies recommended for you:
[2024-10-03T00:32:27.784Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:32:27.784Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:32:27.784Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (103216.865 ms) ======
[2024-10-03T00:32:27.784Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-03T00:32:28.535Z] GC before operation: completed in 575.715 ms, heap usage 124.868 MB -> 47.584 MB.
[2024-10-03T00:32:44.531Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:33:00.392Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:33:16.255Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:33:30.130Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:33:40.118Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:33:48.314Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:33:59.948Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:34:08.238Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:34:09.851Z] 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.
[2024-10-03T00:34:09.851Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:34:10.628Z] Movies recommended for you:
[2024-10-03T00:34:10.628Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:34:10.628Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:34:10.628Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (102264.394 ms) ======
[2024-10-03T00:34:10.628Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-03T00:34:11.399Z] GC before operation: completed in 663.994 ms, heap usage 131.913 MB -> 47.656 MB.
[2024-10-03T00:34:27.702Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:34:44.266Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:35:03.107Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:35:16.645Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:35:24.789Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:35:33.023Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:35:42.816Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:35:51.091Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:35:52.652Z] 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.
[2024-10-03T00:35:52.652Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:35:53.499Z] Movies recommended for you:
[2024-10-03T00:35:53.499Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:35:53.499Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:35:53.499Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (101539.932 ms) ======
[2024-10-03T00:35:53.499Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-03T00:35:53.499Z] GC before operation: completed in 636.721 ms, heap usage 167.556 MB -> 48.069 MB.
[2024-10-03T00:36:09.908Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:36:23.498Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:36:39.486Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:36:55.495Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:37:05.264Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:37:13.414Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:37:23.622Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:37:31.701Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:37:34.110Z] 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.
[2024-10-03T00:37:34.110Z] The best model improves the baseline by 14.52%.
[2024-10-03T00:37:34.861Z] Movies recommended for you:
[2024-10-03T00:37:34.861Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:37:34.861Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:37:34.861Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (101258.277 ms) ======
[2024-10-03T00:37:38.188Z] -----------------------------------
[2024-10-03T00:37:38.188Z] renaissance-movie-lens_0_PASSED
[2024-10-03T00:37:38.188Z] -----------------------------------
[2024-10-03T00:37:38.188Z]
[2024-10-03T00:37:38.188Z] TEST TEARDOWN:
[2024-10-03T00:37:38.188Z] Nothing to be done for teardown.
[2024-10-03T00:37:38.188Z] renaissance-movie-lens_0 Finish Time: Thu Oct 3 00:37:37 2024 Epoch Time (ms): 1727915857815