renaissance-movie-lens_0
[2025-02-27T13:32:46.372Z] Running test renaissance-movie-lens_0 ...
[2025-02-27T13:32:46.372Z] ===============================================
[2025-02-27T13:32:46.372Z] renaissance-movie-lens_0 Start Time: Thu Feb 27 13:32:45 2025 Epoch Time (ms): 1740663165930
[2025-02-27T13:32:46.372Z] variation: NoOptions
[2025-02-27T13:32:46.372Z] JVM_OPTIONS:
[2025-02-27T13:32:46.372Z] { \
[2025-02-27T13:32:46.372Z] echo ""; echo "TEST SETUP:"; \
[2025-02-27T13:32:46.372Z] echo "Nothing to be done for setup."; \
[2025-02-27T13:32:46.372Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406613191788/renaissance-movie-lens_0"; \
[2025-02-27T13:32:46.372Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406613191788/renaissance-movie-lens_0"; \
[2025-02-27T13:32:46.372Z] echo ""; echo "TESTING:"; \
[2025-02-27T13:32:46.372Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406613191788/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-27T13:32:46.372Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406613191788/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-27T13:32:46.372Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-27T13:32:46.372Z] echo "Nothing to be done for teardown."; \
[2025-02-27T13:32:46.372Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406613191788/TestTargetResult";
[2025-02-27T13:32:46.372Z]
[2025-02-27T13:32:46.372Z] TEST SETUP:
[2025-02-27T13:32:46.372Z] Nothing to be done for setup.
[2025-02-27T13:32:46.372Z]
[2025-02-27T13:32:46.372Z] TESTING:
[2025-02-27T13:32:52.674Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-27T13:32:57.282Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-27T13:33:03.057Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-27T13:33:03.872Z] Training: 60056, validation: 20285, test: 19854
[2025-02-27T13:33:03.872Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-27T13:33:04.664Z] GC before operation: completed in 274.170 ms, heap usage 79.305 MB -> 36.421 MB.
[2025-02-27T13:33:23.962Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:33:38.028Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:33:52.194Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:34:02.345Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:34:08.734Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:34:14.550Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:34:21.733Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:34:27.507Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:34:28.323Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:34:29.133Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:34:29.133Z] Movies recommended for you:
[2025-02-27T13:34:29.955Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:34:29.955Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:34:29.955Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (85079.852 ms) ======
[2025-02-27T13:34:29.955Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-27T13:34:29.955Z] GC before operation: completed in 521.136 ms, heap usage 190.791 MB -> 49.034 MB.
[2025-02-27T13:34:40.159Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:34:48.759Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:34:59.249Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:35:08.157Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:35:13.540Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:35:18.411Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:35:24.446Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:35:29.327Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:35:30.182Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:35:30.182Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:35:30.182Z] Movies recommended for you:
[2025-02-27T13:35:30.182Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:35:30.182Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:35:30.182Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (60394.320 ms) ======
[2025-02-27T13:35:30.182Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-27T13:35:31.031Z] GC before operation: completed in 368.198 ms, heap usage 187.048 MB -> 49.031 MB.
[2025-02-27T13:35:39.934Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:35:47.320Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:35:56.196Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:36:03.564Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:36:09.601Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:36:13.927Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:36:20.019Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:36:24.848Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:36:25.705Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:36:25.705Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:36:25.705Z] Movies recommended for you:
[2025-02-27T13:36:25.705Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:36:25.705Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:36:25.705Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (55042.737 ms) ======
[2025-02-27T13:36:25.705Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-27T13:36:26.555Z] GC before operation: completed in 354.710 ms, heap usage 110.252 MB -> 49.215 MB.
[2025-02-27T13:36:35.468Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:36:41.572Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:36:50.460Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:36:57.888Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:37:02.738Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:37:06.594Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:37:12.602Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:37:16.694Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:37:17.537Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:37:17.537Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:37:17.537Z] Movies recommended for you:
[2025-02-27T13:37:17.537Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:37:17.537Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:37:17.537Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (51542.169 ms) ======
[2025-02-27T13:37:17.537Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-27T13:37:18.386Z] GC before operation: completed in 329.429 ms, heap usage 273.048 MB -> 49.701 MB.
[2025-02-27T13:37:25.767Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:37:33.166Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:37:42.086Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:37:48.162Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:37:52.996Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:37:57.869Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:38:02.743Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:38:06.472Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:38:07.323Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:38:07.323Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:38:07.323Z] Movies recommended for you:
[2025-02-27T13:38:07.323Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:38:07.323Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:38:07.323Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (49323.120 ms) ======
[2025-02-27T13:38:07.323Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-27T13:38:08.164Z] GC before operation: completed in 351.261 ms, heap usage 104.175 MB -> 49.728 MB.
[2025-02-27T13:38:15.758Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:38:23.174Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:38:33.741Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:38:42.644Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:38:46.365Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:38:51.204Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:38:54.902Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:38:59.721Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:39:00.574Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:39:00.574Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:39:00.574Z] Movies recommended for you:
[2025-02-27T13:39:00.574Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:39:00.574Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:39:00.574Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (53025.277 ms) ======
[2025-02-27T13:39:00.574Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-27T13:39:01.420Z] GC before operation: completed in 347.564 ms, heap usage 212.488 MB -> 49.775 MB.
[2025-02-27T13:39:08.815Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:39:17.712Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:39:25.081Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:39:32.498Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:39:38.562Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:39:43.391Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:39:49.389Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:39:53.037Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:39:53.859Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:39:54.672Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:39:54.672Z] Movies recommended for you:
[2025-02-27T13:39:54.672Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:39:54.672Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:39:54.672Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (53411.958 ms) ======
[2025-02-27T13:39:54.672Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-27T13:39:54.672Z] GC before operation: completed in 288.355 ms, heap usage 154.635 MB -> 49.953 MB.
[2025-02-27T13:40:01.894Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:40:10.510Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:40:22.865Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:40:30.046Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:40:34.898Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:40:43.493Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:40:48.178Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:40:55.340Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:40:56.175Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:40:56.175Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:40:56.175Z] Movies recommended for you:
[2025-02-27T13:40:56.175Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:40:56.175Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:40:56.175Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (61667.114 ms) ======
[2025-02-27T13:40:56.175Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-27T13:40:56.997Z] GC before operation: completed in 354.904 ms, heap usage 123.182 MB -> 50.116 MB.
[2025-02-27T13:41:05.656Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:41:14.352Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:41:22.146Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:41:28.009Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:41:31.634Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:41:37.565Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:41:43.462Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:41:48.199Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:41:49.006Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:41:49.006Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:41:49.825Z] Movies recommended for you:
[2025-02-27T13:41:49.825Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:41:49.825Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:41:49.825Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (52735.211 ms) ======
[2025-02-27T13:41:49.825Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-27T13:41:49.825Z] GC before operation: completed in 269.560 ms, heap usage 79.121 MB -> 49.904 MB.
[2025-02-27T13:41:58.513Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:42:06.114Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:42:16.435Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:42:22.937Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:42:26.587Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:42:31.303Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:42:36.015Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:42:41.970Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:42:41.970Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:42:41.970Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:42:42.786Z] Movies recommended for you:
[2025-02-27T13:42:42.786Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:42:42.786Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:42:42.786Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (52870.012 ms) ======
[2025-02-27T13:42:42.786Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-27T13:42:42.786Z] GC before operation: completed in 393.677 ms, heap usage 106.589 MB -> 50.234 MB.
[2025-02-27T13:42:53.122Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:43:03.400Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:43:12.101Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:43:22.549Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:43:27.876Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:43:33.778Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:43:39.740Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:43:45.664Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:43:46.500Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:43:46.500Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:43:47.321Z] Movies recommended for you:
[2025-02-27T13:43:47.321Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:43:47.321Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:43:47.321Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (63776.838 ms) ======
[2025-02-27T13:43:47.321Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-27T13:43:47.321Z] GC before operation: completed in 399.281 ms, heap usage 180.516 MB -> 50.019 MB.
[2025-02-27T13:43:59.532Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:44:09.852Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:44:22.071Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:44:30.807Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:44:38.631Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:44:44.600Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:44:51.861Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:44:59.142Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:44:59.956Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:44:59.956Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:45:00.765Z] Movies recommended for you:
[2025-02-27T13:45:00.766Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:45:00.766Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:45:00.766Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (73109.883 ms) ======
[2025-02-27T13:45:00.766Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-27T13:45:00.766Z] GC before operation: completed in 451.415 ms, heap usage 270.373 MB -> 51.293 MB.
[2025-02-27T13:45:12.889Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:45:25.089Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:45:37.195Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:45:47.994Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:45:53.844Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:46:01.047Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:46:08.198Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:46:15.439Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:46:15.439Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:46:16.289Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:46:16.289Z] Movies recommended for you:
[2025-02-27T13:46:16.289Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:46:16.289Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:46:16.289Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (75485.723 ms) ======
[2025-02-27T13:46:16.289Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-27T13:46:17.118Z] GC before operation: completed in 545.385 ms, heap usage 154.000 MB -> 50.225 MB.
[2025-02-27T13:46:29.322Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:46:37.959Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:46:50.612Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:47:02.843Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:47:08.902Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:47:16.083Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:47:21.999Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:47:29.086Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:47:30.735Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:47:30.735Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:47:31.538Z] Movies recommended for you:
[2025-02-27T13:47:31.538Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:47:31.538Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:47:31.538Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (74197.582 ms) ======
[2025-02-27T13:47:31.538Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-27T13:47:31.538Z] GC before operation: completed in 518.687 ms, heap usage 92.795 MB -> 50.026 MB.
[2025-02-27T13:47:43.634Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:47:55.731Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:48:05.946Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:48:16.190Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:48:22.045Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:48:27.909Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:48:35.062Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:48:40.893Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:48:41.673Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:48:42.458Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:48:42.458Z] Movies recommended for you:
[2025-02-27T13:48:42.458Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:48:42.458Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:48:42.458Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (70771.380 ms) ======
[2025-02-27T13:48:42.458Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-27T13:48:42.458Z] GC before operation: completed in 291.708 ms, heap usage 190.743 MB -> 50.148 MB.
[2025-02-27T13:48:53.064Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:49:04.961Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:49:14.952Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:49:23.293Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:49:30.196Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:49:35.879Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:49:42.868Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:49:48.508Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:49:49.293Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:49:49.293Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:49:50.077Z] Movies recommended for you:
[2025-02-27T13:49:50.077Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:49:50.077Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:49:50.077Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (67183.170 ms) ======
[2025-02-27T13:49:50.077Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-27T13:49:50.077Z] GC before operation: completed in 441.216 ms, heap usage 78.009 MB -> 59.328 MB.
[2025-02-27T13:50:01.914Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:50:11.886Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:50:23.847Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:50:33.867Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:50:39.542Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:50:47.950Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:50:54.134Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:51:01.135Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:51:01.922Z] 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.
[2025-02-27T13:51:01.922Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:51:02.712Z] Movies recommended for you:
[2025-02-27T13:51:02.713Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:51:02.713Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:51:02.713Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (72240.353 ms) ======
[2025-02-27T13:51:02.713Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-27T13:51:02.713Z] GC before operation: completed in 386.782 ms, heap usage 115.946 MB -> 50.040 MB.
[2025-02-27T13:51:14.570Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:51:24.601Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:51:36.386Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:51:46.362Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:51:52.055Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:51:57.147Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:52:04.171Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:52:07.817Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:52:08.624Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:52:09.420Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:52:09.420Z] Movies recommended for you:
[2025-02-27T13:52:09.420Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:52:09.420Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:52:09.420Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (66377.389 ms) ======
[2025-02-27T13:52:09.421Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-27T13:52:09.421Z] GC before operation: completed in 316.063 ms, heap usage 189.339 MB -> 50.146 MB.
[2025-02-27T13:52:17.810Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:52:26.161Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:52:36.145Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:52:43.105Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:52:47.649Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:52:53.306Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:52:57.072Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:53:02.735Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:53:02.735Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:53:02.735Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:53:03.513Z] Movies recommended for you:
[2025-02-27T13:53:03.513Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:53:03.513Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:53:03.513Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (53588.823 ms) ======
[2025-02-27T13:53:03.513Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-27T13:53:03.513Z] GC before operation: completed in 334.463 ms, heap usage 169.912 MB -> 50.325 MB.
[2025-02-27T13:53:11.883Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T13:53:21.888Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T13:53:30.289Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T13:53:40.277Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T13:53:45.971Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T13:53:51.695Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T13:53:57.322Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T13:54:03.547Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T13:54:04.336Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T13:54:04.336Z] The best model improves the baseline by 14.52%.
[2025-02-27T13:54:05.131Z] Movies recommended for you:
[2025-02-27T13:54:05.131Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T13:54:05.131Z] There is no way to check that no silent failure occurred.
[2025-02-27T13:54:05.131Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (61448.280 ms) ======
[2025-02-27T13:54:06.728Z] -----------------------------------
[2025-02-27T13:54:06.728Z] renaissance-movie-lens_0_PASSED
[2025-02-27T13:54:06.728Z] -----------------------------------
[2025-02-27T13:54:06.728Z]
[2025-02-27T13:54:06.728Z] TEST TEARDOWN:
[2025-02-27T13:54:06.728Z] Nothing to be done for teardown.
[2025-02-27T13:54:06.728Z] renaissance-movie-lens_0 Finish Time: Thu Feb 27 13:54:06 2025 Epoch Time (ms): 1740664446453