renaissance-movie-lens_0
[2024-09-25T23:34:40.237Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T23:34:40.237Z] ===============================================
[2024-09-25T23:34:40.237Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 23:34:38 2024 Epoch Time (ms): 1727307278931
[2024-09-25T23:34:40.237Z] variation: NoOptions
[2024-09-25T23:34:40.237Z] JVM_OPTIONS:
[2024-09-25T23:34:40.237Z] { \
[2024-09-25T23:34:40.237Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T23:34:40.238Z] echo "Nothing to be done for setup."; \
[2024-09-25T23:34:40.238Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17273013765094/renaissance-movie-lens_0"; \
[2024-09-25T23:34:40.238Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17273013765094/renaissance-movie-lens_0"; \
[2024-09-25T23:34:40.238Z] echo ""; echo "TESTING:"; \
[2024-09-25T23:34:40.238Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-11.0.25+7/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_17273013765094/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T23:34:40.238Z] 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_17273013765094/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T23:34:40.238Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T23:34:40.238Z] echo "Nothing to be done for teardown."; \
[2024-09-25T23:34:40.238Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17273013765094/TestTargetResult";
[2024-09-25T23:34:40.238Z]
[2024-09-25T23:34:40.238Z] TEST SETUP:
[2024-09-25T23:34:40.238Z] Nothing to be done for setup.
[2024-09-25T23:34:40.238Z]
[2024-09-25T23:34:40.238Z] TESTING:
[2024-09-25T23:34:51.760Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T23:35:03.380Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-25T23:35:29.292Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T23:35:30.052Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T23:35:30.052Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T23:35:30.822Z] GC before operation: completed in 547.496 ms, heap usage 94.345 MB -> 36.675 MB.
[2024-09-25T23:36:19.792Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:36:50.590Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:37:16.643Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:37:42.228Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:37:52.037Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:38:04.149Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:38:17.860Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:38:27.646Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:38:28.404Z] 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.
[2024-09-25T23:38:29.156Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:38:29.906Z] Movies recommended for you:
[2024-09-25T23:38:29.906Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:38:29.907Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:38:29.907Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (179399.587 ms) ======
[2024-09-25T23:38:29.907Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T23:38:30.686Z] GC before operation: completed in 767.072 ms, heap usage 226.475 MB -> 49.767 MB.
[2024-09-25T23:38:52.509Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:39:11.205Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:39:29.869Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:39:46.407Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:39:56.204Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:40:05.970Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:40:15.778Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:40:27.279Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:40:28.821Z] 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.
[2024-09-25T23:40:28.821Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:40:29.569Z] Movies recommended for you:
[2024-09-25T23:40:29.569Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:40:29.569Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:40:29.569Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (118498.056 ms) ======
[2024-09-25T23:40:29.569Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T23:40:30.319Z] GC before operation: completed in 654.556 ms, heap usage 270.149 MB -> 49.284 MB.
[2024-09-25T23:40:48.990Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:41:05.063Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:41:21.596Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:41:35.205Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:41:46.829Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:41:56.551Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:42:06.313Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:42:16.101Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:42:17.677Z] 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.
[2024-09-25T23:42:17.677Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:42:18.429Z] Movies recommended for you:
[2024-09-25T23:42:18.429Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:42:18.429Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:42:18.430Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (108717.751 ms) ======
[2024-09-25T23:42:18.430Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T23:42:19.183Z] GC before operation: completed in 665.438 ms, heap usage 298.882 MB -> 49.628 MB.
[2024-09-25T23:42:38.401Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:42:51.958Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:43:07.913Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:43:21.740Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:43:29.916Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:43:38.176Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:43:46.344Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:43:54.500Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:43:55.752Z] 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.
[2024-09-25T23:43:56.511Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:43:56.511Z] Movies recommended for you:
[2024-09-25T23:43:56.511Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:43:56.511Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:43:56.511Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (97439.616 ms) ======
[2024-09-25T23:43:56.511Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T23:43:57.293Z] GC before operation: completed in 529.092 ms, heap usage 300.948 MB -> 49.956 MB.
[2024-09-25T23:44:10.883Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:44:24.744Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:44:40.879Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:44:56.870Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:45:06.724Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:45:17.271Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:45:26.991Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:45:35.180Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:45:35.952Z] 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.
[2024-09-25T23:45:36.709Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:45:36.710Z] Movies recommended for you:
[2024-09-25T23:45:36.710Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:45:36.710Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:45:36.710Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (99666.398 ms) ======
[2024-09-25T23:45:36.710Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T23:45:37.488Z] GC before operation: completed in 761.374 ms, heap usage 202.759 MB -> 50.023 MB.
[2024-09-25T23:45:53.500Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:46:07.115Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:46:25.803Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:46:39.361Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:46:49.333Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:46:59.315Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:47:07.571Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:47:17.468Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:47:18.219Z] 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.
[2024-09-25T23:47:18.219Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:47:18.972Z] Movies recommended for you:
[2024-09-25T23:47:18.972Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:47:18.972Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:47:18.972Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (101573.261 ms) ======
[2024-09-25T23:47:18.972Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T23:47:19.722Z] GC before operation: completed in 762.066 ms, heap usage 84.408 MB -> 49.871 MB.
[2024-09-25T23:47:35.632Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:47:54.295Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:48:07.900Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:48:22.215Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:48:31.979Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:48:40.144Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:48:49.875Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:48:59.627Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:49:00.377Z] 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.
[2024-09-25T23:49:00.377Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:49:01.140Z] Movies recommended for you:
[2024-09-25T23:49:01.140Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:49:01.140Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:49:01.140Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (100838.449 ms) ======
[2024-09-25T23:49:01.140Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T23:49:01.140Z] GC before operation: completed in 552.658 ms, heap usage 223.685 MB -> 50.136 MB.
[2024-09-25T23:49:17.146Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:49:30.828Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:49:44.669Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:49:56.309Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:50:06.134Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:50:11.864Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:50:21.794Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:50:31.507Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:50:32.257Z] 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.
[2024-09-25T23:50:32.257Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:50:33.004Z] Movies recommended for you:
[2024-09-25T23:50:33.004Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:50:33.004Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:50:33.004Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (91520.529 ms) ======
[2024-09-25T23:50:33.004Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T23:50:33.759Z] GC before operation: completed in 719.758 ms, heap usage 69.919 MB -> 53.770 MB.
[2024-09-25T23:50:49.768Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:51:04.054Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:51:17.844Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:51:31.448Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:51:39.584Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:51:47.844Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:51:55.996Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:52:04.148Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:52:05.696Z] 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.
[2024-09-25T23:52:05.696Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:52:05.696Z] Movies recommended for you:
[2024-09-25T23:52:05.696Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:52:05.696Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:52:05.696Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (92214.589 ms) ======
[2024-09-25T23:52:05.696Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T23:52:06.450Z] GC before operation: completed in 600.920 ms, heap usage 252.336 MB -> 50.306 MB.
[2024-09-25T23:52:20.572Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:52:34.172Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:52:50.109Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:53:01.619Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:53:11.404Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:53:18.227Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:53:27.968Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:53:35.285Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:53:36.852Z] 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.
[2024-09-25T23:53:37.620Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:53:37.620Z] Movies recommended for you:
[2024-09-25T23:53:37.620Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:53:37.620Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:53:37.620Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (91206.579 ms) ======
[2024-09-25T23:53:37.620Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T23:53:38.392Z] GC before operation: completed in 588.837 ms, heap usage 250.302 MB -> 50.390 MB.
[2024-09-25T23:53:52.040Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:54:05.768Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:54:19.395Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:54:33.092Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:54:39.972Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:54:49.752Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:54:58.076Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:55:06.300Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:55:07.052Z] 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.
[2024-09-25T23:55:07.809Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:55:07.809Z] Movies recommended for you:
[2024-09-25T23:55:07.809Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:55:07.809Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:55:07.809Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (89783.486 ms) ======
[2024-09-25T23:55:07.809Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T23:55:08.561Z] GC before operation: completed in 662.373 ms, heap usage 81.529 MB -> 49.927 MB.
[2024-09-25T23:55:24.639Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:55:38.212Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:55:51.779Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:56:05.370Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:56:10.996Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:56:18.180Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:56:26.332Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:56:34.485Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:56:35.240Z] 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.
[2024-09-25T23:56:35.240Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:56:35.993Z] Movies recommended for you:
[2024-09-25T23:56:35.993Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:56:35.993Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:56:35.993Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (86958.108 ms) ======
[2024-09-25T23:56:35.993Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T23:56:35.993Z] GC before operation: completed in 483.005 ms, heap usage 175.212 MB -> 50.260 MB.
[2024-09-25T23:56:49.669Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:57:03.240Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:57:16.823Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:57:30.407Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:57:39.155Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:57:45.975Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:57:55.810Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:58:05.679Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:58:07.232Z] 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.
[2024-09-25T23:58:07.232Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:58:07.232Z] Movies recommended for you:
[2024-09-25T23:58:07.232Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:58:07.233Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:58:07.233Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (91146.805 ms) ======
[2024-09-25T23:58:07.233Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T23:58:07.985Z] GC before operation: completed in 696.337 ms, heap usage 213.562 MB -> 50.415 MB.
[2024-09-25T23:58:23.909Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:58:37.608Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:58:51.175Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:59:04.877Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:59:13.160Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:59:21.423Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:59:29.573Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:59:37.757Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:59:39.331Z] 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.
[2024-09-25T23:59:39.332Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:59:40.140Z] Movies recommended for you:
[2024-09-25T23:59:40.140Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:59:40.140Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:59:40.140Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (91777.472 ms) ======
[2024-09-25T23:59:40.140Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T23:59:40.140Z] GC before operation: completed in 468.869 ms, heap usage 323.057 MB -> 49.098 MB.
[2024-09-25T23:59:51.649Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T00:00:03.137Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T00:00:17.273Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T00:00:30.856Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T00:00:37.584Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T00:00:45.928Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T00:00:52.824Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T00:00:59.573Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T00:01:01.126Z] 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.
[2024-09-26T00:01:01.126Z] The best model improves the baseline by 14.52%.
[2024-09-26T00:01:01.876Z] Movies recommended for you:
[2024-09-26T00:01:01.876Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T00:01:01.876Z] There is no way to check that no silent failure occurred.
[2024-09-26T00:01:01.876Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (81378.748 ms) ======
[2024-09-26T00:01:01.876Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-26T00:01:01.876Z] GC before operation: completed in 486.798 ms, heap usage 312.817 MB -> 48.046 MB.
[2024-09-26T00:01:13.352Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T00:01:29.723Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T00:01:43.298Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T00:01:56.978Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T00:02:04.199Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T00:02:14.064Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T00:02:22.188Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T00:02:30.347Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T00:02:31.905Z] 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.
[2024-09-26T00:02:31.905Z] The best model improves the baseline by 14.52%.
[2024-09-26T00:02:32.650Z] Movies recommended for you:
[2024-09-26T00:02:32.650Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T00:02:32.650Z] There is no way to check that no silent failure occurred.
[2024-09-26T00:02:32.650Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (90475.373 ms) ======
[2024-09-26T00:02:32.650Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-26T00:02:33.407Z] GC before operation: completed in 680.581 ms, heap usage 205.100 MB -> 48.059 MB.
[2024-09-26T00:02:47.752Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T00:03:01.346Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T00:03:14.992Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T00:03:28.559Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T00:03:35.322Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T00:03:43.490Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T00:03:51.672Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T00:03:59.837Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T00:04:01.398Z] 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.
[2024-09-26T00:04:01.398Z] The best model improves the baseline by 14.52%.
[2024-09-26T00:04:01.398Z] Movies recommended for you:
[2024-09-26T00:04:01.398Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T00:04:01.398Z] There is no way to check that no silent failure occurred.
[2024-09-26T00:04:01.398Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (88235.333 ms) ======
[2024-09-26T00:04:01.398Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-26T00:04:02.150Z] GC before operation: completed in 616.763 ms, heap usage 327.660 MB -> 48.398 MB.
[2024-09-26T00:04:15.251Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T00:04:28.820Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T00:04:42.467Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T00:04:56.008Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T00:05:05.760Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T00:05:12.544Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T00:05:22.917Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T00:05:31.209Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T00:05:31.960Z] 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.
[2024-09-26T00:05:31.960Z] The best model improves the baseline by 14.52%.
[2024-09-26T00:05:32.711Z] Movies recommended for you:
[2024-09-26T00:05:32.711Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T00:05:32.711Z] There is no way to check that no silent failure occurred.
[2024-09-26T00:05:32.711Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (90270.086 ms) ======
[2024-09-26T00:05:32.712Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-26T00:05:32.712Z] GC before operation: completed in 541.892 ms, heap usage 323.746 MB -> 47.949 MB.
[2024-09-26T00:05:46.291Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T00:05:59.884Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T00:06:13.456Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T00:06:27.032Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T00:06:36.760Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T00:06:43.727Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T00:06:52.740Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T00:07:00.963Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T00:07:02.518Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T00:07:03.272Z] The best model improves the baseline by 14.52%.
[2024-09-26T00:07:03.273Z] Movies recommended for you:
[2024-09-26T00:07:03.273Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T00:07:03.273Z] There is no way to check that no silent failure occurred.
[2024-09-26T00:07:03.273Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (90328.790 ms) ======
[2024-09-26T00:07:03.273Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-26T00:07:04.040Z] GC before operation: completed in 557.204 ms, heap usage 189.723 MB -> 47.947 MB.
[2024-09-26T00:07:17.759Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T00:07:31.306Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T00:07:44.828Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T00:07:56.328Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T00:08:03.056Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T00:08:10.353Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T00:08:18.633Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T00:08:26.785Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T00:08:29.204Z] 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.
[2024-09-26T00:08:29.204Z] The best model improves the baseline by 14.52%.
[2024-09-26T00:08:29.958Z] Movies recommended for you:
[2024-09-26T00:08:29.958Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T00:08:29.958Z] There is no way to check that no silent failure occurred.
[2024-09-26T00:08:29.958Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (85750.135 ms) ======
[2024-09-26T00:08:32.362Z] -----------------------------------
[2024-09-26T00:08:32.362Z] renaissance-movie-lens_0_PASSED
[2024-09-26T00:08:32.362Z] -----------------------------------
[2024-09-26T00:08:32.362Z]
[2024-09-26T00:08:32.362Z] TEST TEARDOWN:
[2024-09-26T00:08:32.362Z] Nothing to be done for teardown.
[2024-09-26T00:08:32.362Z] renaissance-movie-lens_0 Finish Time: Thu Sep 26 00:08:31 2024 Epoch Time (ms): 1727309311958