renaissance-movie-lens_0
[2024-12-04T21:55:11.133Z] Running test renaissance-movie-lens_0 ...
[2024-12-04T21:55:11.133Z] ===============================================
[2024-12-04T21:55:11.134Z] renaissance-movie-lens_0 Start Time: Wed Dec 4 21:55:11 2024 Epoch Time (ms): 1733349311060
[2024-12-04T21:55:11.134Z] variation: NoOptions
[2024-12-04T21:55:11.134Z] JVM_OPTIONS:
[2024-12-04T21:55:11.134Z] { \
[2024-12-04T21:55:11.134Z] echo ""; echo "TEST SETUP:"; \
[2024-12-04T21:55:11.134Z] echo "Nothing to be done for setup."; \
[2024-12-04T21:55:11.134Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17333482907636/renaissance-movie-lens_0"; \
[2024-12-04T21:55:11.134Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17333482907636/renaissance-movie-lens_0"; \
[2024-12-04T21:55:11.134Z] echo ""; echo "TESTING:"; \
[2024-12-04T21:55:11.134Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17333482907636/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-04T21:55:11.134Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17333482907636/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-04T21:55:11.134Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-04T21:55:11.134Z] echo "Nothing to be done for teardown."; \
[2024-12-04T21:55:11.134Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17333482907636/TestTargetResult";
[2024-12-04T21:55:11.134Z]
[2024-12-04T21:55:11.134Z] TEST SETUP:
[2024-12-04T21:55:11.134Z] Nothing to be done for setup.
[2024-12-04T21:55:11.134Z]
[2024-12-04T21:55:11.134Z] TESTING:
[2024-12-04T21:55:15.216Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-04T21:55:17.701Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-12-04T21:55:22.899Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-04T21:55:22.899Z] Training: 60056, validation: 20285, test: 19854
[2024-12-04T21:55:22.899Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-04T21:55:22.899Z] GC before operation: completed in 70.107 ms, heap usage 58.518 MB -> 37.321 MB.
[2024-12-04T21:55:32.489Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:55:36.649Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:55:40.922Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:55:45.120Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:55:47.608Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:55:49.449Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:55:51.982Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:55:53.860Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:55:53.860Z] 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.
[2024-12-04T21:55:53.860Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:55:54.250Z] Movies recommended for you:
[2024-12-04T21:55:54.250Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:55:54.250Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:55:54.250Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (31314.861 ms) ======
[2024-12-04T21:55:54.250Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-04T21:55:54.250Z] GC before operation: completed in 74.577 ms, heap usage 163.666 MB -> 55.580 MB.
[2024-12-04T21:55:57.472Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:56:00.768Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:56:04.033Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:56:07.260Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:56:09.100Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:56:10.951Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:56:13.000Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:56:14.331Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:56:14.705Z] 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.
[2024-12-04T21:56:14.705Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:56:15.075Z] Movies recommended for you:
[2024-12-04T21:56:15.075Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:56:15.075Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:56:15.075Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20741.084 ms) ======
[2024-12-04T21:56:15.075Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-04T21:56:15.075Z] GC before operation: completed in 69.845 ms, heap usage 351.372 MB -> 49.935 MB.
[2024-12-04T21:56:18.321Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:56:21.601Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:56:24.132Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:56:27.382Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:56:29.260Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:56:31.133Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:56:33.091Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:56:34.391Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:56:34.814Z] 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.
[2024-12-04T21:56:34.814Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:56:35.186Z] Movies recommended for you:
[2024-12-04T21:56:35.186Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:56:35.186Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:56:35.186Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (19953.859 ms) ======
[2024-12-04T21:56:35.186Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-04T21:56:35.186Z] GC before operation: completed in 78.601 ms, heap usage 95.073 MB -> 49.996 MB.
[2024-12-04T21:56:37.688Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:56:40.356Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:56:43.599Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:56:46.136Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:56:47.961Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:56:49.269Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:56:51.159Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:56:53.035Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:56:53.035Z] 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.
[2024-12-04T21:56:53.407Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:56:53.407Z] Movies recommended for you:
[2024-12-04T21:56:53.407Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:56:53.407Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:56:53.407Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18302.345 ms) ======
[2024-12-04T21:56:53.407Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-04T21:56:53.407Z] GC before operation: completed in 74.020 ms, heap usage 131.948 MB -> 50.386 MB.
[2024-12-04T21:56:56.680Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:56:59.169Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:57:01.690Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:57:04.999Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:57:06.296Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:57:08.147Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:57:10.011Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:57:11.409Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:57:11.799Z] 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.
[2024-12-04T21:57:11.799Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:57:12.176Z] Movies recommended for you:
[2024-12-04T21:57:12.177Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:57:12.177Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:57:12.177Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (18529.006 ms) ======
[2024-12-04T21:57:12.177Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-04T21:57:12.177Z] GC before operation: completed in 74.632 ms, heap usage 166.966 MB -> 50.703 MB.
[2024-12-04T21:57:14.669Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:57:17.916Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:57:21.189Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:57:23.709Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:57:25.027Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:57:26.904Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:57:28.750Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:57:30.068Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:57:30.460Z] 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.
[2024-12-04T21:57:30.460Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:57:30.460Z] Movies recommended for you:
[2024-12-04T21:57:30.460Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:57:30.460Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:57:30.460Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18549.586 ms) ======
[2024-12-04T21:57:30.460Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-04T21:57:30.829Z] GC before operation: completed in 75.477 ms, heap usage 264.076 MB -> 50.720 MB.
[2024-12-04T21:57:33.329Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:57:35.859Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:57:39.126Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:57:41.639Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:57:42.932Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:57:44.789Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:57:46.090Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:57:47.979Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:57:47.979Z] 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.
[2024-12-04T21:57:47.979Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:57:47.979Z] Movies recommended for you:
[2024-12-04T21:57:47.979Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:57:47.979Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:57:47.979Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17452.445 ms) ======
[2024-12-04T21:57:47.979Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-04T21:57:48.349Z] GC before operation: completed in 76.462 ms, heap usage 288.282 MB -> 50.846 MB.
[2024-12-04T21:57:50.827Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:57:53.337Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:57:55.808Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:57:58.326Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:58:00.181Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:58:01.480Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:58:03.330Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:58:04.699Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:58:05.070Z] 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.
[2024-12-04T21:58:05.070Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:58:05.070Z] Movies recommended for you:
[2024-12-04T21:58:05.070Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:58:05.070Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:58:05.070Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16816.818 ms) ======
[2024-12-04T21:58:05.070Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-04T21:58:05.070Z] GC before operation: completed in 79.620 ms, heap usage 319.352 MB -> 51.190 MB.
[2024-12-04T21:58:07.557Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:58:10.174Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:58:12.657Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:58:15.190Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:58:17.050Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:58:18.343Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:58:20.229Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:58:21.519Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:58:21.895Z] 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.
[2024-12-04T21:58:21.895Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:58:21.895Z] Movies recommended for you:
[2024-12-04T21:58:21.895Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:58:21.895Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:58:21.895Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16867.988 ms) ======
[2024-12-04T21:58:21.895Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-04T21:58:21.895Z] GC before operation: completed in 72.074 ms, heap usage 99.580 MB -> 51.477 MB.
[2024-12-04T21:58:25.156Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:58:27.007Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:58:30.269Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:58:32.779Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:58:34.069Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:58:35.361Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:58:37.349Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:58:38.739Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:58:39.133Z] 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.
[2024-12-04T21:58:39.133Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:58:39.133Z] Movies recommended for you:
[2024-12-04T21:58:39.133Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:58:39.133Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:58:39.133Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17127.453 ms) ======
[2024-12-04T21:58:39.133Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-04T21:58:39.133Z] GC before operation: completed in 77.728 ms, heap usage 256.820 MB -> 51.052 MB.
[2024-12-04T21:58:41.650Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:58:44.951Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:58:47.468Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:58:50.048Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:58:51.331Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:58:53.191Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:58:54.483Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:58:56.352Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:58:56.352Z] 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.
[2024-12-04T21:58:56.352Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:58:56.352Z] Movies recommended for you:
[2024-12-04T21:58:56.352Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:58:56.352Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:58:56.352Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17250.328 ms) ======
[2024-12-04T21:58:56.352Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-04T21:58:56.729Z] GC before operation: completed in 82.854 ms, heap usage 287.828 MB -> 50.786 MB.
[2024-12-04T21:58:59.245Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:59:01.737Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:59:04.254Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:59:06.769Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:59:08.051Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:59:09.896Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:59:11.751Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:59:13.075Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:59:13.075Z] 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.
[2024-12-04T21:59:13.075Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:59:13.460Z] Movies recommended for you:
[2024-12-04T21:59:13.460Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:59:13.460Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:59:13.460Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16758.488 ms) ======
[2024-12-04T21:59:13.460Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-04T21:59:13.460Z] GC before operation: completed in 72.262 ms, heap usage 276.652 MB -> 50.935 MB.
[2024-12-04T21:59:15.957Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:59:18.448Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:59:20.950Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:59:23.436Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:59:25.293Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:59:27.141Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:59:28.429Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:59:30.405Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:59:30.405Z] 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.
[2024-12-04T21:59:30.405Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:59:30.789Z] Movies recommended for you:
[2024-12-04T21:59:30.789Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:59:30.789Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:59:30.789Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17214.035 ms) ======
[2024-12-04T21:59:30.789Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-04T21:59:30.789Z] GC before operation: completed in 73.907 ms, heap usage 287.448 MB -> 51.156 MB.
[2024-12-04T21:59:33.272Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:59:35.748Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:59:38.215Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:59:40.709Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:59:42.572Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:59:43.869Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:59:45.712Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:59:47.545Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:59:47.545Z] 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.
[2024-12-04T21:59:47.545Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:59:47.545Z] Movies recommended for you:
[2024-12-04T21:59:47.545Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:59:47.545Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:59:47.545Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16899.801 ms) ======
[2024-12-04T21:59:47.545Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-04T21:59:47.545Z] GC before operation: completed in 76.550 ms, heap usage 99.554 MB -> 51.748 MB.
[2024-12-04T21:59:50.064Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:59:52.552Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:59:55.820Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:59:57.669Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:59:59.614Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T22:00:00.920Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T22:00:02.269Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T22:00:04.155Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T22:00:04.155Z] 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.
[2024-12-04T22:00:04.155Z] The best model improves the baseline by 14.52%.
[2024-12-04T22:00:04.535Z] Movies recommended for you:
[2024-12-04T22:00:04.535Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T22:00:04.535Z] There is no way to check that no silent failure occurred.
[2024-12-04T22:00:04.535Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16650.451 ms) ======
[2024-12-04T22:00:04.535Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-04T22:00:04.535Z] GC before operation: completed in 80.650 ms, heap usage 79.546 MB -> 53.475 MB.
[2024-12-04T22:00:07.017Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T22:00:09.508Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T22:00:11.999Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T22:00:14.499Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T22:00:16.398Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T22:00:17.689Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T22:00:19.571Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T22:00:21.503Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T22:00:21.504Z] 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.
[2024-12-04T22:00:21.504Z] The best model improves the baseline by 14.52%.
[2024-12-04T22:00:21.504Z] Movies recommended for you:
[2024-12-04T22:00:21.504Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T22:00:21.504Z] There is no way to check that no silent failure occurred.
[2024-12-04T22:00:21.504Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17199.151 ms) ======
[2024-12-04T22:00:21.504Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-04T22:00:21.873Z] GC before operation: completed in 82.940 ms, heap usage 441.019 MB -> 54.572 MB.
[2024-12-04T22:00:24.370Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T22:00:26.975Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T22:00:29.476Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T22:00:31.965Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T22:00:33.287Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T22:00:35.121Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T22:00:36.412Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T22:00:37.699Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T22:00:38.095Z] 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.
[2024-12-04T22:00:38.095Z] The best model improves the baseline by 14.52%.
[2024-12-04T22:00:38.474Z] Movies recommended for you:
[2024-12-04T22:00:38.474Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T22:00:38.474Z] There is no way to check that no silent failure occurred.
[2024-12-04T22:00:38.474Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16576.797 ms) ======
[2024-12-04T22:00:38.474Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-04T22:00:38.474Z] GC before operation: completed in 76.589 ms, heap usage 338.220 MB -> 51.100 MB.
[2024-12-04T22:00:40.976Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T22:00:43.524Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T22:00:46.021Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T22:00:48.519Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T22:00:49.827Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T22:00:51.690Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T22:00:52.978Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T22:00:54.997Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T22:00:54.997Z] 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.
[2024-12-04T22:00:54.997Z] The best model improves the baseline by 14.52%.
[2024-12-04T22:00:54.997Z] Movies recommended for you:
[2024-12-04T22:00:54.997Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T22:00:54.997Z] There is no way to check that no silent failure occurred.
[2024-12-04T22:00:54.997Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16790.073 ms) ======
[2024-12-04T22:00:54.997Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-04T22:00:55.367Z] GC before operation: completed in 75.969 ms, heap usage 257.919 MB -> 51.068 MB.
[2024-12-04T22:00:57.880Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T22:01:00.387Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T22:01:02.854Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T22:01:05.328Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T22:01:06.652Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T22:01:08.569Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T22:01:09.860Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T22:01:11.779Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T22:01:11.779Z] 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.
[2024-12-04T22:01:11.779Z] The best model improves the baseline by 14.52%.
[2024-12-04T22:01:11.779Z] Movies recommended for you:
[2024-12-04T22:01:11.779Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T22:01:11.779Z] There is no way to check that no silent failure occurred.
[2024-12-04T22:01:11.779Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16673.037 ms) ======
[2024-12-04T22:01:11.779Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-04T22:01:12.150Z] GC before operation: completed in 80.154 ms, heap usage 252.846 MB -> 51.309 MB.
[2024-12-04T22:01:14.778Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T22:01:20.560Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T22:01:20.560Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T22:01:21.871Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T22:01:23.729Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T22:01:25.216Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T22:01:26.512Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T22:01:28.355Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T22:01:28.355Z] 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.
[2024-12-04T22:01:28.355Z] The best model improves the baseline by 14.52%.
[2024-12-04T22:01:28.735Z] Movies recommended for you:
[2024-12-04T22:01:28.735Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T22:01:28.735Z] There is no way to check that no silent failure occurred.
[2024-12-04T22:01:28.735Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16600.566 ms) ======
[2024-12-04T22:01:29.104Z] -----------------------------------
[2024-12-04T22:01:29.104Z] renaissance-movie-lens_0_PASSED
[2024-12-04T22:01:29.104Z] -----------------------------------
[2024-12-04T22:01:29.104Z]
[2024-12-04T22:01:29.104Z] TEST TEARDOWN:
[2024-12-04T22:01:29.104Z] Nothing to be done for teardown.
[2024-12-04T22:01:29.104Z] renaissance-movie-lens_0 Finish Time: Wed Dec 4 22:01:29 2024 Epoch Time (ms): 1733349689060