renaissance-movie-lens_0
[2024-09-25T21:01:10.210Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T21:01:10.210Z] ===============================================
[2024-09-25T21:01:10.210Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 21:01:09 2024 Epoch Time (ms): 1727298069267
[2024-09-25T21:01:10.210Z] variation: NoOptions
[2024-09-25T21:01:10.210Z] JVM_OPTIONS:
[2024-09-25T21:01:10.210Z] { \
[2024-09-25T21:01:10.210Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T21:01:10.210Z] echo "Nothing to be done for setup."; \
[2024-09-25T21:01:10.210Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17272970993569/renaissance-movie-lens_0"; \
[2024-09-25T21:01:10.210Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17272970993569/renaissance-movie-lens_0"; \
[2024-09-25T21:01:10.210Z] echo ""; echo "TESTING:"; \
[2024-09-25T21:01:10.210Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_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_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17272970993569/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T21:01:10.210Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17272970993569/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T21:01:10.210Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T21:01:10.210Z] echo "Nothing to be done for teardown."; \
[2024-09-25T21:01:10.210Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17272970993569/TestTargetResult";
[2024-09-25T21:01:10.210Z]
[2024-09-25T21:01:10.210Z] TEST SETUP:
[2024-09-25T21:01:10.210Z] Nothing to be done for setup.
[2024-09-25T21:01:10.210Z]
[2024-09-25T21:01:10.210Z] TESTING:
[2024-09-25T21:01:13.162Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T21:01:16.110Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-25T21:01:20.179Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T21:01:20.179Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T21:01:20.179Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T21:01:20.179Z] GC before operation: completed in 81.391 ms, heap usage 74.042 MB -> 36.435 MB.
[2024-09-25T21:01:26.730Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:01:30.841Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:01:33.790Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:01:36.751Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:01:38.659Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:01:39.589Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:01:41.503Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:01:43.418Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:01:43.418Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:01:43.418Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:01:43.418Z] Movies recommended for you:
[2024-09-25T21:01:43.418Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:01:43.418Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:01:43.418Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23465.053 ms) ======
[2024-09-25T21:01:43.418Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T21:01:43.418Z] GC before operation: completed in 122.494 ms, heap usage 273.586 MB -> 48.764 MB.
[2024-09-25T21:01:46.384Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:01:49.335Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:01:52.296Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:01:54.982Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:01:55.928Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:01:57.837Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:01:58.768Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:02:00.676Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:02:00.676Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:02:00.676Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:02:00.677Z] Movies recommended for you:
[2024-09-25T21:02:00.677Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:02:00.677Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:02:00.677Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17239.761 ms) ======
[2024-09-25T21:02:00.677Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T21:02:00.677Z] GC before operation: completed in 103.231 ms, heap usage 174.783 MB -> 49.115 MB.
[2024-09-25T21:02:03.625Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:02:06.572Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:02:08.492Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:02:10.435Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:02:12.349Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:02:14.375Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:02:15.304Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:02:17.213Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:02:17.213Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:02:17.213Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:02:17.213Z] Movies recommended for you:
[2024-09-25T21:02:17.213Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:02:17.213Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:02:17.213Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16091.366 ms) ======
[2024-09-25T21:02:17.213Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T21:02:17.213Z] GC before operation: completed in 103.343 ms, heap usage 173.261 MB -> 49.364 MB.
[2024-09-25T21:02:19.125Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:02:22.074Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:02:23.982Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:02:25.899Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:02:27.812Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:02:29.725Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:02:30.661Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:02:31.595Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:02:32.524Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:02:32.524Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:02:32.524Z] Movies recommended for you:
[2024-09-25T21:02:32.524Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:02:32.524Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:02:32.524Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15125.787 ms) ======
[2024-09-25T21:02:32.524Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T21:02:32.524Z] GC before operation: completed in 91.989 ms, heap usage 298.528 MB -> 49.798 MB.
[2024-09-25T21:02:34.480Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:02:37.426Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:02:39.338Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:02:41.354Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:02:43.263Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:02:44.209Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:02:46.116Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:02:47.045Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:02:47.975Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:02:47.975Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:02:47.975Z] Movies recommended for you:
[2024-09-25T21:02:47.975Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:02:47.975Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:02:47.975Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15407.713 ms) ======
[2024-09-25T21:02:47.975Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T21:02:47.975Z] GC before operation: completed in 93.086 ms, heap usage 261.124 MB -> 49.947 MB.
[2024-09-25T21:02:49.913Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:02:52.860Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:02:54.778Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:02:56.692Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:02:58.624Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:02:59.553Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:03:00.489Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:03:02.396Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:03:02.396Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:03:02.396Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:03:02.396Z] Movies recommended for you:
[2024-09-25T21:03:02.396Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:03:02.396Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:03:02.396Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14651.873 ms) ======
[2024-09-25T21:03:02.396Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T21:03:02.396Z] GC before operation: completed in 96.028 ms, heap usage 184.289 MB -> 49.861 MB.
[2024-09-25T21:03:05.367Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:03:07.361Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:03:10.301Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:03:11.636Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:03:13.547Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:03:14.490Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:03:16.414Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:03:17.344Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:03:17.344Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:03:17.344Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:03:17.344Z] Movies recommended for you:
[2024-09-25T21:03:17.344Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:03:17.344Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:03:17.344Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15038.099 ms) ======
[2024-09-25T21:03:17.344Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T21:03:18.272Z] GC before operation: completed in 107.237 ms, heap usage 204.900 MB -> 49.992 MB.
[2024-09-25T21:03:20.183Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:03:23.131Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:03:25.044Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:03:26.965Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:03:28.875Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:03:29.803Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:03:31.758Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:03:32.686Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:03:32.686Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:03:32.686Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:03:32.686Z] Movies recommended for you:
[2024-09-25T21:03:32.686Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:03:32.686Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:03:32.686Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15051.356 ms) ======
[2024-09-25T21:03:32.686Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T21:03:32.686Z] GC before operation: completed in 94.877 ms, heap usage 66.602 MB -> 50.136 MB.
[2024-09-25T21:03:35.642Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:03:37.548Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:03:39.461Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:03:41.372Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:03:43.284Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:03:44.212Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:03:46.124Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:03:47.051Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:03:47.983Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:03:47.983Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:03:47.983Z] Movies recommended for you:
[2024-09-25T21:03:47.983Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:03:47.983Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:03:47.983Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14715.691 ms) ======
[2024-09-25T21:03:47.983Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T21:03:47.983Z] GC before operation: completed in 91.595 ms, heap usage 281.798 MB -> 50.143 MB.
[2024-09-25T21:03:49.890Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:03:51.797Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:03:54.745Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:03:56.662Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:03:57.591Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:03:59.496Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:04:00.440Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:04:02.349Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:04:02.349Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:04:02.349Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:04:02.349Z] Movies recommended for you:
[2024-09-25T21:04:02.349Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:04:02.349Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:04:02.349Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14636.874 ms) ======
[2024-09-25T21:04:02.349Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T21:04:02.349Z] GC before operation: completed in 92.351 ms, heap usage 99.432 MB -> 50.134 MB.
[2024-09-25T21:04:05.294Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:04:07.204Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:04:09.150Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:04:11.056Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:04:12.921Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:04:13.868Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:04:14.797Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:04:16.703Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:04:16.703Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:04:16.703Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:04:16.703Z] Movies recommended for you:
[2024-09-25T21:04:16.703Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:04:16.703Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:04:16.703Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14367.152 ms) ======
[2024-09-25T21:04:16.703Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T21:04:16.703Z] GC before operation: completed in 94.419 ms, heap usage 232.660 MB -> 49.921 MB.
[2024-09-25T21:04:19.661Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:04:21.570Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:04:23.490Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:04:26.443Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:04:27.380Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:04:28.308Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:04:30.213Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:04:31.148Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:04:31.148Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:04:31.148Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:04:32.076Z] Movies recommended for you:
[2024-09-25T21:04:32.076Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:04:32.076Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:04:32.076Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14665.917 ms) ======
[2024-09-25T21:04:32.076Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T21:04:32.076Z] GC before operation: completed in 88.736 ms, heap usage 79.259 MB -> 49.988 MB.
[2024-09-25T21:04:33.981Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:04:35.891Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:04:38.834Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:04:40.739Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:04:41.666Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:04:43.574Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:04:44.502Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:04:46.415Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:04:46.415Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:04:46.415Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:04:46.415Z] Movies recommended for you:
[2024-09-25T21:04:46.415Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:04:46.415Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:04:46.415Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14825.400 ms) ======
[2024-09-25T21:04:46.415Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T21:04:46.415Z] GC before operation: completed in 90.743 ms, heap usage 131.082 MB -> 50.195 MB.
[2024-09-25T21:04:49.371Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:04:51.278Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:04:53.184Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:04:55.094Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:04:57.002Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:04:57.931Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:04:59.881Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:05:00.810Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:05:00.810Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:05:00.810Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:05:00.810Z] Movies recommended for you:
[2024-09-25T21:05:00.810Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:05:00.810Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:05:00.810Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14496.286 ms) ======
[2024-09-25T21:05:00.810Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T21:05:01.739Z] GC before operation: completed in 104.728 ms, heap usage 156.933 MB -> 50.022 MB.
[2024-09-25T21:05:03.817Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:05:05.728Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:05:07.644Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:05:09.549Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:05:11.456Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:05:12.383Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:05:14.387Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:05:16.167Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:05:16.167Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:05:16.167Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:05:16.167Z] Movies recommended for you:
[2024-09-25T21:05:16.167Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:05:16.167Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:05:16.167Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14374.907 ms) ======
[2024-09-25T21:05:16.167Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T21:05:17.336Z] GC before operation: completed in 102.509 ms, heap usage 83.877 MB -> 50.089 MB.
[2024-09-25T21:05:18.264Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:05:20.170Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:05:22.078Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:05:24.002Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:05:25.909Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:05:26.839Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:05:28.758Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:05:29.692Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:05:29.692Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:05:29.692Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:05:29.692Z] Movies recommended for you:
[2024-09-25T21:05:29.692Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:05:29.692Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:05:29.692Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14349.407 ms) ======
[2024-09-25T21:05:29.692Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T21:05:30.623Z] GC before operation: completed in 94.784 ms, heap usage 348.442 MB -> 50.441 MB.
[2024-09-25T21:05:32.534Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:05:34.480Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:05:37.432Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:05:39.340Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:05:40.279Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:05:41.214Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:05:43.156Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:05:44.089Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:05:45.018Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:05:45.018Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:05:45.018Z] Movies recommended for you:
[2024-09-25T21:05:45.018Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:05:45.018Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:05:45.018Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14557.489 ms) ======
[2024-09-25T21:05:45.018Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T21:05:45.018Z] GC before operation: completed in 93.749 ms, heap usage 228.769 MB -> 50.093 MB.
[2024-09-25T21:05:46.930Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:05:49.895Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:05:51.816Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:05:53.728Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:05:54.661Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:05:56.569Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:05:57.497Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:05:59.404Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:05:59.404Z] 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.
[2024-09-25T21:05:59.404Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:05:59.404Z] Movies recommended for you:
[2024-09-25T21:05:59.404Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:05:59.404Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:05:59.404Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14620.622 ms) ======
[2024-09-25T21:05:59.404Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T21:05:59.404Z] GC before operation: completed in 97.003 ms, heap usage 70.760 MB -> 50.045 MB.
[2024-09-25T21:06:01.310Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:06:04.261Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:06:06.172Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:06:08.082Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:06:09.042Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:06:10.982Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:06:11.915Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:06:13.910Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:06:13.910Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:06:13.910Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:06:13.910Z] Movies recommended for you:
[2024-09-25T21:06:13.910Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:06:13.910Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:06:13.910Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14232.091 ms) ======
[2024-09-25T21:06:13.910Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T21:06:13.910Z] GC before operation: completed in 98.056 ms, heap usage 180.935 MB -> 50.328 MB.
[2024-09-25T21:06:16.863Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:06:18.780Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:06:20.692Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:06:23.644Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:06:23.644Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:06:25.557Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:06:26.489Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:06:28.396Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:06:28.396Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-25T21:06:28.396Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:06:28.396Z] Movies recommended for you:
[2024-09-25T21:06:28.396Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:06:28.396Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:06:28.396Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14507.782 ms) ======
[2024-09-25T21:06:29.333Z] -----------------------------------
[2024-09-25T21:06:29.333Z] renaissance-movie-lens_0_PASSED
[2024-09-25T21:06:29.333Z] -----------------------------------
[2024-09-25T21:06:29.333Z]
[2024-09-25T21:06:29.333Z] TEST TEARDOWN:
[2024-09-25T21:06:29.333Z] Nothing to be done for teardown.
[2024-09-25T21:06:29.333Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 21:06:28 2024 Epoch Time (ms): 1727298388488