renaissance-movie-lens_0
[2024-10-02T21:49:34.797Z] Running test renaissance-movie-lens_0 ...
[2024-10-02T21:49:34.797Z] ===============================================
[2024-10-02T21:49:34.797Z] renaissance-movie-lens_0 Start Time: Wed Oct 2 21:49:34 2024 Epoch Time (ms): 1727905774447
[2024-10-02T21:49:34.797Z] variation: NoOptions
[2024-10-02T21:49:34.797Z] JVM_OPTIONS:
[2024-10-02T21:49:34.797Z] { \
[2024-10-02T21:49:34.797Z] echo ""; echo "TEST SETUP:"; \
[2024-10-02T21:49:34.797Z] echo "Nothing to be done for setup."; \
[2024-10-02T21:49:34.797Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279049463345/renaissance-movie-lens_0"; \
[2024-10-02T21:49:34.797Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279049463345/renaissance-movie-lens_0"; \
[2024-10-02T21:49:34.797Z] echo ""; echo "TESTING:"; \
[2024-10-02T21:49:34.797Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279049463345/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-02T21:49:34.797Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279049463345/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-02T21:49:34.797Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-02T21:49:34.797Z] echo "Nothing to be done for teardown."; \
[2024-10-02T21:49:34.797Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17279049463345/TestTargetResult";
[2024-10-02T21:49:34.797Z]
[2024-10-02T21:49:34.797Z] TEST SETUP:
[2024-10-02T21:49:34.797Z] Nothing to be done for setup.
[2024-10-02T21:49:34.797Z]
[2024-10-02T21:49:34.797Z] TESTING:
[2024-10-02T21:49:37.787Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-02T21:49:39.722Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-02T21:49:41.680Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-02T21:49:42.623Z] Training: 60056, validation: 20285, test: 19854
[2024-10-02T21:49:42.623Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-02T21:49:42.623Z] GC before operation: completed in 82.607 ms, heap usage 56.700 MB -> 37.210 MB.
[2024-10-02T21:49:46.734Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:49:49.721Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:49:52.726Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:49:55.739Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:49:56.684Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:49:58.619Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:49:59.561Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:50:01.494Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:50:01.494Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:50:01.494Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:50:01.494Z] Movies recommended for you:
[2024-10-02T21:50:01.494Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:50:01.494Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:50:01.494Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (19365.996 ms) ======
[2024-10-02T21:50:01.494Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-02T21:50:01.494Z] GC before operation: completed in 67.987 ms, heap usage 196.309 MB -> 53.325 MB.
[2024-10-02T21:50:04.479Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:50:06.411Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:50:09.428Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:50:11.361Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:50:12.304Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:50:13.245Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:50:15.193Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:50:16.143Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:50:16.143Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:50:16.143Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:50:16.143Z] Movies recommended for you:
[2024-10-02T21:50:16.143Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:50:16.143Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:50:16.143Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (14694.424 ms) ======
[2024-10-02T21:50:16.143Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-02T21:50:17.085Z] GC before operation: completed in 62.214 ms, heap usage 345.679 MB -> 49.876 MB.
[2024-10-02T21:50:19.027Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:50:20.962Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:50:22.897Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:50:24.835Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:50:27.629Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:50:27.629Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:50:28.651Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:50:30.587Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:50:30.587Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:50:30.587Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:50:30.587Z] Movies recommended for you:
[2024-10-02T21:50:30.587Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:50:30.587Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:50:30.587Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (13798.779 ms) ======
[2024-10-02T21:50:30.587Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-02T21:50:30.587Z] GC before operation: completed in 74.704 ms, heap usage 270.720 MB -> 50.161 MB.
[2024-10-02T21:50:32.521Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:50:34.456Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:50:36.390Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:50:38.348Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:50:40.290Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:50:41.233Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:50:42.176Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:50:43.119Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:50:44.062Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:50:44.062Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:50:44.062Z] Movies recommended for you:
[2024-10-02T21:50:44.062Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:50:44.062Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:50:44.062Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13352.568 ms) ======
[2024-10-02T21:50:44.062Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-02T21:50:44.062Z] GC before operation: completed in 60.376 ms, heap usage 249.916 MB -> 50.603 MB.
[2024-10-02T21:50:46.009Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:50:47.943Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:50:50.050Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:50:51.986Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:50:52.928Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:50:53.883Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:50:55.910Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:50:56.855Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:50:56.855Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:50:56.855Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:50:56.855Z] Movies recommended for you:
[2024-10-02T21:50:56.855Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:50:56.855Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:50:56.855Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13037.246 ms) ======
[2024-10-02T21:50:56.855Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-02T21:50:56.855Z] GC before operation: completed in 54.755 ms, heap usage 85.939 MB -> 50.501 MB.
[2024-10-02T21:50:58.796Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:51:00.744Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:51:02.676Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:51:04.610Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:51:05.560Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:51:06.507Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:51:08.442Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:51:09.385Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:51:09.385Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:51:09.385Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:51:09.385Z] Movies recommended for you:
[2024-10-02T21:51:09.385Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:51:09.385Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:51:09.385Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (12393.066 ms) ======
[2024-10-02T21:51:09.385Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-02T21:51:09.385Z] GC before operation: completed in 62.897 ms, heap usage 85.290 MB -> 50.542 MB.
[2024-10-02T21:51:11.323Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:51:13.257Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:51:15.193Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:51:17.129Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:51:18.070Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:51:19.014Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:51:19.957Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:51:21.891Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:51:21.891Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:51:21.891Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:51:21.891Z] Movies recommended for you:
[2024-10-02T21:51:21.891Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:51:21.891Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:51:21.891Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (12249.166 ms) ======
[2024-10-02T21:51:21.891Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-02T21:51:21.891Z] GC before operation: completed in 60.610 ms, heap usage 90.428 MB -> 54.056 MB.
[2024-10-02T21:51:23.825Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:51:25.632Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:51:27.611Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:51:28.554Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:51:30.486Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:51:31.427Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:51:32.371Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:51:33.312Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:51:33.312Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:51:33.312Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:51:34.256Z] Movies recommended for you:
[2024-10-02T21:51:34.256Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:51:34.256Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:51:34.256Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (11976.761 ms) ======
[2024-10-02T21:51:34.256Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-02T21:51:34.256Z] GC before operation: completed in 58.618 ms, heap usage 320.796 MB -> 51.269 MB.
[2024-10-02T21:51:35.198Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:51:37.135Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:51:39.069Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:51:41.003Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:51:41.945Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:51:42.889Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:51:43.831Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:51:45.767Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:51:45.767Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:51:45.767Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:51:45.767Z] Movies recommended for you:
[2024-10-02T21:51:45.767Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:51:45.767Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:51:45.767Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (11773.105 ms) ======
[2024-10-02T21:51:45.767Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-02T21:51:45.767Z] GC before operation: completed in 58.596 ms, heap usage 206.257 MB -> 50.937 MB.
[2024-10-02T21:51:47.707Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:51:49.649Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:51:51.585Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:51:52.528Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:51:53.472Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:51:55.563Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:51:56.506Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:51:57.455Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:51:57.455Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:51:57.455Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:51:57.455Z] Movies recommended for you:
[2024-10-02T21:51:57.455Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:51:57.455Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:51:57.455Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (11854.790 ms) ======
[2024-10-02T21:51:57.455Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-02T21:51:57.455Z] GC before operation: completed in 58.988 ms, heap usage 105.744 MB -> 50.889 MB.
[2024-10-02T21:51:59.388Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:52:01.320Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:52:03.258Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:52:05.192Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:52:06.133Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:52:07.074Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:52:08.015Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:52:09.953Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:52:09.953Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:52:09.953Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:52:09.953Z] Movies recommended for you:
[2024-10-02T21:52:09.953Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:52:09.953Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:52:09.953Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12237.840 ms) ======
[2024-10-02T21:52:09.953Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-02T21:52:09.953Z] GC before operation: completed in 82.891 ms, heap usage 87.363 MB -> 50.587 MB.
[2024-10-02T21:52:11.886Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:52:13.827Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:52:15.767Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:52:16.709Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:52:17.654Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:52:19.590Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:52:20.533Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:52:21.480Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:52:21.480Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:52:21.480Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:52:21.480Z] Movies recommended for you:
[2024-10-02T21:52:21.481Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:52:21.481Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:52:21.481Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (11845.410 ms) ======
[2024-10-02T21:52:21.481Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-02T21:52:21.481Z] GC before operation: completed in 61.211 ms, heap usage 86.741 MB -> 50.744 MB.
[2024-10-02T21:52:23.338Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:52:25.270Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:52:27.210Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:52:29.152Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:52:30.097Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:52:31.040Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:52:33.045Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:52:33.987Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:52:33.987Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:52:33.987Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:52:33.987Z] Movies recommended for you:
[2024-10-02T21:52:33.987Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:52:33.987Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:52:33.987Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (12025.925 ms) ======
[2024-10-02T21:52:33.987Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-02T21:52:33.987Z] GC before operation: completed in 72.958 ms, heap usage 88.613 MB -> 50.963 MB.
[2024-10-02T21:52:35.919Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:52:37.857Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:52:39.792Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:52:40.736Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:52:41.679Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:52:43.622Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:52:44.565Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:52:45.509Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:52:45.509Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:52:45.509Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:52:45.509Z] Movies recommended for you:
[2024-10-02T21:52:45.509Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:52:45.509Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:52:45.509Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (11803.233 ms) ======
[2024-10-02T21:52:45.509Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-02T21:52:45.509Z] GC before operation: completed in 72.236 ms, heap usage 86.732 MB -> 50.655 MB.
[2024-10-02T21:52:47.450Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:52:49.389Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:52:51.329Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:52:53.268Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:52:54.212Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:52:55.156Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:52:56.100Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:52:58.033Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:52:58.033Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:52:58.033Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:52:58.033Z] Movies recommended for you:
[2024-10-02T21:52:58.033Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:52:58.033Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:52:58.033Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12236.287 ms) ======
[2024-10-02T21:52:58.033Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-02T21:52:58.033Z] GC before operation: completed in 80.321 ms, heap usage 122.256 MB -> 50.871 MB.
[2024-10-02T21:52:59.966Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:53:01.900Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:53:03.838Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:53:05.770Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:53:06.712Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:53:07.654Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:53:08.601Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:53:09.545Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:53:10.488Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:53:10.488Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:53:10.488Z] Movies recommended for you:
[2024-10-02T21:53:10.488Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:53:10.488Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:53:10.488Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12148.209 ms) ======
[2024-10-02T21:53:10.488Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-02T21:53:10.488Z] GC before operation: completed in 74.633 ms, heap usage 434.150 MB -> 54.595 MB.
[2024-10-02T21:53:12.432Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:53:14.371Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:53:16.316Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:53:18.252Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:53:20.240Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:53:20.240Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:53:22.204Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:53:23.147Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:53:23.147Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:53:23.147Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:53:23.147Z] Movies recommended for you:
[2024-10-02T21:53:23.147Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:53:23.147Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:53:23.147Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13120.094 ms) ======
[2024-10-02T21:53:23.147Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-02T21:53:23.147Z] GC before operation: completed in 68.404 ms, heap usage 396.008 MB -> 53.455 MB.
[2024-10-02T21:53:26.206Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:53:28.148Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:53:30.086Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:53:32.083Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:53:33.027Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:53:33.970Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:53:35.907Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:53:36.850Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:53:36.850Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:53:36.850Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:53:36.850Z] Movies recommended for you:
[2024-10-02T21:53:36.850Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:53:36.850Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:53:36.850Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13524.372 ms) ======
[2024-10-02T21:53:36.850Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-02T21:53:36.850Z] GC before operation: completed in 65.331 ms, heap usage 380.051 MB -> 53.430 MB.
[2024-10-02T21:53:39.845Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:53:41.779Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:53:43.711Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:53:44.653Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:53:46.586Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:53:47.533Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:53:48.476Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:53:49.420Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:53:49.420Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:53:49.420Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:53:49.420Z] Movies recommended for you:
[2024-10-02T21:53:49.420Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:53:49.420Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:53:49.420Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12659.595 ms) ======
[2024-10-02T21:53:49.420Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-02T21:53:49.420Z] GC before operation: completed in 59.096 ms, heap usage 128.026 MB -> 51.130 MB.
[2024-10-02T21:53:51.359Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T21:53:53.295Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T21:53:55.233Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T21:53:57.171Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T21:53:58.120Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T21:54:00.071Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T21:54:01.012Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T21:54:01.953Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T21:54:01.953Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-02T21:54:01.953Z] The best model improves the baseline by 14.52%.
[2024-10-02T21:54:01.953Z] Movies recommended for you:
[2024-10-02T21:54:01.953Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T21:54:01.953Z] There is no way to check that no silent failure occurred.
[2024-10-02T21:54:01.953Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12479.919 ms) ======
[2024-10-02T21:54:02.893Z] -----------------------------------
[2024-10-02T21:54:02.893Z] renaissance-movie-lens_0_PASSED
[2024-10-02T21:54:02.893Z] -----------------------------------
[2024-10-02T21:54:02.893Z]
[2024-10-02T21:54:02.893Z] TEST TEARDOWN:
[2024-10-02T21:54:02.893Z] Nothing to be done for teardown.
[2024-10-02T21:54:02.893Z] renaissance-movie-lens_0 Finish Time: Wed Oct 2 21:54:02 2024 Epoch Time (ms): 1727906042266