renaissance-movie-lens_0
[2024-12-07T01:09:23.869Z] Running test renaissance-movie-lens_0 ...
[2024-12-07T01:09:23.869Z] ===============================================
[2024-12-07T01:09:23.869Z] renaissance-movie-lens_0 Start Time: Sat Dec 7 01:09:22 2024 Epoch Time (ms): 1733533762219
[2024-12-07T01:09:23.869Z] variation: NoOptions
[2024-12-07T01:09:23.869Z] JVM_OPTIONS:
[2024-12-07T01:09:23.869Z] { \
[2024-12-07T01:09:23.869Z] echo ""; echo "TEST SETUP:"; \
[2024-12-07T01:09:23.869Z] echo "Nothing to be done for setup."; \
[2024-12-07T01:09:23.869Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_1733527164839/renaissance-movie-lens_0"; \
[2024-12-07T01:09:23.869Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_1733527164839/renaissance-movie-lens_0"; \
[2024-12-07T01:09:23.869Z] echo ""; echo "TESTING:"; \
[2024-12-07T01:09:23.869Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_1733527164839/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-07T01:09:23.869Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_1733527164839/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-07T01:09:23.869Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-07T01:09:23.869Z] echo "Nothing to be done for teardown."; \
[2024-12-07T01:09:23.869Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_1733527164839/TestTargetResult";
[2024-12-07T01:09:23.869Z]
[2024-12-07T01:09:23.869Z] TEST SETUP:
[2024-12-07T01:09:23.869Z] Nothing to be done for setup.
[2024-12-07T01:09:23.869Z]
[2024-12-07T01:09:23.869Z] TESTING:
[2024-12-07T01:09:42.515Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-07T01:09:58.265Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-12-07T01:10:28.183Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-07T01:10:31.691Z] Training: 60056, validation: 20285, test: 19854
[2024-12-07T01:10:31.691Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-07T01:10:31.691Z] GC before operation: completed in 503.790 ms, heap usage 66.931 MB -> 36.445 MB.
[2024-12-07T01:11:38.866Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:12:14.443Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:12:49.959Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:13:20.343Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:13:39.439Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:13:53.248Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:14:12.371Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:14:26.701Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:14:28.376Z] 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.
[2024-12-07T01:14:29.191Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:14:30.830Z] Movies recommended for you:
[2024-12-07T01:14:30.830Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:14:30.830Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:14:30.830Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (239060.010 ms) ======
[2024-12-07T01:14:30.830Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-07T01:14:31.608Z] GC before operation: completed in 971.705 ms, heap usage 105.475 MB -> 50.442 MB.
[2024-12-07T01:14:57.483Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:15:23.434Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:15:49.497Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:16:12.369Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:16:28.719Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:16:42.688Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:16:59.323Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:17:15.887Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:17:16.691Z] 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.
[2024-12-07T01:17:17.575Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:17:18.617Z] Movies recommended for you:
[2024-12-07T01:17:18.618Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:17:18.618Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:17:18.618Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (166405.442 ms) ======
[2024-12-07T01:17:18.618Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-07T01:17:19.726Z] GC before operation: completed in 1085.552 ms, heap usage 315.178 MB -> 49.131 MB.
[2024-12-07T01:17:50.619Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:18:17.296Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:18:43.821Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:19:06.922Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:19:26.629Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:19:40.891Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:20:00.321Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:20:14.449Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:20:16.121Z] 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.
[2024-12-07T01:20:16.942Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:20:17.714Z] Movies recommended for you:
[2024-12-07T01:20:17.714Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:20:17.714Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:20:17.714Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (178547.644 ms) ======
[2024-12-07T01:20:17.714Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-07T01:20:19.910Z] GC before operation: completed in 1115.833 ms, heap usage 230.208 MB -> 49.291 MB.
[2024-12-07T01:20:45.823Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:21:08.364Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:21:34.375Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:21:56.790Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:22:11.179Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:22:27.549Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:22:42.079Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:22:55.797Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:22:58.908Z] 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.
[2024-12-07T01:22:58.908Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:22:59.891Z] Movies recommended for you:
[2024-12-07T01:22:59.891Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:22:59.891Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:22:59.891Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (161020.827 ms) ======
[2024-12-07T01:22:59.891Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-07T01:23:00.830Z] GC before operation: completed in 1018.608 ms, heap usage 104.535 MB -> 49.559 MB.
[2024-12-07T01:23:30.302Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:23:54.894Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:24:23.200Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:24:47.867Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:25:03.681Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:25:20.648Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:25:34.236Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:25:48.121Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:25:50.081Z] 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.
[2024-12-07T01:25:51.050Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:25:52.080Z] Movies recommended for you:
[2024-12-07T01:25:52.080Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:25:52.080Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:25:52.080Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (170618.378 ms) ======
[2024-12-07T01:25:52.080Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-07T01:25:53.061Z] GC before operation: completed in 1174.640 ms, heap usage 142.175 MB -> 49.743 MB.
[2024-12-07T01:26:18.782Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:26:43.650Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:27:08.639Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:27:33.629Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:27:47.214Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:28:03.961Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:28:19.818Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:28:33.312Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:28:36.308Z] 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.
[2024-12-07T01:28:37.302Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:28:38.238Z] Movies recommended for you:
[2024-12-07T01:28:38.238Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:28:38.238Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:28:38.238Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (165366.508 ms) ======
[2024-12-07T01:28:38.238Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-07T01:28:39.175Z] GC before operation: completed in 1005.779 ms, heap usage 113.211 MB -> 49.672 MB.
[2024-12-07T01:29:04.097Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:29:29.409Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:29:54.192Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:30:15.989Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:30:31.786Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:30:45.664Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:31:00.191Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:31:16.369Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:31:17.386Z] 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.
[2024-12-07T01:31:18.317Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:31:19.322Z] Movies recommended for you:
[2024-12-07T01:31:19.322Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:31:19.322Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:31:19.322Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (160121.148 ms) ======
[2024-12-07T01:31:19.322Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-07T01:31:21.261Z] GC before operation: completed in 1330.536 ms, heap usage 226.435 MB -> 49.926 MB.
[2024-12-07T01:31:45.892Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:32:10.579Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:32:41.506Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:33:03.343Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:33:19.476Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:33:35.648Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:33:51.728Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:34:08.743Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:34:09.567Z] 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.
[2024-12-07T01:34:10.350Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:34:11.155Z] Movies recommended for you:
[2024-12-07T01:34:11.155Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:34:11.155Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:34:11.155Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (170490.600 ms) ======
[2024-12-07T01:34:11.155Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-07T01:34:11.943Z] GC before operation: completed in 965.605 ms, heap usage 166.318 MB -> 49.618 MB.
[2024-12-07T01:34:42.026Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:35:03.913Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:35:34.620Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:35:57.007Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:36:10.902Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:36:24.716Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:36:41.154Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:36:57.485Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:36:58.276Z] 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.
[2024-12-07T01:36:58.276Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:36:59.324Z] Movies recommended for you:
[2024-12-07T01:36:59.324Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:36:59.325Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:36:59.325Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (167282.887 ms) ======
[2024-12-07T01:36:59.325Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-07T01:37:00.145Z] GC before operation: completed in 924.155 ms, heap usage 125.720 MB -> 47.563 MB.
[2024-12-07T01:37:25.983Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:37:51.764Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:38:22.156Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:38:44.390Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:39:00.680Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:39:14.629Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:39:30.901Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:39:44.963Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:39:47.471Z] 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.
[2024-12-07T01:39:48.341Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:39:49.119Z] Movies recommended for you:
[2024-12-07T01:39:49.119Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:39:49.119Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:39:49.119Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (168516.535 ms) ======
[2024-12-07T01:39:49.119Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-07T01:39:49.888Z] GC before operation: completed in 1126.334 ms, heap usage 180.896 MB -> 47.644 MB.
[2024-12-07T01:40:20.992Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:40:46.570Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:41:12.143Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:41:38.317Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:41:52.261Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:42:06.175Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:42:19.971Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:42:31.837Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:42:34.322Z] 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.
[2024-12-07T01:42:34.322Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:42:35.109Z] Movies recommended for you:
[2024-12-07T01:42:35.109Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:42:35.109Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:42:35.109Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (165419.746 ms) ======
[2024-12-07T01:42:35.109Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-07T01:42:35.942Z] GC before operation: completed in 926.483 ms, heap usage 220.528 MB -> 47.288 MB.
[2024-12-07T01:43:02.039Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:43:24.087Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:43:49.867Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:44:11.765Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:44:25.866Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:44:39.478Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:44:55.418Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:45:09.186Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:45:11.588Z] 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.
[2024-12-07T01:45:11.588Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:45:12.321Z] Movies recommended for you:
[2024-12-07T01:45:12.321Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:45:12.321Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:45:12.321Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (156005.671 ms) ======
[2024-12-07T01:45:12.321Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-07T01:45:13.057Z] GC before operation: completed in 786.457 ms, heap usage 176.811 MB -> 46.963 MB.
[2024-12-07T01:45:35.011Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:45:56.772Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:46:18.850Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:46:37.709Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:46:49.499Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:47:01.207Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:47:13.071Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:47:26.948Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:47:28.526Z] 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.
[2024-12-07T01:47:28.526Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:47:29.276Z] Movies recommended for you:
[2024-12-07T01:47:29.276Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:47:29.276Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:47:29.276Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (136206.289 ms) ======
[2024-12-07T01:47:29.276Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-07T01:47:30.060Z] GC before operation: completed in 652.231 ms, heap usage 119.342 MB -> 47.000 MB.
[2024-12-07T01:47:52.051Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:48:08.330Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:48:27.238Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:48:43.830Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:48:57.656Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:49:05.892Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:49:17.627Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:49:31.273Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:49:32.076Z] 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.
[2024-12-07T01:49:32.076Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:49:32.816Z] Movies recommended for you:
[2024-12-07T01:49:32.816Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:49:32.816Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:49:32.816Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (122571.675 ms) ======
[2024-12-07T01:49:32.816Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-07T01:49:33.593Z] GC before operation: completed in 706.598 ms, heap usage 99.782 MB -> 46.794 MB.
[2024-12-07T01:49:50.401Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:50:09.273Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:50:31.164Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:50:47.224Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:50:59.291Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:51:09.237Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:51:20.914Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:51:32.495Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:51:33.261Z] 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.
[2024-12-07T01:51:34.057Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:51:34.057Z] Movies recommended for you:
[2024-12-07T01:51:34.057Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:51:34.057Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:51:34.057Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (121058.955 ms) ======
[2024-12-07T01:51:34.057Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-07T01:51:34.810Z] GC before operation: completed in 748.006 ms, heap usage 198.362 MB -> 47.017 MB.
[2024-12-07T01:51:56.652Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:52:15.631Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:52:34.409Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:52:56.322Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:53:07.858Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:53:21.381Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:53:38.828Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:53:50.283Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:53:52.689Z] 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.
[2024-12-07T01:53:52.689Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:53:53.443Z] Movies recommended for you:
[2024-12-07T01:53:53.443Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:53:53.443Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:53:53.443Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (138518.680 ms) ======
[2024-12-07T01:53:53.443Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-07T01:53:54.185Z] GC before operation: completed in 684.310 ms, heap usage 129.792 MB -> 47.153 MB.
[2024-12-07T01:54:15.939Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:54:34.470Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:54:56.289Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:55:12.754Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:55:24.194Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:55:37.738Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:55:49.237Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:56:00.704Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:56:02.265Z] 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.
[2024-12-07T01:56:02.265Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:56:03.018Z] Movies recommended for you:
[2024-12-07T01:56:03.018Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:56:03.018Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:56:03.018Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (129034.244 ms) ======
[2024-12-07T01:56:03.018Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-07T01:56:03.770Z] GC before operation: completed in 793.287 ms, heap usage 216.835 MB -> 47.487 MB.
[2024-12-07T01:56:25.695Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:56:47.447Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:57:09.327Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:57:28.182Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:57:36.807Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:57:48.449Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T01:58:02.317Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T01:58:12.183Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T01:58:14.602Z] 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.
[2024-12-07T01:58:14.602Z] The best model improves the baseline by 14.52%.
[2024-12-07T01:58:15.344Z] Movies recommended for you:
[2024-12-07T01:58:15.344Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T01:58:15.344Z] There is no way to check that no silent failure occurred.
[2024-12-07T01:58:15.344Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (131203.834 ms) ======
[2024-12-07T01:58:15.344Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-07T01:58:16.112Z] GC before operation: completed in 976.408 ms, heap usage 64.888 MB -> 47.947 MB.
[2024-12-07T01:58:38.034Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T01:58:57.083Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T01:59:19.215Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T01:59:37.897Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T01:59:49.439Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T01:59:59.335Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T02:00:11.092Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T02:00:20.795Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T02:00:23.213Z] 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.
[2024-12-07T02:00:23.213Z] The best model improves the baseline by 14.52%.
[2024-12-07T02:00:23.969Z] Movies recommended for you:
[2024-12-07T02:00:23.969Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T02:00:23.969Z] There is no way to check that no silent failure occurred.
[2024-12-07T02:00:23.969Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (127457.137 ms) ======
[2024-12-07T02:00:23.969Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-07T02:00:24.729Z] GC before operation: completed in 722.535 ms, heap usage 122.371 MB -> 48.119 MB.
[2024-12-07T02:00:43.375Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-07T02:00:59.241Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-07T02:01:15.313Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-07T02:01:28.967Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-07T02:01:37.708Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-07T02:01:47.516Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-07T02:01:59.094Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-07T02:02:08.985Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-07T02:02:10.515Z] 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.
[2024-12-07T02:02:10.515Z] The best model improves the baseline by 14.52%.
[2024-12-07T02:02:11.257Z] Movies recommended for you:
[2024-12-07T02:02:11.257Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-07T02:02:11.257Z] There is no way to check that no silent failure occurred.
[2024-12-07T02:02:11.257Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (106551.079 ms) ======
[2024-12-07T02:02:13.658Z] -----------------------------------
[2024-12-07T02:02:13.658Z] renaissance-movie-lens_0_PASSED
[2024-12-07T02:02:13.658Z] -----------------------------------
[2024-12-07T02:02:13.658Z]
[2024-12-07T02:02:13.658Z] TEST TEARDOWN:
[2024-12-07T02:02:13.658Z] Nothing to be done for teardown.
[2024-12-07T02:02:13.658Z] renaissance-movie-lens_0 Finish Time: Sat Dec 7 02:02:13 2024 Epoch Time (ms): 1733536933403