renaissance-movie-lens_0
[2024-09-04T21:25:30.176Z] Running test renaissance-movie-lens_0 ...
[2024-09-04T21:25:30.176Z] ===============================================
[2024-09-04T21:25:30.176Z] renaissance-movie-lens_0 Start Time: Wed Sep 4 21:25:29 2024 Epoch Time (ms): 1725485129171
[2024-09-04T21:25:30.176Z] variation: NoOptions
[2024-09-04T21:25:30.176Z] JVM_OPTIONS:
[2024-09-04T21:25:30.176Z] { \
[2024-09-04T21:25:30.176Z] echo ""; echo "TEST SETUP:"; \
[2024-09-04T21:25:30.176Z] echo "Nothing to be done for setup."; \
[2024-09-04T21:25:30.176Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17254842815826/renaissance-movie-lens_0"; \
[2024-09-04T21:25:30.176Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17254842815826/renaissance-movie-lens_0"; \
[2024-09-04T21:25:30.176Z] echo ""; echo "TESTING:"; \
[2024-09-04T21:25:30.176Z] "/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_17254842815826/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-04T21:25:30.176Z] 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_17254842815826/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-04T21:25:30.176Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-04T21:25:30.176Z] echo "Nothing to be done for teardown."; \
[2024-09-04T21:25:30.176Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17254842815826/TestTargetResult";
[2024-09-04T21:25:30.176Z]
[2024-09-04T21:25:30.176Z] TEST SETUP:
[2024-09-04T21:25:30.176Z] Nothing to be done for setup.
[2024-09-04T21:25:30.176Z]
[2024-09-04T21:25:30.177Z] TESTING:
[2024-09-04T21:25:33.090Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-04T21:25:34.009Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-04T21:25:36.944Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-04T21:25:37.862Z] Training: 60056, validation: 20285, test: 19854
[2024-09-04T21:25:37.862Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-04T21:25:37.862Z] GC before operation: completed in 64.916 ms, heap usage 68.125 MB -> 37.183 MB.
[2024-09-04T21:25:43.088Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:25:46.008Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:25:48.931Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:25:51.857Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:25:52.778Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:25:54.685Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:25:56.580Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:25:57.498Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:25:57.498Z] 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.
[2024-09-04T21:25:58.419Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:25:58.419Z] Movies recommended for you:
[2024-09-04T21:25:58.419Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:25:58.419Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:25:58.419Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20535.724 ms) ======
[2024-09-04T21:25:58.419Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-04T21:25:58.419Z] GC before operation: completed in 80.994 ms, heap usage 279.917 MB -> 51.290 MB.
[2024-09-04T21:26:00.310Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:26:03.304Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:26:05.191Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:26:07.612Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:26:09.501Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:26:10.422Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:26:12.318Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:26:13.237Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:26:14.158Z] 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.
[2024-09-04T21:26:14.158Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:26:14.158Z] Movies recommended for you:
[2024-09-04T21:26:14.158Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:26:14.158Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:26:14.158Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15833.179 ms) ======
[2024-09-04T21:26:14.158Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-04T21:26:14.158Z] GC before operation: completed in 65.398 ms, heap usage 210.572 MB -> 49.809 MB.
[2024-09-04T21:26:16.088Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:26:19.027Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:26:20.916Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:26:22.806Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:26:23.734Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:26:25.795Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:26:26.715Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:26:28.609Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:26:28.609Z] 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.
[2024-09-04T21:26:28.609Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:26:28.609Z] Movies recommended for you:
[2024-09-04T21:26:28.609Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:26:28.609Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:26:28.609Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14433.233 ms) ======
[2024-09-04T21:26:28.609Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-04T21:26:28.609Z] GC before operation: completed in 64.844 ms, heap usage 194.568 MB -> 50.171 MB.
[2024-09-04T21:26:30.495Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:26:32.383Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:26:35.298Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:26:37.184Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:26:38.103Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:26:39.996Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:26:40.919Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:26:41.837Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:26:42.780Z] 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.
[2024-09-04T21:26:42.780Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:26:42.780Z] Movies recommended for you:
[2024-09-04T21:26:42.780Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:26:42.780Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:26:42.780Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13965.520 ms) ======
[2024-09-04T21:26:42.780Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-04T21:26:42.780Z] GC before operation: completed in 66.368 ms, heap usage 319.735 MB -> 50.635 MB.
[2024-09-04T21:26:44.671Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:26:46.573Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:26:49.499Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:26:51.387Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:26:52.305Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:26:53.224Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:26:55.110Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:26:56.031Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:26:56.031Z] 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.
[2024-09-04T21:26:56.031Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:26:56.951Z] Movies recommended for you:
[2024-09-04T21:26:56.951Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:26:56.951Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:26:56.951Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13912.211 ms) ======
[2024-09-04T21:26:56.951Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-04T21:26:56.951Z] GC before operation: completed in 65.240 ms, heap usage 129.751 MB -> 50.587 MB.
[2024-09-04T21:26:58.836Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:27:00.723Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:27:02.611Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:27:04.496Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:27:05.413Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:27:06.332Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:27:08.221Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:27:09.988Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:27:09.988Z] 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.
[2024-09-04T21:27:09.988Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:27:09.988Z] Movies recommended for you:
[2024-09-04T21:27:09.988Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:27:09.988Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:27:09.988Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (12954.241 ms) ======
[2024-09-04T21:27:09.988Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-04T21:27:09.988Z] GC before operation: completed in 64.076 ms, heap usage 75.469 MB -> 50.569 MB.
[2024-09-04T21:27:11.875Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:27:13.760Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:27:15.664Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:27:17.620Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:27:18.540Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:27:19.477Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:27:21.364Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:27:22.283Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:27:22.283Z] 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.
[2024-09-04T21:27:22.283Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:27:22.283Z] Movies recommended for you:
[2024-09-04T21:27:22.283Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:27:22.283Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:27:22.283Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (12944.261 ms) ======
[2024-09-04T21:27:22.283Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-04T21:27:22.283Z] GC before operation: completed in 72.929 ms, heap usage 415.662 MB -> 54.222 MB.
[2024-09-04T21:27:24.171Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:27:26.235Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:27:28.125Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:27:30.012Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:27:31.898Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:27:32.820Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:27:33.738Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:27:34.656Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:27:35.575Z] 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.
[2024-09-04T21:27:35.575Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:27:35.575Z] Movies recommended for you:
[2024-09-04T21:27:35.575Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:27:35.575Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:27:35.575Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12788.387 ms) ======
[2024-09-04T21:27:35.575Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-04T21:27:35.575Z] GC before operation: completed in 67.440 ms, heap usage 290.837 MB -> 51.226 MB.
[2024-09-04T21:27:37.459Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:27:39.374Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:27:41.268Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:27:43.156Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:27:44.074Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:27:45.959Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:27:46.877Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:27:47.796Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:27:47.796Z] 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.
[2024-09-04T21:27:47.796Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:27:48.718Z] Movies recommended for you:
[2024-09-04T21:27:48.718Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:27:48.718Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:27:48.718Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12837.512 ms) ======
[2024-09-04T21:27:48.718Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-04T21:27:48.718Z] GC before operation: completed in 67.064 ms, heap usage 66.129 MB -> 52.897 MB.
[2024-09-04T21:27:50.656Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:27:52.549Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:27:54.435Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:27:56.321Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:27:57.241Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:27:58.160Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:28:00.051Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:28:00.969Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:28:00.969Z] 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.
[2024-09-04T21:28:00.969Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:28:00.969Z] Movies recommended for you:
[2024-09-04T21:28:00.969Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:28:00.969Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:28:00.969Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12865.583 ms) ======
[2024-09-04T21:28:00.969Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-04T21:28:00.969Z] GC before operation: completed in 66.415 ms, heap usage 307.118 MB -> 51.198 MB.
[2024-09-04T21:28:02.912Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:28:04.803Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:28:06.707Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:28:08.602Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:28:10.495Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:28:11.413Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:28:12.330Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:28:14.275Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:28:14.275Z] 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.
[2024-09-04T21:28:14.275Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:28:14.275Z] Movies recommended for you:
[2024-09-04T21:28:14.275Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:28:14.275Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:28:14.275Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12768.866 ms) ======
[2024-09-04T21:28:14.275Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-04T21:28:14.275Z] GC before operation: completed in 65.352 ms, heap usage 84.620 MB -> 50.741 MB.
[2024-09-04T21:28:16.174Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:28:17.693Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:28:19.599Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:28:21.491Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:28:23.398Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:28:24.318Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:28:25.250Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:28:26.168Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:28:27.087Z] 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.
[2024-09-04T21:28:27.087Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:28:27.087Z] Movies recommended for you:
[2024-09-04T21:28:27.087Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:28:27.087Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:28:27.087Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12680.066 ms) ======
[2024-09-04T21:28:27.087Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-04T21:28:27.087Z] GC before operation: completed in 70.141 ms, heap usage 415.966 MB -> 54.356 MB.
[2024-09-04T21:28:28.975Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:28:30.870Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:28:32.756Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:28:34.647Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:28:35.567Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:28:37.456Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:28:38.375Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:28:39.295Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:28:40.212Z] 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.
[2024-09-04T21:28:40.212Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:28:40.212Z] Movies recommended for you:
[2024-09-04T21:28:40.212Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:28:40.212Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:28:40.212Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13126.610 ms) ======
[2024-09-04T21:28:40.212Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-04T21:28:40.212Z] GC before operation: completed in 69.636 ms, heap usage 283.441 MB -> 51.196 MB.
[2024-09-04T21:28:42.100Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:28:43.989Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:28:45.894Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:28:47.781Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:28:48.699Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:28:50.589Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:28:51.508Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:28:52.426Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:28:53.344Z] 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.
[2024-09-04T21:28:53.344Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:28:53.344Z] Movies recommended for you:
[2024-09-04T21:28:53.344Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:28:53.344Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:28:53.344Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12908.666 ms) ======
[2024-09-04T21:28:53.344Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-04T21:28:53.344Z] GC before operation: completed in 65.959 ms, heap usage 403.596 MB -> 54.345 MB.
[2024-09-04T21:28:55.234Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:28:57.122Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:28:59.014Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:29:00.901Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:29:01.819Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:29:02.739Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:29:03.659Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:29:05.557Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:29:05.557Z] 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.
[2024-09-04T21:29:05.557Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:29:05.557Z] Movies recommended for you:
[2024-09-04T21:29:05.557Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:29:05.557Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:29:05.557Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12484.760 ms) ======
[2024-09-04T21:29:05.557Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-04T21:29:05.557Z] GC before operation: completed in 69.122 ms, heap usage 400.525 MB -> 54.512 MB.
[2024-09-04T21:29:07.443Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:29:09.330Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:29:11.219Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:29:13.107Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:29:14.026Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:29:15.914Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:29:16.842Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:29:17.761Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:29:17.761Z] 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.
[2024-09-04T21:29:17.761Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:29:18.704Z] Movies recommended for you:
[2024-09-04T21:29:18.704Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:29:18.704Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:29:18.704Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12575.050 ms) ======
[2024-09-04T21:29:18.704Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-04T21:29:18.704Z] GC before operation: completed in 65.518 ms, heap usage 103.851 MB -> 51.112 MB.
[2024-09-04T21:29:20.592Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:29:23.288Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:29:24.209Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:29:26.122Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:29:27.046Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:29:27.970Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:29:29.863Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:29:30.809Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:29:30.809Z] 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.
[2024-09-04T21:29:30.809Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:29:30.809Z] Movies recommended for you:
[2024-09-04T21:29:30.809Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:29:30.809Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:29:30.809Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12689.556 ms) ======
[2024-09-04T21:29:30.809Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-04T21:29:30.809Z] GC before operation: completed in 68.471 ms, heap usage 416.635 MB -> 54.402 MB.
[2024-09-04T21:29:32.698Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:29:34.590Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:29:36.477Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:29:38.365Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:29:39.283Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:29:41.170Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:29:42.091Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:29:43.012Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:29:43.012Z] 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.
[2024-09-04T21:29:43.012Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:29:43.932Z] Movies recommended for you:
[2024-09-04T21:29:43.932Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:29:43.932Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:29:43.932Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12502.276 ms) ======
[2024-09-04T21:29:43.932Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-04T21:29:43.932Z] GC before operation: completed in 80.845 ms, heap usage 76.779 MB -> 51.716 MB.
[2024-09-04T21:29:45.826Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:29:47.744Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:29:49.652Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:29:51.547Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:29:52.471Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:29:53.392Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:29:54.312Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:29:56.202Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:29:56.202Z] 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.
[2024-09-04T21:29:56.202Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:29:56.202Z] Movies recommended for you:
[2024-09-04T21:29:56.202Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:29:56.202Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:29:56.202Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12579.642 ms) ======
[2024-09-04T21:29:56.202Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-04T21:29:56.202Z] GC before operation: completed in 76.132 ms, heap usage 402.185 MB -> 54.664 MB.
[2024-09-04T21:29:58.096Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T21:29:59.986Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T21:30:01.876Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T21:30:03.763Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T21:30:04.684Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T21:30:06.624Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T21:30:07.544Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T21:30:08.465Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T21:30:08.465Z] 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.
[2024-09-04T21:30:08.465Z] The best model improves the baseline by 14.52%.
[2024-09-04T21:30:09.386Z] Movies recommended for you:
[2024-09-04T21:30:09.386Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T21:30:09.386Z] There is no way to check that no silent failure occurred.
[2024-09-04T21:30:09.386Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12653.610 ms) ======
[2024-09-04T21:30:09.386Z] -----------------------------------
[2024-09-04T21:30:09.386Z] renaissance-movie-lens_0_PASSED
[2024-09-04T21:30:09.386Z] -----------------------------------
[2024-09-04T21:30:09.386Z]
[2024-09-04T21:30:09.386Z] TEST TEARDOWN:
[2024-09-04T21:30:09.386Z] Nothing to be done for teardown.
[2024-09-04T21:30:09.386Z] renaissance-movie-lens_0 Finish Time: Wed Sep 4 21:30:08 2024 Epoch Time (ms): 1725485408994