renaissance-movie-lens_0
[2024-11-14T00:35:55.017Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T00:35:55.017Z] ===============================================
[2024-11-14T00:35:55.017Z] renaissance-movie-lens_0 Start Time: Thu Nov 14 00:35:54 2024 Epoch Time (ms): 1731544554161
[2024-11-14T00:35:55.017Z] variation: NoOptions
[2024-11-14T00:35:55.017Z] JVM_OPTIONS:
[2024-11-14T00:35:55.017Z] { \
[2024-11-14T00:35:55.017Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T00:35:55.017Z] echo "Nothing to be done for setup."; \
[2024-11-14T00:35:55.017Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17315435913636/renaissance-movie-lens_0"; \
[2024-11-14T00:35:55.017Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17315435913636/renaissance-movie-lens_0"; \
[2024-11-14T00:35:55.017Z] echo ""; echo "TESTING:"; \
[2024-11-14T00:35:55.017Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17315435913636/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T00:35:55.017Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17315435913636/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T00:35:55.017Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T00:35:55.017Z] echo "Nothing to be done for teardown."; \
[2024-11-14T00:35:55.017Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17315435913636/TestTargetResult";
[2024-11-14T00:35:55.017Z]
[2024-11-14T00:35:55.017Z] TEST SETUP:
[2024-11-14T00:35:55.017Z] Nothing to be done for setup.
[2024-11-14T00:35:55.017Z]
[2024-11-14T00:35:55.017Z] TESTING:
[2024-11-14T00:35:57.982Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T00:35:59.903Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-14T00:36:03.985Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T00:36:03.985Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T00:36:03.985Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T00:36:03.985Z] GC before operation: completed in 90.765 ms, heap usage 47.948 MB -> 36.463 MB.
[2024-11-14T00:36:10.588Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:36:14.681Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:36:17.000Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:36:19.972Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:36:21.895Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:36:23.815Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:36:25.739Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:36:26.683Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:36:27.621Z] 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.
[2024-11-14T00:36:27.621Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:36:27.621Z] Movies recommended for you:
[2024-11-14T00:36:27.621Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:36:27.621Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:36:27.621Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23408.797 ms) ======
[2024-11-14T00:36:27.621Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T00:36:27.621Z] GC before operation: completed in 91.082 ms, heap usage 300.549 MB -> 48.260 MB.
[2024-11-14T00:36:30.657Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:36:33.663Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:36:35.582Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:36:38.553Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:36:39.491Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:36:41.416Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:36:43.339Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:36:44.282Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:36:44.282Z] 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.
[2024-11-14T00:36:44.282Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:36:44.282Z] Movies recommended for you:
[2024-11-14T00:36:44.282Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:36:44.282Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:36:44.282Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17107.932 ms) ======
[2024-11-14T00:36:44.282Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T00:36:45.217Z] GC before operation: completed in 98.275 ms, heap usage 163.941 MB -> 49.063 MB.
[2024-11-14T00:36:47.146Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:36:50.116Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:36:52.036Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:36:55.014Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:36:55.949Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:36:57.873Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:36:58.815Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:37:00.749Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:37:00.749Z] 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.
[2024-11-14T00:37:00.749Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:37:00.749Z] Movies recommended for you:
[2024-11-14T00:37:00.749Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:37:00.749Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:37:00.749Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (15895.394 ms) ======
[2024-11-14T00:37:00.749Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T00:37:00.749Z] GC before operation: completed in 93.339 ms, heap usage 78.882 MB -> 49.193 MB.
[2024-11-14T00:37:02.671Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:37:05.705Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:37:07.635Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:37:10.605Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:37:11.540Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:37:13.463Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:37:14.398Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:37:15.331Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:37:16.266Z] 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.
[2024-11-14T00:37:16.266Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:37:16.266Z] Movies recommended for you:
[2024-11-14T00:37:16.266Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:37:16.266Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:37:16.266Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15346.707 ms) ======
[2024-11-14T00:37:16.266Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T00:37:16.266Z] GC before operation: completed in 90.909 ms, heap usage 316.724 MB -> 49.834 MB.
[2024-11-14T00:37:18.202Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:37:21.856Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:37:22.792Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:37:25.773Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:37:26.709Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:37:28.794Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:37:29.729Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:37:30.665Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:37:31.602Z] 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.
[2024-11-14T00:37:31.602Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:37:31.602Z] Movies recommended for you:
[2024-11-14T00:37:31.602Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:37:31.602Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:37:31.602Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15160.162 ms) ======
[2024-11-14T00:37:31.602Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T00:37:31.602Z] GC before operation: completed in 83.641 ms, heap usage 194.616 MB -> 49.858 MB.
[2024-11-14T00:37:33.521Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:37:36.486Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:37:38.408Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:37:40.358Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:37:42.283Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:37:43.218Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:37:45.145Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:37:46.086Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:37:46.086Z] 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.
[2024-11-14T00:37:46.086Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:37:46.086Z] Movies recommended for you:
[2024-11-14T00:37:46.086Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:37:46.086Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:37:46.086Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15020.278 ms) ======
[2024-11-14T00:37:46.086Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T00:37:47.028Z] GC before operation: completed in 103.947 ms, heap usage 151.506 MB -> 49.780 MB.
[2024-11-14T00:37:48.953Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:37:50.876Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:37:53.850Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:37:55.781Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:37:56.717Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:37:57.654Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:37:59.586Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:38:00.522Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:38:01.467Z] 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.
[2024-11-14T00:38:01.467Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:38:01.467Z] Movies recommended for you:
[2024-11-14T00:38:01.467Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:38:01.467Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:38:01.467Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14689.091 ms) ======
[2024-11-14T00:38:01.467Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T00:38:01.467Z] GC before operation: completed in 97.767 ms, heap usage 170.630 MB -> 49.941 MB.
[2024-11-14T00:38:03.387Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:38:06.356Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:38:08.281Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:38:10.213Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:38:12.139Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:38:13.075Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:38:15.001Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:38:15.936Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:38:15.936Z] 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.
[2024-11-14T00:38:15.936Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:38:15.936Z] Movies recommended for you:
[2024-11-14T00:38:15.936Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:38:15.936Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:38:15.936Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14869.549 ms) ======
[2024-11-14T00:38:15.936Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T00:38:15.936Z] GC before operation: completed in 95.305 ms, heap usage 212.711 MB -> 50.249 MB.
[2024-11-14T00:38:18.914Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:38:20.836Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:38:22.758Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:38:25.545Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:38:26.483Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:38:28.403Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:38:29.339Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:38:30.280Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:38:31.225Z] 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.
[2024-11-14T00:38:31.225Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:38:31.225Z] Movies recommended for you:
[2024-11-14T00:38:31.225Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:38:31.225Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:38:31.225Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14766.519 ms) ======
[2024-11-14T00:38:31.225Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T00:38:31.225Z] GC before operation: completed in 92.202 ms, heap usage 172.481 MB -> 50.038 MB.
[2024-11-14T00:38:34.193Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:38:36.148Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:38:38.076Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:38:39.999Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:38:41.921Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:38:42.859Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:38:43.797Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:38:45.721Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:38:45.721Z] 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.
[2024-11-14T00:38:45.721Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:38:45.721Z] Movies recommended for you:
[2024-11-14T00:38:45.721Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:38:45.721Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:38:45.721Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14680.402 ms) ======
[2024-11-14T00:38:45.721Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T00:38:45.721Z] GC before operation: completed in 84.909 ms, heap usage 108.135 MB -> 50.090 MB.
[2024-11-14T00:38:47.661Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:38:50.630Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:38:52.553Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:38:54.477Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:38:55.414Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:38:57.338Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:38:58.274Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:39:00.197Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:39:00.197Z] 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.
[2024-11-14T00:39:00.197Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:39:00.197Z] Movies recommended for you:
[2024-11-14T00:39:00.197Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:39:00.197Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:39:00.197Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14370.813 ms) ======
[2024-11-14T00:39:00.197Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T00:39:00.197Z] GC before operation: completed in 97.084 ms, heap usage 188.407 MB -> 49.871 MB.
[2024-11-14T00:39:03.178Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:39:05.103Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:39:07.024Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:39:08.944Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:39:10.862Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:39:11.797Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:39:13.717Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:39:14.651Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:39:14.651Z] 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.
[2024-11-14T00:39:14.651Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:39:15.588Z] Movies recommended for you:
[2024-11-14T00:39:15.588Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:39:15.588Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:39:15.588Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14686.457 ms) ======
[2024-11-14T00:39:15.588Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T00:39:15.588Z] GC before operation: completed in 92.326 ms, heap usage 121.548 MB -> 50.008 MB.
[2024-11-14T00:39:17.506Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:39:19.424Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:39:22.387Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:39:24.312Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:39:25.979Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:39:26.914Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:39:27.851Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:39:29.784Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:39:29.784Z] 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.
[2024-11-14T00:39:29.784Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:39:29.784Z] Movies recommended for you:
[2024-11-14T00:39:29.784Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:39:29.784Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:39:29.784Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14807.860 ms) ======
[2024-11-14T00:39:29.784Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T00:39:29.784Z] GC before operation: completed in 89.268 ms, heap usage 254.069 MB -> 50.281 MB.
[2024-11-14T00:39:32.754Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:39:34.685Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:39:36.606Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:39:38.530Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:39:40.454Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:39:41.390Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:39:43.313Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:39:44.248Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:39:45.188Z] 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.
[2024-11-14T00:39:45.188Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:39:45.188Z] Movies recommended for you:
[2024-11-14T00:39:45.188Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:39:45.188Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:39:45.188Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14867.986 ms) ======
[2024-11-14T00:39:45.188Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T00:39:45.188Z] GC before operation: completed in 91.328 ms, heap usage 156.242 MB -> 49.974 MB.
[2024-11-14T00:39:47.109Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:39:50.079Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:39:52.003Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:39:53.936Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:39:55.045Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:39:56.968Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:39:57.907Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:39:58.842Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:39:59.780Z] 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.
[2024-11-14T00:39:59.780Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:39:59.780Z] Movies recommended for you:
[2024-11-14T00:39:59.780Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:39:59.780Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:39:59.780Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14458.923 ms) ======
[2024-11-14T00:39:59.780Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T00:39:59.780Z] GC before operation: completed in 94.709 ms, heap usage 256.223 MB -> 50.282 MB.
[2024-11-14T00:40:01.702Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:40:03.623Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:40:06.594Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:40:08.524Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:40:09.467Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:40:11.394Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:40:12.331Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:40:14.259Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:40:14.259Z] 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.
[2024-11-14T00:40:14.259Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:40:14.259Z] Movies recommended for you:
[2024-11-14T00:40:14.259Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:40:14.259Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:40:14.259Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14590.407 ms) ======
[2024-11-14T00:40:14.259Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T00:40:14.259Z] GC before operation: completed in 92.036 ms, heap usage 263.248 MB -> 50.322 MB.
[2024-11-14T00:40:16.187Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:40:19.162Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:40:21.101Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:40:23.022Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:40:23.958Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:40:25.881Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:40:26.816Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:40:27.753Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:40:29.478Z] 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.
[2024-11-14T00:40:29.478Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:40:29.478Z] Movies recommended for you:
[2024-11-14T00:40:29.478Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:40:29.478Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:40:29.478Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14139.929 ms) ======
[2024-11-14T00:40:29.478Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T00:40:29.478Z] GC before operation: completed in 106.424 ms, heap usage 190.031 MB -> 50.133 MB.
[2024-11-14T00:40:31.400Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:40:33.332Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:40:35.259Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:40:37.191Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:40:39.113Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:40:40.049Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:40:41.971Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:40:42.909Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:40:43.845Z] 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.
[2024-11-14T00:40:43.845Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:40:43.845Z] Movies recommended for you:
[2024-11-14T00:40:43.845Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:40:43.845Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:40:43.845Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14918.296 ms) ======
[2024-11-14T00:40:43.845Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T00:40:43.845Z] GC before operation: completed in 93.139 ms, heap usage 333.885 MB -> 50.273 MB.
[2024-11-14T00:40:45.784Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:40:48.757Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:40:50.680Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:40:52.603Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:40:53.540Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:40:55.465Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:40:56.402Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:40:58.332Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:40:58.332Z] 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.
[2024-11-14T00:40:58.332Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:40:58.332Z] Movies recommended for you:
[2024-11-14T00:40:58.332Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:40:58.332Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:40:58.332Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14812.907 ms) ======
[2024-11-14T00:40:58.332Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T00:40:58.332Z] GC before operation: completed in 90.734 ms, heap usage 152.061 MB -> 50.326 MB.
[2024-11-14T00:41:00.256Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:41:03.228Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:41:05.150Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:41:07.073Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:41:08.996Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:41:09.933Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:41:10.870Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:41:12.796Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:41:12.796Z] 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.
[2024-11-14T00:41:12.796Z] The best model improves the baseline by 14.52%.
[2024-11-14T00:41:12.796Z] Movies recommended for you:
[2024-11-14T00:41:12.796Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:41:12.796Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:41:12.796Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14375.082 ms) ======
[2024-11-14T00:41:13.733Z] -----------------------------------
[2024-11-14T00:41:13.733Z] renaissance-movie-lens_0_PASSED
[2024-11-14T00:41:13.733Z] -----------------------------------
[2024-11-14T00:41:13.733Z]
[2024-11-14T00:41:13.733Z] TEST TEARDOWN:
[2024-11-14T00:41:13.733Z] Nothing to be done for teardown.
[2024-11-14T00:41:13.733Z] renaissance-movie-lens_0 Finish Time: Thu Nov 14 00:41:12 2024 Epoch Time (ms): 1731544872913