renaissance-movie-lens_0
[2024-07-31T21:00:36.848Z] Running test renaissance-movie-lens_0 ...
[2024-07-31T21:00:36.848Z] ===============================================
[2024-07-31T21:00:36.848Z] renaissance-movie-lens_0 Start Time: Wed Jul 31 21:00:35 2024 Epoch Time (ms): 1722459635874
[2024-07-31T21:00:36.848Z] variation: NoOptions
[2024-07-31T21:00:36.848Z] JVM_OPTIONS:
[2024-07-31T21:00:36.848Z] { \
[2024-07-31T21:00:36.848Z] echo ""; echo "TEST SETUP:"; \
[2024-07-31T21:00:36.848Z] echo "Nothing to be done for setup."; \
[2024-07-31T21:00:36.848Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17224587411344/renaissance-movie-lens_0"; \
[2024-07-31T21:00:36.848Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17224587411344/renaissance-movie-lens_0"; \
[2024-07-31T21:00:36.848Z] echo ""; echo "TESTING:"; \
[2024-07-31T21:00:36.848Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/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_aarch64_linux_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17224587411344/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-07-31T21:00:36.848Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17224587411344/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-07-31T21:00:36.848Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-07-31T21:00:36.848Z] echo "Nothing to be done for teardown."; \
[2024-07-31T21:00:36.848Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17224587411344/TestTargetResult";
[2024-07-31T21:00:36.848Z]
[2024-07-31T21:00:36.848Z] TEST SETUP:
[2024-07-31T21:00:36.848Z] Nothing to be done for setup.
[2024-07-31T21:00:36.848Z]
[2024-07-31T21:00:36.848Z] TESTING:
[2024-07-31T21:00:41.012Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-07-31T21:00:46.505Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-07-31T21:00:50.637Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-07-31T21:00:50.637Z] Training: 60056, validation: 20285, test: 19854
[2024-07-31T21:00:50.637Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-07-31T21:00:50.637Z] GC before operation: completed in 48.976 ms, heap usage 78.027 MB -> 39.370 MB.
[2024-07-31T21:00:58.728Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:01:02.854Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:01:06.957Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:01:09.937Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:01:12.923Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:01:14.860Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:01:16.795Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:01:19.782Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:01:19.782Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:01:19.782Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:01:20.725Z] Movies recommended for you:
[2024-07-31T21:01:20.725Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:01:20.725Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:01:20.725Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (29411.037 ms) ======
[2024-07-31T21:01:20.725Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-07-31T21:01:20.725Z] GC before operation: completed in 131.641 ms, heap usage 836.302 MB -> 57.848 MB.
[2024-07-31T21:01:23.733Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:01:26.716Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:01:29.708Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:01:32.710Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:01:34.646Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:01:35.593Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:01:37.528Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:01:39.462Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:01:39.462Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:01:39.462Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:01:39.462Z] Movies recommended for you:
[2024-07-31T21:01:39.462Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:01:39.462Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:01:39.462Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (19159.915 ms) ======
[2024-07-31T21:01:39.462Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-07-31T21:01:39.462Z] GC before operation: completed in 133.631 ms, heap usage 315.763 MB -> 58.333 MB.
[2024-07-31T21:01:42.467Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:01:46.584Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:01:49.581Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:01:51.559Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:01:53.506Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:01:55.440Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:01:56.386Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:01:59.845Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:01:59.845Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:01:59.845Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:02:00.803Z] Movies recommended for you:
[2024-07-31T21:02:00.803Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:02:00.803Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:02:00.803Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20585.016 ms) ======
[2024-07-31T21:02:00.803Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-07-31T21:02:00.803Z] GC before operation: completed in 204.034 ms, heap usage 1.044 GB -> 58.948 MB.
[2024-07-31T21:02:03.784Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:02:06.765Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:02:08.701Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:02:11.700Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:02:13.636Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:02:15.575Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:02:17.512Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:02:18.456Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:02:18.456Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:02:18.456Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:02:19.400Z] Movies recommended for you:
[2024-07-31T21:02:19.400Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:02:19.400Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:02:19.400Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18525.700 ms) ======
[2024-07-31T21:02:19.400Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-07-31T21:02:19.400Z] GC before operation: completed in 97.866 ms, heap usage 1.038 GB -> 58.106 MB.
[2024-07-31T21:02:21.337Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:02:25.456Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:02:27.390Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:02:29.388Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:02:30.327Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:02:31.266Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:02:33.193Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:02:34.131Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:02:34.131Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:02:34.131Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:02:34.131Z] Movies recommended for you:
[2024-07-31T21:02:34.131Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:02:34.131Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:02:34.131Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15328.189 ms) ======
[2024-07-31T21:02:34.131Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-07-31T21:02:34.131Z] GC before operation: completed in 78.514 ms, heap usage 1.784 GB -> 62.420 MB.
[2024-07-31T21:02:37.108Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:02:39.037Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:02:39.976Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:02:41.905Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:02:43.833Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:02:45.759Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:02:46.698Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:02:47.637Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:02:48.574Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:02:48.574Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:02:48.574Z] Movies recommended for you:
[2024-07-31T21:02:48.574Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:02:48.574Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:02:48.574Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13770.884 ms) ======
[2024-07-31T21:02:48.574Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-07-31T21:02:48.574Z] GC before operation: completed in 83.489 ms, heap usage 169.980 MB -> 56.344 MB.
[2024-07-31T21:02:50.500Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:02:53.471Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:02:55.396Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:02:57.333Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:02:59.258Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:03:00.195Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:03:01.137Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:03:03.080Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:03:03.080Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:03:03.080Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:03:04.021Z] Movies recommended for you:
[2024-07-31T21:03:04.021Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:03:04.021Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:03:04.021Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15153.276 ms) ======
[2024-07-31T21:03:04.021Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-07-31T21:03:04.021Z] GC before operation: completed in 116.156 ms, heap usage 611.786 MB -> 60.992 MB.
[2024-07-31T21:03:05.954Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:03:08.948Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:03:11.938Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:03:13.869Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:03:15.803Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:03:17.732Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:03:18.675Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:03:20.610Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:03:20.610Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:03:20.610Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:03:20.610Z] Movies recommended for you:
[2024-07-31T21:03:20.610Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:03:20.610Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:03:20.610Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17155.460 ms) ======
[2024-07-31T21:03:20.610Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-07-31T21:03:20.610Z] GC before operation: completed in 101.544 ms, heap usage 1.913 GB -> 61.248 MB.
[2024-07-31T21:03:24.723Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:03:27.710Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:03:30.061Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:03:33.050Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:03:34.986Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:03:35.928Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:03:37.862Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:03:39.790Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:03:39.790Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:03:39.790Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:03:39.790Z] Movies recommended for you:
[2024-07-31T21:03:39.790Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:03:39.790Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:03:39.790Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19014.210 ms) ======
[2024-07-31T21:03:39.790Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-07-31T21:03:39.790Z] GC before operation: completed in 101.442 ms, heap usage 1.915 GB -> 62.596 MB.
[2024-07-31T21:03:42.771Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:03:45.761Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:03:47.700Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:03:50.729Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:03:52.666Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:03:53.604Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:03:55.536Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:03:56.478Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:03:57.428Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:03:57.428Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:03:57.428Z] Movies recommended for you:
[2024-07-31T21:03:57.428Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:03:57.428Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:03:57.428Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17112.459 ms) ======
[2024-07-31T21:03:57.428Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-07-31T21:03:57.428Z] GC before operation: completed in 93.104 ms, heap usage 315.984 MB -> 54.461 MB.
[2024-07-31T21:04:00.416Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:04:03.418Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:04:05.347Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:04:08.332Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:04:10.264Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:04:11.203Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:04:13.137Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:04:15.082Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:04:15.082Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:04:15.082Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:04:15.082Z] Movies recommended for you:
[2024-07-31T21:04:15.082Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:04:15.082Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:04:15.082Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17765.746 ms) ======
[2024-07-31T21:04:15.082Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-07-31T21:04:15.082Z] GC before operation: completed in 90.993 ms, heap usage 245.558 MB -> 56.025 MB.
[2024-07-31T21:04:18.067Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:04:21.068Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:04:24.060Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:04:27.048Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:04:28.007Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:04:30.045Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:04:30.991Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:04:32.930Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:04:32.930Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:04:32.930Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:04:32.930Z] Movies recommended for you:
[2024-07-31T21:04:32.930Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:04:32.930Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:04:32.930Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (18058.196 ms) ======
[2024-07-31T21:04:32.930Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-07-31T21:04:32.930Z] GC before operation: completed in 105.336 ms, heap usage 718.179 MB -> 61.372 MB.
[2024-07-31T21:04:35.921Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:04:38.913Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:04:41.905Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:04:44.891Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:04:46.825Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:04:47.769Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:04:49.705Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:04:50.649Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:04:50.649Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:04:50.649Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:04:51.591Z] Movies recommended for you:
[2024-07-31T21:04:51.591Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:04:51.591Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:04:51.591Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17886.938 ms) ======
[2024-07-31T21:04:51.591Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-07-31T21:04:51.591Z] GC before operation: completed in 99.392 ms, heap usage 175.920 MB -> 58.671 MB.
[2024-07-31T21:04:54.576Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:04:56.509Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:04:59.497Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:05:01.432Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:05:04.076Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:05:05.022Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:05:05.962Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:05:06.903Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:05:06.904Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:05:06.904Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:05:06.904Z] Movies recommended for you:
[2024-07-31T21:05:06.904Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:05:06.904Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:05:06.904Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15759.430 ms) ======
[2024-07-31T21:05:06.904Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-07-31T21:05:06.904Z] GC before operation: completed in 83.063 ms, heap usage 807.842 MB -> 59.076 MB.
[2024-07-31T21:05:08.835Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:05:11.820Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:05:14.806Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:05:16.738Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:05:17.680Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:05:19.611Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:05:20.549Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:05:21.493Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:05:22.434Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:05:22.434Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:05:22.434Z] Movies recommended for you:
[2024-07-31T21:05:22.434Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:05:22.434Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:05:22.434Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15071.773 ms) ======
[2024-07-31T21:05:22.434Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-07-31T21:05:22.434Z] GC before operation: completed in 107.249 ms, heap usage 1.906 GB -> 60.616 MB.
[2024-07-31T21:05:25.418Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:05:28.402Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:05:30.356Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:05:33.344Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:05:35.279Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:05:37.214Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:05:39.143Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:05:40.085Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:05:41.029Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:05:41.029Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:05:41.029Z] Movies recommended for you:
[2024-07-31T21:05:41.029Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:05:41.029Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:05:41.029Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (18398.116 ms) ======
[2024-07-31T21:05:41.029Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-07-31T21:05:41.029Z] GC before operation: completed in 85.766 ms, heap usage 734.501 MB -> 60.494 MB.
[2024-07-31T21:05:44.207Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:05:47.190Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:05:49.135Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:05:52.123Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:05:53.069Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:05:54.999Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:05:56.933Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:05:57.875Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:05:58.817Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:05:58.817Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:05:58.817Z] Movies recommended for you:
[2024-07-31T21:05:58.817Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:05:58.817Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:05:58.817Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17692.236 ms) ======
[2024-07-31T21:05:58.817Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-07-31T21:05:58.817Z] GC before operation: completed in 192.458 ms, heap usage 1.916 GB -> 61.500 MB.
[2024-07-31T21:06:01.804Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:06:04.859Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:06:07.854Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:06:10.852Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:06:11.798Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:06:13.738Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:06:14.679Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:06:16.614Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:06:16.614Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:06:16.614Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:06:17.559Z] Movies recommended for you:
[2024-07-31T21:06:17.559Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:06:17.559Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:06:17.559Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (18494.062 ms) ======
[2024-07-31T21:06:17.559Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-07-31T21:06:17.559Z] GC before operation: completed in 99.690 ms, heap usage 243.793 MB -> 56.861 MB.
[2024-07-31T21:06:20.557Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:06:22.496Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:06:24.452Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:06:27.447Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:06:28.388Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:06:30.378Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:06:32.316Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:06:33.958Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:06:33.958Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:06:33.958Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:06:33.958Z] Movies recommended for you:
[2024-07-31T21:06:33.958Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:06:33.958Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:06:33.958Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16367.484 ms) ======
[2024-07-31T21:06:33.958Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-07-31T21:06:34.913Z] GC before operation: completed in 126.517 ms, heap usage 1.753 GB -> 59.541 MB.
[2024-07-31T21:06:36.844Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T21:06:39.832Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T21:06:42.814Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T21:06:45.798Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T21:06:46.741Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T21:06:48.681Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T21:06:50.619Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T21:06:52.557Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T21:06:52.557Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-07-31T21:06:53.501Z] The best model improves the baseline by 14.43%.
[2024-07-31T21:06:53.501Z] Movies recommended for you:
[2024-07-31T21:06:53.501Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T21:06:53.501Z] There is no way to check that no silent failure occurred.
[2024-07-31T21:06:53.501Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19391.684 ms) ======
[2024-07-31T21:06:55.437Z] -----------------------------------
[2024-07-31T21:06:55.437Z] renaissance-movie-lens_0_PASSED
[2024-07-31T21:06:55.437Z] -----------------------------------
[2024-07-31T21:06:55.437Z]
[2024-07-31T21:06:55.437Z] TEST TEARDOWN:
[2024-07-31T21:06:55.437Z] Nothing to be done for teardown.
[2024-07-31T21:06:55.437Z] renaissance-movie-lens_0 Finish Time: Wed Jul 31 21:06:54 2024 Epoch Time (ms): 1722460014637