renaissance-movie-lens_0
[2025-02-06T02:05:22.741Z] Running test renaissance-movie-lens_0 ...
[2025-02-06T02:05:22.741Z] ===============================================
[2025-02-06T02:05:22.741Z] renaissance-movie-lens_0 Start Time: Thu Feb 6 02:05:22 2025 Epoch Time (ms): 1738807522364
[2025-02-06T02:05:22.741Z] variation: NoOptions
[2025-02-06T02:05:22.741Z] JVM_OPTIONS:
[2025-02-06T02:05:22.741Z] { \
[2025-02-06T02:05:22.741Z] echo ""; echo "TEST SETUP:"; \
[2025-02-06T02:05:22.741Z] echo "Nothing to be done for setup."; \
[2025-02-06T02:05:22.741Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17388070283729/renaissance-movie-lens_0"; \
[2025-02-06T02:05:22.741Z] cd "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17388070283729/renaissance-movie-lens_0"; \
[2025-02-06T02:05:22.741Z] echo ""; echo "TESTING:"; \
[2025-02-06T02:05:22.741Z] "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/jdkbinary/j2sdk-image/Contents/Home/bin/..//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 "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17388070283729/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-06T02:05:22.741Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17388070283729/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-06T02:05:22.741Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-06T02:05:22.741Z] echo "Nothing to be done for teardown."; \
[2025-02-06T02:05:22.741Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17388070283729/TestTargetResult";
[2025-02-06T02:05:22.741Z]
[2025-02-06T02:05:22.741Z] TEST SETUP:
[2025-02-06T02:05:22.741Z] Nothing to be done for setup.
[2025-02-06T02:05:22.741Z]
[2025-02-06T02:05:22.741Z] TESTING:
[2025-02-06T02:05:26.719Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-06T02:05:27.951Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2025-02-06T02:05:31.085Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-06T02:05:31.449Z] Training: 60056, validation: 20285, test: 19854
[2025-02-06T02:05:31.449Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-06T02:05:31.449Z] GC before operation: completed in 39.407 ms, heap usage 61.511 MB -> 38.153 MB.
[2025-02-06T02:05:59.684Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:06:15.986Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:06:39.500Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:06:55.826Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:07:06.996Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:07:14.566Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:07:25.738Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:07:34.933Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:07:34.933Z] 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.
[2025-02-06T02:07:34.933Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:07:34.933Z] Movies recommended for you:
[2025-02-06T02:07:34.933Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:07:34.933Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:07:34.933Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (123325.942 ms) ======
[2025-02-06T02:07:34.933Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-06T02:07:34.933Z] GC before operation: completed in 70.690 ms, heap usage 374.544 MB -> 74.443 MB.
[2025-02-06T02:07:54.571Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:08:14.170Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:08:33.794Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:08:50.068Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:09:01.232Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:09:08.816Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:09:19.990Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:09:29.209Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:09:29.209Z] 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.
[2025-02-06T02:09:29.209Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:09:29.209Z] Movies recommended for you:
[2025-02-06T02:09:29.209Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:09:29.209Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:09:29.209Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (114275.900 ms) ======
[2025-02-06T02:09:29.209Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-06T02:09:29.209Z] GC before operation: completed in 69.852 ms, heap usage 744.968 MB -> 80.401 MB.
[2025-02-06T02:09:48.808Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:10:08.425Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:10:28.062Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:10:44.341Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:10:55.516Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:11:03.064Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:11:14.228Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:11:23.433Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:11:23.433Z] 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.
[2025-02-06T02:11:23.433Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:11:23.433Z] Movies recommended for you:
[2025-02-06T02:11:23.433Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:11:23.433Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:11:23.433Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (114314.510 ms) ======
[2025-02-06T02:11:23.433Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-06T02:11:23.433Z] GC before operation: completed in 59.530 ms, heap usage 659.490 MB -> 58.295 MB.
[2025-02-06T02:11:46.949Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:12:03.210Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:12:26.746Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:12:40.236Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:12:51.411Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:12:58.961Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:13:10.139Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:13:19.339Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:13:19.339Z] 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.
[2025-02-06T02:13:19.339Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:13:19.339Z] Movies recommended for you:
[2025-02-06T02:13:19.339Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:13:19.339Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:13:19.340Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (115243.112 ms) ======
[2025-02-06T02:13:19.340Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-06T02:13:19.340Z] GC before operation: completed in 82.561 ms, heap usage 932.443 MB -> 81.256 MB.
[2025-02-06T02:13:38.905Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:13:58.478Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:14:23.632Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:14:37.139Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:14:48.316Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:14:57.524Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:15:13.801Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:15:21.355Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:15:21.355Z] 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.
[2025-02-06T02:15:21.355Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:15:21.355Z] Movies recommended for you:
[2025-02-06T02:15:21.355Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:15:21.355Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:15:21.355Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (122566.802 ms) ======
[2025-02-06T02:15:21.355Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-06T02:15:21.355Z] GC before operation: completed in 77.279 ms, heap usage 399.013 MB -> 81.325 MB.
[2025-02-06T02:15:45.122Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:16:01.382Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:16:25.038Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:16:38.526Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:16:47.728Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:16:56.938Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:17:08.165Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:17:15.755Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:17:15.755Z] 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.
[2025-02-06T02:17:15.755Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:17:15.755Z] Movies recommended for you:
[2025-02-06T02:17:15.755Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:17:15.755Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:17:15.755Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (114212.951 ms) ======
[2025-02-06T02:17:15.755Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-06T02:17:15.756Z] GC before operation: completed in 73.949 ms, heap usage 651.255 MB -> 81.317 MB.
[2025-02-06T02:17:35.341Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:17:54.960Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:18:18.503Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:18:38.088Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:18:45.660Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:18:54.876Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:19:06.038Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:19:13.600Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:19:13.962Z] 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.
[2025-02-06T02:19:13.962Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:19:13.962Z] Movies recommended for you:
[2025-02-06T02:19:13.962Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:19:13.962Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:19:13.962Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (118236.274 ms) ======
[2025-02-06T02:19:13.962Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-06T02:19:13.962Z] GC before operation: completed in 77.549 ms, heap usage 384.179 MB -> 81.492 MB.
[2025-02-06T02:19:37.479Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:19:57.049Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:20:16.713Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:20:32.987Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:20:42.195Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:20:49.767Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:21:00.963Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:21:10.182Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:21:10.538Z] 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.
[2025-02-06T02:21:10.538Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:21:10.538Z] Movies recommended for you:
[2025-02-06T02:21:10.538Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:21:10.538Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:21:10.538Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (116486.401 ms) ======
[2025-02-06T02:21:10.538Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-06T02:21:10.538Z] GC before operation: completed in 62.539 ms, heap usage 300.582 MB -> 56.780 MB.
[2025-02-06T02:21:30.115Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:21:49.691Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:22:13.243Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:22:32.819Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:22:40.372Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:22:49.575Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:23:00.772Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:23:08.319Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:23:08.681Z] 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.
[2025-02-06T02:23:08.681Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:23:08.681Z] Movies recommended for you:
[2025-02-06T02:23:08.681Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:23:08.681Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:23:08.681Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (118087.688 ms) ======
[2025-02-06T02:23:08.681Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-06T02:23:08.681Z] GC before operation: completed in 78.123 ms, heap usage 578.258 MB -> 81.509 MB.
[2025-02-06T02:23:32.219Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:23:51.787Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:24:11.351Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:24:27.632Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:24:36.838Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:24:44.390Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:24:55.575Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:25:03.166Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:25:03.933Z] 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.
[2025-02-06T02:25:03.933Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:25:03.933Z] Movies recommended for you:
[2025-02-06T02:25:03.933Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:25:03.933Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:25:03.933Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (115086.797 ms) ======
[2025-02-06T02:25:03.933Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-06T02:25:03.933Z] GC before operation: completed in 74.382 ms, heap usage 513.605 MB -> 81.632 MB.
[2025-02-06T02:25:23.506Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:25:50.315Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:26:18.180Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:26:34.450Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:26:43.658Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:26:51.213Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:27:02.385Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:27:11.634Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:27:11.634Z] 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.
[2025-02-06T02:27:11.634Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:27:11.634Z] Movies recommended for you:
[2025-02-06T02:27:11.634Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:27:11.634Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:27:11.634Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (127045.791 ms) ======
[2025-02-06T02:27:11.634Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-06T02:27:11.634Z] GC before operation: completed in 78.970 ms, heap usage 924.098 MB -> 81.401 MB.
[2025-02-06T02:27:31.247Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:27:54.947Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:28:14.532Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:28:30.795Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:28:38.373Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:28:47.579Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:28:58.742Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:29:06.325Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:29:06.685Z] 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.
[2025-02-06T02:29:06.685Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:29:07.043Z] Movies recommended for you:
[2025-02-06T02:29:07.043Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:29:07.043Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:29:07.043Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (115829.765 ms) ======
[2025-02-06T02:29:07.043Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-06T02:29:07.043Z] GC before operation: completed in 74.300 ms, heap usage 133.743 MB -> 81.457 MB.
[2025-02-06T02:29:26.606Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:29:46.179Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:30:09.700Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:30:25.966Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:30:37.134Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:30:44.696Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:30:58.194Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:31:04.352Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:31:04.710Z] 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.
[2025-02-06T02:31:04.710Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:31:05.113Z] Movies recommended for you:
[2025-02-06T02:31:05.113Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:31:05.113Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:31:05.113Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (118025.920 ms) ======
[2025-02-06T02:31:05.113Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-06T02:31:05.113Z] GC before operation: completed in 76.496 ms, heap usage 548.085 MB -> 81.810 MB.
[2025-02-06T02:31:28.636Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:31:44.906Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:32:08.446Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:32:21.974Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:32:31.187Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:32:40.415Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:32:51.581Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:33:00.786Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:33:00.786Z] 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.
[2025-02-06T02:33:00.786Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:33:00.786Z] Movies recommended for you:
[2025-02-06T02:33:00.786Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:33:00.786Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:33:00.786Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (115080.788 ms) ======
[2025-02-06T02:33:00.786Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-06T02:33:00.786Z] GC before operation: completed in 67.955 ms, heap usage 494.129 MB -> 76.751 MB.
[2025-02-06T02:33:20.363Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:33:36.635Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:34:00.214Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:34:20.649Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:34:31.804Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:34:42.804Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:34:56.301Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:35:02.477Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:35:03.245Z] 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.
[2025-02-06T02:35:03.245Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:35:03.245Z] Movies recommended for you:
[2025-02-06T02:35:03.245Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:35:03.245Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:35:03.245Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (122971.333 ms) ======
[2025-02-06T02:35:03.245Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-06T02:35:03.245Z] GC before operation: completed in 69.070 ms, heap usage 759.110 MB -> 74.837 MB.
[2025-02-06T02:35:26.853Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:35:46.424Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:36:09.951Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:36:26.227Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:36:37.452Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:36:46.695Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:36:57.867Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:37:05.421Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:37:05.780Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:37:05.780Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:37:05.780Z] Movies recommended for you:
[2025-02-06T02:37:05.780Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:37:05.780Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:37:05.780Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (122589.982 ms) ======
[2025-02-06T02:37:05.780Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-06T02:37:05.780Z] GC before operation: completed in 74.221 ms, heap usage 129.686 MB -> 78.253 MB.
[2025-02-06T02:37:29.324Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:37:45.588Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:38:09.188Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:38:22.704Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:38:31.917Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:38:41.121Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:38:52.291Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:38:59.855Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:38:59.856Z] 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.
[2025-02-06T02:38:59.856Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:39:00.214Z] Movies recommended for you:
[2025-02-06T02:39:00.214Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:39:00.214Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:39:00.214Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (114237.445 ms) ======
[2025-02-06T02:39:00.214Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-06T02:39:00.214Z] GC before operation: completed in 77.243 ms, heap usage 273.797 MB -> 81.512 MB.
[2025-02-06T02:39:19.811Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:39:39.404Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:40:02.939Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:40:20.489Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:40:28.039Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:40:37.236Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:40:48.394Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:40:57.664Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:40:57.664Z] 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.
[2025-02-06T02:40:57.664Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:40:57.664Z] Movies recommended for you:
[2025-02-06T02:40:57.664Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:40:57.664Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:40:57.664Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (116638.854 ms) ======
[2025-02-06T02:40:57.664Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-06T02:40:57.664Z] GC before operation: completed in 77.241 ms, heap usage 1.023 GB -> 81.751 MB.
[2025-02-06T02:41:17.234Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:41:33.512Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:41:57.057Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:42:10.563Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:42:21.774Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:42:29.321Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:42:40.497Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:42:49.696Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:42:49.696Z] 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.
[2025-02-06T02:42:49.696Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:42:49.696Z] Movies recommended for you:
[2025-02-06T02:42:49.696Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:42:49.696Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:42:49.696Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (112541.126 ms) ======
[2025-02-06T02:42:49.696Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-06T02:42:49.696Z] GC before operation: completed in 74.856 ms, heap usage 328.079 MB -> 81.942 MB.
[2025-02-06T02:43:09.275Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:43:28.848Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:43:52.362Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:44:05.864Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:44:17.023Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:44:24.579Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:44:35.738Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:44:44.976Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:44:44.976Z] 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.
[2025-02-06T02:44:44.976Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:44:44.976Z] Movies recommended for you:
[2025-02-06T02:44:44.976Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:44:44.976Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:44:44.976Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (115329.178 ms) ======
[2025-02-06T02:44:45.332Z] -----------------------------------
[2025-02-06T02:44:45.332Z] renaissance-movie-lens_0_PASSED
[2025-02-06T02:44:45.332Z] -----------------------------------
[2025-02-06T02:44:46.096Z]
[2025-02-06T02:44:46.096Z] TEST TEARDOWN:
[2025-02-06T02:44:46.096Z] Nothing to be done for teardown.
[2025-02-06T02:44:46.096Z] renaissance-movie-lens_0 Finish Time: Thu Feb 6 02:44:45 2025 Epoch Time (ms): 1738809885721