renaissance-movie-lens_0
[2024-08-08T00:34:12.156Z] Running test renaissance-movie-lens_0 ...
[2024-08-08T00:34:12.156Z] ===============================================
[2024-08-08T00:34:12.156Z] renaissance-movie-lens_0 Start Time: Wed Aug 7 19:34:11 2024 Epoch Time (ms): 1723077251867
[2024-08-08T00:34:12.156Z] variation: NoOptions
[2024-08-08T00:34:12.156Z] JVM_OPTIONS:
[2024-08-08T00:34:12.156Z] { \
[2024-08-08T00:34:12.156Z] echo ""; echo "TEST SETUP:"; \
[2024-08-08T00:34:12.156Z] echo "Nothing to be done for setup."; \
[2024-08-08T00:34:12.156Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230766079807/renaissance-movie-lens_0"; \
[2024-08-08T00:34:12.156Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230766079807/renaissance-movie-lens_0"; \
[2024-08-08T00:34:12.156Z] echo ""; echo "TESTING:"; \
[2024-08-08T00:34:12.156Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/jdkbinary/j2sdk-image/jdk-17.0.13+2/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 "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230766079807/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-08T00:34:12.156Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230766079807/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-08T00:34:12.156Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-08T00:34:12.156Z] echo "Nothing to be done for teardown."; \
[2024-08-08T00:34:12.156Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230766079807/TestTargetResult";
[2024-08-08T00:34:12.156Z]
[2024-08-08T00:34:12.156Z] TEST SETUP:
[2024-08-08T00:34:12.156Z] Nothing to be done for setup.
[2024-08-08T00:34:12.156Z]
[2024-08-08T00:34:12.156Z] TESTING:
[2024-08-08T00:34:15.167Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-08T00:34:16.552Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-08-08T00:34:19.573Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-08T00:34:19.573Z] Training: 60056, validation: 20285, test: 19854
[2024-08-08T00:34:20.249Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-08T00:34:20.249Z] GC before operation: completed in 52.252 ms, heap usage 76.358 MB -> 37.905 MB.
[2024-08-08T00:34:27.765Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:34:30.791Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:34:33.828Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:34:36.848Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:34:38.240Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:34:40.410Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:34:41.830Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:34:44.014Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:34:44.014Z] 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.
[2024-08-08T00:34:44.014Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:34:44.014Z] Movies recommended for you:
[2024-08-08T00:34:44.014Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:34:44.014Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:34:44.014Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24294.034 ms) ======
[2024-08-08T00:34:44.014Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-08T00:34:44.688Z] GC before operation: completed in 118.599 ms, heap usage 223.064 MB -> 53.122 MB.
[2024-08-08T00:34:47.707Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:34:50.315Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:34:53.379Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:34:55.590Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:34:56.981Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:34:58.374Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:35:00.546Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:35:01.947Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:35:01.947Z] 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.
[2024-08-08T00:35:01.947Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:35:01.947Z] Movies recommended for you:
[2024-08-08T00:35:01.947Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:35:01.947Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:35:01.947Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17835.287 ms) ======
[2024-08-08T00:35:01.947Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-08T00:35:01.947Z] GC before operation: completed in 62.151 ms, heap usage 513.632 MB -> 55.121 MB.
[2024-08-08T00:35:05.922Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:35:08.106Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:35:10.301Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:35:13.389Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:35:14.772Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:35:16.167Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:35:17.570Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:35:18.960Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:35:18.960Z] 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.
[2024-08-08T00:35:18.960Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:35:19.628Z] Movies recommended for you:
[2024-08-08T00:35:19.628Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:35:19.628Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:35:19.628Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17067.793 ms) ======
[2024-08-08T00:35:19.628Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-08T00:35:19.628Z] GC before operation: completed in 61.930 ms, heap usage 197.954 MB -> 52.060 MB.
[2024-08-08T00:35:21.810Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:35:24.008Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:35:27.036Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:35:28.441Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:35:30.617Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:35:31.300Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:35:32.708Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:35:34.867Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:35:34.867Z] 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.
[2024-08-08T00:35:34.867Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:35:34.867Z] Movies recommended for you:
[2024-08-08T00:35:34.867Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:35:34.867Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:35:34.867Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15501.141 ms) ======
[2024-08-08T00:35:34.867Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-08T00:35:34.867Z] GC before operation: completed in 82.895 ms, heap usage 201.479 MB -> 55.721 MB.
[2024-08-08T00:35:37.030Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:35:40.055Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:35:42.655Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:35:44.068Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:35:46.252Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:35:46.924Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:35:48.318Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:35:49.746Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:35:50.419Z] 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.
[2024-08-08T00:35:50.419Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:35:50.419Z] Movies recommended for you:
[2024-08-08T00:35:50.419Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:35:50.419Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:35:50.419Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15305.849 ms) ======
[2024-08-08T00:35:50.419Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-08T00:35:50.419Z] GC before operation: completed in 58.450 ms, heap usage 468.315 MB -> 56.083 MB.
[2024-08-08T00:35:52.593Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:35:55.631Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:35:57.803Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:35:59.964Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:36:01.375Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:36:02.780Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:36:05.017Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:36:05.683Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:36:06.402Z] 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.
[2024-08-08T00:36:06.403Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:36:06.403Z] Movies recommended for you:
[2024-08-08T00:36:06.403Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:36:06.403Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:36:06.403Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15883.920 ms) ======
[2024-08-08T00:36:06.403Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-08T00:36:06.403Z] GC before operation: completed in 70.184 ms, heap usage 432.405 MB -> 52.662 MB.
[2024-08-08T00:36:08.571Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:36:10.775Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:36:13.825Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:36:15.248Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:36:16.689Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:36:18.087Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:36:19.479Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:36:20.870Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:36:20.870Z] 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.
[2024-08-08T00:36:20.870Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:36:21.543Z] Movies recommended for you:
[2024-08-08T00:36:21.543Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:36:21.543Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:36:21.543Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15014.569 ms) ======
[2024-08-08T00:36:21.543Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-08T00:36:21.543Z] GC before operation: completed in 72.213 ms, heap usage 354.454 MB -> 52.762 MB.
[2024-08-08T00:36:23.758Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:36:25.966Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:36:28.149Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:36:30.323Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:36:31.737Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:36:33.903Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:36:35.714Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:36:36.399Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:36:36.399Z] 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.
[2024-08-08T00:36:36.399Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:36:36.399Z] Movies recommended for you:
[2024-08-08T00:36:36.399Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:36:36.399Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:36:36.399Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15247.205 ms) ======
[2024-08-08T00:36:36.399Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-08T00:36:36.399Z] GC before operation: completed in 57.943 ms, heap usage 854.161 MB -> 57.031 MB.
[2024-08-08T00:36:39.443Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:36:41.647Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:36:43.856Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:36:46.030Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:36:47.436Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:36:48.831Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:36:50.255Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:36:50.924Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:36:51.592Z] 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.
[2024-08-08T00:36:51.592Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:36:51.592Z] Movies recommended for you:
[2024-08-08T00:36:51.592Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:36:51.592Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:36:51.592Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14794.749 ms) ======
[2024-08-08T00:36:51.592Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-08T00:36:51.592Z] GC before operation: completed in 57.422 ms, heap usage 421.359 MB -> 52.939 MB.
[2024-08-08T00:36:53.772Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:36:55.959Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:36:58.151Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:37:00.333Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:37:01.727Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:37:03.113Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:37:04.512Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:37:05.899Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:37:05.899Z] 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.
[2024-08-08T00:37:05.899Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:37:06.567Z] Movies recommended for you:
[2024-08-08T00:37:06.567Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:37:06.567Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:37:06.567Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14749.932 ms) ======
[2024-08-08T00:37:06.567Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-08T00:37:06.567Z] GC before operation: completed in 55.905 ms, heap usage 155.054 MB -> 52.922 MB.
[2024-08-08T00:37:08.757Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:37:10.955Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:37:13.157Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:37:15.356Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:37:17.522Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:37:18.201Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:37:19.606Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:37:21.022Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:37:21.022Z] 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.
[2024-08-08T00:37:21.695Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:37:21.695Z] Movies recommended for you:
[2024-08-08T00:37:21.695Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:37:21.695Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:37:21.695Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15107.142 ms) ======
[2024-08-08T00:37:21.695Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-08T00:37:21.695Z] GC before operation: completed in 86.464 ms, heap usage 218.845 MB -> 52.752 MB.
[2024-08-08T00:37:24.722Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:37:26.661Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:37:28.882Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:37:31.062Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:37:32.450Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:37:33.975Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:37:35.372Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:37:36.839Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:37:36.839Z] 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.
[2024-08-08T00:37:36.839Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:37:36.839Z] Movies recommended for you:
[2024-08-08T00:37:36.839Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:37:36.839Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:37:36.839Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15301.353 ms) ======
[2024-08-08T00:37:36.839Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-08T00:37:36.839Z] GC before operation: completed in 84.940 ms, heap usage 146.015 MB -> 52.718 MB.
[2024-08-08T00:37:39.025Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:37:42.042Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:37:44.216Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:37:46.406Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:37:47.813Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:37:49.209Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:37:50.636Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:37:51.321Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:37:51.989Z] 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.
[2024-08-08T00:37:51.989Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:37:51.989Z] Movies recommended for you:
[2024-08-08T00:37:51.989Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:37:51.989Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:37:51.989Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15011.973 ms) ======
[2024-08-08T00:37:51.989Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-08T00:37:51.989Z] GC before operation: completed in 80.998 ms, heap usage 145.274 MB -> 53.034 MB.
[2024-08-08T00:37:54.160Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:37:56.386Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:37:59.440Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:38:00.843Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:38:02.229Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:38:03.612Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:38:05.016Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:38:06.410Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:38:07.078Z] 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.
[2024-08-08T00:38:07.078Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:38:07.078Z] Movies recommended for you:
[2024-08-08T00:38:07.078Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:38:07.078Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:38:07.078Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14919.960 ms) ======
[2024-08-08T00:38:07.078Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-08T00:38:07.078Z] GC before operation: completed in 87.344 ms, heap usage 255.511 MB -> 52.772 MB.
[2024-08-08T00:38:09.261Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:38:11.435Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:38:14.470Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:38:15.879Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:38:17.275Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:38:18.657Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:38:20.044Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:38:21.437Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:38:21.437Z] 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.
[2024-08-08T00:38:21.437Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:38:22.103Z] Movies recommended for you:
[2024-08-08T00:38:22.103Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:38:22.103Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:38:22.103Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14781.838 ms) ======
[2024-08-08T00:38:22.103Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-08T00:38:22.103Z] GC before operation: completed in 53.195 ms, heap usage 223.716 MB -> 53.013 MB.
[2024-08-08T00:38:24.269Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:38:26.453Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:38:28.626Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:38:30.895Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:38:32.292Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:38:33.697Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:38:35.498Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:38:36.922Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:38:36.922Z] 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.
[2024-08-08T00:38:36.922Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:38:36.922Z] Movies recommended for you:
[2024-08-08T00:38:36.922Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:38:36.922Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:38:36.922Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15133.625 ms) ======
[2024-08-08T00:38:36.922Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-08T00:38:36.922Z] GC before operation: completed in 61.965 ms, heap usage 575.427 MB -> 56.562 MB.
[2024-08-08T00:38:39.950Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:38:42.153Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:38:44.352Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:38:45.760Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:38:47.162Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:38:48.548Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:38:49.940Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:38:51.341Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:38:51.341Z] 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.
[2024-08-08T00:38:52.022Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:38:52.022Z] Movies recommended for you:
[2024-08-08T00:38:52.022Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:38:52.022Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:38:52.022Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14733.312 ms) ======
[2024-08-08T00:38:52.022Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-08T00:38:52.022Z] GC before operation: completed in 64.279 ms, heap usage 316.556 MB -> 53.017 MB.
[2024-08-08T00:38:54.207Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:38:56.390Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:38:58.567Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:39:00.781Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:39:02.187Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:39:04.354Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:39:05.743Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:39:07.134Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:39:07.134Z] 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.
[2024-08-08T00:39:07.134Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:39:07.134Z] Movies recommended for you:
[2024-08-08T00:39:07.134Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:39:07.134Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:39:07.134Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15460.008 ms) ======
[2024-08-08T00:39:07.134Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-08T00:39:07.134Z] GC before operation: completed in 61.074 ms, heap usage 303.427 MB -> 55.474 MB.
[2024-08-08T00:39:10.164Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:39:12.337Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:39:14.521Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:39:16.676Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:39:18.067Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:39:19.459Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:39:20.851Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:39:22.285Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:39:22.285Z] 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.
[2024-08-08T00:39:22.285Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:39:22.285Z] Movies recommended for you:
[2024-08-08T00:39:22.285Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:39:22.285Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:39:22.285Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14859.388 ms) ======
[2024-08-08T00:39:22.285Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-08T00:39:22.285Z] GC before operation: completed in 62.155 ms, heap usage 153.698 MB -> 54.936 MB.
[2024-08-08T00:39:25.338Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:39:27.516Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:39:29.692Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:39:31.881Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:39:32.549Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:39:33.953Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:39:35.353Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:39:36.824Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:39:36.824Z] 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.
[2024-08-08T00:39:36.824Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:39:37.493Z] Movies recommended for you:
[2024-08-08T00:39:37.493Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:39:37.493Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:39:37.493Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14917.407 ms) ======
[2024-08-08T00:39:38.160Z] -----------------------------------
[2024-08-08T00:39:38.160Z] renaissance-movie-lens_0_PASSED
[2024-08-08T00:39:38.160Z] -----------------------------------
[2024-08-08T00:39:38.160Z]
[2024-08-08T00:39:38.160Z] TEST TEARDOWN:
[2024-08-08T00:39:38.160Z] Nothing to be done for teardown.
[2024-08-08T00:39:38.160Z] renaissance-movie-lens_0 Finish Time: Wed Aug 7 19:39:38 2024 Epoch Time (ms): 1723077578019