renaissance-movie-lens_0
[2024-11-22T22:09:42.677Z] Running test renaissance-movie-lens_0 ...
[2024-11-22T22:09:42.677Z] ===============================================
[2024-11-22T22:09:42.677Z] renaissance-movie-lens_0 Start Time: Fri Nov 22 22:09:41 2024 Epoch Time (ms): 1732313381767
[2024-11-22T22:09:42.677Z] variation: NoOptions
[2024-11-22T22:09:42.677Z] JVM_OPTIONS:
[2024-11-22T22:09:42.677Z] { \
[2024-11-22T22:09:42.677Z] echo ""; echo "TEST SETUP:"; \
[2024-11-22T22:09:42.677Z] echo "Nothing to be done for setup."; \
[2024-11-22T22:09:42.677Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323125913444/renaissance-movie-lens_0"; \
[2024-11-22T22:09:42.677Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323125913444/renaissance-movie-lens_0"; \
[2024-11-22T22:09:42.677Z] echo ""; echo "TESTING:"; \
[2024-11-22T22:09:42.677Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323125913444/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-22T22:09:42.677Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323125913444/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-22T22:09:42.677Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-22T22:09:42.677Z] echo "Nothing to be done for teardown."; \
[2024-11-22T22:09:42.677Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323125913444/TestTargetResult";
[2024-11-22T22:09:42.677Z]
[2024-11-22T22:09:42.677Z] TEST SETUP:
[2024-11-22T22:09:42.677Z] Nothing to be done for setup.
[2024-11-22T22:09:42.677Z]
[2024-11-22T22:09:42.677Z] TESTING:
[2024-11-22T22:09:45.600Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-22T22:09:48.521Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-22T22:09:52.545Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-22T22:09:53.466Z] Training: 60056, validation: 20285, test: 19854
[2024-11-22T22:09:53.466Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-22T22:09:53.466Z] GC before operation: completed in 65.895 ms, heap usage 79.462 MB -> 39.433 MB.
[2024-11-22T22:10:01.382Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:10:05.412Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:10:15.128Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:10:15.128Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:10:15.128Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:10:17.024Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:10:19.944Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:10:21.838Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:10:21.839Z] 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.
[2024-11-22T22:10:21.839Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:10:22.761Z] Movies recommended for you:
[2024-11-22T22:10:22.761Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:10:22.761Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:10:22.761Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (29058.399 ms) ======
[2024-11-22T22:10:22.761Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-22T22:10:22.761Z] GC before operation: completed in 113.329 ms, heap usage 936.865 MB -> 56.888 MB.
[2024-11-22T22:10:26.789Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:10:29.712Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:10:32.635Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:10:35.696Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:10:37.586Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:10:39.478Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:10:41.371Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:10:43.259Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:10:43.259Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-22T22:10:43.259Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:10:43.259Z] Movies recommended for you:
[2024-11-22T22:10:43.259Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:10:43.259Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:10:43.259Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21021.924 ms) ======
[2024-11-22T22:10:43.259Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-22T22:10:43.260Z] GC before operation: completed in 105.064 ms, heap usage 393.721 MB -> 56.602 MB.
[2024-11-22T22:10:47.296Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:10:50.227Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:10:53.156Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:10:55.064Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:10:56.956Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:10:58.845Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:11:00.737Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:11:02.672Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:11:02.672Z] 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.
[2024-11-22T22:11:02.672Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:11:02.672Z] Movies recommended for you:
[2024-11-22T22:11:02.672Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:11:02.672Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:11:02.672Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (19382.394 ms) ======
[2024-11-22T22:11:02.672Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-22T22:11:03.593Z] GC before operation: completed in 93.268 ms, heap usage 394.002 MB -> 53.621 MB.
[2024-11-22T22:11:06.513Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:11:08.411Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:11:11.337Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:11:15.616Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:11:15.616Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:11:17.509Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:11:19.409Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:11:20.330Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:11:21.250Z] 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.
[2024-11-22T22:11:21.250Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:11:21.250Z] Movies recommended for you:
[2024-11-22T22:11:21.250Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:11:21.250Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:11:21.250Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18217.153 ms) ======
[2024-11-22T22:11:21.250Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-22T22:11:21.250Z] GC before operation: completed in 100.744 ms, heap usage 405.950 MB -> 53.920 MB.
[2024-11-22T22:11:24.176Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:11:27.613Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:11:29.505Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:11:32.428Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:11:33.349Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:11:35.573Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:11:37.472Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:11:38.393Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:11:39.314Z] 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.
[2024-11-22T22:11:39.314Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:11:39.314Z] Movies recommended for you:
[2024-11-22T22:11:39.314Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:11:39.314Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:11:39.314Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17800.995 ms) ======
[2024-11-22T22:11:39.314Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-22T22:11:39.314Z] GC before operation: completed in 100.728 ms, heap usage 718.976 MB -> 57.643 MB.
[2024-11-22T22:11:42.232Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:11:45.153Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:11:47.044Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:11:49.980Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:11:51.875Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:11:52.796Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:11:54.687Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:11:56.615Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:11:56.615Z] 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.
[2024-11-22T22:11:56.615Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:11:57.910Z] Movies recommended for you:
[2024-11-22T22:11:57.910Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:11:57.910Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:11:57.910Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (17768.457 ms) ======
[2024-11-22T22:11:57.910Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-22T22:11:57.910Z] GC before operation: completed in 105.343 ms, heap usage 312.486 MB -> 54.043 MB.
[2024-11-22T22:11:59.807Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:12:02.732Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:12:04.622Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:12:07.544Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:12:09.434Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:12:11.325Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:12:12.246Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:12:20.308Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:12:20.308Z] 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.
[2024-11-22T22:12:20.308Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:12:20.308Z] Movies recommended for you:
[2024-11-22T22:12:20.308Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:12:20.308Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:12:20.308Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17561.714 ms) ======
[2024-11-22T22:12:20.308Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-22T22:12:20.308Z] GC before operation: completed in 106.368 ms, heap usage 302.346 MB -> 54.328 MB.
[2024-11-22T22:12:20.308Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:12:20.308Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:12:22.198Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:12:25.121Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:12:27.009Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:12:27.930Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:12:29.819Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:12:31.710Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:12:31.710Z] 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.
[2024-11-22T22:12:31.710Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:12:31.710Z] Movies recommended for you:
[2024-11-22T22:12:31.710Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:12:31.710Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:12:31.710Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17163.218 ms) ======
[2024-11-22T22:12:31.710Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-22T22:12:32.632Z] GC before operation: completed in 111.199 ms, heap usage 522.897 MB -> 58.038 MB.
[2024-11-22T22:12:35.556Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:12:37.454Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:12:40.374Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:12:42.263Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:12:44.155Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:12:46.046Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:12:46.967Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:12:48.858Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:12:48.858Z] 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.
[2024-11-22T22:12:48.858Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:12:49.781Z] Movies recommended for you:
[2024-11-22T22:12:49.781Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:12:49.781Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:12:49.781Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17171.273 ms) ======
[2024-11-22T22:12:49.781Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-22T22:12:49.781Z] GC before operation: completed in 100.652 ms, heap usage 353.156 MB -> 54.386 MB.
[2024-11-22T22:12:51.675Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:12:54.597Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:12:56.485Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:12:59.404Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:13:00.323Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:13:02.213Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:13:03.133Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:13:05.026Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:13:05.026Z] 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.
[2024-11-22T22:13:05.026Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:13:05.947Z] Movies recommended for you:
[2024-11-22T22:13:05.947Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:13:05.947Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:13:05.947Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16112.649 ms) ======
[2024-11-22T22:13:05.947Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-22T22:13:05.947Z] GC before operation: completed in 99.520 ms, heap usage 410.762 MB -> 54.555 MB.
[2024-11-22T22:13:08.356Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:13:10.250Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:13:13.172Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:13:15.062Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:13:21.020Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:13:21.021Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:13:23.811Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:13:23.811Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:13:23.811Z] 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.
[2024-11-22T22:13:23.811Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:13:23.811Z] Movies recommended for you:
[2024-11-22T22:13:23.811Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:13:23.811Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:13:23.811Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16103.611 ms) ======
[2024-11-22T22:13:23.811Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-22T22:13:23.811Z] GC before operation: completed in 100.196 ms, heap usage 515.067 MB -> 57.629 MB.
[2024-11-22T22:13:24.731Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:13:26.621Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:13:29.555Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:13:31.444Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:13:33.348Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:13:34.267Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:13:36.184Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:13:38.075Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:13:38.075Z] 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.
[2024-11-22T22:13:38.075Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:13:38.075Z] Movies recommended for you:
[2024-11-22T22:13:38.075Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:13:38.075Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:13:38.075Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16359.104 ms) ======
[2024-11-22T22:13:38.075Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-22T22:13:38.075Z] GC before operation: completed in 101.117 ms, heap usage 205.224 MB -> 54.370 MB.
[2024-11-22T22:13:41.016Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:13:42.908Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:13:45.830Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:13:47.724Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:13:49.615Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:13:51.508Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:13:52.429Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:13:54.320Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:13:55.241Z] 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.
[2024-11-22T22:13:55.241Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:13:55.241Z] Movies recommended for you:
[2024-11-22T22:13:55.241Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:13:55.241Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:13:55.241Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16769.156 ms) ======
[2024-11-22T22:13:55.241Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-22T22:13:55.241Z] GC before operation: completed in 108.063 ms, heap usage 571.906 MB -> 58.101 MB.
[2024-11-22T22:13:58.163Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:14:00.054Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:14:02.987Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:14:05.908Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:14:07.805Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:14:08.727Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:14:10.619Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:14:12.511Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:14:12.511Z] 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.
[2024-11-22T22:14:12.511Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:14:12.511Z] Movies recommended for you:
[2024-11-22T22:14:12.511Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:14:12.511Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:14:12.511Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17562.649 ms) ======
[2024-11-22T22:14:12.511Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-22T22:14:12.511Z] GC before operation: completed in 104.097 ms, heap usage 310.380 MB -> 54.311 MB.
[2024-11-22T22:14:15.430Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:14:19.518Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:14:20.439Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:14:23.358Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:14:25.244Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:14:26.163Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:14:28.050Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:14:28.969Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:14:29.890Z] 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.
[2024-11-22T22:14:29.890Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:14:29.890Z] Movies recommended for you:
[2024-11-22T22:14:29.890Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:14:29.890Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:14:29.890Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (17159.439 ms) ======
[2024-11-22T22:14:29.890Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-22T22:14:29.890Z] GC before operation: completed in 99.433 ms, heap usage 347.842 MB -> 54.485 MB.
[2024-11-22T22:14:32.812Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:14:34.701Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:14:37.773Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:14:39.665Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:14:41.554Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:14:43.467Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:14:44.386Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:14:46.280Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:14:46.280Z] 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.
[2024-11-22T22:14:46.280Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:14:47.200Z] Movies recommended for you:
[2024-11-22T22:14:47.200Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:14:47.200Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:14:47.200Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16627.449 ms) ======
[2024-11-22T22:14:47.200Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-22T22:14:47.200Z] GC before operation: completed in 121.556 ms, heap usage 408.974 MB -> 54.695 MB.
[2024-11-22T22:14:49.090Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:14:52.011Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:14:53.903Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:14:56.829Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:14:57.750Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:14:59.640Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:15:00.560Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:15:02.450Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:15:02.450Z] 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.
[2024-11-22T22:15:02.450Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:15:03.374Z] Movies recommended for you:
[2024-11-22T22:15:03.374Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:15:03.374Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:15:03.374Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16087.679 ms) ======
[2024-11-22T22:15:03.374Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-22T22:15:03.374Z] GC before operation: completed in 99.737 ms, heap usage 563.963 MB -> 57.978 MB.
[2024-11-22T22:15:05.263Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:15:08.177Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:15:10.066Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:15:12.981Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:15:13.900Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:15:15.790Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:15:18.364Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:15:19.284Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:15:19.284Z] 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.
[2024-11-22T22:15:19.284Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:15:19.284Z] Movies recommended for you:
[2024-11-22T22:15:19.284Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:15:19.284Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:15:19.284Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16473.712 ms) ======
[2024-11-22T22:15:19.284Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-22T22:15:19.284Z] GC before operation: completed in 99.630 ms, heap usage 330.080 MB -> 56.914 MB.
[2024-11-22T22:15:22.203Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:15:24.090Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:15:27.012Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:15:28.902Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:15:30.792Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:15:31.717Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:15:33.612Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:15:35.536Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:15:35.536Z] 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.
[2024-11-22T22:15:35.536Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:15:35.536Z] Movies recommended for you:
[2024-11-22T22:15:35.536Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:15:35.536Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:15:35.536Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15965.471 ms) ======
[2024-11-22T22:15:35.536Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-22T22:15:35.536Z] GC before operation: completed in 100.497 ms, heap usage 307.779 MB -> 54.747 MB.
[2024-11-22T22:15:38.464Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T22:15:40.354Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T22:15:43.311Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T22:15:45.206Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T22:15:47.098Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T22:15:48.026Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T22:15:49.925Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T22:15:51.819Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T22:15:51.820Z] 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.
[2024-11-22T22:15:51.820Z] The best model improves the baseline by 14.43%.
[2024-11-22T22:15:51.820Z] Movies recommended for you:
[2024-11-22T22:15:51.820Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T22:15:51.820Z] There is no way to check that no silent failure occurred.
[2024-11-22T22:15:51.820Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16249.395 ms) ======
[2024-11-22T22:15:53.713Z] -----------------------------------
[2024-11-22T22:15:53.713Z] renaissance-movie-lens_0_PASSED
[2024-11-22T22:15:53.713Z] -----------------------------------
[2024-11-22T22:15:53.713Z]
[2024-11-22T22:15:53.713Z] TEST TEARDOWN:
[2024-11-22T22:15:53.713Z] Nothing to be done for teardown.
[2024-11-22T22:15:53.713Z] renaissance-movie-lens_0 Finish Time: Fri Nov 22 22:15:53 2024 Epoch Time (ms): 1732313753109