renaissance-movie-lens_0
[2025-02-26T22:53:23.751Z] Running test renaissance-movie-lens_0 ...
[2025-02-26T22:53:23.751Z] ===============================================
[2025-02-26T22:53:23.751Z] renaissance-movie-lens_0 Start Time: Wed Feb 26 16:53:23 2025 Epoch Time (ms): 1740610403353
[2025-02-26T22:53:23.751Z] variation: NoOptions
[2025-02-26T22:53:23.751Z] JVM_OPTIONS:
[2025-02-26T22:53:23.751Z] { \
[2025-02-26T22:53:23.751Z] echo ""; echo "TEST SETUP:"; \
[2025-02-26T22:53:23.751Z] echo "Nothing to be done for setup."; \
[2025-02-26T22:53:23.751Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17406095219659/renaissance-movie-lens_0"; \
[2025-02-26T22:53:23.751Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17406095219659/renaissance-movie-lens_0"; \
[2025-02-26T22:53:23.751Z] echo ""; echo "TESTING:"; \
[2025-02-26T22:53:23.751Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17406095219659/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-26T22:53:23.751Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17406095219659/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-26T22:53:23.751Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-26T22:53:23.751Z] echo "Nothing to be done for teardown."; \
[2025-02-26T22:53:23.751Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17406095219659/TestTargetResult";
[2025-02-26T22:53:23.751Z]
[2025-02-26T22:53:23.751Z] TEST SETUP:
[2025-02-26T22:53:23.751Z] Nothing to be done for setup.
[2025-02-26T22:53:23.751Z]
[2025-02-26T22:53:23.751Z] TESTING:
[2025-02-26T22:53:25.966Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-26T22:53:28.182Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2025-02-26T22:53:32.220Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-26T22:53:32.220Z] Training: 60056, validation: 20285, test: 19854
[2025-02-26T22:53:32.220Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-26T22:53:32.220Z] GC before operation: completed in 112.118 ms, heap usage 112.984 MB -> 37.163 MB.
[2025-02-26T22:53:39.861Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:53:43.921Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:53:47.146Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:53:50.271Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:53:51.703Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:53:53.925Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:53:56.152Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:53:57.633Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:53:57.633Z] 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.
[2025-02-26T22:53:57.633Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:53:58.321Z] Movies recommended for you:
[2025-02-26T22:53:58.321Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:53:58.321Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:53:58.321Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25763.404 ms) ======
[2025-02-26T22:53:58.321Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-26T22:53:58.321Z] GC before operation: completed in 136.460 ms, heap usage 129.389 MB -> 50.926 MB.
[2025-02-26T22:54:01.414Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:54:04.537Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:54:06.810Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:54:09.917Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:54:11.381Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:54:13.617Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:54:15.038Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:54:17.262Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:54:17.262Z] 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.
[2025-02-26T22:54:17.262Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:54:17.945Z] Movies recommended for you:
[2025-02-26T22:54:17.946Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:54:17.946Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:54:17.946Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (19501.024 ms) ======
[2025-02-26T22:54:17.946Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-26T22:54:17.946Z] GC before operation: completed in 128.836 ms, heap usage 104.331 MB -> 53.425 MB.
[2025-02-26T22:54:21.049Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:54:23.302Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:54:26.422Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:54:28.679Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:54:30.905Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:54:31.612Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:54:33.837Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:54:35.279Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:54:35.967Z] 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.
[2025-02-26T22:54:35.967Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:54:35.967Z] Movies recommended for you:
[2025-02-26T22:54:35.967Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:54:35.967Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:54:35.967Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (18076.436 ms) ======
[2025-02-26T22:54:35.967Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-26T22:54:35.967Z] GC before operation: completed in 123.678 ms, heap usage 224.218 MB -> 51.393 MB.
[2025-02-26T22:54:39.091Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:54:41.335Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:54:43.585Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:54:45.838Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:54:48.095Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:54:49.537Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:54:50.976Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:54:52.416Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:54:53.105Z] 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.
[2025-02-26T22:54:53.105Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:54:53.105Z] Movies recommended for you:
[2025-02-26T22:54:53.105Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:54:53.105Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:54:53.105Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16946.318 ms) ======
[2025-02-26T22:54:53.105Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-26T22:54:53.105Z] GC before operation: completed in 134.402 ms, heap usage 125.830 MB -> 51.627 MB.
[2025-02-26T22:54:56.263Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:54:58.532Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:55:00.768Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:55:03.003Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:55:04.438Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:55:06.668Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:55:08.109Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:55:09.538Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:55:09.538Z] 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.
[2025-02-26T22:55:09.538Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:55:09.538Z] Movies recommended for you:
[2025-02-26T22:55:09.538Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:55:09.538Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:55:09.538Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16769.937 ms) ======
[2025-02-26T22:55:09.538Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-26T22:55:10.255Z] GC before operation: completed in 118.683 ms, heap usage 203.470 MB -> 51.860 MB.
[2025-02-26T22:55:13.378Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:55:15.623Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:55:17.870Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:55:20.095Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:55:21.550Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:55:23.795Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:55:25.228Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:55:26.663Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:55:26.663Z] 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.
[2025-02-26T22:55:26.663Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:55:26.663Z] Movies recommended for you:
[2025-02-26T22:55:26.663Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:55:26.663Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:55:26.663Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16900.903 ms) ======
[2025-02-26T22:55:26.663Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-26T22:55:26.663Z] GC before operation: completed in 123.876 ms, heap usage 331.349 MB -> 55.088 MB.
[2025-02-26T22:55:29.746Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:55:31.988Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:55:34.222Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:55:36.469Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:55:38.708Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:55:40.147Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:55:41.568Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:55:43.001Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:55:43.001Z] 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.
[2025-02-26T22:55:43.001Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:55:43.001Z] Movies recommended for you:
[2025-02-26T22:55:43.001Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:55:43.001Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:55:43.001Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16255.075 ms) ======
[2025-02-26T22:55:43.001Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-26T22:55:43.686Z] GC before operation: completed in 113.818 ms, heap usage 247.704 MB -> 51.911 MB.
[2025-02-26T22:55:45.899Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:55:48.137Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:55:51.248Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:55:53.492Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:55:54.948Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:55:56.387Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:55:58.648Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:55:59.360Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:56:00.059Z] 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.
[2025-02-26T22:56:00.059Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:56:00.059Z] Movies recommended for you:
[2025-02-26T22:56:00.059Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:56:00.059Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:56:00.059Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16635.879 ms) ======
[2025-02-26T22:56:00.059Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-26T22:56:00.059Z] GC before operation: completed in 119.515 ms, heap usage 315.783 MB -> 52.230 MB.
[2025-02-26T22:56:02.291Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:56:05.398Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:56:07.650Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:56:09.880Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:56:12.116Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:56:13.537Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:56:14.962Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:56:16.390Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:56:16.390Z] 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.
[2025-02-26T22:56:16.390Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:56:16.390Z] Movies recommended for you:
[2025-02-26T22:56:16.390Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:56:16.390Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:56:16.390Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16297.804 ms) ======
[2025-02-26T22:56:16.390Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-26T22:56:16.390Z] GC before operation: completed in 124.610 ms, heap usage 229.142 MB -> 52.082 MB.
[2025-02-26T22:56:19.484Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:56:21.716Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:56:23.954Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:56:27.068Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:56:28.501Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:56:29.926Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:56:31.366Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:56:32.831Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:56:32.831Z] 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.
[2025-02-26T22:56:33.515Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:56:33.515Z] Movies recommended for you:
[2025-02-26T22:56:33.515Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:56:33.515Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:56:33.515Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16761.594 ms) ======
[2025-02-26T22:56:33.515Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-26T22:56:33.515Z] GC before operation: completed in 126.673 ms, heap usage 226.436 MB -> 52.183 MB.
[2025-02-26T22:56:35.752Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:56:38.855Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:56:41.072Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:56:43.831Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:56:44.551Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:56:45.993Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:56:47.417Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:56:48.882Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:56:49.601Z] 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.
[2025-02-26T22:56:49.601Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:56:49.601Z] Movies recommended for you:
[2025-02-26T22:56:49.601Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:56:49.601Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:56:49.601Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15978.788 ms) ======
[2025-02-26T22:56:49.601Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-26T22:56:49.601Z] GC before operation: completed in 128.846 ms, heap usage 218.104 MB -> 51.865 MB.
[2025-02-26T22:56:51.816Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:56:54.897Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:56:57.159Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:56:59.376Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:57:00.799Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:57:02.251Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:57:03.695Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:57:05.142Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:57:05.843Z] 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.
[2025-02-26T22:57:05.843Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:57:05.843Z] Movies recommended for you:
[2025-02-26T22:57:05.843Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:57:05.843Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:57:05.843Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16250.042 ms) ======
[2025-02-26T22:57:05.843Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-26T22:57:05.843Z] GC before operation: completed in 136.413 ms, heap usage 255.527 MB -> 52.114 MB.
[2025-02-26T22:57:08.946Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:57:11.177Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:57:13.448Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:57:15.709Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:57:17.941Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:57:19.387Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:57:20.825Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:57:22.251Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:57:22.965Z] 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.
[2025-02-26T22:57:22.965Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:57:22.965Z] Movies recommended for you:
[2025-02-26T22:57:22.965Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:57:22.965Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:57:22.965Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16898.881 ms) ======
[2025-02-26T22:57:22.966Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-26T22:57:22.966Z] GC before operation: completed in 118.311 ms, heap usage 436.754 MB -> 52.425 MB.
[2025-02-26T22:57:26.078Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:57:28.306Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:57:30.523Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:57:32.746Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:57:34.192Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:57:35.619Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:57:37.055Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:57:38.500Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:57:39.185Z] 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.
[2025-02-26T22:57:39.185Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:57:39.185Z] Movies recommended for you:
[2025-02-26T22:57:39.185Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:57:39.185Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:57:39.186Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16160.855 ms) ======
[2025-02-26T22:57:39.186Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-26T22:57:39.186Z] GC before operation: completed in 128.065 ms, heap usage 340.343 MB -> 52.032 MB.
[2025-02-26T22:57:42.315Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:57:44.563Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:57:46.782Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:57:49.013Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:57:50.499Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:57:52.743Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:57:54.161Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:57:55.596Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:57:55.596Z] 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.
[2025-02-26T22:57:55.596Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:57:55.596Z] Movies recommended for you:
[2025-02-26T22:57:55.596Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:57:55.596Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:57:55.596Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16573.803 ms) ======
[2025-02-26T22:57:55.596Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-26T22:57:56.294Z] GC before operation: completed in 145.451 ms, heap usage 308.527 MB -> 52.283 MB.
[2025-02-26T22:57:58.533Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:58:00.782Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:58:03.904Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:58:06.147Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:58:07.573Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:58:09.078Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:58:10.500Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:58:11.934Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:58:11.934Z] 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.
[2025-02-26T22:58:11.934Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:58:11.934Z] Movies recommended for you:
[2025-02-26T22:58:11.934Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:58:11.934Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:58:11.934Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16283.791 ms) ======
[2025-02-26T22:58:11.934Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-26T22:58:12.618Z] GC before operation: completed in 124.160 ms, heap usage 328.446 MB -> 52.329 MB.
[2025-02-26T22:58:14.835Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:58:17.073Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:58:20.177Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:58:22.407Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:58:23.847Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:58:25.287Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:58:26.720Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:58:28.160Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:58:28.847Z] 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.
[2025-02-26T22:58:28.847Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:58:28.847Z] Movies recommended for you:
[2025-02-26T22:58:28.847Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:58:28.847Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:58:28.847Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16525.133 ms) ======
[2025-02-26T22:58:28.847Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-26T22:58:28.847Z] GC before operation: completed in 128.331 ms, heap usage 227.515 MB -> 52.081 MB.
[2025-02-26T22:58:31.933Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:58:34.158Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:58:36.409Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:58:39.494Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:58:40.922Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:58:42.359Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:58:43.826Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:58:45.401Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:58:45.401Z] 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.
[2025-02-26T22:58:45.401Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:58:45.401Z] Movies recommended for you:
[2025-02-26T22:58:45.401Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:58:45.401Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:58:45.401Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16313.169 ms) ======
[2025-02-26T22:58:45.401Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-26T22:58:45.401Z] GC before operation: completed in 125.453 ms, heap usage 364.411 MB -> 52.308 MB.
[2025-02-26T22:58:48.489Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:58:50.720Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:58:52.970Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:58:55.226Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:58:56.649Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:58:58.082Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:58:59.512Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:59:00.971Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:59:01.683Z] 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.
[2025-02-26T22:59:01.683Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:59:01.683Z] Movies recommended for you:
[2025-02-26T22:59:01.683Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:59:01.683Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:59:01.683Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16203.396 ms) ======
[2025-02-26T22:59:01.683Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-26T22:59:01.683Z] GC before operation: completed in 120.632 ms, heap usage 320.345 MB -> 52.419 MB.
[2025-02-26T22:59:04.791Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:59:07.022Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:59:09.306Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:59:11.528Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:59:12.956Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:59:14.395Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:59:15.836Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:59:18.073Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:59:18.074Z] 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.
[2025-02-26T22:59:18.074Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:59:18.074Z] Movies recommended for you:
[2025-02-26T22:59:18.074Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:59:18.074Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:59:18.074Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16251.002 ms) ======
[2025-02-26T22:59:19.494Z] -----------------------------------
[2025-02-26T22:59:19.494Z] renaissance-movie-lens_0_PASSED
[2025-02-26T22:59:19.495Z] -----------------------------------
[2025-02-26T22:59:19.495Z]
[2025-02-26T22:59:19.495Z] TEST TEARDOWN:
[2025-02-26T22:59:19.495Z] Nothing to be done for teardown.
[2025-02-26T22:59:19.495Z] renaissance-movie-lens_0 Finish Time: Wed Feb 26 16:59:19 2025 Epoch Time (ms): 1740610759094