renaissance-movie-lens_0
[2025-02-26T22:08:07.053Z] Running test renaissance-movie-lens_0 ...
[2025-02-26T22:08:07.053Z] ===============================================
[2025-02-26T22:08:07.053Z] renaissance-movie-lens_0 Start Time: Wed Feb 26 22:08:06 2025 Epoch Time (ms): 1740607686448
[2025-02-26T22:08:07.053Z] variation: NoOptions
[2025-02-26T22:08:07.053Z] JVM_OPTIONS:
[2025-02-26T22:08:07.053Z] { \
[2025-02-26T22:08:07.053Z] echo ""; echo "TEST SETUP:"; \
[2025-02-26T22:08:07.053Z] echo "Nothing to be done for setup."; \
[2025-02-26T22:08:07.053Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406068737315/renaissance-movie-lens_0"; \
[2025-02-26T22:08:07.053Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406068737315/renaissance-movie-lens_0"; \
[2025-02-26T22:08:07.053Z] echo ""; echo "TESTING:"; \
[2025-02-26T22:08:07.053Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/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_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406068737315/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-26T22:08:07.053Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406068737315/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-26T22:08:07.053Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-26T22:08:07.053Z] echo "Nothing to be done for teardown."; \
[2025-02-26T22:08:07.053Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406068737315/TestTargetResult";
[2025-02-26T22:08:07.053Z]
[2025-02-26T22:08:07.053Z] TEST SETUP:
[2025-02-26T22:08:07.053Z] Nothing to be done for setup.
[2025-02-26T22:08:07.053Z]
[2025-02-26T22:08:07.053Z] TESTING:
[2025-02-26T22:08:11.648Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-26T22:08:13.271Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2025-02-26T22:08:16.790Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-26T22:08:16.790Z] Training: 60056, validation: 20285, test: 19854
[2025-02-26T22:08:16.790Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-26T22:08:16.790Z] GC before operation: completed in 47.131 ms, heap usage 217.964 MB -> 37.433 MB.
[2025-02-26T22:08:21.314Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:08:24.810Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:08:27.317Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:08:30.830Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:08:32.452Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:08:34.075Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:08:35.707Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:08:37.341Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:08:37.341Z] 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.
[2025-02-26T22:08:37.341Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:08:37.341Z] Movies recommended for you:
[2025-02-26T22:08:37.341Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:08:37.341Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:08:37.341Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20809.644 ms) ======
[2025-02-26T22:08:37.341Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-26T22:08:38.122Z] GC before operation: completed in 89.704 ms, heap usage 2.381 GB -> 55.673 MB.
[2025-02-26T22:08:40.676Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:08:43.233Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:08:45.751Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:08:48.279Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:08:49.894Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:08:51.524Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:08:53.148Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:08:54.766Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:08:54.766Z] 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.
[2025-02-26T22:08:54.766Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:08:54.766Z] Movies recommended for you:
[2025-02-26T22:08:54.766Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:08:54.766Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:08:54.766Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17295.920 ms) ======
[2025-02-26T22:08:54.766Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-26T22:08:54.766Z] GC before operation: completed in 75.543 ms, heap usage 801.167 MB -> 54.745 MB.
[2025-02-26T22:08:58.243Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:08:59.930Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:09:03.420Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:09:05.941Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:09:06.730Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:09:08.362Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:09:09.975Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:09:11.616Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:09:11.616Z] 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.
[2025-02-26T22:09:11.616Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:09:11.616Z] Movies recommended for you:
[2025-02-26T22:09:11.616Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:09:11.616Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:09:11.616Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16853.994 ms) ======
[2025-02-26T22:09:11.616Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-26T22:09:12.400Z] GC before operation: completed in 72.958 ms, heap usage 205.424 MB -> 51.575 MB.
[2025-02-26T22:09:14.901Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:09:17.515Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:09:20.043Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:09:22.580Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:09:23.363Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:09:24.975Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:09:26.639Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:09:28.264Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:09:28.264Z] 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.
[2025-02-26T22:09:28.264Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:09:28.264Z] Movies recommended for you:
[2025-02-26T22:09:28.264Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:09:28.264Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:09:28.264Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16578.532 ms) ======
[2025-02-26T22:09:28.264Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-26T22:09:29.212Z] GC before operation: completed in 72.129 ms, heap usage 95.528 MB -> 55.562 MB.
[2025-02-26T22:09:30.915Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:09:33.449Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:09:36.145Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:09:38.876Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:09:40.536Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:09:42.228Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:09:43.844Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:09:45.456Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:09:45.456Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-26T22:09:45.456Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:09:45.456Z] Movies recommended for you:
[2025-02-26T22:09:45.456Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:09:45.456Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:09:45.456Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16719.143 ms) ======
[2025-02-26T22:09:45.456Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-26T22:09:45.456Z] GC before operation: completed in 83.229 ms, heap usage 1.770 GB -> 56.943 MB.
[2025-02-26T22:09:47.979Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:09:50.494Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:09:53.016Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:09:55.529Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:09:57.148Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:09:58.816Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:10:00.448Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:10:01.246Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:10:02.037Z] 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.
[2025-02-26T22:10:02.037Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:10:02.037Z] Movies recommended for you:
[2025-02-26T22:10:02.037Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:10:02.037Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:10:02.037Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16492.590 ms) ======
[2025-02-26T22:10:02.037Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-26T22:10:02.037Z] GC before operation: completed in 73.056 ms, heap usage 228.840 MB -> 52.051 MB.
[2025-02-26T22:10:04.554Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:10:07.117Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:10:09.637Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:10:12.179Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:10:13.802Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:10:15.460Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:10:17.112Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:10:17.903Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:10:18.689Z] 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.
[2025-02-26T22:10:18.689Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:10:18.689Z] Movies recommended for you:
[2025-02-26T22:10:18.689Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:10:18.689Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:10:18.689Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16495.259 ms) ======
[2025-02-26T22:10:18.689Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-26T22:10:18.689Z] GC before operation: completed in 86.716 ms, heap usage 486.191 MB -> 52.323 MB.
[2025-02-26T22:10:21.207Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:10:23.746Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:10:26.292Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:10:28.799Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:10:30.452Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:10:32.078Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:10:32.858Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:10:34.479Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:10:35.259Z] 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.
[2025-02-26T22:10:35.260Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:10:35.260Z] Movies recommended for you:
[2025-02-26T22:10:35.260Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:10:35.260Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:10:35.260Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16401.310 ms) ======
[2025-02-26T22:10:35.260Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-26T22:10:35.260Z] GC before operation: completed in 76.275 ms, heap usage 494.722 MB -> 52.584 MB.
[2025-02-26T22:10:37.781Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:10:40.317Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:10:42.845Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:10:45.346Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:10:46.961Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:10:48.574Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:10:50.197Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:10:51.844Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:10:51.844Z] 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.
[2025-02-26T22:10:51.844Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:10:51.844Z] Movies recommended for you:
[2025-02-26T22:10:51.844Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:10:51.844Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:10:51.844Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16693.960 ms) ======
[2025-02-26T22:10:51.844Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-26T22:10:51.844Z] GC before operation: completed in 90.873 ms, heap usage 1.516 GB -> 57.037 MB.
[2025-02-26T22:10:54.344Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:10:56.861Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:10:59.386Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:11:01.898Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:11:03.518Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:11:05.132Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:11:06.769Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:11:08.386Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:11:08.386Z] 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.
[2025-02-26T22:11:08.386Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:11:08.386Z] Movies recommended for you:
[2025-02-26T22:11:08.386Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:11:08.386Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:11:08.386Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16455.228 ms) ======
[2025-02-26T22:11:08.386Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-26T22:11:08.386Z] GC before operation: completed in 77.055 ms, heap usage 779.687 MB -> 55.986 MB.
[2025-02-26T22:11:10.883Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:11:13.377Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:11:15.889Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:11:18.415Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:11:20.029Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:11:21.637Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:11:23.251Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:11:24.891Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:11:24.891Z] 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.
[2025-02-26T22:11:24.891Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:11:24.891Z] Movies recommended for you:
[2025-02-26T22:11:24.891Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:11:24.891Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:11:24.891Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16568.802 ms) ======
[2025-02-26T22:11:24.891Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-26T22:11:24.891Z] GC before operation: completed in 87.218 ms, heap usage 2.198 GB -> 56.998 MB.
[2025-02-26T22:11:27.394Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:11:29.900Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:11:32.422Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:11:34.933Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:11:36.559Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:11:38.170Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:11:39.783Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:11:41.401Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:11:41.401Z] 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.
[2025-02-26T22:11:41.401Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:11:41.401Z] Movies recommended for you:
[2025-02-26T22:11:41.401Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:11:41.401Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:11:41.401Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16524.525 ms) ======
[2025-02-26T22:11:41.401Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-26T22:11:41.401Z] GC before operation: completed in 81.838 ms, heap usage 103.484 MB -> 56.126 MB.
[2025-02-26T22:11:43.898Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:11:46.399Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:11:48.909Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:11:51.447Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:11:53.053Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:11:54.668Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:11:56.280Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:11:57.911Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:11:57.911Z] 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.
[2025-02-26T22:11:57.911Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:11:57.911Z] Movies recommended for you:
[2025-02-26T22:11:57.911Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:11:57.911Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:11:57.911Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16186.894 ms) ======
[2025-02-26T22:11:57.911Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-26T22:11:57.911Z] GC before operation: completed in 76.415 ms, heap usage 1.717 GB -> 57.260 MB.
[2025-02-26T22:12:00.439Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:12:02.935Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:12:05.432Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:12:07.954Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:12:09.559Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:12:11.173Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:12:12.797Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:12:13.576Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:12:14.354Z] 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.
[2025-02-26T22:12:14.354Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:12:14.354Z] Movies recommended for you:
[2025-02-26T22:12:14.354Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:12:14.354Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:12:14.354Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16221.217 ms) ======
[2025-02-26T22:12:14.354Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-26T22:12:14.354Z] GC before operation: completed in 83.825 ms, heap usage 1.359 GB -> 56.741 MB.
[2025-02-26T22:12:16.867Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:12:19.389Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:12:21.934Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:12:24.466Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:12:26.086Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:12:26.870Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:12:28.480Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:12:30.169Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:12:30.169Z] 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.
[2025-02-26T22:12:30.169Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:12:30.169Z] Movies recommended for you:
[2025-02-26T22:12:30.169Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:12:30.169Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:12:30.169Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16250.355 ms) ======
[2025-02-26T22:12:30.169Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-26T22:12:30.952Z] GC before operation: completed in 77.902 ms, heap usage 1.515 GB -> 57.148 MB.
[2025-02-26T22:12:33.456Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:12:35.981Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:12:38.504Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:12:41.016Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:12:41.799Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:12:43.408Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:12:45.024Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:12:46.657Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:12:46.657Z] 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.
[2025-02-26T22:12:46.657Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:12:46.657Z] Movies recommended for you:
[2025-02-26T22:12:46.657Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:12:46.657Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:12:46.657Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16278.947 ms) ======
[2025-02-26T22:12:46.657Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-26T22:12:46.657Z] GC before operation: completed in 75.054 ms, heap usage 128.270 MB -> 56.780 MB.
[2025-02-26T22:12:49.164Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:12:52.591Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:12:54.893Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:12:56.919Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:12:58.676Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:12:59.456Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:13:01.076Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:13:02.692Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:13:03.470Z] 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.
[2025-02-26T22:13:03.470Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:13:03.470Z] Movies recommended for you:
[2025-02-26T22:13:03.470Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:13:03.470Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:13:03.470Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16255.639 ms) ======
[2025-02-26T22:13:03.470Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-26T22:13:03.470Z] GC before operation: completed in 76.226 ms, heap usage 298.693 MB -> 52.286 MB.
[2025-02-26T22:13:05.992Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:13:08.505Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:13:11.005Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:13:13.529Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:13:15.151Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:13:15.929Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:13:17.535Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:13:19.142Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:13:19.142Z] 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.
[2025-02-26T22:13:19.142Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:13:19.919Z] Movies recommended for you:
[2025-02-26T22:13:19.919Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:13:19.919Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:13:19.919Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16308.947 ms) ======
[2025-02-26T22:13:19.919Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-26T22:13:19.919Z] GC before operation: completed in 79.749 ms, heap usage 1.325 GB -> 56.910 MB.
[2025-02-26T22:13:22.417Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:13:24.945Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:13:27.438Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:13:29.953Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:13:30.733Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:13:32.348Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:13:33.953Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:13:35.558Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:13:35.558Z] 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.
[2025-02-26T22:13:35.558Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:13:35.558Z] Movies recommended for you:
[2025-02-26T22:13:35.558Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:13:35.558Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:13:35.558Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16178.678 ms) ======
[2025-02-26T22:13:35.558Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-26T22:13:35.558Z] GC before operation: completed in 76.074 ms, heap usage 107.544 MB -> 56.215 MB.
[2025-02-26T22:13:38.058Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:13:40.615Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:13:43.129Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:13:45.874Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:13:47.489Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:13:49.093Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:13:50.700Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:13:52.304Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:13:53.081Z] 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.
[2025-02-26T22:13:53.081Z] The best model improves the baseline by 14.43%.
[2025-02-26T22:13:53.081Z] Movies recommended for you:
[2025-02-26T22:13:53.081Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:13:53.081Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:13:53.081Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16915.283 ms) ======
[2025-02-26T22:13:53.081Z] -----------------------------------
[2025-02-26T22:13:53.081Z] renaissance-movie-lens_0_PASSED
[2025-02-26T22:13:53.081Z] -----------------------------------
[2025-02-26T22:13:53.081Z]
[2025-02-26T22:13:53.081Z] TEST TEARDOWN:
[2025-02-26T22:13:53.081Z] Nothing to be done for teardown.
[2025-02-26T22:13:53.081Z] renaissance-movie-lens_0 Finish Time: Wed Feb 26 22:13:52 2025 Epoch Time (ms): 1740608032947