renaissance-movie-lens_0
[2025-02-27T02:48:18.339Z] Running test renaissance-movie-lens_0 ...
[2025-02-27T02:48:18.339Z] ===============================================
[2025-02-27T02:48:18.339Z] renaissance-movie-lens_0 Start Time: Thu Feb 27 02:48:17 2025 Epoch Time (ms): 1740624497967
[2025-02-27T02:48:18.339Z] variation: NoOptions
[2025-02-27T02:48:18.339Z] JVM_OPTIONS:
[2025-02-27T02:48:18.339Z] { \
[2025-02-27T02:48:18.339Z] echo ""; echo "TEST SETUP:"; \
[2025-02-27T02:48:18.339Z] echo "Nothing to be done for setup."; \
[2025-02-27T02:48:18.339Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406235016313/renaissance-movie-lens_0"; \
[2025-02-27T02:48:18.339Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406235016313/renaissance-movie-lens_0"; \
[2025-02-27T02:48:18.339Z] echo ""; echo "TESTING:"; \
[2025-02-27T02:48:18.339Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406235016313/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-27T02:48:18.339Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406235016313/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-27T02:48:18.339Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-27T02:48:18.339Z] echo "Nothing to be done for teardown."; \
[2025-02-27T02:48:18.339Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17406235016313/TestTargetResult";
[2025-02-27T02:48:18.339Z]
[2025-02-27T02:48:18.339Z] TEST SETUP:
[2025-02-27T02:48:18.339Z] Nothing to be done for setup.
[2025-02-27T02:48:18.339Z]
[2025-02-27T02:48:18.339Z] TESTING:
[2025-02-27T02:48:22.795Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-27T02:48:26.221Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2025-02-27T02:48:29.668Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-27T02:48:30.436Z] Training: 60056, validation: 20285, test: 19854
[2025-02-27T02:48:30.436Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-27T02:48:30.436Z] GC before operation: completed in 63.941 ms, heap usage 56.423 MB -> 38.061 MB.
[2025-02-27T02:48:36.057Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:48:40.514Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:48:43.933Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:48:47.396Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:48:48.985Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:48:51.471Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:48:53.061Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:48:55.530Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:48:55.531Z] 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.
[2025-02-27T02:48:55.531Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:48:56.299Z] Movies recommended for you:
[2025-02-27T02:48:56.299Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:48:56.299Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:48:56.299Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25630.379 ms) ======
[2025-02-27T02:48:56.299Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-27T02:48:56.299Z] GC before operation: completed in 107.281 ms, heap usage 75.945 MB -> 50.502 MB.
[2025-02-27T02:48:59.743Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:49:02.217Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:49:05.648Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:49:09.077Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:49:10.669Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:49:13.157Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:49:14.749Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:49:17.223Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:49:17.223Z] 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.
[2025-02-27T02:49:17.223Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:49:17.223Z] Movies recommended for you:
[2025-02-27T02:49:17.223Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:49:17.223Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:49:17.223Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21208.107 ms) ======
[2025-02-27T02:49:17.223Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-27T02:49:17.223Z] GC before operation: completed in 109.684 ms, heap usage 268.234 MB -> 51.762 MB.
[2025-02-27T02:49:20.646Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:49:24.073Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:49:26.551Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:49:29.976Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:49:31.570Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:49:33.163Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:49:35.628Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:49:37.223Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:49:37.223Z] 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.
[2025-02-27T02:49:37.994Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:49:37.994Z] Movies recommended for you:
[2025-02-27T02:49:37.994Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:49:37.994Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:49:37.994Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20322.343 ms) ======
[2025-02-27T02:49:37.994Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-27T02:49:37.994Z] GC before operation: completed in 112.816 ms, heap usage 241.486 MB -> 52.169 MB.
[2025-02-27T02:49:40.465Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:49:43.922Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:49:47.516Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:49:49.992Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:49:51.580Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:49:54.066Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:49:55.662Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:49:57.271Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:49:58.041Z] 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.
[2025-02-27T02:49:58.041Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:49:58.041Z] Movies recommended for you:
[2025-02-27T02:49:58.041Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:49:58.041Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:49:58.041Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20103.809 ms) ======
[2025-02-27T02:49:58.041Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-27T02:49:58.041Z] GC before operation: completed in 122.695 ms, heap usage 97.755 MB -> 55.216 MB.
[2025-02-27T02:50:01.465Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:50:03.942Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:50:07.369Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:50:10.788Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:50:12.377Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:50:13.976Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:50:16.448Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:50:18.048Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:50:18.048Z] 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.
[2025-02-27T02:50:18.048Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:50:18.048Z] Movies recommended for you:
[2025-02-27T02:50:18.048Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:50:18.048Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:50:18.048Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20246.691 ms) ======
[2025-02-27T02:50:18.048Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-27T02:50:18.817Z] GC before operation: completed in 111.652 ms, heap usage 236.176 MB -> 52.679 MB.
[2025-02-27T02:50:21.283Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:50:24.711Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:50:28.186Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:50:30.660Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:50:32.251Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:50:34.731Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:50:36.319Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:50:37.907Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:50:38.678Z] 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.
[2025-02-27T02:50:38.678Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:50:38.678Z] Movies recommended for you:
[2025-02-27T02:50:38.678Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:50:38.678Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:50:38.678Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20041.721 ms) ======
[2025-02-27T02:50:38.678Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-27T02:50:38.678Z] GC before operation: completed in 112.928 ms, heap usage 336.523 MB -> 52.675 MB.
[2025-02-27T02:50:42.117Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:50:44.595Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:50:48.085Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:50:50.556Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:50:53.035Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:50:54.625Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:50:56.223Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:50:58.712Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:50:58.712Z] 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.
[2025-02-27T02:50:58.712Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:50:58.712Z] Movies recommended for you:
[2025-02-27T02:50:58.712Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:50:58.712Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:50:58.712Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20103.734 ms) ======
[2025-02-27T02:50:58.712Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-27T02:50:58.712Z] GC before operation: completed in 108.920 ms, heap usage 296.452 MB -> 52.831 MB.
[2025-02-27T02:51:02.136Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:51:04.612Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:51:08.035Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:51:10.517Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:51:12.987Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:51:14.577Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:51:16.166Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:51:18.646Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:51:18.646Z] 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.
[2025-02-27T02:51:18.646Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:51:18.646Z] Movies recommended for you:
[2025-02-27T02:51:18.646Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:51:18.646Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:51:18.646Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19827.450 ms) ======
[2025-02-27T02:51:18.646Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-27T02:51:18.646Z] GC before operation: completed in 147.403 ms, heap usage 300.128 MB -> 53.122 MB.
[2025-02-27T02:51:22.065Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:51:24.548Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:51:27.969Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:51:31.394Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:51:32.991Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:51:34.583Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:51:36.174Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:51:38.652Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:51:38.652Z] 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.
[2025-02-27T02:51:38.652Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:51:38.652Z] Movies recommended for you:
[2025-02-27T02:51:38.652Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:51:38.652Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:51:38.652Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19913.450 ms) ======
[2025-02-27T02:51:38.652Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-27T02:51:38.652Z] GC before operation: completed in 110.344 ms, heap usage 244.289 MB -> 52.867 MB.
[2025-02-27T02:51:42.073Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:51:44.716Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:51:48.338Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:51:50.807Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:51:52.404Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:51:54.882Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:51:56.480Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:51:58.084Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:51:58.853Z] 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.
[2025-02-27T02:51:58.853Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:51:58.853Z] Movies recommended for you:
[2025-02-27T02:51:58.853Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:51:58.853Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:51:58.853Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19874.052 ms) ======
[2025-02-27T02:51:58.853Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-27T02:51:58.853Z] GC before operation: completed in 123.356 ms, heap usage 189.075 MB -> 52.949 MB.
[2025-02-27T02:52:02.273Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:52:04.753Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:52:08.200Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:52:10.665Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:52:13.139Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:52:14.736Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:52:16.327Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:52:17.924Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:52:18.696Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T02:52:18.696Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:52:18.696Z] Movies recommended for you:
[2025-02-27T02:52:18.696Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:52:18.696Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:52:18.696Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19884.294 ms) ======
[2025-02-27T02:52:18.696Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-27T02:52:18.696Z] GC before operation: completed in 112.300 ms, heap usage 247.151 MB -> 52.696 MB.
[2025-02-27T02:52:22.121Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:52:24.599Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:52:28.019Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:52:31.458Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:52:33.045Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:52:34.643Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:52:36.236Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:52:38.736Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:52:38.736Z] 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.
[2025-02-27T02:52:38.736Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:52:38.736Z] Movies recommended for you:
[2025-02-27T02:52:38.736Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:52:38.736Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:52:38.736Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20134.280 ms) ======
[2025-02-27T02:52:38.736Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-27T02:52:38.736Z] GC before operation: completed in 121.208 ms, heap usage 117.012 MB -> 53.335 MB.
[2025-02-27T02:52:42.162Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:52:45.597Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:52:48.557Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:52:51.032Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:52:53.530Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:52:55.118Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:52:56.708Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:52:58.306Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:52:59.075Z] 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.
[2025-02-27T02:52:59.075Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:52:59.075Z] Movies recommended for you:
[2025-02-27T02:52:59.075Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:52:59.075Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:52:59.075Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20063.263 ms) ======
[2025-02-27T02:52:59.075Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-27T02:52:59.075Z] GC before operation: completed in 117.592 ms, heap usage 462.198 MB -> 53.230 MB.
[2025-02-27T02:53:02.508Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:53:04.978Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:53:08.402Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:53:10.873Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:53:13.349Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:53:14.941Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:53:16.528Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:53:18.122Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:53:18.895Z] 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.
[2025-02-27T02:53:18.895Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:53:18.895Z] Movies recommended for you:
[2025-02-27T02:53:18.895Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:53:18.895Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:53:18.895Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19746.224 ms) ======
[2025-02-27T02:53:18.895Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-27T02:53:18.895Z] GC before operation: completed in 109.766 ms, heap usage 174.433 MB -> 52.732 MB.
[2025-02-27T02:53:22.329Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:53:24.807Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:53:28.232Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:53:31.659Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:53:33.268Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:53:34.878Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:53:36.474Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:53:38.947Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:53:38.947Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T02:53:38.947Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:53:38.947Z] Movies recommended for you:
[2025-02-27T02:53:38.947Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:53:38.947Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:53:38.947Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19958.839 ms) ======
[2025-02-27T02:53:38.947Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-27T02:53:38.947Z] GC before operation: completed in 112.366 ms, heap usage 196.529 MB -> 52.964 MB.
[2025-02-27T02:53:42.386Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:53:44.868Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:53:48.316Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:53:51.755Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:53:53.365Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:53:54.952Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:53:56.545Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:53:59.022Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:53:59.022Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T02:53:59.022Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:53:59.022Z] Movies recommended for you:
[2025-02-27T02:53:59.022Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:53:59.022Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:53:59.022Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19910.168 ms) ======
[2025-02-27T02:53:59.022Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-27T02:53:59.022Z] GC before operation: completed in 114.118 ms, heap usage 253.473 MB -> 53.075 MB.
[2025-02-27T02:54:02.451Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:54:04.928Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:54:08.347Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:54:11.777Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:54:13.367Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:54:14.972Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:54:16.566Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:54:18.159Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:54:18.928Z] 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.
[2025-02-27T02:54:18.928Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:54:18.928Z] Movies recommended for you:
[2025-02-27T02:54:18.928Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:54:18.928Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:54:18.928Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19782.003 ms) ======
[2025-02-27T02:54:18.928Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-27T02:54:18.928Z] GC before operation: completed in 117.490 ms, heap usage 305.396 MB -> 52.978 MB.
[2025-02-27T02:54:22.376Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:54:24.851Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:54:28.278Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:54:31.705Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:54:33.319Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:54:34.926Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:54:36.516Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:54:38.115Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:54:38.888Z] 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.
[2025-02-27T02:54:38.888Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:54:38.888Z] Movies recommended for you:
[2025-02-27T02:54:38.888Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:54:38.888Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:54:38.888Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19733.285 ms) ======
[2025-02-27T02:54:38.888Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-27T02:54:38.888Z] GC before operation: completed in 115.168 ms, heap usage 216.742 MB -> 52.971 MB.
[2025-02-27T02:54:42.316Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:54:44.798Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:54:48.217Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:54:50.710Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:54:53.177Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:54:54.768Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:54:56.369Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:54:57.963Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:54:58.734Z] 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.
[2025-02-27T02:54:58.734Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:54:58.734Z] Movies recommended for you:
[2025-02-27T02:54:58.734Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:54:58.734Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:54:58.734Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19657.028 ms) ======
[2025-02-27T02:54:58.734Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-27T02:54:58.734Z] GC before operation: completed in 111.348 ms, heap usage 319.269 MB -> 53.232 MB.
[2025-02-27T02:55:02.175Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:55:04.671Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:55:08.100Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:55:10.573Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:55:12.162Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:55:13.758Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:55:16.252Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:55:17.851Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:55:17.851Z] 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.
[2025-02-27T02:55:17.851Z] The best model improves the baseline by 14.43%.
[2025-02-27T02:55:17.851Z] Movies recommended for you:
[2025-02-27T02:55:17.851Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:55:17.851Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:55:17.851Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19440.602 ms) ======
[2025-02-27T02:55:18.620Z] -----------------------------------
[2025-02-27T02:55:18.620Z] renaissance-movie-lens_0_PASSED
[2025-02-27T02:55:18.620Z] -----------------------------------
[2025-02-27T02:55:18.620Z]
[2025-02-27T02:55:18.620Z] TEST TEARDOWN:
[2025-02-27T02:55:18.620Z] Nothing to be done for teardown.
[2025-02-27T02:55:18.620Z] renaissance-movie-lens_0 Finish Time: Thu Feb 27 02:55:18 2025 Epoch Time (ms): 1740624918356