renaissance-movie-lens_0
[2024-12-06T22:44:12.046Z] Running test renaissance-movie-lens_0 ...
[2024-12-06T22:44:12.046Z] ===============================================
[2024-12-06T22:44:12.046Z] renaissance-movie-lens_0 Start Time: Fri Dec 6 22:44:10 2024 Epoch Time (ms): 1733525050829
[2024-12-06T22:44:12.046Z] variation: NoOptions
[2024-12-06T22:44:12.046Z] JVM_OPTIONS:
[2024-12-06T22:44:12.046Z] { \
[2024-12-06T22:44:12.046Z] echo ""; echo "TEST SETUP:"; \
[2024-12-06T22:44:12.046Z] echo "Nothing to be done for setup."; \
[2024-12-06T22:44:12.046Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17335240229460/renaissance-movie-lens_0"; \
[2024-12-06T22:44:12.046Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17335240229460/renaissance-movie-lens_0"; \
[2024-12-06T22:44:12.046Z] echo ""; echo "TESTING:"; \
[2024-12-06T22:44:12.046Z] "/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_17335240229460/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-06T22:44:12.046Z] 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_17335240229460/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-06T22:44:12.046Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-06T22:44:12.046Z] echo "Nothing to be done for teardown."; \
[2024-12-06T22:44:12.046Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17335240229460/TestTargetResult";
[2024-12-06T22:44:12.046Z]
[2024-12-06T22:44:12.046Z] TEST SETUP:
[2024-12-06T22:44:12.046Z] Nothing to be done for setup.
[2024-12-06T22:44:12.046Z]
[2024-12-06T22:44:12.046Z] TESTING:
[2024-12-06T22:44:16.516Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-06T22:44:19.006Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-12-06T22:44:23.484Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-06T22:44:23.484Z] Training: 60056, validation: 20285, test: 19854
[2024-12-06T22:44:23.484Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-06T22:44:23.484Z] GC before operation: completed in 63.798 ms, heap usage 150.649 MB -> 37.969 MB.
[2024-12-06T22:44:30.366Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:44:33.804Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:44:37.245Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:44:40.673Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:44:43.152Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:44:44.749Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:44:47.226Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:44:49.706Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:44:49.706Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:44:49.706Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:44:49.706Z] Movies recommended for you:
[2024-12-06T22:44:49.706Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:44:49.706Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:44:49.706Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26500.807 ms) ======
[2024-12-06T22:44:49.706Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-06T22:44:49.706Z] GC before operation: completed in 113.520 ms, heap usage 97.577 MB -> 52.860 MB.
[2024-12-06T22:44:53.147Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:44:56.577Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:45:00.035Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:45:03.468Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:45:05.061Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:45:07.542Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:45:10.019Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:45:11.617Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:45:11.617Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:45:11.617Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:45:12.389Z] Movies recommended for you:
[2024-12-06T22:45:12.389Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:45:12.389Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:45:12.389Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21994.583 ms) ======
[2024-12-06T22:45:12.389Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-06T22:45:12.389Z] GC before operation: completed in 153.178 ms, heap usage 276.450 MB -> 51.780 MB.
[2024-12-06T22:45:15.819Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:45:19.260Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:45:22.696Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:45:25.183Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:45:27.660Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:45:29.253Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:45:30.850Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:45:33.345Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:45:33.345Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:45:33.345Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:45:33.345Z] Movies recommended for you:
[2024-12-06T22:45:33.345Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:45:33.345Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:45:33.345Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (21265.016 ms) ======
[2024-12-06T22:45:33.345Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-06T22:45:33.345Z] GC before operation: completed in 120.255 ms, heap usage 484.393 MB -> 55.568 MB.
[2024-12-06T22:45:36.784Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:45:40.231Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:45:43.671Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:45:46.153Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:45:48.637Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:45:50.240Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:45:51.844Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:45:54.348Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:45:54.348Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:45:54.348Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:45:54.348Z] Movies recommended for you:
[2024-12-06T22:45:54.348Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:45:54.348Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:45:54.348Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20852.482 ms) ======
[2024-12-06T22:45:54.348Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-06T22:45:54.348Z] GC before operation: completed in 117.607 ms, heap usage 232.237 MB -> 55.663 MB.
[2024-12-06T22:45:57.802Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:46:01.251Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:46:04.682Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:46:07.155Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:46:09.640Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:46:11.231Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:46:12.829Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:46:15.310Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:46:15.310Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:46:15.310Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:46:15.310Z] Movies recommended for you:
[2024-12-06T22:46:15.310Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:46:15.310Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:46:15.310Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20857.916 ms) ======
[2024-12-06T22:46:15.310Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-06T22:46:15.310Z] GC before operation: completed in 120.027 ms, heap usage 221.618 MB -> 52.592 MB.
[2024-12-06T22:46:18.746Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:46:22.193Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:46:24.672Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:46:28.125Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:46:29.722Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:46:32.220Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:46:33.815Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:46:36.302Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:46:36.302Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:46:36.302Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:46:36.302Z] Movies recommended for you:
[2024-12-06T22:46:36.302Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:46:36.302Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:46:36.302Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20837.557 ms) ======
[2024-12-06T22:46:36.302Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-06T22:46:36.302Z] GC before operation: completed in 130.012 ms, heap usage 604.226 MB -> 56.034 MB.
[2024-12-06T22:46:39.733Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:46:43.178Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:46:46.618Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:46:49.096Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:46:51.587Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:46:53.181Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:46:54.778Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:46:57.257Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:46:57.257Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:46:57.257Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:46:57.257Z] Movies recommended for you:
[2024-12-06T22:46:57.257Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:46:57.257Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:46:57.257Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20979.672 ms) ======
[2024-12-06T22:46:57.257Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-06T22:46:57.257Z] GC before operation: completed in 122.800 ms, heap usage 551.239 MB -> 56.192 MB.
[2024-12-06T22:47:00.696Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:47:04.316Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:47:06.804Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:47:10.245Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:47:11.842Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:47:14.325Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:47:15.917Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:47:17.513Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:47:18.285Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:47:18.285Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:47:18.285Z] Movies recommended for you:
[2024-12-06T22:47:18.285Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:47:18.285Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:47:18.285Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20762.476 ms) ======
[2024-12-06T22:47:18.285Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-06T22:47:18.285Z] GC before operation: completed in 116.858 ms, heap usage 490.078 MB -> 56.478 MB.
[2024-12-06T22:47:21.726Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:47:25.158Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:47:27.645Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:47:31.073Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:47:32.686Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:47:35.167Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:47:36.763Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:47:38.355Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:47:39.131Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:47:39.131Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:47:39.131Z] Movies recommended for you:
[2024-12-06T22:47:39.131Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:47:39.131Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:47:39.131Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20630.510 ms) ======
[2024-12-06T22:47:39.131Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-06T22:47:39.131Z] GC before operation: completed in 114.630 ms, heap usage 319.135 MB -> 52.903 MB.
[2024-12-06T22:47:42.561Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:47:46.001Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:47:48.492Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:47:51.927Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:47:53.528Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:47:56.015Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:47:57.616Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:47:59.229Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:48:00.000Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:48:00.000Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:48:00.000Z] Movies recommended for you:
[2024-12-06T22:48:00.000Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:48:00.000Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:48:00.000Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20833.315 ms) ======
[2024-12-06T22:48:00.000Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-06T22:48:00.000Z] GC before operation: completed in 135.242 ms, heap usage 503.884 MB -> 56.418 MB.
[2024-12-06T22:48:03.440Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:48:06.877Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:48:10.315Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:48:12.794Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:48:15.281Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:48:16.887Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:48:18.490Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:48:20.987Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:48:20.987Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:48:20.987Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:48:20.987Z] Movies recommended for you:
[2024-12-06T22:48:20.987Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:48:20.987Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:48:20.987Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20850.095 ms) ======
[2024-12-06T22:48:20.987Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-06T22:48:20.987Z] GC before operation: completed in 116.405 ms, heap usage 293.608 MB -> 52.751 MB.
[2024-12-06T22:48:24.437Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:48:27.885Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:48:30.370Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:48:33.807Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:48:35.401Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:48:37.893Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:48:39.517Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:48:41.108Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:48:41.879Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:48:41.879Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:48:41.879Z] Movies recommended for you:
[2024-12-06T22:48:41.879Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:48:41.879Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:48:41.879Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20783.104 ms) ======
[2024-12-06T22:48:41.879Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-06T22:48:41.879Z] GC before operation: completed in 113.876 ms, heap usage 264.720 MB -> 52.897 MB.
[2024-12-06T22:48:45.319Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:48:48.842Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:48:51.329Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:48:54.764Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:48:56.359Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:48:58.833Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:49:00.444Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:49:02.049Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:49:02.819Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:49:02.819Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:49:02.819Z] Movies recommended for you:
[2024-12-06T22:49:02.819Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:49:02.819Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:49:02.819Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20676.539 ms) ======
[2024-12-06T22:49:02.819Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-06T22:49:02.819Z] GC before operation: completed in 127.016 ms, heap usage 235.825 MB -> 53.030 MB.
[2024-12-06T22:49:06.261Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:49:08.743Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:49:12.184Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:49:15.616Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:49:17.209Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:49:18.802Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:49:21.301Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:49:22.913Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:49:24.388Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:49:24.388Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:49:24.388Z] Movies recommended for you:
[2024-12-06T22:49:24.388Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:49:24.388Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:49:24.388Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (20597.228 ms) ======
[2024-12-06T22:49:24.388Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-06T22:49:24.388Z] GC before operation: completed in 127.762 ms, heap usage 257.445 MB -> 52.754 MB.
[2024-12-06T22:49:27.243Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:49:30.241Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:49:32.723Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:49:36.150Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:49:37.770Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:49:39.363Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:49:41.840Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:49:43.452Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:49:43.452Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:49:43.452Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:49:43.452Z] Movies recommended for you:
[2024-12-06T22:49:43.452Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:49:43.452Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:49:43.452Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20202.329 ms) ======
[2024-12-06T22:49:43.452Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-06T22:49:44.225Z] GC before operation: completed in 125.759 ms, heap usage 267.654 MB -> 52.953 MB.
[2024-12-06T22:49:46.699Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:49:50.134Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:49:53.573Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:49:56.051Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:49:57.647Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:50:00.132Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:50:01.732Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:50:03.328Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:50:03.328Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:50:03.328Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:50:04.098Z] Movies recommended for you:
[2024-12-06T22:50:04.099Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:50:04.099Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:50:04.099Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19835.063 ms) ======
[2024-12-06T22:50:04.099Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-06T22:50:04.099Z] GC before operation: completed in 116.445 ms, heap usage 276.642 MB -> 53.022 MB.
[2024-12-06T22:50:06.578Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:50:10.014Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:50:13.443Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:50:15.922Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:50:17.514Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:50:20.006Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:50:21.596Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:50:23.191Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:50:23.191Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:50:23.962Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:50:23.962Z] Movies recommended for you:
[2024-12-06T22:50:23.962Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:50:23.962Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:50:23.962Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19839.674 ms) ======
[2024-12-06T22:50:23.962Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-06T22:50:23.962Z] GC before operation: completed in 118.808 ms, heap usage 214.563 MB -> 52.864 MB.
[2024-12-06T22:50:26.445Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:50:29.880Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:50:33.322Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:50:35.805Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:50:37.402Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:50:39.881Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:50:41.487Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:50:43.134Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:50:43.134Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:50:43.903Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:50:43.904Z] Movies recommended for you:
[2024-12-06T22:50:43.904Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:50:43.904Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:50:43.904Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19781.234 ms) ======
[2024-12-06T22:50:43.904Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-06T22:50:43.904Z] GC before operation: completed in 117.563 ms, heap usage 131.838 MB -> 52.827 MB.
[2024-12-06T22:50:46.405Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:50:49.838Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:50:53.281Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:50:55.757Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:50:57.349Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:50:59.826Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:51:01.430Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:51:03.044Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:51:03.814Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:51:03.814Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:51:03.814Z] Movies recommended for you:
[2024-12-06T22:51:03.814Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:51:03.814Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:51:03.814Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19859.918 ms) ======
[2024-12-06T22:51:03.814Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-06T22:51:03.814Z] GC before operation: completed in 117.206 ms, heap usage 389.846 MB -> 53.239 MB.
[2024-12-06T22:51:07.247Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:51:09.719Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:51:13.164Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:51:15.642Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:51:18.120Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:51:19.714Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:51:21.307Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:51:22.902Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:51:23.674Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-06T22:51:23.675Z] The best model improves the baseline by 14.43%.
[2024-12-06T22:51:23.675Z] Movies recommended for you:
[2024-12-06T22:51:23.675Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:51:23.675Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:51:23.675Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19914.353 ms) ======
[2024-12-06T22:51:24.446Z] -----------------------------------
[2024-12-06T22:51:24.446Z] renaissance-movie-lens_0_PASSED
[2024-12-06T22:51:24.446Z] -----------------------------------
[2024-12-06T22:51:24.446Z]
[2024-12-06T22:51:24.446Z] TEST TEARDOWN:
[2024-12-06T22:51:24.446Z] Nothing to be done for teardown.
[2024-12-06T22:51:24.446Z] renaissance-movie-lens_0 Finish Time: Fri Dec 6 22:51:23 2024 Epoch Time (ms): 1733525483814