renaissance-movie-lens_0
[2024-10-30T22:19:44.053Z] Running test renaissance-movie-lens_0 ...
[2024-10-30T22:19:44.053Z] ===============================================
[2024-10-30T22:19:44.053Z] renaissance-movie-lens_0 Start Time: Wed Oct 30 22:19:43 2024 Epoch Time (ms): 1730326783940
[2024-10-30T22:19:44.053Z] variation: NoOptions
[2024-10-30T22:19:44.053Z] JVM_OPTIONS:
[2024-10-30T22:19:44.053Z] { \
[2024-10-30T22:19:44.053Z] echo ""; echo "TEST SETUP:"; \
[2024-10-30T22:19:44.053Z] echo "Nothing to be done for setup."; \
[2024-10-30T22:19:44.053Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17303256196677/renaissance-movie-lens_0"; \
[2024-10-30T22:19:44.053Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17303256196677/renaissance-movie-lens_0"; \
[2024-10-30T22:19:44.053Z] echo ""; echo "TESTING:"; \
[2024-10-30T22:19:44.053Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17303256196677/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-30T22:19:44.053Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17303256196677/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-30T22:19:44.053Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-30T22:19:44.053Z] echo "Nothing to be done for teardown."; \
[2024-10-30T22:19:44.053Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17303256196677/TestTargetResult";
[2024-10-30T22:19:44.053Z]
[2024-10-30T22:19:44.053Z] TEST SETUP:
[2024-10-30T22:19:44.053Z] Nothing to be done for setup.
[2024-10-30T22:19:44.053Z]
[2024-10-30T22:19:44.053Z] TESTING:
[2024-10-30T22:19:48.080Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-30T22:19:49.973Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-10-30T22:19:55.185Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-30T22:19:55.185Z] Training: 60056, validation: 20285, test: 19854
[2024-10-30T22:19:55.185Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-30T22:19:56.113Z] GC before operation: completed in 89.865 ms, heap usage 95.476 MB -> 39.290 MB.
[2024-10-30T22:20:05.622Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:20:11.127Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:20:16.354Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:20:20.377Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:20:22.273Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:20:24.354Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:20:27.293Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:20:29.184Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:20:30.106Z] 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.
[2024-10-30T22:20:30.106Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:20:30.106Z] Movies recommended for you:
[2024-10-30T22:20:30.106Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:20:30.106Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:20:30.106Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (34381.497 ms) ======
[2024-10-30T22:20:30.106Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-30T22:20:30.106Z] GC before operation: completed in 136.124 ms, heap usage 391.316 MB -> 52.312 MB.
[2024-10-30T22:20:34.130Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:20:37.056Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:20:41.076Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:20:44.020Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:20:45.910Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:20:48.889Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:20:50.779Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:20:52.675Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:20:53.597Z] 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.
[2024-10-30T22:20:53.597Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:20:53.597Z] Movies recommended for you:
[2024-10-30T22:20:53.597Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:20:53.597Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:20:53.597Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (23105.622 ms) ======
[2024-10-30T22:20:53.597Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-30T22:20:53.597Z] GC before operation: completed in 132.118 ms, heap usage 419.485 MB -> 53.256 MB.
[2024-10-30T22:20:57.628Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:21:00.569Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:21:03.545Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:21:07.621Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:21:09.518Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:21:12.130Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:21:13.077Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:21:14.991Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:21:15.915Z] 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.
[2024-10-30T22:21:15.915Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:21:15.915Z] Movies recommended for you:
[2024-10-30T22:21:15.915Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:21:15.915Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:21:15.915Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (22263.734 ms) ======
[2024-10-30T22:21:15.915Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-30T22:21:15.915Z] GC before operation: completed in 122.768 ms, heap usage 365.319 MB -> 53.448 MB.
[2024-10-30T22:21:19.956Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:21:22.887Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:21:25.855Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:21:29.887Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:21:31.787Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:21:33.685Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:21:35.627Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:21:37.525Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:21:37.525Z] 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.
[2024-10-30T22:21:37.525Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:21:38.448Z] Movies recommended for you:
[2024-10-30T22:21:38.448Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:21:38.448Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:21:38.448Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (22222.357 ms) ======
[2024-10-30T22:21:38.449Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-30T22:21:38.449Z] GC before operation: completed in 151.655 ms, heap usage 572.479 MB -> 57.325 MB.
[2024-10-30T22:21:41.376Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:21:44.312Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:21:47.294Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:21:50.240Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:21:52.139Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:21:54.046Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:21:55.943Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:21:58.877Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:21:58.877Z] 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.
[2024-10-30T22:21:58.877Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:21:58.877Z] Movies recommended for you:
[2024-10-30T22:21:58.877Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:21:58.877Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:21:58.877Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20854.186 ms) ======
[2024-10-30T22:21:58.877Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-30T22:21:58.878Z] GC before operation: completed in 105.840 ms, heap usage 313.894 MB -> 54.045 MB.
[2024-10-30T22:22:02.910Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:22:05.848Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:22:08.782Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:22:11.100Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:22:12.998Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:22:14.898Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:22:16.794Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:22:18.689Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:22:19.615Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-30T22:22:19.615Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:22:19.615Z] Movies recommended for you:
[2024-10-30T22:22:19.615Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:22:19.615Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:22:19.615Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20310.132 ms) ======
[2024-10-30T22:22:19.615Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-30T22:22:19.615Z] GC before operation: completed in 102.080 ms, heap usage 544.590 MB -> 57.257 MB.
[2024-10-30T22:22:22.550Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:22:25.483Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:22:28.422Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:22:31.352Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:22:33.258Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:22:35.152Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:22:37.052Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:22:37.977Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:22:38.906Z] 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.
[2024-10-30T22:22:38.906Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:22:38.906Z] Movies recommended for you:
[2024-10-30T22:22:38.906Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:22:38.906Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:22:38.906Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (19271.653 ms) ======
[2024-10-30T22:22:38.906Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-30T22:22:38.906Z] GC before operation: completed in 104.504 ms, heap usage 354.170 MB -> 54.064 MB.
[2024-10-30T22:22:41.836Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:22:45.891Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:22:48.861Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:22:51.801Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:22:53.695Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:22:55.590Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:22:57.485Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:22:59.383Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:22:59.383Z] 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.
[2024-10-30T22:22:59.383Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:22:59.383Z] Movies recommended for you:
[2024-10-30T22:22:59.383Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:22:59.383Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:22:59.383Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20532.636 ms) ======
[2024-10-30T22:22:59.383Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-30T22:22:59.383Z] GC before operation: completed in 125.601 ms, heap usage 326.073 MB -> 54.341 MB.
[2024-10-30T22:23:03.585Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:23:06.516Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:23:10.127Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:23:13.058Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:23:14.952Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:23:16.851Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:23:18.748Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:23:20.648Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:23:20.648Z] 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.
[2024-10-30T22:23:20.648Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:23:20.648Z] Movies recommended for you:
[2024-10-30T22:23:20.648Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:23:20.648Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:23:20.648Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (21340.750 ms) ======
[2024-10-30T22:23:20.648Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-30T22:23:21.572Z] GC before operation: completed in 115.263 ms, heap usage 189.037 MB -> 54.164 MB.
[2024-10-30T22:23:24.507Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:23:27.584Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:23:30.512Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:23:33.438Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:23:35.340Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:23:37.239Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:23:39.138Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:23:41.039Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:23:41.039Z] 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.
[2024-10-30T22:23:41.039Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:23:41.039Z] Movies recommended for you:
[2024-10-30T22:23:41.039Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:23:41.039Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:23:41.039Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20046.737 ms) ======
[2024-10-30T22:23:41.039Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-30T22:23:41.039Z] GC before operation: completed in 138.702 ms, heap usage 312.227 MB -> 54.347 MB.
[2024-10-30T22:23:45.080Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:23:48.009Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:23:50.938Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:23:53.871Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:23:55.771Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:23:57.668Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:23:59.570Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:24:01.467Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:24:01.467Z] 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.
[2024-10-30T22:24:01.467Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:24:01.467Z] Movies recommended for you:
[2024-10-30T22:24:01.467Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:24:01.467Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:24:01.467Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20363.523 ms) ======
[2024-10-30T22:24:01.467Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-30T22:24:01.467Z] GC before operation: completed in 111.972 ms, heap usage 305.302 MB -> 54.198 MB.
[2024-10-30T22:24:04.402Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:24:07.772Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:24:10.815Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:24:13.747Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:24:15.641Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:24:17.538Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:24:19.435Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:24:22.361Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:24:22.361Z] 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.
[2024-10-30T22:24:22.361Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:24:22.361Z] Movies recommended for you:
[2024-10-30T22:24:22.361Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:24:22.361Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:24:22.361Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20900.347 ms) ======
[2024-10-30T22:24:22.361Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-30T22:24:22.361Z] GC before operation: completed in 127.843 ms, heap usage 347.721 MB -> 54.298 MB.
[2024-10-30T22:24:26.401Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:24:30.526Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:24:32.418Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:24:35.339Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:24:37.229Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:24:39.120Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:24:41.010Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:24:42.903Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:24:43.827Z] 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.
[2024-10-30T22:24:43.827Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:24:43.827Z] Movies recommended for you:
[2024-10-30T22:24:43.827Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:24:43.827Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:24:43.827Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (21024.841 ms) ======
[2024-10-30T22:24:43.827Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-30T22:24:43.827Z] GC before operation: completed in 123.728 ms, heap usage 345.381 MB -> 54.406 MB.
[2024-10-30T22:24:47.858Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:24:50.779Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:24:53.702Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:24:56.624Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:24:57.546Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:24:59.435Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:25:01.323Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:25:03.217Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:25:04.810Z] 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.
[2024-10-30T22:25:04.810Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:25:04.810Z] Movies recommended for you:
[2024-10-30T22:25:04.810Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:25:04.810Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:25:04.810Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (20091.987 ms) ======
[2024-10-30T22:25:04.810Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-30T22:25:04.810Z] GC before operation: completed in 131.706 ms, heap usage 303.933 MB -> 54.207 MB.
[2024-10-30T22:25:07.740Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:25:09.635Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:25:12.565Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:25:15.486Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:25:17.377Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:25:19.268Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:25:21.159Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:25:23.049Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:25:23.049Z] 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.
[2024-10-30T22:25:23.049Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:25:23.049Z] Movies recommended for you:
[2024-10-30T22:25:23.049Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:25:23.049Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:25:23.049Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19078.428 ms) ======
[2024-10-30T22:25:23.049Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-30T22:25:23.049Z] GC before operation: completed in 101.651 ms, heap usage 307.888 MB -> 54.371 MB.
[2024-10-30T22:25:26.145Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:25:29.279Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:25:32.199Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:25:35.170Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:25:37.062Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:25:38.963Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:25:39.884Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:25:41.775Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:25:42.696Z] 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.
[2024-10-30T22:25:42.696Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:25:42.696Z] Movies recommended for you:
[2024-10-30T22:25:42.696Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:25:42.696Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:25:42.696Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19086.575 ms) ======
[2024-10-30T22:25:42.696Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-30T22:25:42.696Z] GC before operation: completed in 118.765 ms, heap usage 350.396 MB -> 54.515 MB.
[2024-10-30T22:25:45.616Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:25:48.543Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:25:51.469Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:25:54.399Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:25:56.288Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:25:58.177Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:25:59.535Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:26:01.430Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:26:02.350Z] 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.
[2024-10-30T22:26:02.350Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:26:02.350Z] Movies recommended for you:
[2024-10-30T22:26:02.350Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:26:02.350Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:26:02.350Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19797.170 ms) ======
[2024-10-30T22:26:02.350Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-30T22:26:02.350Z] GC before operation: completed in 136.392 ms, heap usage 720.643 MB -> 57.960 MB.
[2024-10-30T22:26:05.271Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:26:08.188Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:26:11.109Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:26:14.047Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:26:15.935Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:26:17.829Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:26:19.718Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:26:21.631Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:26:21.631Z] 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.
[2024-10-30T22:26:21.631Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:26:21.631Z] Movies recommended for you:
[2024-10-30T22:26:21.631Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:26:21.631Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:26:21.631Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19321.082 ms) ======
[2024-10-30T22:26:21.631Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-30T22:26:21.631Z] GC before operation: completed in 112.701 ms, heap usage 278.687 MB -> 54.388 MB.
[2024-10-30T22:26:24.552Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:26:27.579Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:26:30.503Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:26:33.426Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:26:35.315Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:26:37.209Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:26:39.112Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:26:41.013Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:26:41.934Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-30T22:26:41.934Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:26:41.935Z] Movies recommended for you:
[2024-10-30T22:26:41.935Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:26:41.935Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:26:41.935Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (20153.210 ms) ======
[2024-10-30T22:26:41.935Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-30T22:26:41.935Z] GC before operation: completed in 114.829 ms, heap usage 406.316 MB -> 54.775 MB.
[2024-10-30T22:26:45.961Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:26:48.890Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:26:51.818Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:26:54.742Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:26:56.634Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:26:58.965Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:27:00.887Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:27:01.807Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:27:02.731Z] 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.
[2024-10-30T22:27:02.731Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:27:02.731Z] Movies recommended for you:
[2024-10-30T22:27:02.731Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:27:02.731Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:27:02.731Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (20442.678 ms) ======
[2024-10-30T22:27:04.624Z] -----------------------------------
[2024-10-30T22:27:04.624Z] renaissance-movie-lens_0_PASSED
[2024-10-30T22:27:04.624Z] -----------------------------------
[2024-10-30T22:27:04.624Z]
[2024-10-30T22:27:04.624Z] TEST TEARDOWN:
[2024-10-30T22:27:04.624Z] Nothing to be done for teardown.
[2024-10-30T22:27:04.624Z] renaissance-movie-lens_0 Finish Time: Wed Oct 30 22:27:04 2024 Epoch Time (ms): 1730327224457