renaissance-movie-lens_0
[2024-10-30T22:27:14.210Z] Running test renaissance-movie-lens_0 ...
[2024-10-30T22:27:14.210Z] ===============================================
[2024-10-30T22:27:14.210Z] renaissance-movie-lens_0 Start Time: Wed Oct 30 17:27:13 2024 Epoch Time (ms): 1730327233543
[2024-10-30T22:27:14.210Z] variation: NoOptions
[2024-10-30T22:27:14.210Z] JVM_OPTIONS:
[2024-10-30T22:27:14.210Z] { \
[2024-10-30T22:27:14.210Z] echo ""; echo "TEST SETUP:"; \
[2024-10-30T22:27:14.210Z] echo "Nothing to be done for setup."; \
[2024-10-30T22:27:14.210Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17303265957862/renaissance-movie-lens_0"; \
[2024-10-30T22:27:14.210Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17303265957862/renaissance-movie-lens_0"; \
[2024-10-30T22:27:14.210Z] echo ""; echo "TESTING:"; \
[2024-10-30T22:27:14.210Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-17.0.14+1/bin/..//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_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17303265957862/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-30T22:27:14.210Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17303265957862/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-30T22:27:14.210Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-30T22:27:14.210Z] echo "Nothing to be done for teardown."; \
[2024-10-30T22:27:14.210Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17303265957862/TestTargetResult";
[2024-10-30T22:27:14.210Z]
[2024-10-30T22:27:14.210Z] TEST SETUP:
[2024-10-30T22:27:14.210Z] Nothing to be done for setup.
[2024-10-30T22:27:14.210Z]
[2024-10-30T22:27:14.210Z] TESTING:
[2024-10-30T22:27:16.448Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-30T22:27:18.691Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-10-30T22:27:22.788Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-30T22:27:22.788Z] Training: 60056, validation: 20285, test: 19854
[2024-10-30T22:27:22.788Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-30T22:27:22.788Z] GC before operation: completed in 57.427 ms, heap usage 101.105 MB -> 37.799 MB.
[2024-10-30T22:27:29.127Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:27:33.224Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:27:36.315Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:27:38.551Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:27:40.936Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:27:42.370Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:27:43.807Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:27:46.077Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:27:46.077Z] 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.
[2024-10-30T22:27:46.077Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:27:46.077Z] Movies recommended for you:
[2024-10-30T22:27:46.077Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:27:46.077Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:27:46.077Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23551.339 ms) ======
[2024-10-30T22:27:46.077Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-30T22:27:46.077Z] GC before operation: completed in 96.986 ms, heap usage 575.937 MB -> 56.795 MB.
[2024-10-30T22:27:49.194Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:27:52.312Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:27:54.580Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:27:56.833Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:27:58.268Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:27:59.706Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:28:01.993Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:28:03.423Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:28:03.423Z] 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.
[2024-10-30T22:28:03.423Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:28:03.423Z] Movies recommended for you:
[2024-10-30T22:28:03.423Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:28:03.423Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:28:03.423Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17395.923 ms) ======
[2024-10-30T22:28:03.423Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-30T22:28:03.423Z] GC before operation: completed in 58.597 ms, heap usage 84.978 MB -> 54.937 MB.
[2024-10-30T22:28:06.537Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:28:09.653Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:28:11.900Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:28:14.140Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:28:15.572Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:28:17.033Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:28:18.492Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:28:19.949Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:28:20.652Z] 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.
[2024-10-30T22:28:20.652Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:28:20.652Z] Movies recommended for you:
[2024-10-30T22:28:20.652Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:28:20.652Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:28:20.652Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16892.496 ms) ======
[2024-10-30T22:28:20.652Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-30T22:28:20.652Z] GC before operation: completed in 77.457 ms, heap usage 779.725 MB -> 55.536 MB.
[2024-10-30T22:28:22.906Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:28:26.054Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:28:27.493Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:28:29.736Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:28:31.177Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:28:32.629Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:28:34.102Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:28:35.545Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:28:35.545Z] 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.
[2024-10-30T22:28:35.545Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:28:35.545Z] Movies recommended for you:
[2024-10-30T22:28:35.545Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:28:35.545Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:28:35.545Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15020.782 ms) ======
[2024-10-30T22:28:35.545Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-30T22:28:35.545Z] GC before operation: completed in 58.212 ms, heap usage 343.676 MB -> 52.404 MB.
[2024-10-30T22:28:38.663Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:28:40.090Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:28:42.329Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:28:44.592Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:28:46.028Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:28:47.452Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:28:48.481Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:28:49.973Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:28:49.973Z] 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.
[2024-10-30T22:28:49.973Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:28:49.973Z] Movies recommended for you:
[2024-10-30T22:28:49.973Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:28:49.973Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:28:49.973Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14554.422 ms) ======
[2024-10-30T22:28:49.973Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-30T22:28:50.664Z] GC before operation: completed in 61.953 ms, heap usage 163.304 MB -> 52.436 MB.
[2024-10-30T22:28:52.898Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:28:55.155Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:28:57.457Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:28:59.687Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:29:00.375Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:29:01.819Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:29:03.256Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:29:04.680Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:29:04.680Z] 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.
[2024-10-30T22:29:04.680Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:29:04.680Z] Movies recommended for you:
[2024-10-30T22:29:04.680Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:29:04.680Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:29:04.680Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14535.920 ms) ======
[2024-10-30T22:29:04.680Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-30T22:29:04.680Z] GC before operation: completed in 82.949 ms, heap usage 422.910 MB -> 52.510 MB.
[2024-10-30T22:29:07.824Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:29:10.061Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:29:11.516Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:29:13.783Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:29:15.219Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:29:16.647Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:29:18.096Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:29:19.534Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:29:19.534Z] 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.
[2024-10-30T22:29:19.534Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:29:19.534Z] Movies recommended for you:
[2024-10-30T22:29:19.534Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:29:19.534Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:29:19.534Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14554.785 ms) ======
[2024-10-30T22:29:19.534Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-30T22:29:19.534Z] GC before operation: completed in 63.314 ms, heap usage 531.149 MB -> 56.017 MB.
[2024-10-30T22:29:21.852Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:29:24.096Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:29:26.509Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:29:28.754Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:29:29.454Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:29:30.900Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:29:32.325Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:29:33.754Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:29:33.754Z] 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.
[2024-10-30T22:29:33.754Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:29:33.754Z] Movies recommended for you:
[2024-10-30T22:29:33.754Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:29:33.754Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:29:33.754Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14431.810 ms) ======
[2024-10-30T22:29:33.754Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-30T22:29:33.754Z] GC before operation: completed in 55.072 ms, heap usage 347.257 MB -> 52.895 MB.
[2024-10-30T22:29:36.854Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:29:38.301Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:29:40.541Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:29:42.768Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:29:44.259Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:29:45.715Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:29:47.139Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:29:48.583Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:29:48.583Z] 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.
[2024-10-30T22:29:48.583Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:29:48.583Z] Movies recommended for you:
[2024-10-30T22:29:48.583Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:29:48.583Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:29:48.584Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14579.005 ms) ======
[2024-10-30T22:29:48.584Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-30T22:29:48.584Z] GC before operation: completed in 58.726 ms, heap usage 866.729 MB -> 56.492 MB.
[2024-10-30T22:29:50.809Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:29:53.040Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:29:55.286Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:29:57.512Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:29:58.943Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:29:59.640Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:30:01.108Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:30:02.546Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:30:03.237Z] 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.
[2024-10-30T22:30:03.237Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:30:03.237Z] Movies recommended for you:
[2024-10-30T22:30:03.237Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:30:03.237Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:30:03.237Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14324.864 ms) ======
[2024-10-30T22:30:03.237Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-30T22:30:03.237Z] GC before operation: completed in 55.302 ms, heap usage 526.349 MB -> 56.350 MB.
[2024-10-30T22:30:05.473Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:30:07.739Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:30:09.992Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:30:12.226Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:30:12.915Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:30:14.350Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:30:15.773Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:30:17.212Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:30:17.212Z] 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.
[2024-10-30T22:30:17.212Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:30:17.212Z] Movies recommended for you:
[2024-10-30T22:30:17.212Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:30:17.212Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:30:17.212Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14288.701 ms) ======
[2024-10-30T22:30:17.212Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-30T22:30:17.212Z] GC before operation: completed in 71.549 ms, heap usage 442.902 MB -> 52.638 MB.
[2024-10-30T22:30:20.338Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:30:21.776Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:30:24.072Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:30:26.322Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:30:27.743Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:30:29.172Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:30:30.611Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:30:31.309Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:30:32.018Z] 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.
[2024-10-30T22:30:32.018Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:30:32.018Z] Movies recommended for you:
[2024-10-30T22:30:32.018Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:30:32.018Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:30:32.018Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14468.342 ms) ======
[2024-10-30T22:30:32.018Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-30T22:30:32.018Z] GC before operation: completed in 88.969 ms, heap usage 363.705 MB -> 52.830 MB.
[2024-10-30T22:30:34.254Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:30:36.497Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:30:38.759Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:30:41.000Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:30:42.423Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:30:43.859Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:30:45.293Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:30:46.802Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:30:46.802Z] 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.
[2024-10-30T22:30:46.802Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:30:46.802Z] Movies recommended for you:
[2024-10-30T22:30:46.802Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:30:46.802Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:30:46.802Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15066.141 ms) ======
[2024-10-30T22:30:46.802Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-30T22:30:46.802Z] GC before operation: completed in 56.152 ms, heap usage 531.163 MB -> 56.370 MB.
[2024-10-30T22:30:49.035Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:30:51.364Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:30:53.648Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:30:55.881Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:30:57.322Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:30:58.785Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:31:00.229Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:31:01.664Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:31:01.664Z] 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.
[2024-10-30T22:31:01.664Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:31:01.664Z] Movies recommended for you:
[2024-10-30T22:31:01.664Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:31:01.664Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:31:01.664Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14587.982 ms) ======
[2024-10-30T22:31:01.664Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-30T22:31:01.664Z] GC before operation: completed in 55.306 ms, heap usage 519.883 MB -> 56.106 MB.
[2024-10-30T22:31:03.915Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:31:06.191Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:31:08.476Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:31:10.707Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:31:12.156Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:31:12.855Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:31:14.352Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:31:16.079Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:31:16.079Z] 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.
[2024-10-30T22:31:16.079Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:31:16.079Z] Movies recommended for you:
[2024-10-30T22:31:16.079Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:31:16.079Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:31:16.079Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14380.205 ms) ======
[2024-10-30T22:31:16.079Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-30T22:31:16.079Z] GC before operation: completed in 78.031 ms, heap usage 547.084 MB -> 56.244 MB.
[2024-10-30T22:31:18.619Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:31:20.852Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:31:23.079Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:31:25.310Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:31:26.754Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:31:28.181Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:31:29.621Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:31:30.304Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:31:31.002Z] 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.
[2024-10-30T22:31:31.002Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:31:31.002Z] Movies recommended for you:
[2024-10-30T22:31:31.002Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:31:31.002Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:31:31.002Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14698.054 ms) ======
[2024-10-30T22:31:31.002Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-30T22:31:31.002Z] GC before operation: completed in 51.125 ms, heap usage 138.639 MB -> 52.816 MB.
[2024-10-30T22:31:33.232Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:31:35.494Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:31:37.839Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:31:40.164Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:31:40.850Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:31:42.283Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:31:43.717Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:31:45.172Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:31:45.172Z] 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.
[2024-10-30T22:31:45.172Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:31:45.172Z] Movies recommended for you:
[2024-10-30T22:31:45.172Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:31:45.172Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:31:45.172Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14512.537 ms) ======
[2024-10-30T22:31:45.172Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-30T22:31:45.858Z] GC before operation: completed in 82.615 ms, heap usage 386.123 MB -> 52.812 MB.
[2024-10-30T22:31:48.089Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:31:50.358Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:31:52.600Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:31:54.839Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:31:55.542Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:31:56.998Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:31:58.450Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:31:59.870Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:31:59.870Z] 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.
[2024-10-30T22:31:59.870Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:31:59.870Z] Movies recommended for you:
[2024-10-30T22:31:59.870Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:31:59.870Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:31:59.870Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14562.841 ms) ======
[2024-10-30T22:31:59.870Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-30T22:32:00.555Z] GC before operation: completed in 59.705 ms, heap usage 604.912 MB -> 56.387 MB.
[2024-10-30T22:32:02.815Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:32:05.058Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:32:07.322Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:32:08.797Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:32:10.247Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:32:11.672Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:32:13.147Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:32:14.585Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:32:14.585Z] 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.
[2024-10-30T22:32:14.585Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:32:14.585Z] Movies recommended for you:
[2024-10-30T22:32:14.585Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:32:14.585Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:32:14.585Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14365.974 ms) ======
[2024-10-30T22:32:14.585Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-30T22:32:14.585Z] GC before operation: completed in 73.927 ms, heap usage 544.890 MB -> 56.500 MB.
[2024-10-30T22:32:16.838Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T22:32:19.132Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T22:32:21.361Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T22:32:23.597Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T22:32:24.288Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T22:32:25.713Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T22:32:27.338Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T22:32:28.788Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T22:32:28.788Z] 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.
[2024-10-30T22:32:28.788Z] The best model improves the baseline by 14.43%.
[2024-10-30T22:32:28.788Z] Movies recommended for you:
[2024-10-30T22:32:28.788Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T22:32:28.788Z] There is no way to check that no silent failure occurred.
[2024-10-30T22:32:28.788Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14194.231 ms) ======
[2024-10-30T22:32:30.207Z] -----------------------------------
[2024-10-30T22:32:30.207Z] renaissance-movie-lens_0_PASSED
[2024-10-30T22:32:30.207Z] -----------------------------------
[2024-10-30T22:32:30.207Z]
[2024-10-30T22:32:30.207Z] TEST TEARDOWN:
[2024-10-30T22:32:30.207Z] Nothing to be done for teardown.
[2024-10-30T22:32:30.207Z] renaissance-movie-lens_0 Finish Time: Wed Oct 30 17:32:29 2024 Epoch Time (ms): 1730327549618