renaissance-movie-lens_0
[2025-02-27T01:44:39.333Z] Running test renaissance-movie-lens_0 ...
[2025-02-27T01:44:39.333Z] ===============================================
[2025-02-27T01:44:39.333Z] renaissance-movie-lens_0 Start Time: Thu Feb 27 01:44:39 2025 Epoch Time (ms): 1740620679105
[2025-02-27T01:44:39.333Z] variation: NoOptions
[2025-02-27T01:44:39.333Z] JVM_OPTIONS:
[2025-02-27T01:44:39.333Z] { \
[2025-02-27T01:44:39.333Z] echo ""; echo "TEST SETUP:"; \
[2025-02-27T01:44:39.333Z] echo "Nothing to be done for setup."; \
[2025-02-27T01:44:39.333Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1740619888174/renaissance-movie-lens_0"; \
[2025-02-27T01:44:39.333Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1740619888174/renaissance-movie-lens_0"; \
[2025-02-27T01:44:39.333Z] echo ""; echo "TESTING:"; \
[2025-02-27T01:44:39.333Z] "/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_1740619888174/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-27T01:44:39.333Z] 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_1740619888174/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-27T01:44:39.333Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-27T01:44:39.333Z] echo "Nothing to be done for teardown."; \
[2025-02-27T01:44:39.333Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1740619888174/TestTargetResult";
[2025-02-27T01:44:39.333Z]
[2025-02-27T01:44:39.333Z] TEST SETUP:
[2025-02-27T01:44:39.333Z] Nothing to be done for setup.
[2025-02-27T01:44:39.333Z]
[2025-02-27T01:44:39.333Z] TESTING:
[2025-02-27T01:44:42.383Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-27T01:44:45.430Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2025-02-27T01:44:49.627Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-27T01:44:50.592Z] Training: 60056, validation: 20285, test: 19854
[2025-02-27T01:44:50.592Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-27T01:44:50.592Z] GC before operation: completed in 80.030 ms, heap usage 74.742 MB -> 39.299 MB.
[2025-02-27T01:44:57.363Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:45:00.410Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:45:04.608Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:45:06.587Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:45:08.565Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:45:09.559Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:45:11.534Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:45:13.512Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:45:13.512Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:45:13.512Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:45:13.512Z] Movies recommended for you:
[2025-02-27T01:45:13.513Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:45:13.513Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:45:13.513Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23214.927 ms) ======
[2025-02-27T01:45:13.513Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-27T01:45:13.513Z] GC before operation: completed in 97.110 ms, heap usage 363.542 MB -> 53.922 MB.
[2025-02-27T01:45:16.566Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:45:19.616Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:45:21.717Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:45:23.693Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:45:25.669Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:45:26.632Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:45:28.609Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:45:29.572Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:45:29.572Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:45:29.572Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:45:29.572Z] Movies recommended for you:
[2025-02-27T01:45:29.572Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:45:29.572Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:45:29.572Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16283.736 ms) ======
[2025-02-27T01:45:29.572Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-27T01:45:29.572Z] GC before operation: completed in 88.471 ms, heap usage 653.871 MB -> 56.597 MB.
[2025-02-27T01:45:32.623Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:45:34.600Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:45:36.577Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:45:38.620Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:45:40.595Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:45:41.559Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:45:42.522Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:45:44.503Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:45:44.503Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:45:44.503Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:45:44.503Z] Movies recommended for you:
[2025-02-27T01:45:44.503Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:45:44.503Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:45:44.503Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14365.663 ms) ======
[2025-02-27T01:45:44.503Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-27T01:45:44.503Z] GC before operation: completed in 88.476 ms, heap usage 308.581 MB -> 56.785 MB.
[2025-02-27T01:45:46.479Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:45:49.531Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:45:51.525Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:45:53.505Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:45:54.469Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:45:55.432Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:45:56.395Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:45:58.370Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:45:58.370Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:45:58.370Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:45:58.370Z] Movies recommended for you:
[2025-02-27T01:45:58.370Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:45:58.370Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:45:58.370Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13926.190 ms) ======
[2025-02-27T01:45:58.370Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-27T01:45:58.370Z] GC before operation: completed in 120.833 ms, heap usage 1.374 GB -> 58.486 MB.
[2025-02-27T01:46:00.352Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:46:03.402Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:46:05.378Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:46:07.355Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:46:08.317Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:46:09.280Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:46:10.243Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:46:11.206Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:46:12.170Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:46:12.170Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:46:12.170Z] Movies recommended for you:
[2025-02-27T01:46:12.170Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:46:12.170Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:46:12.170Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13379.664 ms) ======
[2025-02-27T01:46:12.170Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-27T01:46:12.170Z] GC before operation: completed in 109.232 ms, heap usage 1.325 GB -> 58.640 MB.
[2025-02-27T01:46:14.146Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:46:16.124Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:46:18.102Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:46:20.079Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:46:21.043Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:46:23.152Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:46:24.116Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:46:25.080Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:46:25.080Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:46:25.080Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:46:25.080Z] Movies recommended for you:
[2025-02-27T01:46:25.080Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:46:25.080Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:46:25.080Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13293.004 ms) ======
[2025-02-27T01:46:25.080Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-27T01:46:25.080Z] GC before operation: completed in 96.183 ms, heap usage 1.355 GB -> 58.695 MB.
[2025-02-27T01:46:28.131Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:46:29.274Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:46:31.257Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:46:33.236Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:46:35.217Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:46:36.182Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:46:37.145Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:46:38.118Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:46:38.118Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:46:38.118Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:46:38.118Z] Movies recommended for you:
[2025-02-27T01:46:38.118Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:46:38.118Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:46:38.118Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13230.748 ms) ======
[2025-02-27T01:46:38.118Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-27T01:46:39.087Z] GC before operation: completed in 129.416 ms, heap usage 279.998 MB -> 54.047 MB.
[2025-02-27T01:46:41.065Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:46:43.046Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:46:45.024Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:46:47.001Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:46:47.966Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:46:48.930Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:46:49.894Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:46:50.860Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:46:51.825Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:46:51.825Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:46:51.825Z] Movies recommended for you:
[2025-02-27T01:46:51.825Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:46:51.825Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:46:51.825Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12834.548 ms) ======
[2025-02-27T01:46:51.825Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-27T01:46:51.825Z] GC before operation: completed in 100.055 ms, heap usage 345.772 MB -> 54.433 MB.
[2025-02-27T01:46:53.800Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:46:55.779Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:46:57.754Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:46:59.733Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:47:00.697Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:47:01.659Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:47:03.637Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:47:04.601Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:47:04.601Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:47:04.601Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:47:04.601Z] Movies recommended for you:
[2025-02-27T01:47:04.601Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:47:04.601Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:47:04.601Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12995.084 ms) ======
[2025-02-27T01:47:04.601Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-27T01:47:04.601Z] GC before operation: completed in 95.721 ms, heap usage 348.263 MB -> 54.253 MB.
[2025-02-27T01:47:06.575Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:47:08.554Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:47:10.531Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:47:12.508Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:47:13.475Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:47:14.438Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:47:16.417Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:47:17.395Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:47:17.395Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:47:17.395Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:47:17.395Z] Movies recommended for you:
[2025-02-27T01:47:17.395Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:47:17.395Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:47:17.395Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12860.079 ms) ======
[2025-02-27T01:47:17.395Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-27T01:47:17.395Z] GC before operation: completed in 96.417 ms, heap usage 346.302 MB -> 54.321 MB.
[2025-02-27T01:47:20.109Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:47:22.090Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:47:24.070Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:47:26.048Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:47:27.012Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:47:27.977Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:47:28.941Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:47:29.903Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:47:30.868Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:47:30.868Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:47:30.868Z] Movies recommended for you:
[2025-02-27T01:47:30.868Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:47:30.868Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:47:30.868Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12994.910 ms) ======
[2025-02-27T01:47:30.868Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-27T01:47:30.868Z] GC before operation: completed in 98.995 ms, heap usage 346.293 MB -> 54.063 MB.
[2025-02-27T01:47:32.845Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:47:34.824Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:47:36.801Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:47:38.777Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:47:39.741Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:47:40.705Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:47:42.684Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:47:43.647Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:47:43.647Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:47:43.647Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:47:43.647Z] Movies recommended for you:
[2025-02-27T01:47:43.647Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:47:43.647Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:47:43.647Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12953.435 ms) ======
[2025-02-27T01:47:43.647Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-27T01:47:43.647Z] GC before operation: completed in 96.722 ms, heap usage 304.016 MB -> 54.359 MB.
[2025-02-27T01:47:45.626Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:47:47.603Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:47:49.578Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:47:51.560Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:47:53.536Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:47:54.501Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:47:55.465Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:47:57.443Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:47:57.443Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:47:57.443Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:47:57.443Z] Movies recommended for you:
[2025-02-27T01:47:57.443Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:47:57.443Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:47:57.443Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13392.438 ms) ======
[2025-02-27T01:47:57.443Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-27T01:47:57.443Z] GC before operation: completed in 103.832 ms, heap usage 344.948 MB -> 54.417 MB.
[2025-02-27T01:47:59.421Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:48:01.398Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:48:03.374Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:48:05.350Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:48:06.317Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:48:07.281Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:48:09.256Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:48:10.219Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:48:10.219Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:48:10.219Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:48:10.219Z] Movies recommended for you:
[2025-02-27T01:48:10.219Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:48:10.219Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:48:10.219Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12950.744 ms) ======
[2025-02-27T01:48:10.219Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-27T01:48:10.219Z] GC before operation: completed in 117.479 ms, heap usage 300.418 MB -> 54.171 MB.
[2025-02-27T01:48:12.257Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:48:14.233Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:48:16.209Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:48:18.187Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:48:19.151Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:48:20.114Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:48:22.202Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:48:23.167Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:48:23.167Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:48:23.167Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:48:23.167Z] Movies recommended for you:
[2025-02-27T01:48:23.167Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:48:23.167Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:48:23.167Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12796.656 ms) ======
[2025-02-27T01:48:23.167Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-27T01:48:23.167Z] GC before operation: completed in 93.732 ms, heap usage 302.886 MB -> 54.399 MB.
[2025-02-27T01:48:25.144Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:48:27.119Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:48:29.096Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:48:31.072Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:48:33.076Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:48:34.039Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:48:35.006Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:48:35.970Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:48:36.934Z] 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.
[2025-02-27T01:48:36.934Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:48:36.934Z] Movies recommended for you:
[2025-02-27T01:48:36.934Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:48:36.934Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:48:36.934Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13461.192 ms) ======
[2025-02-27T01:48:36.934Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-27T01:48:36.934Z] GC before operation: completed in 92.237 ms, heap usage 301.540 MB -> 54.400 MB.
[2025-02-27T01:48:38.908Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:48:40.888Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:48:42.865Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:48:44.843Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:48:45.806Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:48:46.769Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:48:48.747Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:48:49.709Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:48:49.709Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:48:49.709Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:48:49.709Z] Movies recommended for you:
[2025-02-27T01:48:49.709Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:48:49.709Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:48:49.709Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13172.878 ms) ======
[2025-02-27T01:48:49.709Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-27T01:48:49.709Z] GC before operation: completed in 91.928 ms, heap usage 300.439 MB -> 54.276 MB.
[2025-02-27T01:48:52.759Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:48:54.736Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:48:56.712Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:48:58.689Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:48:59.651Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:49:00.615Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:49:01.577Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:49:03.553Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:49:03.553Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:49:03.553Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:49:03.553Z] Movies recommended for you:
[2025-02-27T01:49:03.553Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:49:03.553Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:49:03.553Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13228.655 ms) ======
[2025-02-27T01:49:03.553Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-27T01:49:03.553Z] GC before operation: completed in 93.878 ms, heap usage 305.383 MB -> 54.359 MB.
[2025-02-27T01:49:05.528Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:49:07.504Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:49:09.482Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:49:11.459Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:49:12.426Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:49:13.391Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:49:14.358Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:49:16.344Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:49:16.344Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:49:16.344Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:49:16.344Z] Movies recommended for you:
[2025-02-27T01:49:16.344Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:49:16.344Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:49:16.344Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12770.049 ms) ======
[2025-02-27T01:49:16.344Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-27T01:49:16.344Z] GC before operation: completed in 89.203 ms, heap usage 534.486 MB -> 57.965 MB.
[2025-02-27T01:49:18.322Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T01:49:20.301Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T01:49:22.364Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T01:49:24.343Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T01:49:25.307Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T01:49:26.271Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T01:49:27.237Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T01:49:28.201Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T01:49:29.166Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-27T01:49:29.166Z] The best model improves the baseline by 14.43%.
[2025-02-27T01:49:29.166Z] Movies recommended for you:
[2025-02-27T01:49:29.166Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T01:49:29.166Z] There is no way to check that no silent failure occurred.
[2025-02-27T01:49:29.166Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12675.424 ms) ======
[2025-02-27T01:49:30.129Z] -----------------------------------
[2025-02-27T01:49:30.129Z] renaissance-movie-lens_0_PASSED
[2025-02-27T01:49:30.129Z] -----------------------------------
[2025-02-27T01:49:30.129Z]
[2025-02-27T01:49:30.129Z] TEST TEARDOWN:
[2025-02-27T01:49:30.129Z] Nothing to be done for teardown.
[2025-02-27T01:49:30.129Z] renaissance-movie-lens_0 Finish Time: Thu Feb 27 01:49:30 2025 Epoch Time (ms): 1740620970025