renaissance-movie-lens_0
[2024-11-28T03:23:05.541Z] Running test renaissance-movie-lens_0 ...
[2024-11-28T03:23:05.541Z] ===============================================
[2024-11-28T03:23:05.541Z] renaissance-movie-lens_0 Start Time: Wed Nov 27 21:23:04 2024 Epoch Time (ms): 1732764184855
[2024-11-28T03:23:05.541Z] variation: NoOptions
[2024-11-28T03:23:05.541Z] JVM_OPTIONS:
[2024-11-28T03:23:05.541Z] { \
[2024-11-28T03:23:05.541Z] echo ""; echo "TEST SETUP:"; \
[2024-11-28T03:23:05.541Z] echo "Nothing to be done for setup."; \
[2024-11-28T03:23:05.541Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17327635686846/renaissance-movie-lens_0"; \
[2024-11-28T03:23:05.542Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17327635686846/renaissance-movie-lens_0"; \
[2024-11-28T03:23:05.542Z] echo ""; echo "TESTING:"; \
[2024-11-28T03:23:05.542Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_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_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_17327635686846/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-28T03:23:05.542Z] 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_17327635686846/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-28T03:23:05.542Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-28T03:23:05.542Z] echo "Nothing to be done for teardown."; \
[2024-11-28T03:23:05.542Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17327635686846/TestTargetResult";
[2024-11-28T03:23:05.542Z]
[2024-11-28T03:23:05.542Z] TEST SETUP:
[2024-11-28T03:23:05.542Z] Nothing to be done for setup.
[2024-11-28T03:23:05.542Z]
[2024-11-28T03:23:05.542Z] TESTING:
[2024-11-28T03:23:07.761Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-28T03:23:09.979Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-28T03:23:13.117Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-28T03:23:13.117Z] Training: 60056, validation: 20285, test: 19854
[2024-11-28T03:23:13.117Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-28T03:23:13.117Z] GC before operation: completed in 41.946 ms, heap usage 95.330 MB -> 37.719 MB.
[2024-11-28T03:23:20.859Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:23:23.971Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:23:27.107Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:23:30.244Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:23:31.685Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:23:33.130Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:23:35.376Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:23:36.834Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:23:36.834Z] 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.
[2024-11-28T03:23:36.834Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:23:36.834Z] Movies recommended for you:
[2024-11-28T03:23:36.834Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:23:36.834Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:23:36.834Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23845.238 ms) ======
[2024-11-28T03:23:36.834Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-28T03:23:36.834Z] GC before operation: completed in 61.065 ms, heap usage 290.953 MB -> 51.242 MB.
[2024-11-28T03:23:39.936Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:23:42.172Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:23:45.282Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:23:47.529Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:23:48.959Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:23:51.194Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:23:52.636Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:23:54.086Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:23:54.086Z] 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.
[2024-11-28T03:23:54.086Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:23:54.086Z] Movies recommended for you:
[2024-11-28T03:23:54.086Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:23:54.086Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:23:54.086Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17204.961 ms) ======
[2024-11-28T03:23:54.086Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-28T03:23:54.086Z] GC before operation: completed in 65.075 ms, heap usage 300.542 MB -> 51.552 MB.
[2024-11-28T03:23:57.184Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:24:00.332Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:24:02.575Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:24:04.810Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:24:06.238Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:24:07.669Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:24:09.586Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:24:10.284Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:24:10.980Z] 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.
[2024-11-28T03:24:10.980Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:24:10.980Z] Movies recommended for you:
[2024-11-28T03:24:10.980Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:24:10.980Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:24:10.980Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16655.491 ms) ======
[2024-11-28T03:24:10.980Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-28T03:24:10.980Z] GC before operation: completed in 63.936 ms, heap usage 348.460 MB -> 52.064 MB.
[2024-11-28T03:24:14.097Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:24:16.339Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:24:18.640Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:24:20.076Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:24:21.513Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:24:22.987Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:24:24.442Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:24:25.883Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:24:26.570Z] 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.
[2024-11-28T03:24:26.570Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:24:26.570Z] Movies recommended for you:
[2024-11-28T03:24:26.570Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:24:26.570Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:24:26.570Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15266.585 ms) ======
[2024-11-28T03:24:26.570Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-28T03:24:26.570Z] GC before operation: completed in 85.824 ms, heap usage 296.458 MB -> 52.390 MB.
[2024-11-28T03:24:28.827Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:24:31.085Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:24:33.311Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:24:35.541Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:24:36.995Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:24:37.684Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:24:39.114Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:24:40.550Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:24:40.550Z] 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.
[2024-11-28T03:24:40.550Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:24:41.240Z] Movies recommended for you:
[2024-11-28T03:24:41.240Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:24:41.240Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:24:41.240Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14469.330 ms) ======
[2024-11-28T03:24:41.240Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-28T03:24:41.240Z] GC before operation: completed in 63.793 ms, heap usage 243.534 MB -> 55.687 MB.
[2024-11-28T03:24:43.485Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:24:45.749Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:24:48.001Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:24:50.248Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:24:51.671Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:24:53.111Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:24:54.556Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:24:55.992Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:24:55.992Z] 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.
[2024-11-28T03:24:55.992Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:24:55.992Z] Movies recommended for you:
[2024-11-28T03:24:55.992Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:24:55.992Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:24:55.992Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15013.117 ms) ======
[2024-11-28T03:24:55.992Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-28T03:24:55.992Z] GC before operation: completed in 62.376 ms, heap usage 572.757 MB -> 55.828 MB.
[2024-11-28T03:24:59.093Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:25:00.538Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:25:02.763Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:25:04.984Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:25:06.449Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:25:07.881Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:25:09.323Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:25:10.760Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:25:10.760Z] 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.
[2024-11-28T03:25:10.760Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:25:10.760Z] Movies recommended for you:
[2024-11-28T03:25:10.760Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:25:10.760Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:25:10.760Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14771.586 ms) ======
[2024-11-28T03:25:10.761Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-28T03:25:10.761Z] GC before operation: completed in 58.265 ms, heap usage 317.933 MB -> 52.679 MB.
[2024-11-28T03:25:13.872Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:25:15.302Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:25:17.613Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:25:19.842Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:25:21.302Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:25:21.990Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:25:23.423Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:25:24.852Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:25:24.852Z] 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.
[2024-11-28T03:25:24.852Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:25:24.852Z] Movies recommended for you:
[2024-11-28T03:25:24.852Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:25:24.853Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:25:24.853Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14126.403 ms) ======
[2024-11-28T03:25:24.853Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-28T03:25:24.853Z] GC before operation: completed in 58.894 ms, heap usage 372.127 MB -> 52.964 MB.
[2024-11-28T03:25:27.954Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:25:30.202Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:25:31.631Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:25:33.864Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:25:35.320Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:25:36.752Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:25:38.203Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:25:38.898Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:25:39.599Z] 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.
[2024-11-28T03:25:39.599Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:25:39.599Z] Movies recommended for you:
[2024-11-28T03:25:39.599Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:25:39.599Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:25:39.599Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14469.335 ms) ======
[2024-11-28T03:25:39.599Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-28T03:25:39.599Z] GC before operation: completed in 76.608 ms, heap usage 309.015 MB -> 52.788 MB.
[2024-11-28T03:25:41.860Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:25:44.106Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:25:46.352Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:25:48.611Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:25:50.036Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:25:51.474Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:25:52.920Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:25:53.609Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:25:54.299Z] 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.
[2024-11-28T03:25:54.299Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:25:54.299Z] Movies recommended for you:
[2024-11-28T03:25:54.299Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:25:54.299Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:25:54.299Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14572.078 ms) ======
[2024-11-28T03:25:54.299Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-28T03:25:54.299Z] GC before operation: completed in 79.775 ms, heap usage 351.005 MB -> 52.888 MB.
[2024-11-28T03:25:56.529Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:25:59.638Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:26:01.086Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:26:03.335Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:26:04.785Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:26:06.215Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:26:07.640Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:26:09.078Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:26:09.078Z] 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.
[2024-11-28T03:26:09.078Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:26:09.078Z] Movies recommended for you:
[2024-11-28T03:26:09.078Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:26:09.078Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:26:09.078Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14857.319 ms) ======
[2024-11-28T03:26:09.078Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-28T03:26:09.078Z] GC before operation: completed in 70.025 ms, heap usage 308.292 MB -> 52.547 MB.
[2024-11-28T03:26:11.300Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:26:13.527Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:26:15.748Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:26:17.981Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:26:19.449Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:26:20.890Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:26:21.588Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:26:23.033Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:26:23.719Z] 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.
[2024-11-28T03:26:23.719Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:26:23.719Z] Movies recommended for you:
[2024-11-28T03:26:23.719Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:26:23.719Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:26:23.719Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14414.443 ms) ======
[2024-11-28T03:26:23.719Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-28T03:26:23.719Z] GC before operation: completed in 71.448 ms, heap usage 616.226 MB -> 56.275 MB.
[2024-11-28T03:26:25.950Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:26:28.215Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:26:30.252Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:26:32.509Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:26:33.934Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:26:35.365Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:26:36.808Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:26:38.265Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:26:38.265Z] 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.
[2024-11-28T03:26:38.265Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:26:38.265Z] Movies recommended for you:
[2024-11-28T03:26:38.265Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:26:38.265Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:26:38.265Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14764.687 ms) ======
[2024-11-28T03:26:38.265Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-28T03:26:38.265Z] GC before operation: completed in 81.175 ms, heap usage 337.062 MB -> 53.020 MB.
[2024-11-28T03:26:41.375Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:26:42.814Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:26:45.928Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:26:47.362Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:26:48.812Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:26:50.252Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:26:51.707Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:26:53.136Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:26:53.136Z] 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.
[2024-11-28T03:26:53.136Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:26:53.136Z] Movies recommended for you:
[2024-11-28T03:26:53.136Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:26:53.136Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:26:53.136Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14663.565 ms) ======
[2024-11-28T03:26:53.136Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-28T03:26:53.136Z] GC before operation: completed in 77.807 ms, heap usage 365.037 MB -> 52.670 MB.
[2024-11-28T03:26:56.265Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:26:58.527Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:26:59.965Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:27:02.211Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:27:03.639Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:27:05.074Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:27:06.567Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:27:07.263Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:27:07.967Z] 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.
[2024-11-28T03:27:07.967Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:27:07.967Z] Movies recommended for you:
[2024-11-28T03:27:07.967Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:27:07.967Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:27:07.967Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14568.141 ms) ======
[2024-11-28T03:27:07.967Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-28T03:27:07.967Z] GC before operation: completed in 108.143 ms, heap usage 299.219 MB -> 52.950 MB.
[2024-11-28T03:27:10.224Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:27:12.461Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:27:14.703Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:27:16.971Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:27:17.714Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:27:19.172Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:27:20.603Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:27:22.035Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:27:22.035Z] 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.
[2024-11-28T03:27:22.035Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:27:22.035Z] Movies recommended for you:
[2024-11-28T03:27:22.035Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:27:22.035Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:27:22.035Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14378.665 ms) ======
[2024-11-28T03:27:22.036Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-28T03:27:22.722Z] GC before operation: completed in 80.309 ms, heap usage 160.685 MB -> 52.780 MB.
[2024-11-28T03:27:25.046Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:27:27.278Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:27:29.526Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:27:30.974Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:27:32.403Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:27:33.834Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:27:35.260Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:27:36.689Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:27:36.689Z] 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.
[2024-11-28T03:27:36.689Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:27:36.689Z] Movies recommended for you:
[2024-11-28T03:27:36.689Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:27:36.689Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:27:36.689Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14340.842 ms) ======
[2024-11-28T03:27:36.689Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-28T03:27:36.689Z] GC before operation: completed in 54.053 ms, heap usage 182.482 MB -> 52.668 MB.
[2024-11-28T03:27:38.927Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:27:41.159Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:27:43.392Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:27:45.622Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:27:47.086Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:27:48.527Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:27:49.232Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:27:50.781Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:27:51.471Z] 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.
[2024-11-28T03:27:51.471Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:27:51.471Z] Movies recommended for you:
[2024-11-28T03:27:51.471Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:27:51.471Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:27:51.471Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14428.926 ms) ======
[2024-11-28T03:27:51.471Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-28T03:27:51.471Z] GC before operation: completed in 87.770 ms, heap usage 513.024 MB -> 56.279 MB.
[2024-11-28T03:27:53.722Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:27:55.942Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:27:58.182Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:28:00.434Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:28:01.146Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:28:02.585Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:28:04.023Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:28:05.466Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:28:05.466Z] 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.
[2024-11-28T03:28:05.466Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:28:05.466Z] Movies recommended for you:
[2024-11-28T03:28:05.466Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:28:05.466Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:28:05.466Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14240.728 ms) ======
[2024-11-28T03:28:05.466Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-28T03:28:05.466Z] GC before operation: completed in 80.038 ms, heap usage 539.358 MB -> 56.562 MB.
[2024-11-28T03:28:07.720Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:28:09.957Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:28:12.197Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:28:14.459Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:28:15.879Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:28:17.310Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:28:18.734Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:28:20.172Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:28:20.172Z] 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.
[2024-11-28T03:28:20.172Z] The best model improves the baseline by 14.43%.
[2024-11-28T03:28:20.172Z] Movies recommended for you:
[2024-11-28T03:28:20.172Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:28:20.172Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:28:20.172Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14650.969 ms) ======
[2024-11-28T03:28:21.595Z] -----------------------------------
[2024-11-28T03:28:21.595Z] renaissance-movie-lens_0_PASSED
[2024-11-28T03:28:21.595Z] -----------------------------------
[2024-11-28T03:28:21.595Z]
[2024-11-28T03:28:21.595Z] TEST TEARDOWN:
[2024-11-28T03:28:21.595Z] Nothing to be done for teardown.
[2024-11-28T03:28:21.595Z] renaissance-movie-lens_0 Finish Time: Wed Nov 27 21:28:21 2024 Epoch Time (ms): 1732764501232