renaissance-movie-lens_0
[2024-11-13T21:59:37.177Z] Running test renaissance-movie-lens_0 ...
[2024-11-13T21:59:37.177Z] ===============================================
[2024-11-13T21:59:37.177Z] renaissance-movie-lens_0 Start Time: Wed Nov 13 21:59:36 2024 Epoch Time (ms): 1731535176549
[2024-11-13T21:59:37.177Z] variation: NoOptions
[2024-11-13T21:59:37.177Z] JVM_OPTIONS:
[2024-11-13T21:59:37.177Z] { \
[2024-11-13T21:59:37.177Z] echo ""; echo "TEST SETUP:"; \
[2024-11-13T21:59:37.177Z] echo "Nothing to be done for setup."; \
[2024-11-13T21:59:37.177Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315343321240/renaissance-movie-lens_0"; \
[2024-11-13T21:59:37.177Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315343321240/renaissance-movie-lens_0"; \
[2024-11-13T21:59:37.177Z] echo ""; echo "TESTING:"; \
[2024-11-13T21:59:37.177Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315343321240/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-13T21:59:37.177Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315343321240/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-13T21:59:37.177Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-13T21:59:37.177Z] echo "Nothing to be done for teardown."; \
[2024-11-13T21:59:37.177Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315343321240/TestTargetResult";
[2024-11-13T21:59:37.177Z]
[2024-11-13T21:59:37.177Z] TEST SETUP:
[2024-11-13T21:59:37.177Z] Nothing to be done for setup.
[2024-11-13T21:59:37.177Z]
[2024-11-13T21:59:37.177Z] TESTING:
[2024-11-13T21:59:41.597Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-13T21:59:44.041Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-11-13T21:59:46.495Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-13T21:59:47.258Z] Training: 60056, validation: 20285, test: 19854
[2024-11-13T21:59:47.258Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-13T21:59:47.258Z] GC before operation: completed in 53.446 ms, heap usage 250.228 MB -> 37.571 MB.
[2024-11-13T21:59:51.672Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T21:59:55.059Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T21:59:58.447Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:00:00.902Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:00:02.481Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:00:04.055Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:00:06.502Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:00:08.079Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:00:08.080Z] 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.
[2024-11-13T22:00:08.080Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:00:08.080Z] Movies recommended for you:
[2024-11-13T22:00:08.080Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:00:08.080Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:00:08.080Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21255.865 ms) ======
[2024-11-13T22:00:08.080Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-13T22:00:08.080Z] GC before operation: completed in 89.217 ms, heap usage 1.993 GB -> 55.847 MB.
[2024-11-13T22:00:11.498Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:00:13.955Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:00:16.417Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:00:19.815Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:00:21.386Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:00:22.975Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:00:24.550Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:00:26.134Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:00:26.134Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-13T22:00:26.134Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:00:26.134Z] Movies recommended for you:
[2024-11-13T22:00:26.134Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:00:26.134Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:00:26.134Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (18106.788 ms) ======
[2024-11-13T22:00:26.134Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-13T22:00:26.902Z] GC before operation: completed in 85.188 ms, heap usage 3.434 GB -> 60.605 MB.
[2024-11-13T22:00:29.367Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:00:31.812Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:00:35.385Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:00:36.963Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:00:38.546Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:00:40.119Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:00:41.694Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:00:43.271Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:00:44.030Z] 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.
[2024-11-13T22:00:44.030Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:00:44.030Z] Movies recommended for you:
[2024-11-13T22:00:44.030Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:00:44.030Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:00:44.030Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17379.110 ms) ======
[2024-11-13T22:00:44.030Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-13T22:00:44.030Z] GC before operation: completed in 75.858 ms, heap usage 139.518 MB -> 51.683 MB.
[2024-11-13T22:00:46.473Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:00:48.946Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:00:52.336Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:00:54.962Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:00:56.574Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:00:58.149Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:00:59.741Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:01:01.318Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:01:01.318Z] 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.
[2024-11-13T22:01:01.318Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:01:01.318Z] Movies recommended for you:
[2024-11-13T22:01:01.318Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:01:01.318Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:01:01.318Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (17445.660 ms) ======
[2024-11-13T22:01:01.318Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-13T22:01:01.318Z] GC before operation: completed in 82.446 ms, heap usage 1.758 GB -> 57.034 MB.
[2024-11-13T22:01:03.765Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:01:07.175Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:01:09.620Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:01:12.074Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:01:13.661Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:01:15.234Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:01:16.814Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:01:18.412Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:01:18.412Z] 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.
[2024-11-13T22:01:18.412Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:01:18.412Z] Movies recommended for you:
[2024-11-13T22:01:18.412Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:01:18.412Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:01:18.412Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17186.249 ms) ======
[2024-11-13T22:01:18.412Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-13T22:01:19.174Z] GC before operation: completed in 78.202 ms, heap usage 83.607 MB -> 56.582 MB.
[2024-11-13T22:01:21.621Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:01:24.091Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:01:26.537Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:01:28.983Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:01:30.563Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:01:32.133Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:01:33.702Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:01:35.306Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:01:36.082Z] 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.
[2024-11-13T22:01:36.082Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:01:36.082Z] Movies recommended for you:
[2024-11-13T22:01:36.082Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:01:36.082Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:01:36.082Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (17039.974 ms) ======
[2024-11-13T22:01:36.082Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-13T22:01:36.082Z] GC before operation: completed in 79.005 ms, heap usage 1.709 GB -> 57.007 MB.
[2024-11-13T22:01:38.524Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:01:40.984Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:01:43.428Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:01:45.872Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:01:47.451Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:01:49.026Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:01:50.613Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:01:52.194Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:01:52.954Z] 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.
[2024-11-13T22:01:52.954Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:01:52.954Z] Movies recommended for you:
[2024-11-13T22:01:52.954Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:01:52.954Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:01:52.954Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16971.982 ms) ======
[2024-11-13T22:01:52.954Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-13T22:01:52.954Z] GC before operation: completed in 77.577 ms, heap usage 169.710 MB -> 52.282 MB.
[2024-11-13T22:01:55.401Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:01:57.852Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:02:01.237Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:02:03.689Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:02:05.266Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:02:06.840Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:02:08.421Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:02:09.200Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:02:09.959Z] 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.
[2024-11-13T22:02:09.959Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:02:09.959Z] Movies recommended for you:
[2024-11-13T22:02:09.959Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:02:09.959Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:02:09.959Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16987.609 ms) ======
[2024-11-13T22:02:09.959Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-13T22:02:09.959Z] GC before operation: completed in 80.523 ms, heap usage 1.518 GB -> 57.436 MB.
[2024-11-13T22:02:12.422Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:02:14.872Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:02:18.259Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:02:20.707Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:02:22.300Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:02:23.895Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:02:24.820Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:02:26.412Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:02:27.180Z] 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.
[2024-11-13T22:02:27.180Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:02:27.180Z] Movies recommended for you:
[2024-11-13T22:02:27.180Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:02:27.180Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:02:27.180Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16984.829 ms) ======
[2024-11-13T22:02:27.180Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-13T22:02:27.180Z] GC before operation: completed in 90.629 ms, heap usage 1.766 GB -> 57.354 MB.
[2024-11-13T22:02:29.628Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:02:32.071Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:02:34.520Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:02:36.970Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:02:38.540Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:02:40.118Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:02:41.697Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:02:43.271Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:02:43.271Z] 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.
[2024-11-13T22:02:43.271Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:02:44.030Z] Movies recommended for you:
[2024-11-13T22:02:44.030Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:02:44.030Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:02:44.030Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16631.276 ms) ======
[2024-11-13T22:02:44.030Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-13T22:02:44.030Z] GC before operation: completed in 80.864 ms, heap usage 1.410 GB -> 57.197 MB.
[2024-11-13T22:02:46.471Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:02:48.921Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:02:51.365Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:02:53.829Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:02:55.400Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:02:56.982Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:02:58.555Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:03:00.129Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:03:00.129Z] 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.
[2024-11-13T22:03:00.129Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:03:00.899Z] Movies recommended for you:
[2024-11-13T22:03:00.899Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:03:00.899Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:03:00.899Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16733.336 ms) ======
[2024-11-13T22:03:00.899Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-13T22:03:00.899Z] GC before operation: completed in 77.654 ms, heap usage 136.729 MB -> 52.206 MB.
[2024-11-13T22:03:03.369Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:03:05.827Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:03:08.271Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:03:10.722Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:03:12.303Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:03:13.884Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:03:15.460Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:03:17.031Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:03:17.031Z] 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.
[2024-11-13T22:03:17.031Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:03:17.031Z] Movies recommended for you:
[2024-11-13T22:03:17.031Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:03:17.031Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:03:17.031Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16581.437 ms) ======
[2024-11-13T22:03:17.031Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-13T22:03:17.031Z] GC before operation: completed in 82.589 ms, heap usage 366.267 MB -> 52.525 MB.
[2024-11-13T22:03:19.484Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:03:22.903Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:03:25.356Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:03:27.848Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:03:29.427Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:03:30.190Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:03:31.789Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:03:33.367Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:03:34.129Z] 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.
[2024-11-13T22:03:34.129Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:03:34.129Z] Movies recommended for you:
[2024-11-13T22:03:34.129Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:03:34.129Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:03:34.129Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16663.525 ms) ======
[2024-11-13T22:03:34.129Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-13T22:03:34.129Z] GC before operation: completed in 88.271 ms, heap usage 465.146 MB -> 52.817 MB.
[2024-11-13T22:03:36.588Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:03:39.029Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:03:42.421Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:03:44.006Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:03:45.609Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:03:47.188Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:03:48.758Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:03:50.339Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:03:50.339Z] 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.
[2024-11-13T22:03:50.339Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:03:50.339Z] Movies recommended for you:
[2024-11-13T22:03:50.339Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:03:50.339Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:03:50.339Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16634.506 ms) ======
[2024-11-13T22:03:50.339Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-13T22:03:51.100Z] GC before operation: completed in 88.586 ms, heap usage 1.964 GB -> 57.375 MB.
[2024-11-13T22:03:53.030Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:03:56.431Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:03:58.878Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:04:01.318Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:04:02.896Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:04:04.467Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:04:06.047Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:04:07.702Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:04:07.702Z] 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.
[2024-11-13T22:04:07.702Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:04:07.702Z] Movies recommended for you:
[2024-11-13T22:04:07.702Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:04:07.702Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:04:07.702Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16859.227 ms) ======
[2024-11-13T22:04:07.702Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-13T22:04:07.702Z] GC before operation: completed in 86.660 ms, heap usage 1.751 GB -> 57.474 MB.
[2024-11-13T22:04:10.148Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:04:12.591Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:04:15.983Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:04:18.446Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:04:19.213Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:04:20.787Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:04:22.372Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:04:23.957Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:04:24.717Z] 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.
[2024-11-13T22:04:24.717Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:04:24.717Z] Movies recommended for you:
[2024-11-13T22:04:24.717Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:04:24.717Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:04:24.717Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16878.271 ms) ======
[2024-11-13T22:04:24.717Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-13T22:04:24.717Z] GC before operation: completed in 80.603 ms, heap usage 827.713 MB -> 56.285 MB.
[2024-11-13T22:04:27.158Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:04:29.601Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:04:32.991Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:04:35.467Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:04:36.238Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:04:37.807Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:04:39.380Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:04:40.951Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:04:41.713Z] 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.
[2024-11-13T22:04:41.713Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:04:41.713Z] Movies recommended for you:
[2024-11-13T22:04:41.713Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:04:41.713Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:04:41.713Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16873.817 ms) ======
[2024-11-13T22:04:41.713Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-13T22:04:41.713Z] GC before operation: completed in 84.494 ms, heap usage 911.848 MB -> 56.366 MB.
[2024-11-13T22:04:44.153Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:04:46.605Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:04:49.074Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:04:52.465Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:04:53.226Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:04:54.796Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:04:56.372Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:04:57.955Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:04:58.734Z] 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.
[2024-11-13T22:04:58.734Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:04:58.734Z] Movies recommended for you:
[2024-11-13T22:04:58.734Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:04:58.734Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:04:58.734Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16854.940 ms) ======
[2024-11-13T22:04:58.734Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-13T22:04:58.734Z] GC before operation: completed in 87.087 ms, heap usage 1.725 GB -> 57.407 MB.
[2024-11-13T22:05:01.184Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:05:03.632Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:05:06.078Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:05:09.481Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:05:10.275Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:05:11.848Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:05:13.422Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:05:14.992Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:05:15.752Z] 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.
[2024-11-13T22:05:15.752Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:05:15.752Z] Movies recommended for you:
[2024-11-13T22:05:15.752Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:05:15.752Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:05:15.752Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16997.869 ms) ======
[2024-11-13T22:05:15.752Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-13T22:05:15.752Z] GC before operation: completed in 88.348 ms, heap usage 2.538 GB -> 57.755 MB.
[2024-11-13T22:05:18.195Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-13T22:05:20.640Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-13T22:05:23.284Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-13T22:05:25.726Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-13T22:05:27.299Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-13T22:05:28.876Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-13T22:05:30.478Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-13T22:05:32.058Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-13T22:05:32.058Z] 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.
[2024-11-13T22:05:32.059Z] The best model improves the baseline by 14.43%.
[2024-11-13T22:05:32.825Z] Movies recommended for you:
[2024-11-13T22:05:32.825Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-13T22:05:32.825Z] There is no way to check that no silent failure occurred.
[2024-11-13T22:05:32.825Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16850.724 ms) ======
[2024-11-13T22:05:33.593Z] -----------------------------------
[2024-11-13T22:05:33.593Z] renaissance-movie-lens_0_PASSED
[2024-11-13T22:05:33.593Z] -----------------------------------
[2024-11-13T22:05:33.593Z]
[2024-11-13T22:05:33.593Z] TEST TEARDOWN:
[2024-11-13T22:05:33.593Z] Nothing to be done for teardown.
[2024-11-13T22:05:33.593Z] renaissance-movie-lens_0 Finish Time: Wed Nov 13 22:05:32 2024 Epoch Time (ms): 1731535532802