renaissance-movie-lens_0
[2024-08-15T03:40:12.124Z] Running test renaissance-movie-lens_0 ...
[2024-08-15T03:40:12.124Z] ===============================================
[2024-08-15T03:40:12.124Z] renaissance-movie-lens_0 Start Time: Thu Aug 15 03:40:11 2024 Epoch Time (ms): 1723693211416
[2024-08-15T03:40:12.124Z] variation: NoOptions
[2024-08-15T03:40:12.124Z] JVM_OPTIONS:
[2024-08-15T03:40:12.124Z] { \
[2024-08-15T03:40:12.124Z] echo ""; echo "TEST SETUP:"; \
[2024-08-15T03:40:12.124Z] echo "Nothing to be done for setup."; \
[2024-08-15T03:40:12.124Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17236923896642/renaissance-movie-lens_0"; \
[2024-08-15T03:40:12.124Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17236923896642/renaissance-movie-lens_0"; \
[2024-08-15T03:40:12.124Z] echo ""; echo "TESTING:"; \
[2024-08-15T03:40:12.124Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/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_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17236923896642/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-15T03:40:12.124Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17236923896642/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-15T03:40:12.124Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-15T03:40:12.124Z] echo "Nothing to be done for teardown."; \
[2024-08-15T03:40:12.124Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17236923896642/TestTargetResult";
[2024-08-15T03:40:12.124Z]
[2024-08-15T03:40:12.124Z] TEST SETUP:
[2024-08-15T03:40:12.124Z] Nothing to be done for setup.
[2024-08-15T03:40:12.124Z]
[2024-08-15T03:40:12.124Z] TESTING:
[2024-08-15T03:40:16.600Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-15T03:40:18.198Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-08-15T03:40:21.641Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-15T03:40:21.641Z] Training: 60056, validation: 20285, test: 19854
[2024-08-15T03:40:21.641Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-15T03:40:21.641Z] GC before operation: completed in 56.716 ms, heap usage 185.319 MB -> 37.641 MB.
[2024-08-15T03:40:26.121Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:40:29.558Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:40:32.391Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:40:34.874Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:40:36.470Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:40:38.066Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:40:39.663Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:40:41.260Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:40:42.032Z] 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.
[2024-08-15T03:40:42.033Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:40:42.033Z] Movies recommended for you:
[2024-08-15T03:40:42.033Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:40:42.033Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:40:42.033Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20337.478 ms) ======
[2024-08-15T03:40:42.033Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-15T03:40:42.033Z] GC before operation: completed in 85.790 ms, heap usage 1.171 GB -> 55.399 MB.
[2024-08-15T03:40:44.522Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:40:47.956Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:40:50.442Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:40:52.923Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:40:54.527Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:40:56.128Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:40:57.738Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:40:59.343Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:40:59.343Z] 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.
[2024-08-15T03:40:59.343Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:40:59.343Z] Movies recommended for you:
[2024-08-15T03:40:59.343Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:40:59.343Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:40:59.343Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17493.644 ms) ======
[2024-08-15T03:40:59.343Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-15T03:40:59.343Z] GC before operation: completed in 79.765 ms, heap usage 2.652 GB -> 56.411 MB.
[2024-08-15T03:41:02.802Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:41:05.290Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:41:07.776Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:41:10.263Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:41:11.881Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:41:13.487Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:41:15.100Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:41:15.886Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:41:16.658Z] 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.
[2024-08-15T03:41:16.658Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:41:16.658Z] Movies recommended for you:
[2024-08-15T03:41:16.658Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:41:16.658Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:41:16.658Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16857.094 ms) ======
[2024-08-15T03:41:16.658Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-15T03:41:16.658Z] GC before operation: completed in 84.519 ms, heap usage 121.267 MB -> 54.314 MB.
[2024-08-15T03:41:19.141Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:41:21.634Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:41:24.118Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:41:26.612Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:41:28.207Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:41:29.813Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:41:31.609Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:41:33.715Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:41:33.715Z] 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.
[2024-08-15T03:41:33.715Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:41:33.715Z] Movies recommended for you:
[2024-08-15T03:41:33.715Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:41:33.715Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:41:33.715Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16615.821 ms) ======
[2024-08-15T03:41:33.715Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-15T03:41:33.715Z] GC before operation: completed in 79.448 ms, heap usage 2.194 GB -> 57.102 MB.
[2024-08-15T03:41:36.201Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:41:38.688Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:41:41.174Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:41:43.659Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:41:45.254Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:41:46.030Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:41:47.630Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:41:49.234Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:41:50.013Z] 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.
[2024-08-15T03:41:50.013Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:41:50.013Z] Movies recommended for you:
[2024-08-15T03:41:50.013Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:41:50.013Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:41:50.013Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16453.103 ms) ======
[2024-08-15T03:41:50.013Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-15T03:41:50.013Z] GC before operation: completed in 78.788 ms, heap usage 1.348 GB -> 56.954 MB.
[2024-08-15T03:41:52.555Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:41:55.044Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:41:57.515Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:41:59.984Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:42:01.574Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:42:03.172Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:42:04.763Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:42:06.354Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:42:06.354Z] 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.
[2024-08-15T03:42:06.354Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:42:06.354Z] Movies recommended for you:
[2024-08-15T03:42:06.354Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:42:06.354Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:42:06.354Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16532.626 ms) ======
[2024-08-15T03:42:06.354Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-15T03:42:06.354Z] GC before operation: completed in 81.569 ms, heap usage 1.369 GB -> 56.962 MB.
[2024-08-15T03:42:08.849Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:42:11.352Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:42:13.845Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:42:16.352Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:42:17.944Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:42:19.560Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:42:21.150Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:42:22.746Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:42:22.746Z] 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.
[2024-08-15T03:42:22.746Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:42:22.746Z] Movies recommended for you:
[2024-08-15T03:42:22.746Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:42:22.746Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:42:22.746Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16498.628 ms) ======
[2024-08-15T03:42:22.746Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-15T03:42:22.746Z] GC before operation: completed in 79.947 ms, heap usage 87.116 MB -> 56.149 MB.
[2024-08-15T03:42:25.238Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:42:27.725Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:42:31.172Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:42:32.765Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:42:34.371Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:42:36.318Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:42:38.047Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:42:38.828Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:42:39.600Z] 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.
[2024-08-15T03:42:39.600Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:42:39.600Z] Movies recommended for you:
[2024-08-15T03:42:39.600Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:42:39.600Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:42:39.600Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16452.432 ms) ======
[2024-08-15T03:42:39.600Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-15T03:42:39.600Z] GC before operation: completed in 80.578 ms, heap usage 1.957 GB -> 57.666 MB.
[2024-08-15T03:42:42.074Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:42:44.558Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:42:47.047Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:42:49.551Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:42:51.151Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:42:52.766Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:42:54.361Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:42:55.963Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:42:55.963Z] 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.
[2024-08-15T03:42:55.963Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:42:55.963Z] Movies recommended for you:
[2024-08-15T03:42:55.963Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:42:55.963Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:42:55.963Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16347.086 ms) ======
[2024-08-15T03:42:55.963Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-15T03:42:55.963Z] GC before operation: completed in 74.442 ms, heap usage 158.928 MB -> 53.073 MB.
[2024-08-15T03:42:58.441Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:43:00.927Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:43:03.429Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:43:05.943Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:43:07.539Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:43:09.132Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:43:10.740Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:43:12.348Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:43:12.348Z] 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.
[2024-08-15T03:43:12.348Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:43:12.348Z] Movies recommended for you:
[2024-08-15T03:43:12.348Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:43:12.348Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:43:12.348Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16467.687 ms) ======
[2024-08-15T03:43:12.348Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-15T03:43:12.348Z] GC before operation: completed in 95.707 ms, heap usage 1.949 GB -> 57.615 MB.
[2024-08-15T03:43:14.831Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:43:17.335Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:43:19.821Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:43:22.317Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:43:23.918Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:43:25.530Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:43:27.141Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:43:28.749Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:43:28.749Z] 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.
[2024-08-15T03:43:28.749Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:43:28.749Z] Movies recommended for you:
[2024-08-15T03:43:28.749Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:43:28.749Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:43:28.749Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16308.937 ms) ======
[2024-08-15T03:43:28.749Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-15T03:43:28.749Z] GC before operation: completed in 82.885 ms, heap usage 1.915 GB -> 57.300 MB.
[2024-08-15T03:43:31.235Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:43:33.717Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:43:37.152Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:43:38.755Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:43:40.571Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:43:42.171Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:43:43.770Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:43:45.373Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:43:45.373Z] 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.
[2024-08-15T03:43:45.373Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:43:45.373Z] Movies recommended for you:
[2024-08-15T03:43:45.373Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:43:45.373Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:43:45.373Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16470.559 ms) ======
[2024-08-15T03:43:45.373Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-15T03:43:45.373Z] GC before operation: completed in 99.292 ms, heap usage 254.546 MB -> 52.633 MB.
[2024-08-15T03:43:47.852Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:43:50.344Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:43:52.847Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:43:55.325Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:43:56.928Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:43:58.528Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:44:00.124Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:44:01.729Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:44:01.729Z] 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.
[2024-08-15T03:44:01.729Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:44:01.729Z] Movies recommended for you:
[2024-08-15T03:44:01.729Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:44:01.729Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:44:01.729Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16336.770 ms) ======
[2024-08-15T03:44:01.729Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-15T03:44:01.729Z] GC before operation: completed in 87.520 ms, heap usage 1.400 GB -> 57.452 MB.
[2024-08-15T03:44:04.230Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:44:06.714Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:44:10.168Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:44:11.770Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:44:13.366Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:44:14.968Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:44:16.559Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:44:18.171Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:44:18.171Z] 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.
[2024-08-15T03:44:18.171Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:44:18.171Z] Movies recommended for you:
[2024-08-15T03:44:18.171Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:44:18.171Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:44:18.171Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16507.206 ms) ======
[2024-08-15T03:44:18.171Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-15T03:44:18.171Z] GC before operation: completed in 84.280 ms, heap usage 87.047 MB -> 55.244 MB.
[2024-08-15T03:44:20.666Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:44:23.152Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:44:26.605Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:44:28.210Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:44:29.810Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:44:31.405Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:44:32.996Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:44:34.599Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:44:34.599Z] 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.
[2024-08-15T03:44:34.599Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:44:34.599Z] Movies recommended for you:
[2024-08-15T03:44:34.599Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:44:34.599Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:44:34.599Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16398.521 ms) ======
[2024-08-15T03:44:34.599Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-15T03:44:35.371Z] GC before operation: completed in 84.164 ms, heap usage 1.281 GB -> 57.263 MB.
[2024-08-15T03:44:37.854Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:44:40.334Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:44:43.165Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:44:45.659Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:44:46.434Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:44:48.029Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:44:49.622Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:44:51.221Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:44:51.221Z] 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.
[2024-08-15T03:44:51.221Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:44:51.221Z] Movies recommended for you:
[2024-08-15T03:44:51.221Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:44:51.221Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:44:51.221Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16548.317 ms) ======
[2024-08-15T03:44:51.221Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-15T03:44:51.999Z] GC before operation: completed in 96.198 ms, heap usage 1.799 GB -> 57.703 MB.
[2024-08-15T03:44:54.482Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:44:56.967Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:44:59.475Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:45:01.958Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:45:02.729Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:45:04.325Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:45:05.924Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:45:07.517Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:45:07.517Z] 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.
[2024-08-15T03:45:07.517Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:45:08.291Z] Movies recommended for you:
[2024-08-15T03:45:08.291Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:45:08.291Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:45:08.291Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16248.122 ms) ======
[2024-08-15T03:45:08.291Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-15T03:45:08.291Z] GC before operation: completed in 84.053 ms, heap usage 1.664 GB -> 57.379 MB.
[2024-08-15T03:45:10.785Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:45:13.288Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:45:15.768Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:45:18.255Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:45:19.862Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:45:20.653Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:45:22.253Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:45:23.859Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:45:24.632Z] 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.
[2024-08-15T03:45:24.632Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:45:24.632Z] Movies recommended for you:
[2024-08-15T03:45:24.632Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:45:24.632Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:45:24.632Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16400.918 ms) ======
[2024-08-15T03:45:24.632Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-15T03:45:24.632Z] GC before operation: completed in 82.098 ms, heap usage 158.544 MB -> 52.607 MB.
[2024-08-15T03:45:27.106Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:45:29.590Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:45:32.066Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:45:34.544Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:45:36.161Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:45:36.942Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:45:38.541Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:45:40.139Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:45:40.139Z] 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.
[2024-08-15T03:45:40.139Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:45:40.910Z] Movies recommended for you:
[2024-08-15T03:45:40.910Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:45:40.910Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:45:40.910Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16093.658 ms) ======
[2024-08-15T03:45:40.910Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-15T03:45:40.910Z] GC before operation: completed in 79.132 ms, heap usage 175.491 MB -> 52.797 MB.
[2024-08-15T03:45:43.574Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:45:45.209Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:45:48.657Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:45:50.260Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:45:51.863Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:45:53.455Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:45:55.048Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:45:56.641Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:45:56.641Z] 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.
[2024-08-15T03:45:56.641Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:45:56.641Z] Movies recommended for you:
[2024-08-15T03:45:56.641Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:45:56.641Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:45:56.641Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16254.030 ms) ======
[2024-08-15T03:45:57.420Z] -----------------------------------
[2024-08-15T03:45:57.420Z] renaissance-movie-lens_0_PASSED
[2024-08-15T03:45:57.420Z] -----------------------------------
[2024-08-15T03:45:57.420Z]
[2024-08-15T03:45:57.420Z] TEST TEARDOWN:
[2024-08-15T03:45:57.420Z] Nothing to be done for teardown.
[2024-08-15T03:45:57.420Z] renaissance-movie-lens_0 Finish Time: Thu Aug 15 03:45:57 2024 Epoch Time (ms): 1723693557241