renaissance-movie-lens_0
[2024-11-21T02:22:58.651Z] Running test renaissance-movie-lens_0 ...
[2024-11-21T02:22:58.651Z] ===============================================
[2024-11-21T02:22:58.651Z] renaissance-movie-lens_0 Start Time: Thu Nov 21 02:22:58 2024 Epoch Time (ms): 1732155778076
[2024-11-21T02:22:58.651Z] variation: NoOptions
[2024-11-21T02:22:58.651Z] JVM_OPTIONS:
[2024-11-21T02:22:58.651Z] { \
[2024-11-21T02:22:58.651Z] echo ""; echo "TEST SETUP:"; \
[2024-11-21T02:22:58.651Z] echo "Nothing to be done for setup."; \
[2024-11-21T02:22:58.651Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17321549567348/renaissance-movie-lens_0"; \
[2024-11-21T02:22:58.651Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17321549567348/renaissance-movie-lens_0"; \
[2024-11-21T02:22:58.651Z] echo ""; echo "TESTING:"; \
[2024-11-21T02:22:58.651Z] "/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_17321549567348/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-21T02:22:58.651Z] 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_17321549567348/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-21T02:22:58.651Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-21T02:22:58.651Z] echo "Nothing to be done for teardown."; \
[2024-11-21T02:22:58.651Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17321549567348/TestTargetResult";
[2024-11-21T02:22:58.651Z]
[2024-11-21T02:22:58.651Z] TEST SETUP:
[2024-11-21T02:22:58.651Z] Nothing to be done for setup.
[2024-11-21T02:22:58.651Z]
[2024-11-21T02:22:58.651Z] TESTING:
[2024-11-21T02:23:03.103Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-21T02:23:04.691Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-11-21T02:23:08.114Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-21T02:23:08.114Z] Training: 60056, validation: 20285, test: 19854
[2024-11-21T02:23:08.114Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-21T02:23:08.114Z] GC before operation: completed in 48.967 ms, heap usage 178.275 MB -> 37.865 MB.
[2024-11-21T02:23:12.561Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:23:15.978Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:23:18.447Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:23:20.925Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:23:22.508Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:23:24.091Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:23:25.686Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:23:27.270Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:23:27.270Z] 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.
[2024-11-21T02:23:28.042Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:23:28.042Z] Movies recommended for you:
[2024-11-21T02:23:28.042Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:23:28.042Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:23:28.042Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (19803.691 ms) ======
[2024-11-21T02:23:28.042Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-21T02:23:28.042Z] GC before operation: completed in 86.453 ms, heap usage 866.065 MB -> 54.697 MB.
[2024-11-21T02:23:30.506Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:23:32.973Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:23:35.449Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:23:37.934Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:23:39.530Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:23:41.129Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:23:42.716Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:23:44.307Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:23:44.307Z] 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.
[2024-11-21T02:23:44.307Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:23:45.075Z] Movies recommended for you:
[2024-11-21T02:23:45.076Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:23:45.076Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:23:45.076Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16894.461 ms) ======
[2024-11-21T02:23:45.076Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-21T02:23:45.076Z] GC before operation: completed in 66.241 ms, heap usage 204.812 MB -> 54.660 MB.
[2024-11-21T02:23:47.541Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:23:50.016Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:23:52.488Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:23:54.963Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:23:56.551Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:23:58.154Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:23:59.778Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:24:01.371Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:24:01.371Z] 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.
[2024-11-21T02:24:01.371Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:24:01.371Z] Movies recommended for you:
[2024-11-21T02:24:01.371Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:24:01.371Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:24:01.371Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16641.730 ms) ======
[2024-11-21T02:24:01.371Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-21T02:24:01.371Z] GC before operation: completed in 75.061 ms, heap usage 243.254 MB -> 51.818 MB.
[2024-11-21T02:24:03.921Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:24:06.389Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:24:09.813Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:24:11.405Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:24:12.994Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:24:14.585Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:24:16.202Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:24:17.801Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:24:17.801Z] 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.
[2024-11-21T02:24:17.801Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:24:17.801Z] Movies recommended for you:
[2024-11-21T02:24:17.801Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:24:17.801Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:24:17.801Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16386.305 ms) ======
[2024-11-21T02:24:17.801Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-21T02:24:17.801Z] GC before operation: completed in 84.019 ms, heap usage 3.118 GB -> 60.837 MB.
[2024-11-21T02:24:20.453Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:24:22.944Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:24:25.412Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:24:27.885Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:24:29.500Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:24:31.103Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:24:32.693Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:24:33.460Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:24:34.227Z] 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.
[2024-11-21T02:24:34.227Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:24:34.227Z] Movies recommended for you:
[2024-11-21T02:24:34.228Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:24:34.228Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:24:34.228Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16092.573 ms) ======
[2024-11-21T02:24:34.228Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-21T02:24:34.228Z] GC before operation: completed in 74.296 ms, heap usage 1.234 GB -> 56.822 MB.
[2024-11-21T02:24:36.694Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:24:39.174Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:24:41.647Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:24:44.128Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:24:45.742Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:24:47.338Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:24:48.943Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:24:49.733Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:24:50.507Z] 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.
[2024-11-21T02:24:50.507Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:24:50.507Z] Movies recommended for you:
[2024-11-21T02:24:50.507Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:24:50.507Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:24:50.507Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16253.946 ms) ======
[2024-11-21T02:24:50.507Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-21T02:24:50.507Z] GC before operation: completed in 83.367 ms, heap usage 1.764 GB -> 57.143 MB.
[2024-11-21T02:24:52.984Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:24:55.455Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:24:57.932Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:25:00.412Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:25:02.012Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:25:03.606Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:25:05.208Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:25:05.999Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:25:06.771Z] 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.
[2024-11-21T02:25:06.771Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:25:06.771Z] Movies recommended for you:
[2024-11-21T02:25:06.771Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:25:06.771Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:25:06.771Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16207.270 ms) ======
[2024-11-21T02:25:06.771Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-21T02:25:06.771Z] GC before operation: completed in 77.688 ms, heap usage 175.819 MB -> 52.529 MB.
[2024-11-21T02:25:09.238Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:25:11.706Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:25:14.176Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:25:16.655Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:25:18.256Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:25:19.843Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:25:21.435Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:25:23.051Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:25:23.051Z] 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.
[2024-11-21T02:25:23.051Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:25:23.051Z] Movies recommended for you:
[2024-11-21T02:25:23.051Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:25:23.051Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:25:23.051Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16352.609 ms) ======
[2024-11-21T02:25:23.051Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-21T02:25:23.051Z] GC before operation: completed in 75.251 ms, heap usage 358.868 MB -> 54.794 MB.
[2024-11-21T02:25:25.525Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:25:28.005Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:25:30.481Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:25:32.948Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:25:34.549Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:25:36.140Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:25:37.735Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:25:39.319Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:25:39.319Z] 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.
[2024-11-21T02:25:39.319Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:25:39.319Z] Movies recommended for you:
[2024-11-21T02:25:39.319Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:25:39.319Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:25:39.319Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16154.580 ms) ======
[2024-11-21T02:25:39.319Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-21T02:25:39.319Z] GC before operation: completed in 81.315 ms, heap usage 84.848 MB -> 56.210 MB.
[2024-11-21T02:25:41.793Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:25:44.353Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:25:46.820Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:25:49.293Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:25:50.882Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:25:52.477Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:25:54.080Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:25:55.685Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:25:55.685Z] 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.
[2024-11-21T02:25:55.685Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:25:55.685Z] Movies recommended for you:
[2024-11-21T02:25:55.685Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:25:55.685Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:25:55.685Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16255.423 ms) ======
[2024-11-21T02:25:55.685Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-21T02:25:55.685Z] GC before operation: completed in 87.120 ms, heap usage 2.416 GB -> 57.664 MB.
[2024-11-21T02:25:58.154Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:26:00.634Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:26:03.107Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:26:05.571Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:26:07.158Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:26:08.744Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:26:10.351Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:26:11.123Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:26:11.899Z] 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.
[2024-11-21T02:26:11.899Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:26:11.899Z] Movies recommended for you:
[2024-11-21T02:26:11.899Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:26:11.899Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:26:11.899Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15991.275 ms) ======
[2024-11-21T02:26:11.899Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-21T02:26:11.899Z] GC before operation: completed in 76.243 ms, heap usage 1.785 GB -> 57.239 MB.
[2024-11-21T02:26:14.517Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:26:16.987Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:26:19.456Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:26:21.919Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:26:23.519Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:26:24.286Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:26:25.875Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:26:27.486Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:26:27.486Z] 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.
[2024-11-21T02:26:27.486Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:26:28.271Z] Movies recommended for you:
[2024-11-21T02:26:28.271Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:26:28.271Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:26:28.271Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16036.534 ms) ======
[2024-11-21T02:26:28.271Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-21T02:26:28.271Z] GC before operation: completed in 71.515 ms, heap usage 1.357 GB -> 57.100 MB.
[2024-11-21T02:26:30.762Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:26:33.226Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:26:35.707Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:26:38.180Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:26:38.960Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:26:40.545Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:26:42.156Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:26:43.743Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:26:43.743Z] 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.
[2024-11-21T02:26:43.743Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:26:43.743Z] Movies recommended for you:
[2024-11-21T02:26:43.743Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:26:43.743Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:26:43.743Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16026.946 ms) ======
[2024-11-21T02:26:43.743Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-21T02:26:43.743Z] GC before operation: completed in 80.853 ms, heap usage 1.735 GB -> 57.577 MB.
[2024-11-21T02:26:46.213Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:26:48.683Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:26:52.110Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:26:53.705Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:26:55.295Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:26:56.880Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:26:58.475Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:27:00.072Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:27:00.072Z] 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.
[2024-11-21T02:27:00.072Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:27:00.072Z] Movies recommended for you:
[2024-11-21T02:27:00.072Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:27:00.072Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:27:00.072Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16017.621 ms) ======
[2024-11-21T02:27:00.072Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-21T02:27:00.072Z] GC before operation: completed in 80.322 ms, heap usage 1.104 GB -> 56.679 MB.
[2024-11-21T02:27:02.539Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:27:05.014Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:27:07.498Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:27:09.970Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:27:11.558Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:27:13.155Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:27:14.753Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:27:16.338Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:27:16.338Z] 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.
[2024-11-21T02:27:16.338Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:27:16.338Z] Movies recommended for you:
[2024-11-21T02:27:16.338Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:27:16.338Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:27:16.338Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16135.131 ms) ======
[2024-11-21T02:27:16.338Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-21T02:27:16.338Z] GC before operation: completed in 71.565 ms, heap usage 167.642 MB -> 52.621 MB.
[2024-11-21T02:27:18.805Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:27:21.271Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:27:23.753Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:27:26.226Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:27:27.813Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:27:29.400Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:27:30.994Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:27:32.597Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:27:32.597Z] 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.
[2024-11-21T02:27:32.597Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:27:32.597Z] Movies recommended for you:
[2024-11-21T02:27:32.597Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:27:32.597Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:27:32.597Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16196.826 ms) ======
[2024-11-21T02:27:32.597Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-21T02:27:32.597Z] GC before operation: completed in 93.153 ms, heap usage 2.531 GB -> 57.781 MB.
[2024-11-21T02:27:35.067Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:27:37.535Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:27:40.008Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:27:42.479Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:27:44.077Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:27:45.669Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:27:47.261Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:27:48.038Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:27:48.815Z] 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.
[2024-11-21T02:27:48.815Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:27:48.815Z] Movies recommended for you:
[2024-11-21T02:27:48.815Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:27:48.815Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:27:48.815Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16009.243 ms) ======
[2024-11-21T02:27:48.815Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-21T02:27:48.815Z] GC before operation: completed in 86.743 ms, heap usage 1.736 GB -> 57.367 MB.
[2024-11-21T02:27:51.287Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:27:53.755Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:27:56.230Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:27:58.694Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:28:00.282Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:28:01.869Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:28:03.490Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:28:04.265Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:28:05.036Z] 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.
[2024-11-21T02:28:05.036Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:28:05.036Z] Movies recommended for you:
[2024-11-21T02:28:05.036Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:28:05.036Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:28:05.036Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16075.830 ms) ======
[2024-11-21T02:28:05.036Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-21T02:28:05.036Z] GC before operation: completed in 78.646 ms, heap usage 80.883 MB -> 56.689 MB.
[2024-11-21T02:28:07.513Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:28:09.978Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:28:12.446Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:28:14.910Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:28:16.498Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:28:17.286Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:28:18.898Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:28:20.500Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:28:20.500Z] 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.
[2024-11-21T02:28:20.500Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:28:21.269Z] Movies recommended for you:
[2024-11-21T02:28:21.270Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:28:21.270Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:28:21.270Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15961.495 ms) ======
[2024-11-21T02:28:21.270Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-21T02:28:21.270Z] GC before operation: completed in 84.930 ms, heap usage 1.987 GB -> 57.745 MB.
[2024-11-21T02:28:23.747Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T02:28:26.217Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T02:28:28.712Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T02:28:31.180Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T02:28:31.954Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T02:28:33.541Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T02:28:35.131Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T02:28:36.730Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T02:28:36.730Z] 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.
[2024-11-21T02:28:36.730Z] The best model improves the baseline by 14.43%.
[2024-11-21T02:28:36.730Z] Movies recommended for you:
[2024-11-21T02:28:36.730Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T02:28:36.730Z] There is no way to check that no silent failure occurred.
[2024-11-21T02:28:36.730Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16023.952 ms) ======
[2024-11-21T02:28:37.500Z] -----------------------------------
[2024-11-21T02:28:37.500Z] renaissance-movie-lens_0_PASSED
[2024-11-21T02:28:37.500Z] -----------------------------------
[2024-11-21T02:28:37.500Z]
[2024-11-21T02:28:37.500Z] TEST TEARDOWN:
[2024-11-21T02:28:37.500Z] Nothing to be done for teardown.
[2024-11-21T02:28:37.500Z] renaissance-movie-lens_0 Finish Time: Thu Nov 21 02:28:37 2024 Epoch Time (ms): 1732156117081