renaissance-movie-lens_0
[2024-08-15T00:40:26.595Z] Running test renaissance-movie-lens_0 ...
[2024-08-15T00:40:26.595Z] ===============================================
[2024-08-15T00:40:26.595Z] renaissance-movie-lens_0 Start Time: Thu Aug 15 00:40:26 2024 Epoch Time (ms): 1723682426478
[2024-08-15T00:40:26.595Z] variation: NoOptions
[2024-08-15T00:40:26.595Z] JVM_OPTIONS:
[2024-08-15T00:40:26.595Z] { \
[2024-08-15T00:40:26.595Z] echo ""; echo "TEST SETUP:"; \
[2024-08-15T00:40:26.595Z] echo "Nothing to be done for setup."; \
[2024-08-15T00:40:26.595Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17236814486853/renaissance-movie-lens_0"; \
[2024-08-15T00:40:26.595Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17236814486853/renaissance-movie-lens_0"; \
[2024-08-15T00:40:26.595Z] echo ""; echo "TESTING:"; \
[2024-08-15T00:40:26.595Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_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_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17236814486853/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-15T00:40:26.595Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17236814486853/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-15T00:40:26.595Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-15T00:40:26.595Z] echo "Nothing to be done for teardown."; \
[2024-08-15T00:40:26.595Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17236814486853/TestTargetResult";
[2024-08-15T00:40:26.595Z]
[2024-08-15T00:40:26.595Z] TEST SETUP:
[2024-08-15T00:40:26.595Z] Nothing to be done for setup.
[2024-08-15T00:40:26.595Z]
[2024-08-15T00:40:26.595Z] TESTING:
[2024-08-15T00:40:30.711Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-15T00:40:33.701Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-08-15T00:40:38.612Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-15T00:40:38.612Z] Training: 60056, validation: 20285, test: 19854
[2024-08-15T00:40:38.612Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-15T00:40:38.612Z] GC before operation: completed in 70.871 ms, heap usage 48.117 MB -> 37.163 MB.
[2024-08-15T00:40:46.747Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:40:52.195Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:40:55.183Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:40:58.178Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:41:00.117Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:41:02.056Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:41:03.993Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:41:05.935Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:41:05.935Z] 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.
[2024-08-15T00:41:06.879Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:41:06.879Z] Movies recommended for you:
[2024-08-15T00:41:06.879Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:41:06.879Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:41:06.879Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (28163.397 ms) ======
[2024-08-15T00:41:06.879Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-15T00:41:06.879Z] GC before operation: completed in 141.397 ms, heap usage 401.490 MB -> 48.381 MB.
[2024-08-15T00:41:09.870Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:41:12.864Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:41:15.864Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:41:17.804Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:41:19.745Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:41:21.685Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:41:23.624Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:41:24.569Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:41:25.512Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T00:41:25.512Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:41:25.512Z] Movies recommended for you:
[2024-08-15T00:41:25.512Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:41:25.512Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:41:25.512Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (18731.208 ms) ======
[2024-08-15T00:41:25.512Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-15T00:41:25.512Z] GC before operation: completed in 161.506 ms, heap usage 390.239 MB -> 51.092 MB.
[2024-08-15T00:41:28.503Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:41:31.496Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:41:34.502Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:41:37.499Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:41:39.437Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:41:41.387Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:41:42.334Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:41:44.961Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:41:44.961Z] 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.
[2024-08-15T00:41:44.961Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:41:44.961Z] Movies recommended for you:
[2024-08-15T00:41:44.961Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:41:44.961Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:41:44.961Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (18891.327 ms) ======
[2024-08-15T00:41:44.961Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-15T00:41:44.961Z] GC before operation: completed in 175.267 ms, heap usage 277.716 MB -> 51.360 MB.
[2024-08-15T00:41:47.956Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:41:49.897Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:41:53.069Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:41:55.008Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:41:56.947Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:41:58.886Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:41:59.829Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:42:01.773Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:42:01.773Z] 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.
[2024-08-15T00:42:01.773Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:42:01.773Z] Movies recommended for you:
[2024-08-15T00:42:01.773Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:42:01.773Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:42:01.773Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (17373.796 ms) ======
[2024-08-15T00:42:01.773Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-15T00:42:01.773Z] GC before operation: completed in 135.694 ms, heap usage 294.759 MB -> 51.747 MB.
[2024-08-15T00:42:04.848Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:42:07.848Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:42:09.796Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:42:12.793Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:42:13.739Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:42:15.685Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:42:17.626Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:42:18.571Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:42:18.571Z] 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.
[2024-08-15T00:42:18.571Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:42:19.517Z] Movies recommended for you:
[2024-08-15T00:42:19.517Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:42:19.517Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:42:19.517Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16905.881 ms) ======
[2024-08-15T00:42:19.517Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-15T00:42:19.517Z] GC before operation: completed in 153.320 ms, heap usage 315.082 MB -> 51.968 MB.
[2024-08-15T00:42:22.515Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:42:24.457Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:42:27.450Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:42:29.388Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:42:31.327Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:42:33.261Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:42:35.200Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:42:37.135Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:42:37.135Z] 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.
[2024-08-15T00:42:37.135Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:42:37.135Z] Movies recommended for you:
[2024-08-15T00:42:37.135Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:42:37.135Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:42:37.135Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18047.075 ms) ======
[2024-08-15T00:42:37.135Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-15T00:42:37.135Z] GC before operation: completed in 146.036 ms, heap usage 261.014 MB -> 51.827 MB.
[2024-08-15T00:42:40.136Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:42:43.123Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:42:45.059Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:42:48.776Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:42:49.719Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:42:50.663Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:42:52.607Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:42:54.551Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:42:54.551Z] 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.
[2024-08-15T00:42:54.551Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:42:54.551Z] Movies recommended for you:
[2024-08-15T00:42:54.551Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:42:54.551Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:42:54.551Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17020.365 ms) ======
[2024-08-15T00:42:54.551Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-15T00:42:54.551Z] GC before operation: completed in 135.719 ms, heap usage 317.297 MB -> 52.030 MB.
[2024-08-15T00:42:57.549Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:42:59.499Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:43:02.516Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:43:04.456Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:43:06.398Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:43:08.336Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:43:09.282Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:43:11.224Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:43:11.224Z] 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.
[2024-08-15T00:43:11.224Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:43:11.224Z] Movies recommended for you:
[2024-08-15T00:43:11.224Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:43:11.224Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:43:11.224Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16859.039 ms) ======
[2024-08-15T00:43:11.224Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-15T00:43:11.224Z] GC before operation: completed in 128.694 ms, heap usage 314.274 MB -> 52.292 MB.
[2024-08-15T00:43:14.220Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:43:17.215Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:43:19.153Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:43:21.091Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:43:23.030Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:43:23.975Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:43:24.919Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:43:26.857Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:43:26.857Z] 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.
[2024-08-15T00:43:26.857Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:43:26.857Z] Movies recommended for you:
[2024-08-15T00:43:26.857Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:43:26.857Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:43:26.857Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15099.991 ms) ======
[2024-08-15T00:43:26.857Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-15T00:43:26.857Z] GC before operation: completed in 143.930 ms, heap usage 553.752 MB -> 55.529 MB.
[2024-08-15T00:43:28.798Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:43:30.737Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:43:33.757Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:43:35.698Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:43:36.650Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:43:38.590Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:43:39.534Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:43:41.478Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:43:42.424Z] 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.
[2024-08-15T00:43:42.424Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:43:42.424Z] Movies recommended for you:
[2024-08-15T00:43:42.424Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:43:42.424Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:43:42.424Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15406.022 ms) ======
[2024-08-15T00:43:42.424Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-15T00:43:42.424Z] GC before operation: completed in 170.711 ms, heap usage 538.169 MB -> 55.565 MB.
[2024-08-15T00:43:45.418Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:43:48.410Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:43:51.855Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:43:53.792Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:43:55.732Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:43:57.671Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:43:59.610Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:44:00.555Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:44:01.497Z] 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.
[2024-08-15T00:44:01.497Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:44:01.497Z] Movies recommended for you:
[2024-08-15T00:44:01.497Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:44:01.497Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:44:01.497Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (18870.533 ms) ======
[2024-08-15T00:44:01.497Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-15T00:44:01.497Z] GC before operation: completed in 161.629 ms, heap usage 179.461 MB -> 51.767 MB.
[2024-08-15T00:44:04.489Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:44:07.477Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:44:09.418Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:44:12.594Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:44:14.559Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:44:15.501Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:44:17.436Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:44:19.371Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:44:19.371Z] 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.
[2024-08-15T00:44:19.371Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:44:19.371Z] Movies recommended for you:
[2024-08-15T00:44:19.371Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:44:19.371Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:44:19.371Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (18292.098 ms) ======
[2024-08-15T00:44:19.371Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-15T00:44:20.317Z] GC before operation: completed in 145.628 ms, heap usage 313.320 MB -> 52.133 MB.
[2024-08-15T00:44:22.319Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:44:25.310Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:44:28.298Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:44:30.243Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:44:32.182Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:44:34.119Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:44:35.064Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:44:37.007Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:44:37.950Z] 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.
[2024-08-15T00:44:37.950Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:44:37.950Z] Movies recommended for you:
[2024-08-15T00:44:37.950Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:44:37.950Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:44:37.950Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17715.733 ms) ======
[2024-08-15T00:44:37.950Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-15T00:44:37.950Z] GC before operation: completed in 149.145 ms, heap usage 226.464 MB -> 52.296 MB.
[2024-08-15T00:44:40.954Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:44:43.944Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:44:45.879Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:44:48.868Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:44:50.837Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:44:53.477Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:44:54.422Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:44:56.361Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:44:56.361Z] 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.
[2024-08-15T00:44:56.361Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:44:56.361Z] Movies recommended for you:
[2024-08-15T00:44:56.361Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:44:56.361Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:44:56.361Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (18991.146 ms) ======
[2024-08-15T00:44:56.361Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-15T00:44:57.307Z] GC before operation: completed in 142.898 ms, heap usage 175.043 MB -> 51.954 MB.
[2024-08-15T00:45:00.306Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:45:03.296Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:45:06.288Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:45:09.280Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:45:11.217Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:45:12.162Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:45:14.102Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:45:16.044Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:45:16.044Z] 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.
[2024-08-15T00:45:16.044Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:45:16.044Z] Movies recommended for you:
[2024-08-15T00:45:16.044Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:45:16.044Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:45:16.044Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19222.115 ms) ======
[2024-08-15T00:45:16.044Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-15T00:45:16.044Z] GC before operation: completed in 145.310 ms, heap usage 149.886 MB -> 52.118 MB.
[2024-08-15T00:45:19.036Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:45:22.029Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:45:25.026Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:45:26.964Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:45:28.927Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:45:30.865Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:45:31.807Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:45:33.745Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:45:33.745Z] 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.
[2024-08-15T00:45:33.745Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:45:33.745Z] Movies recommended for you:
[2024-08-15T00:45:33.745Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:45:33.745Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:45:33.745Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17844.226 ms) ======
[2024-08-15T00:45:33.745Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-15T00:45:34.688Z] GC before operation: completed in 168.519 ms, heap usage 119.042 MB -> 52.166 MB.
[2024-08-15T00:45:37.678Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:45:39.615Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:45:42.609Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:45:45.602Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:45:46.545Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:45:48.487Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:45:50.440Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:45:52.378Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:45:52.378Z] 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.
[2024-08-15T00:45:52.378Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:45:52.378Z] Movies recommended for you:
[2024-08-15T00:45:52.378Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:45:52.378Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:45:52.378Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (18309.216 ms) ======
[2024-08-15T00:45:52.378Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-15T00:45:52.378Z] GC before operation: completed in 142.046 ms, heap usage 265.972 MB -> 52.090 MB.
[2024-08-15T00:45:55.374Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:45:58.779Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:46:00.721Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:46:03.705Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:46:05.639Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:46:07.581Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:46:08.524Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:46:10.461Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:46:11.403Z] 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.
[2024-08-15T00:46:11.403Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:46:11.403Z] Movies recommended for you:
[2024-08-15T00:46:11.403Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:46:11.403Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:46:11.403Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (18465.292 ms) ======
[2024-08-15T00:46:11.403Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-15T00:46:11.403Z] GC before operation: completed in 143.236 ms, heap usage 312.867 MB -> 52.194 MB.
[2024-08-15T00:46:14.392Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:46:17.380Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:46:19.314Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:46:22.301Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:46:24.236Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:46:25.181Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:46:27.118Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:46:29.057Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:46:29.057Z] 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.
[2024-08-15T00:46:29.057Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:46:29.057Z] Movies recommended for you:
[2024-08-15T00:46:29.057Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:46:29.057Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:46:29.057Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (17951.156 ms) ======
[2024-08-15T00:46:29.057Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-15T00:46:29.057Z] GC before operation: completed in 143.097 ms, heap usage 263.419 MB -> 52.390 MB.
[2024-08-15T00:46:32.078Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T00:46:35.070Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T00:46:38.064Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T00:46:39.999Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T00:46:41.934Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T00:46:43.872Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T00:46:44.814Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T00:46:46.754Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T00:46:46.754Z] 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.
[2024-08-15T00:46:46.754Z] The best model improves the baseline by 14.43%.
[2024-08-15T00:46:46.754Z] Movies recommended for you:
[2024-08-15T00:46:46.754Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T00:46:46.754Z] There is no way to check that no silent failure occurred.
[2024-08-15T00:46:46.754Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (17369.075 ms) ======
[2024-08-15T00:46:48.688Z] -----------------------------------
[2024-08-15T00:46:48.688Z] renaissance-movie-lens_0_PASSED
[2024-08-15T00:46:48.688Z] -----------------------------------
[2024-08-15T00:46:48.688Z]
[2024-08-15T00:46:48.688Z] TEST TEARDOWN:
[2024-08-15T00:46:48.688Z] Nothing to be done for teardown.
[2024-08-15T00:46:48.688Z] renaissance-movie-lens_0 Finish Time: Thu Aug 15 00:46:47 2024 Epoch Time (ms): 1723682808001