renaissance-movie-lens_0
[2024-11-14T05:00:11.023Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T05:00:11.023Z] ===============================================
[2024-11-14T05:00:11.023Z] renaissance-movie-lens_0 Start Time: Thu Nov 14 05:00:10 2024 Epoch Time (ms): 1731560410337
[2024-11-14T05:00:11.023Z] variation: NoOptions
[2024-11-14T05:00:11.023Z] JVM_OPTIONS:
[2024-11-14T05:00:11.023Z] { \
[2024-11-14T05:00:11.023Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T05:00:11.023Z] echo "Nothing to be done for setup."; \
[2024-11-14T05:00:11.023Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_17315487018996/renaissance-movie-lens_0"; \
[2024-11-14T05:00:11.023Z] cd "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_17315487018996/renaissance-movie-lens_0"; \
[2024-11-14T05:00:11.023Z] echo ""; echo "TESTING:"; \
[2024-11-14T05:00:11.023Z] "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/jdkbinary/j2sdk-image/Contents/Home/bin/..//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 "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_17315487018996/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T05:00:11.023Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_17315487018996/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T05:00:11.023Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T05:00:11.023Z] echo "Nothing to be done for teardown."; \
[2024-11-14T05:00:11.023Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_17315487018996/TestTargetResult";
[2024-11-14T05:00:11.023Z]
[2024-11-14T05:00:11.023Z] TEST SETUP:
[2024-11-14T05:00:11.023Z] Nothing to be done for setup.
[2024-11-14T05:00:11.023Z]
[2024-11-14T05:00:11.023Z] TESTING:
[2024-11-14T05:00:14.147Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T05:00:15.920Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-14T05:00:19.052Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T05:00:19.415Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T05:00:19.415Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T05:00:19.415Z] GC before operation: completed in 47.667 ms, heap usage 95.828 MB -> 38.073 MB.
[2024-11-14T05:00:47.674Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:01:11.204Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:01:34.738Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:01:54.332Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:02:05.514Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:02:14.740Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:02:31.018Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:02:40.243Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:02:40.243Z] 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.
[2024-11-14T05:02:40.243Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:02:40.243Z] Movies recommended for you:
[2024-11-14T05:02:40.243Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:02:40.243Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:02:40.243Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (140801.717 ms) ======
[2024-11-14T05:02:40.243Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T05:02:40.243Z] GC before operation: completed in 78.699 ms, heap usage 789.099 MB -> 74.482 MB.
[2024-11-14T05:03:08.487Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:03:32.019Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:04:00.275Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:04:23.824Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:04:35.058Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:04:44.304Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:05:00.582Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:05:09.781Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:05:09.781Z] 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.
[2024-11-14T05:05:09.781Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:05:09.781Z] Movies recommended for you:
[2024-11-14T05:05:09.781Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:05:09.781Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:05:09.781Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (149661.461 ms) ======
[2024-11-14T05:05:09.781Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T05:05:10.139Z] GC before operation: completed in 78.318 ms, heap usage 552.857 MB -> 80.438 MB.
[2024-11-14T05:05:39.551Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:06:03.069Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:06:26.614Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:06:46.182Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:06:57.378Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:07:06.586Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:07:20.139Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:07:31.310Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:07:31.310Z] 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.
[2024-11-14T05:07:31.310Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:07:31.310Z] Movies recommended for you:
[2024-11-14T05:07:31.310Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:07:31.310Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:07:31.310Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (140847.479 ms) ======
[2024-11-14T05:07:31.310Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T05:07:31.310Z] GC before operation: completed in 81.323 ms, heap usage 396.134 MB -> 80.776 MB.
[2024-11-14T05:07:54.850Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:08:18.388Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:08:46.634Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:09:06.223Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:09:15.441Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:09:26.613Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:09:40.125Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:09:51.304Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:09:51.304Z] 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.
[2024-11-14T05:09:51.304Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:09:51.304Z] Movies recommended for you:
[2024-11-14T05:09:51.304Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:09:51.304Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:09:51.304Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (139476.806 ms) ======
[2024-11-14T05:09:51.304Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T05:09:51.304Z] GC before operation: completed in 80.118 ms, heap usage 241.254 MB -> 80.843 MB.
[2024-11-14T05:10:14.836Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:10:38.383Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:11:06.615Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:11:22.893Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:11:36.418Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:11:45.661Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:12:01.924Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:12:11.122Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:12:11.122Z] 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.
[2024-11-14T05:12:11.122Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:12:11.122Z] Movies recommended for you:
[2024-11-14T05:12:11.122Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:12:11.122Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:12:11.122Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (140047.336 ms) ======
[2024-11-14T05:12:11.122Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T05:12:11.122Z] GC before operation: completed in 72.752 ms, heap usage 482.521 MB -> 69.042 MB.
[2024-11-14T05:12:39.424Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:13:02.939Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:13:26.456Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:13:46.040Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:13:59.558Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:14:08.776Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:14:25.044Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:14:34.248Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:14:34.248Z] 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.
[2024-11-14T05:14:34.248Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:14:34.248Z] Movies recommended for you:
[2024-11-14T05:14:34.248Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:14:34.248Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:14:34.248Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (142938.550 ms) ======
[2024-11-14T05:14:34.248Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T05:14:34.248Z] GC before operation: completed in 78.467 ms, heap usage 764.601 MB -> 81.208 MB.
[2024-11-14T05:14:57.780Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:15:17.387Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:15:45.688Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:16:01.949Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:16:15.456Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:16:23.013Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:16:39.285Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:16:48.501Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:16:48.501Z] 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.
[2024-11-14T05:16:48.501Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:16:48.501Z] Movies recommended for you:
[2024-11-14T05:16:48.501Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:16:48.501Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:16:48.501Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (134127.262 ms) ======
[2024-11-14T05:16:48.501Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T05:16:48.501Z] GC before operation: completed in 87.689 ms, heap usage 498.189 MB -> 81.420 MB.
[2024-11-14T05:17:12.133Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:17:35.662Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:18:03.929Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:18:20.190Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:18:31.403Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:18:40.605Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:18:54.092Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:19:05.258Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:19:05.258Z] 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.
[2024-11-14T05:19:05.258Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:19:05.258Z] Movies recommended for you:
[2024-11-14T05:19:05.258Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:19:05.258Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:19:05.258Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (136872.875 ms) ======
[2024-11-14T05:19:05.258Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T05:19:05.258Z] GC before operation: completed in 80.233 ms, heap usage 675.652 MB -> 81.656 MB.
[2024-11-14T05:19:28.769Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:19:52.275Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:20:20.501Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:20:40.085Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:20:53.569Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:21:02.790Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:21:19.081Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:21:26.627Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:21:26.627Z] 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.
[2024-11-14T05:21:26.627Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:21:26.987Z] Movies recommended for you:
[2024-11-14T05:21:26.987Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:21:26.987Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:21:26.987Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (142161.961 ms) ======
[2024-11-14T05:21:26.987Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T05:21:26.987Z] GC before operation: completed in 79.650 ms, heap usage 602.087 MB -> 81.612 MB.
[2024-11-14T05:21:50.490Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:22:14.009Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:22:42.223Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:22:58.583Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:23:09.767Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:23:18.981Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:23:32.506Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:23:43.665Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:23:43.665Z] 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.
[2024-11-14T05:23:43.665Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:23:43.665Z] Movies recommended for you:
[2024-11-14T05:23:43.665Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:23:43.665Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:23:43.665Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (135786.245 ms) ======
[2024-11-14T05:23:43.666Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T05:23:43.666Z] GC before operation: completed in 80.009 ms, heap usage 491.940 MB -> 81.604 MB.
[2024-11-14T05:24:07.199Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:24:30.720Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:25:04.827Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:25:21.125Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:25:34.623Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:25:43.896Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:25:57.416Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:26:06.664Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:26:06.664Z] 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.
[2024-11-14T05:26:06.664Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:26:06.664Z] Movies recommended for you:
[2024-11-14T05:26:06.664Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:26:06.664Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:26:06.664Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (143696.118 ms) ======
[2024-11-14T05:26:06.664Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T05:26:06.664Z] GC before operation: completed in 83.738 ms, heap usage 749.197 MB -> 81.334 MB.
[2024-11-14T05:26:34.939Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:26:54.568Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:27:18.092Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:27:37.676Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:27:48.837Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:27:58.049Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:28:11.545Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:28:22.723Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:28:22.723Z] 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.
[2024-11-14T05:28:22.723Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:28:22.723Z] Movies recommended for you:
[2024-11-14T05:28:22.723Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:28:22.723Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:28:22.723Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (135432.890 ms) ======
[2024-11-14T05:28:22.723Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T05:28:22.723Z] GC before operation: completed in 79.108 ms, heap usage 490.480 MB -> 81.467 MB.
[2024-11-14T05:28:46.263Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:29:05.931Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:29:34.177Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:29:54.082Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:30:12.419Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:30:23.578Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:30:41.238Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:30:50.435Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:30:50.435Z] 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.
[2024-11-14T05:30:50.435Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:30:50.435Z] Movies recommended for you:
[2024-11-14T05:30:50.436Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:30:50.436Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:30:50.436Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (147502.725 ms) ======
[2024-11-14T05:30:50.436Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T05:30:50.436Z] GC before operation: completed in 67.894 ms, heap usage 473.612 MB -> 63.834 MB.
[2024-11-14T05:31:13.955Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:31:33.526Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:32:01.760Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:32:18.030Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:32:29.216Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:32:38.423Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:32:51.910Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:33:01.116Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:33:01.116Z] 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.
[2024-11-14T05:33:01.116Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:33:01.480Z] Movies recommended for you:
[2024-11-14T05:33:01.480Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:33:01.480Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:33:01.480Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (131665.871 ms) ======
[2024-11-14T05:33:01.480Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T05:33:01.480Z] GC before operation: completed in 77.553 ms, heap usage 305.326 MB -> 81.345 MB.
[2024-11-14T05:33:25.012Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:33:52.833Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:34:16.354Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:34:35.938Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:34:47.098Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:34:58.301Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:35:14.581Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:35:23.782Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:35:24.145Z] 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.
[2024-11-14T05:35:24.145Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:35:24.145Z] Movies recommended for you:
[2024-11-14T05:35:24.145Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:35:24.145Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:35:24.145Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (142670.529 ms) ======
[2024-11-14T05:35:24.145Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T05:35:24.145Z] GC before operation: completed in 89.486 ms, heap usage 498.936 MB -> 81.593 MB.
[2024-11-14T05:35:55.571Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:36:19.099Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:36:42.678Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:37:02.264Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:37:13.442Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:37:22.665Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:37:36.161Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:37:45.382Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:37:45.739Z] 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.
[2024-11-14T05:37:45.740Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:37:45.740Z] Movies recommended for you:
[2024-11-14T05:37:45.740Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:37:45.740Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:37:45.740Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (141592.533 ms) ======
[2024-11-14T05:37:45.740Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T05:37:45.740Z] GC before operation: completed in 83.577 ms, heap usage 196.805 MB -> 81.541 MB.
[2024-11-14T05:38:09.279Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:38:32.804Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:39:01.069Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:39:17.418Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:39:30.924Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:39:40.134Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:39:56.410Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:40:03.976Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:40:03.976Z] 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.
[2024-11-14T05:40:03.976Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:40:04.339Z] Movies recommended for you:
[2024-11-14T05:40:04.339Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:40:04.339Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:40:04.339Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (138276.757 ms) ======
[2024-11-14T05:40:04.339Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T05:40:04.339Z] GC before operation: completed in 78.565 ms, heap usage 501.688 MB -> 81.543 MB.
[2024-11-14T05:40:32.573Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:40:52.181Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:41:15.785Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:41:35.378Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:41:46.552Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:41:55.811Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:42:09.349Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:42:20.505Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:42:20.863Z] 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.
[2024-11-14T05:42:20.863Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:42:20.863Z] Movies recommended for you:
[2024-11-14T05:42:20.863Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:42:20.863Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:42:20.863Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (136678.690 ms) ======
[2024-11-14T05:42:20.863Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T05:42:20.863Z] GC before operation: completed in 92.730 ms, heap usage 223.557 MB -> 81.471 MB.
[2024-11-14T05:42:49.084Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:43:12.612Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:43:36.158Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:43:55.761Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:44:04.974Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:44:14.192Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:44:27.701Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:44:38.875Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:44:38.875Z] 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.
[2024-11-14T05:44:38.875Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:44:38.875Z] Movies recommended for you:
[2024-11-14T05:44:38.875Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:44:38.875Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:44:38.875Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (136968.998 ms) ======
[2024-11-14T05:44:38.875Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T05:44:38.875Z] GC before operation: completed in 74.467 ms, heap usage 612.696 MB -> 70.907 MB.
[2024-11-14T05:45:02.396Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T05:45:25.919Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T05:45:49.485Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T05:46:09.077Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T05:46:20.257Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T05:46:29.475Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T05:46:42.994Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T05:46:52.202Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T05:46:52.967Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T05:46:52.967Z] The best model improves the baseline by 14.43%.
[2024-11-14T05:46:52.967Z] Movies recommended for you:
[2024-11-14T05:46:52.967Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T05:46:52.967Z] There is no way to check that no silent failure occurred.
[2024-11-14T05:46:52.967Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (134922.313 ms) ======
[2024-11-14T05:46:54.741Z] -----------------------------------
[2024-11-14T05:46:54.741Z] renaissance-movie-lens_0_PASSED
[2024-11-14T05:46:54.741Z] -----------------------------------
[2024-11-14T05:46:54.741Z]
[2024-11-14T05:46:54.741Z] TEST TEARDOWN:
[2024-11-14T05:46:54.741Z] Nothing to be done for teardown.
[2024-11-14T05:46:54.741Z] renaissance-movie-lens_0 Finish Time: Thu Nov 14 05:46:54 2024 Epoch Time (ms): 1731563214295