renaissance-movie-lens_0
[2024-11-21T00:06:48.538Z] Running test renaissance-movie-lens_0 ...
[2024-11-21T00:06:48.538Z] ===============================================
[2024-11-21T00:06:48.538Z] renaissance-movie-lens_0 Start Time: Thu Nov 21 00:06:47 2024 Epoch Time (ms): 1732147607721
[2024-11-21T00:06:48.538Z] variation: NoOptions
[2024-11-21T00:06:48.538Z] JVM_OPTIONS:
[2024-11-21T00:06:48.538Z] { \
[2024-11-21T00:06:48.538Z] echo ""; echo "TEST SETUP:"; \
[2024-11-21T00:06:48.538Z] echo "Nothing to be done for setup."; \
[2024-11-21T00:06:48.538Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17321466308517/renaissance-movie-lens_0"; \
[2024-11-21T00:06:48.538Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17321466308517/renaissance-movie-lens_0"; \
[2024-11-21T00:06:48.538Z] echo ""; echo "TESTING:"; \
[2024-11-21T00:06:48.538Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/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/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17321466308517/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-21T00:06:48.538Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17321466308517/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-21T00:06:48.538Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-21T00:06:48.538Z] echo "Nothing to be done for teardown."; \
[2024-11-21T00:06:48.538Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17321466308517/TestTargetResult";
[2024-11-21T00:06:48.538Z]
[2024-11-21T00:06:48.538Z] TEST SETUP:
[2024-11-21T00:06:48.538Z] Nothing to be done for setup.
[2024-11-21T00:06:48.538Z]
[2024-11-21T00:06:48.538Z] TESTING:
[2024-11-21T00:06:51.617Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-21T00:06:54.696Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-21T00:07:00.176Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-21T00:07:00.176Z] Training: 60056, validation: 20285, test: 19854
[2024-11-21T00:07:00.176Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-21T00:07:00.176Z] GC before operation: completed in 96.726 ms, heap usage 190.263 MB -> 37.188 MB.
[2024-11-21T00:07:10.090Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:07:15.564Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:07:21.039Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:07:24.119Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:07:27.199Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:07:30.279Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:07:32.274Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:07:35.352Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:07:35.352Z] 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.
[2024-11-21T00:07:35.352Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:07:35.352Z] Movies recommended for you:
[2024-11-21T00:07:35.352Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:07:35.352Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:07:35.352Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (35325.136 ms) ======
[2024-11-21T00:07:35.352Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-21T00:07:36.325Z] GC before operation: completed in 167.720 ms, heap usage 195.154 MB -> 50.433 MB.
[2024-11-21T00:07:40.214Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:07:44.447Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:07:47.526Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:07:51.759Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:07:53.759Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:07:55.755Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:07:57.755Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:08:00.844Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:08:00.844Z] 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.
[2024-11-21T00:08:00.844Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:08:01.817Z] Movies recommended for you:
[2024-11-21T00:08:01.817Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:08:01.817Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:08:01.817Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (25377.237 ms) ======
[2024-11-21T00:08:01.817Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-21T00:08:01.817Z] GC before operation: completed in 167.854 ms, heap usage 334.296 MB -> 50.989 MB.
[2024-11-21T00:08:04.908Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:08:09.144Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:08:12.227Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:08:15.335Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:08:17.333Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:08:19.329Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:08:22.408Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:08:24.404Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:08:24.404Z] 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.
[2024-11-21T00:08:24.404Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:08:24.404Z] Movies recommended for you:
[2024-11-21T00:08:24.404Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:08:24.404Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:08:24.404Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (23339.522 ms) ======
[2024-11-21T00:08:24.404Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-21T00:08:25.378Z] GC before operation: completed in 171.344 ms, heap usage 569.688 MB -> 54.793 MB.
[2024-11-21T00:08:28.484Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:08:31.564Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:08:34.648Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:08:37.729Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:08:40.456Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:08:42.455Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:08:44.468Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:08:46.471Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:08:47.444Z] 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.
[2024-11-21T00:08:47.444Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:08:47.444Z] Movies recommended for you:
[2024-11-21T00:08:47.444Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:08:47.444Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:08:47.444Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (22225.682 ms) ======
[2024-11-21T00:08:47.444Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-21T00:08:47.444Z] GC before operation: completed in 189.240 ms, heap usage 191.805 MB -> 51.627 MB.
[2024-11-21T00:08:50.528Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:08:53.609Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:08:56.689Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:08:59.771Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:09:01.825Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:09:03.822Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:09:06.905Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:09:08.927Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:09:08.927Z] 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.
[2024-11-21T00:09:08.927Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:09:08.927Z] Movies recommended for you:
[2024-11-21T00:09:08.927Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:09:08.927Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:09:08.927Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21724.067 ms) ======
[2024-11-21T00:09:08.927Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-21T00:09:08.927Z] GC before operation: completed in 187.182 ms, heap usage 574.740 MB -> 55.284 MB.
[2024-11-21T00:09:13.165Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:09:16.245Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:09:19.329Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:09:22.408Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:09:24.405Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:09:26.405Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:09:28.401Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:09:30.401Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:09:31.375Z] 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.
[2024-11-21T00:09:31.375Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:09:31.375Z] Movies recommended for you:
[2024-11-21T00:09:31.375Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:09:31.375Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:09:31.375Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (21910.066 ms) ======
[2024-11-21T00:09:31.375Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-21T00:09:31.375Z] GC before operation: completed in 167.786 ms, heap usage 280.212 MB -> 51.784 MB.
[2024-11-21T00:09:34.472Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:09:37.554Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:09:41.371Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:09:44.490Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:09:45.465Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:09:48.552Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:09:50.554Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:09:52.552Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:09:52.552Z] 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.
[2024-11-21T00:09:52.552Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:09:52.552Z] Movies recommended for you:
[2024-11-21T00:09:52.552Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:09:52.552Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:09:52.552Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (21371.378 ms) ======
[2024-11-21T00:09:52.552Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-21T00:09:52.552Z] GC before operation: completed in 168.950 ms, heap usage 222.413 MB -> 51.951 MB.
[2024-11-21T00:09:56.786Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:09:58.782Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:10:03.026Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:10:05.020Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:10:07.013Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:10:10.090Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:10:12.085Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:10:14.082Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:10:14.082Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-21T00:10:14.082Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:10:14.082Z] Movies recommended for you:
[2024-11-21T00:10:14.082Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:10:14.082Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:10:14.082Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (21454.058 ms) ======
[2024-11-21T00:10:14.082Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-21T00:10:15.054Z] GC before operation: completed in 167.115 ms, heap usage 233.485 MB -> 55.433 MB.
[2024-11-21T00:10:18.132Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:10:21.213Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:10:24.315Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:10:27.395Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:10:29.390Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:10:31.445Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:10:33.623Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:10:35.621Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:10:35.621Z] 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.
[2024-11-21T00:10:35.621Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:10:36.606Z] Movies recommended for you:
[2024-11-21T00:10:36.606Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:10:36.606Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:10:36.606Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (21669.308 ms) ======
[2024-11-21T00:10:36.606Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-21T00:10:36.606Z] GC before operation: completed in 184.576 ms, heap usage 158.221 MB -> 51.860 MB.
[2024-11-21T00:10:39.692Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:10:42.516Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:10:45.596Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:10:48.680Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:10:51.777Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:10:52.753Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:10:55.847Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:10:57.847Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:10:57.847Z] 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.
[2024-11-21T00:10:57.847Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:10:57.847Z] Movies recommended for you:
[2024-11-21T00:10:57.847Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:10:57.847Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:10:57.847Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (21614.643 ms) ======
[2024-11-21T00:10:57.847Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-21T00:10:57.847Z] GC before operation: completed in 180.266 ms, heap usage 271.811 MB -> 52.079 MB.
[2024-11-21T00:11:00.989Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:11:04.071Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:11:08.307Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:11:10.304Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:11:13.387Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:11:15.387Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:11:17.382Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:11:19.381Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:11:19.381Z] 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.
[2024-11-21T00:11:19.381Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:11:19.381Z] Movies recommended for you:
[2024-11-21T00:11:19.381Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:11:19.381Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:11:19.381Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (21409.887 ms) ======
[2024-11-21T00:11:19.381Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-21T00:11:19.381Z] GC before operation: completed in 181.149 ms, heap usage 567.155 MB -> 55.295 MB.
[2024-11-21T00:11:23.617Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:11:26.699Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:11:28.699Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:11:31.789Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:11:34.875Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:11:35.850Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:11:38.933Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:11:40.929Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:11:40.929Z] 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.
[2024-11-21T00:11:40.929Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:11:40.929Z] Movies recommended for you:
[2024-11-21T00:11:40.929Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:11:40.929Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:11:40.929Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (21384.263 ms) ======
[2024-11-21T00:11:40.929Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-21T00:11:40.929Z] GC before operation: completed in 178.207 ms, heap usage 157.658 MB -> 51.978 MB.
[2024-11-21T00:11:44.825Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:11:47.909Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:11:50.990Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:11:54.072Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:11:56.067Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:11:58.064Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:12:00.060Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:12:02.055Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:12:03.029Z] 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.
[2024-11-21T00:12:03.029Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:12:03.029Z] Movies recommended for you:
[2024-11-21T00:12:03.029Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:12:03.029Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:12:03.029Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (21601.530 ms) ======
[2024-11-21T00:12:03.029Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-21T00:12:03.029Z] GC before operation: completed in 179.615 ms, heap usage 213.949 MB -> 52.251 MB.
[2024-11-21T00:12:06.109Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:12:09.186Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:12:13.422Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:12:15.420Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:12:18.500Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:12:20.497Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:12:22.494Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:12:24.490Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:12:24.490Z] 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.
[2024-11-21T00:12:24.490Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:12:24.490Z] Movies recommended for you:
[2024-11-21T00:12:24.490Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:12:24.490Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:12:24.490Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (21668.728 ms) ======
[2024-11-21T00:12:24.490Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-21T00:12:25.463Z] GC before operation: completed in 181.762 ms, heap usage 172.149 MB -> 51.916 MB.
[2024-11-21T00:12:28.567Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:12:31.651Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:12:34.727Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:12:37.806Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:12:39.802Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:12:41.960Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:12:43.955Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:12:46.701Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:12:46.701Z] 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.
[2024-11-21T00:12:46.701Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:12:47.685Z] Movies recommended for you:
[2024-11-21T00:12:47.685Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:12:47.685Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:12:47.685Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (21521.001 ms) ======
[2024-11-21T00:12:47.685Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-21T00:12:47.685Z] GC before operation: completed in 181.979 ms, heap usage 170.526 MB -> 52.070 MB.
[2024-11-21T00:12:49.728Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:12:52.808Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:12:55.882Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:12:58.957Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:13:02.034Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:13:04.028Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:13:06.021Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:13:08.017Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:13:08.017Z] 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.
[2024-11-21T00:13:08.017Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:13:08.990Z] Movies recommended for you:
[2024-11-21T00:13:08.990Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:13:08.990Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:13:08.990Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (21914.554 ms) ======
[2024-11-21T00:13:08.990Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-21T00:13:08.990Z] GC before operation: completed in 185.299 ms, heap usage 204.645 MB -> 52.219 MB.
[2024-11-21T00:13:12.069Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:13:15.146Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:13:18.223Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:13:21.304Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:13:23.301Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:13:25.294Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:13:27.293Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:13:29.287Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:13:30.260Z] 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.
[2024-11-21T00:13:30.260Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:13:30.260Z] Movies recommended for you:
[2024-11-21T00:13:30.260Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:13:30.260Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:13:30.260Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (21543.260 ms) ======
[2024-11-21T00:13:30.260Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-21T00:13:30.260Z] GC before operation: completed in 174.617 ms, heap usage 124.833 MB -> 51.981 MB.
[2024-11-21T00:13:34.498Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:13:37.617Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:13:40.692Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:13:42.821Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:13:44.820Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:13:46.816Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:13:49.543Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:13:51.538Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:13:51.538Z] 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.
[2024-11-21T00:13:51.538Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:13:52.511Z] Movies recommended for you:
[2024-11-21T00:13:52.511Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:13:52.511Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:13:52.511Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (21523.584 ms) ======
[2024-11-21T00:13:52.511Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-21T00:13:52.511Z] GC before operation: completed in 180.400 ms, heap usage 100.859 MB -> 51.987 MB.
[2024-11-21T00:13:55.589Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:13:58.674Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:14:01.756Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:14:04.844Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:14:07.639Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:14:09.641Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:14:11.641Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:14:13.642Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:14:14.616Z] 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.
[2024-11-21T00:14:14.616Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:14:14.616Z] Movies recommended for you:
[2024-11-21T00:14:14.616Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:14:14.616Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:14:14.616Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (22126.338 ms) ======
[2024-11-21T00:14:14.616Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-21T00:14:14.616Z] GC before operation: completed in 178.103 ms, heap usage 200.536 MB -> 52.351 MB.
[2024-11-21T00:14:17.707Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-21T00:14:20.787Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-21T00:14:23.929Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-21T00:14:27.012Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-21T00:14:29.024Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-21T00:14:31.024Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-21T00:14:33.025Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-21T00:14:35.040Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-21T00:14:36.021Z] 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.
[2024-11-21T00:14:36.021Z] The best model improves the baseline by 14.43%.
[2024-11-21T00:14:36.021Z] Movies recommended for you:
[2024-11-21T00:14:36.021Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-21T00:14:36.021Z] There is no way to check that no silent failure occurred.
[2024-11-21T00:14:36.021Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (21681.014 ms) ======
[2024-11-21T00:14:38.046Z] -----------------------------------
[2024-11-21T00:14:38.046Z] renaissance-movie-lens_0_PASSED
[2024-11-21T00:14:38.046Z] -----------------------------------
[2024-11-21T00:14:38.046Z]
[2024-11-21T00:14:38.046Z] TEST TEARDOWN:
[2024-11-21T00:14:38.046Z] Nothing to be done for teardown.
[2024-11-21T00:14:38.046Z] renaissance-movie-lens_0 Finish Time: Thu Nov 21 00:14:37 2024 Epoch Time (ms): 1732148077447