renaissance-movie-lens_0
[2024-09-05T06:19:12.990Z] Running test renaissance-movie-lens_0 ...
[2024-09-05T06:19:12.990Z] ===============================================
[2024-09-05T06:19:13.677Z] renaissance-movie-lens_0 Start Time: Thu Sep 5 01:19:12 2024 Epoch Time (ms): 1725517152953
[2024-09-05T06:19:13.677Z] variation: NoOptions
[2024-09-05T06:19:13.677Z] JVM_OPTIONS:
[2024-09-05T06:19:13.677Z] { \
[2024-09-05T06:19:13.677Z] echo ""; echo "TEST SETUP:"; \
[2024-09-05T06:19:13.677Z] echo "Nothing to be done for setup."; \
[2024-09-05T06:19:13.677Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17255165195221/renaissance-movie-lens_0"; \
[2024-09-05T06:19:13.677Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17255165195221/renaissance-movie-lens_0"; \
[2024-09-05T06:19:13.677Z] echo ""; echo "TESTING:"; \
[2024-09-05T06:19:13.677Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-17.0.13+6/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 "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17255165195221/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-05T06:19:13.678Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17255165195221/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-05T06:19:13.678Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-05T06:19:13.678Z] echo "Nothing to be done for teardown."; \
[2024-09-05T06:19:13.678Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17255165195221/TestTargetResult";
[2024-09-05T06:19:13.678Z]
[2024-09-05T06:19:13.678Z] TEST SETUP:
[2024-09-05T06:19:13.678Z] Nothing to be done for setup.
[2024-09-05T06:19:13.678Z]
[2024-09-05T06:19:13.678Z] TESTING:
[2024-09-05T06:19:15.920Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-05T06:19:17.342Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-09-05T06:19:21.414Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-05T06:19:21.414Z] Training: 60056, validation: 20285, test: 19854
[2024-09-05T06:19:21.414Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-05T06:19:21.414Z] GC before operation: completed in 47.819 ms, heap usage 135.106 MB -> 37.842 MB.
[2024-09-05T06:19:29.080Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:19:33.136Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:19:36.288Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:19:39.396Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:19:40.832Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:19:43.063Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:19:44.495Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:19:46.764Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:19:46.764Z] 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.
[2024-09-05T06:19:46.764Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:19:46.764Z] Movies recommended for you:
[2024-09-05T06:19:46.764Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:19:46.764Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:19:46.764Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25676.546 ms) ======
[2024-09-05T06:19:46.764Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-05T06:19:46.765Z] GC before operation: completed in 110.666 ms, heap usage 873.879 MB -> 54.976 MB.
[2024-09-05T06:19:50.819Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:19:53.923Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:19:57.056Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:19:59.298Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:20:00.726Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:20:02.149Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:20:03.584Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:20:05.829Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:20:05.829Z] 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.
[2024-09-05T06:20:05.829Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:20:05.829Z] Movies recommended for you:
[2024-09-05T06:20:05.829Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:20:05.830Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:20:05.830Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (19040.427 ms) ======
[2024-09-05T06:20:05.830Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-05T06:20:05.830Z] GC before operation: completed in 98.628 ms, heap usage 435.597 MB -> 51.719 MB.
[2024-09-05T06:20:08.933Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:20:12.041Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:20:14.260Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:20:16.506Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:20:17.946Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:20:19.375Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:20:20.806Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:20:23.051Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:20:23.051Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-05T06:20:23.051Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:20:23.051Z] Movies recommended for you:
[2024-09-05T06:20:23.051Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:20:23.051Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:20:23.051Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17084.565 ms) ======
[2024-09-05T06:20:23.051Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-05T06:20:23.051Z] GC before operation: completed in 65.913 ms, heap usage 350.372 MB -> 52.097 MB.
[2024-09-05T06:20:26.223Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:20:28.455Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:20:30.688Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:20:32.910Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:20:34.338Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:20:35.767Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:20:37.212Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:20:38.647Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:20:38.647Z] 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.
[2024-09-05T06:20:39.340Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:20:39.341Z] Movies recommended for you:
[2024-09-05T06:20:39.341Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:20:39.341Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:20:39.341Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15813.744 ms) ======
[2024-09-05T06:20:39.341Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-05T06:20:39.341Z] GC before operation: completed in 86.358 ms, heap usage 345.363 MB -> 52.567 MB.
[2024-09-05T06:20:41.597Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:20:43.823Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:20:46.067Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:20:48.294Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:20:49.721Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:20:51.169Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:20:52.604Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:20:54.042Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:20:54.042Z] 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.
[2024-09-05T06:20:54.042Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:20:54.731Z] Movies recommended for you:
[2024-09-05T06:20:54.731Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:20:54.731Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:20:54.731Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15286.017 ms) ======
[2024-09-05T06:20:54.731Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-05T06:20:54.731Z] GC before operation: completed in 65.311 ms, heap usage 261.998 MB -> 55.798 MB.
[2024-09-05T06:20:57.006Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:20:59.271Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:21:02.373Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:21:04.616Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:21:05.312Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:21:07.550Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:21:09.001Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:21:09.724Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:21:10.411Z] 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.
[2024-09-05T06:21:10.411Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:21:10.411Z] Movies recommended for you:
[2024-09-05T06:21:10.411Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:21:10.411Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:21:10.411Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15789.324 ms) ======
[2024-09-05T06:21:10.411Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-05T06:21:10.411Z] GC before operation: completed in 89.417 ms, heap usage 345.320 MB -> 52.433 MB.
[2024-09-05T06:21:12.629Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:21:14.876Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:21:17.986Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:21:20.208Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:21:21.661Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:21:22.368Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:21:24.135Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:21:25.567Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:21:25.567Z] 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.
[2024-09-05T06:21:25.567Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:21:25.567Z] Movies recommended for you:
[2024-09-05T06:21:25.567Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:21:25.567Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:21:25.567Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15385.508 ms) ======
[2024-09-05T06:21:25.567Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-05T06:21:26.254Z] GC before operation: completed in 77.397 ms, heap usage 359.447 MB -> 52.634 MB.
[2024-09-05T06:21:28.489Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:21:30.718Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:21:32.943Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:21:35.173Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:21:36.609Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:21:38.058Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:21:39.494Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:21:40.927Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:21:40.927Z] 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.
[2024-09-05T06:21:40.927Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:21:40.927Z] Movies recommended for you:
[2024-09-05T06:21:40.927Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:21:40.927Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:21:40.927Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15102.211 ms) ======
[2024-09-05T06:21:40.927Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-05T06:21:40.927Z] GC before operation: completed in 82.526 ms, heap usage 783.677 MB -> 56.544 MB.
[2024-09-05T06:21:44.038Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:21:46.285Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:21:48.521Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:21:50.775Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:21:52.205Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:21:52.908Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:21:54.343Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:21:55.803Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:21:56.498Z] 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.
[2024-09-05T06:21:56.498Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:21:56.498Z] Movies recommended for you:
[2024-09-05T06:21:56.498Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:21:56.498Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:21:56.498Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15219.819 ms) ======
[2024-09-05T06:21:56.498Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-05T06:21:56.498Z] GC before operation: completed in 84.457 ms, heap usage 510.691 MB -> 56.271 MB.
[2024-09-05T06:21:58.729Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:22:01.846Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:22:04.075Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:22:06.325Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:22:07.023Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:22:08.447Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:22:09.892Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:22:11.349Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:22:12.055Z] 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.
[2024-09-05T06:22:12.055Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:22:12.055Z] Movies recommended for you:
[2024-09-05T06:22:12.055Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:22:12.055Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:22:12.055Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15556.873 ms) ======
[2024-09-05T06:22:12.055Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-05T06:22:12.055Z] GC before operation: completed in 56.155 ms, heap usage 357.716 MB -> 56.587 MB.
[2024-09-05T06:22:14.741Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:22:16.990Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:22:19.227Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:22:21.485Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:22:22.173Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:22:23.605Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:22:25.137Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:22:26.575Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:22:26.575Z] 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.
[2024-09-05T06:22:26.575Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:22:26.575Z] Movies recommended for you:
[2024-09-05T06:22:26.575Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:22:26.575Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:22:26.575Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14805.408 ms) ======
[2024-09-05T06:22:26.575Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-05T06:22:26.575Z] GC before operation: completed in 66.502 ms, heap usage 581.571 MB -> 56.014 MB.
[2024-09-05T06:22:29.697Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:22:31.915Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:22:34.142Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:22:36.363Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:22:37.786Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:22:38.479Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:22:39.905Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:22:41.346Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:22:42.034Z] 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.
[2024-09-05T06:22:42.034Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:22:42.034Z] Movies recommended for you:
[2024-09-05T06:22:42.034Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:22:42.034Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:22:42.034Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15163.479 ms) ======
[2024-09-05T06:22:42.034Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-05T06:22:42.034Z] GC before operation: completed in 67.881 ms, heap usage 443.059 MB -> 52.831 MB.
[2024-09-05T06:22:44.266Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:22:46.509Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:22:49.613Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:22:51.072Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:22:53.317Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:22:54.016Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:22:55.454Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:22:57.686Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:22:57.686Z] 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.
[2024-09-05T06:22:57.686Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:22:57.686Z] Movies recommended for you:
[2024-09-05T06:22:57.686Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:22:57.686Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:22:57.686Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15617.463 ms) ======
[2024-09-05T06:22:57.686Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-05T06:22:57.686Z] GC before operation: completed in 88.199 ms, heap usage 321.418 MB -> 53.002 MB.
[2024-09-05T06:23:00.797Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:23:02.222Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:23:05.383Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:23:06.833Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:23:08.276Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:23:09.727Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:23:11.163Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:23:12.614Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:23:12.614Z] 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.
[2024-09-05T06:23:12.614Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:23:13.316Z] Movies recommended for you:
[2024-09-05T06:23:13.316Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:23:13.316Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:23:13.316Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15183.979 ms) ======
[2024-09-05T06:23:13.316Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-05T06:23:13.316Z] GC before operation: completed in 82.744 ms, heap usage 230.445 MB -> 52.651 MB.
[2024-09-05T06:23:15.567Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:23:17.809Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:23:20.050Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:23:22.296Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:23:23.741Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:23:25.226Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:23:26.658Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:23:28.109Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:23:28.109Z] 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.
[2024-09-05T06:23:28.109Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:23:28.109Z] Movies recommended for you:
[2024-09-05T06:23:28.109Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:23:28.109Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:23:28.109Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15106.095 ms) ======
[2024-09-05T06:23:28.109Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-05T06:23:28.109Z] GC before operation: completed in 70.354 ms, heap usage 250.610 MB -> 52.953 MB.
[2024-09-05T06:23:31.208Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:23:32.639Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:23:35.785Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:23:37.864Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:23:38.565Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:23:39.995Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:23:41.438Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:23:42.976Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:23:42.976Z] 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.
[2024-09-05T06:23:43.666Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:23:43.666Z] Movies recommended for you:
[2024-09-05T06:23:43.666Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:23:43.666Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:23:43.666Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15158.596 ms) ======
[2024-09-05T06:23:43.666Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-05T06:23:43.666Z] GC before operation: completed in 90.046 ms, heap usage 261.411 MB -> 52.980 MB.
[2024-09-05T06:23:45.920Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:23:48.150Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:23:50.379Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:23:52.613Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:23:54.057Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:23:55.479Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:23:56.902Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:23:58.352Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:23:58.352Z] 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.
[2024-09-05T06:23:58.352Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:23:58.352Z] Movies recommended for you:
[2024-09-05T06:23:58.352Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:23:58.352Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:23:58.352Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14824.961 ms) ======
[2024-09-05T06:23:58.352Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-05T06:23:58.352Z] GC before operation: completed in 91.848 ms, heap usage 69.165 MB -> 56.594 MB.
[2024-09-05T06:24:00.594Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:24:03.711Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:24:05.949Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:24:07.386Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:24:08.821Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:24:10.249Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:24:11.668Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:24:13.120Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:24:13.120Z] 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.
[2024-09-05T06:24:13.120Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:24:13.120Z] Movies recommended for you:
[2024-09-05T06:24:13.120Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:24:13.120Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:24:13.120Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14974.480 ms) ======
[2024-09-05T06:24:13.120Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-05T06:24:13.806Z] GC before operation: completed in 64.315 ms, heap usage 570.392 MB -> 56.351 MB.
[2024-09-05T06:24:16.031Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:24:18.268Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:24:20.501Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:24:22.736Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:24:24.160Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:24:25.596Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:24:26.294Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:24:27.717Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:24:28.404Z] 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.
[2024-09-05T06:24:28.404Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:24:28.404Z] Movies recommended for you:
[2024-09-05T06:24:28.404Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:24:28.404Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:24:28.404Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14911.487 ms) ======
[2024-09-05T06:24:28.404Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-05T06:24:28.404Z] GC before operation: completed in 59.802 ms, heap usage 285.890 MB -> 53.100 MB.
[2024-09-05T06:24:30.656Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T06:24:32.913Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T06:24:35.163Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T06:24:37.400Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T06:24:38.822Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T06:24:40.256Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T06:24:41.689Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T06:24:43.131Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T06:24:43.131Z] 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.
[2024-09-05T06:24:43.131Z] The best model improves the baseline by 14.43%.
[2024-09-05T06:24:43.131Z] Movies recommended for you:
[2024-09-05T06:24:43.131Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T06:24:43.131Z] There is no way to check that no silent failure occurred.
[2024-09-05T06:24:43.131Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14789.073 ms) ======
[2024-09-05T06:24:44.550Z] -----------------------------------
[2024-09-05T06:24:44.550Z] renaissance-movie-lens_0_PASSED
[2024-09-05T06:24:44.550Z] -----------------------------------
[2024-09-05T06:24:44.550Z]
[2024-09-05T06:24:44.550Z] TEST TEARDOWN:
[2024-09-05T06:24:44.550Z] Nothing to be done for teardown.
[2024-09-05T06:24:44.550Z] renaissance-movie-lens_0 Finish Time: Thu Sep 5 01:24:44 2024 Epoch Time (ms): 1725517484026