renaissance-movie-lens_0
[2024-09-25T21:24:07.299Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T21:24:07.299Z] ===============================================
[2024-09-25T21:24:07.299Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 21:24:06 2024 Epoch Time (ms): 1727299446609
[2024-09-25T21:24:07.299Z] variation: NoOptions
[2024-09-25T21:24:07.299Z] JVM_OPTIONS:
[2024-09-25T21:24:07.299Z] { \
[2024-09-25T21:24:07.299Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T21:24:07.299Z] echo "Nothing to be done for setup."; \
[2024-09-25T21:24:07.299Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17272985863724/renaissance-movie-lens_0"; \
[2024-09-25T21:24:07.299Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17272985863724/renaissance-movie-lens_0"; \
[2024-09-25T21:24:07.299Z] echo ""; echo "TESTING:"; \
[2024-09-25T21:24:07.299Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17272985863724/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T21:24:07.300Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17272985863724/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T21:24:07.300Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T21:24:07.300Z] echo "Nothing to be done for teardown."; \
[2024-09-25T21:24:07.300Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17272985863724/TestTargetResult";
[2024-09-25T21:24:07.300Z]
[2024-09-25T21:24:07.300Z] TEST SETUP:
[2024-09-25T21:24:07.300Z] Nothing to be done for setup.
[2024-09-25T21:24:07.300Z]
[2024-09-25T21:24:07.300Z] TESTING:
[2024-09-25T21:24:11.719Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T21:24:14.476Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-09-25T21:24:17.084Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T21:24:17.084Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T21:24:17.084Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T21:24:17.084Z] GC before operation: completed in 54.672 ms, heap usage 217.995 MB -> 37.412 MB.
[2024-09-25T21:24:21.520Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:24:24.917Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:24:28.324Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:24:30.776Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:24:32.353Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:24:33.932Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:24:35.508Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:24:37.955Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:24:37.955Z] 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.
[2024-09-25T21:24:37.955Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:24:37.955Z] Movies recommended for you:
[2024-09-25T21:24:37.955Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:24:37.955Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:24:37.955Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20804.550 ms) ======
[2024-09-25T21:24:37.955Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T21:24:37.955Z] GC before operation: completed in 91.844 ms, heap usage 3.360 GB -> 59.249 MB.
[2024-09-25T21:24:41.367Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:24:43.833Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:24:46.289Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:24:48.920Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:24:50.498Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:24:52.119Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:24:53.694Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:24:55.278Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:24:56.042Z] 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.
[2024-09-25T21:24:56.042Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:24:56.042Z] Movies recommended for you:
[2024-09-25T21:24:56.042Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:24:56.042Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:24:56.042Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17849.856 ms) ======
[2024-09-25T21:24:56.042Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T21:24:56.042Z] GC before operation: completed in 82.841 ms, heap usage 1.270 GB -> 55.619 MB.
[2024-09-25T21:24:58.489Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:25:00.936Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:25:04.363Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:25:06.812Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:25:08.385Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:25:09.968Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:25:11.550Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:25:13.312Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:25:13.312Z] 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.
[2024-09-25T21:25:13.312Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:25:13.312Z] Movies recommended for you:
[2024-09-25T21:25:13.312Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:25:13.312Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:25:13.312Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17194.641 ms) ======
[2024-09-25T21:25:13.312Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T21:25:13.312Z] GC before operation: completed in 81.976 ms, heap usage 81.481 MB -> 55.545 MB.
[2024-09-25T21:25:15.924Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:25:18.366Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:25:20.819Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:25:23.294Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:25:24.868Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:25:26.447Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:25:28.039Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:25:29.623Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:25:30.392Z] 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.
[2024-09-25T21:25:30.392Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:25:30.392Z] Movies recommended for you:
[2024-09-25T21:25:30.392Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:25:30.392Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:25:30.392Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16925.137 ms) ======
[2024-09-25T21:25:30.392Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T21:25:30.392Z] GC before operation: completed in 76.311 ms, heap usage 281.446 MB -> 51.781 MB.
[2024-09-25T21:25:32.839Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:25:35.285Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:25:38.684Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:25:41.130Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:25:42.716Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:25:44.303Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:25:45.881Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:25:47.465Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:25:47.465Z] 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.
[2024-09-25T21:25:47.465Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:25:47.465Z] Movies recommended for you:
[2024-09-25T21:25:47.465Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:25:47.465Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:25:47.465Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17133.475 ms) ======
[2024-09-25T21:25:47.465Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T21:25:47.465Z] GC before operation: completed in 89.090 ms, heap usage 4.304 GB -> 62.153 MB.
[2024-09-25T21:25:49.992Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:25:52.442Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:25:55.854Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:25:57.444Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:25:59.031Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:26:00.609Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:26:02.197Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:26:03.771Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:26:04.531Z] 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.
[2024-09-25T21:26:04.531Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:26:04.531Z] Movies recommended for you:
[2024-09-25T21:26:04.531Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:26:04.531Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:26:04.531Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16822.299 ms) ======
[2024-09-25T21:26:04.531Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T21:26:04.531Z] GC before operation: completed in 83.922 ms, heap usage 1.539 GB -> 56.713 MB.
[2024-09-25T21:26:06.987Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:26:09.449Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:26:12.117Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:26:14.616Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:26:16.196Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:26:17.776Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:26:20.235Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:26:21.817Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:26:21.817Z] 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.
[2024-09-25T21:26:21.817Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:26:21.817Z] Movies recommended for you:
[2024-09-25T21:26:21.817Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:26:21.817Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:26:21.817Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17499.987 ms) ======
[2024-09-25T21:26:21.817Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T21:26:21.817Z] GC before operation: completed in 75.606 ms, heap usage 310.946 MB -> 52.155 MB.
[2024-09-25T21:26:25.210Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:26:27.659Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:26:30.126Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:26:32.583Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:26:34.163Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:26:35.737Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:26:37.314Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:26:38.886Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:26:39.647Z] 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.
[2024-09-25T21:26:39.647Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:26:39.647Z] Movies recommended for you:
[2024-09-25T21:26:39.647Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:26:39.647Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:26:39.647Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17521.002 ms) ======
[2024-09-25T21:26:39.647Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T21:26:39.647Z] GC before operation: completed in 99.122 ms, heap usage 2.766 GB -> 59.696 MB.
[2024-09-25T21:26:42.098Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:26:44.541Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:26:47.936Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:26:50.390Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:26:51.970Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:26:53.545Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:26:55.143Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:26:56.722Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:26:56.722Z] 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.
[2024-09-25T21:26:56.722Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:26:56.722Z] Movies recommended for you:
[2024-09-25T21:26:56.722Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:26:56.722Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:26:56.722Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17124.068 ms) ======
[2024-09-25T21:26:56.722Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T21:26:56.722Z] GC before operation: completed in 105.629 ms, heap usage 3.127 GB -> 64.384 MB.
[2024-09-25T21:26:59.180Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:27:02.570Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:27:05.019Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:27:07.464Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:27:09.215Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:27:10.839Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:27:12.421Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:27:14.015Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:27:14.015Z] 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.
[2024-09-25T21:27:14.015Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:27:14.015Z] Movies recommended for you:
[2024-09-25T21:27:14.015Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:27:14.015Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:27:14.015Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17286.230 ms) ======
[2024-09-25T21:27:14.015Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T21:27:14.015Z] GC before operation: completed in 94.693 ms, heap usage 1.071 GB -> 58.952 MB.
[2024-09-25T21:27:17.413Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:27:19.892Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:27:23.285Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:27:25.741Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:27:27.320Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:27:28.891Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:27:30.489Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:27:32.069Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:27:32.070Z] 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.
[2024-09-25T21:27:32.070Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:27:32.070Z] Movies recommended for you:
[2024-09-25T21:27:32.070Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:27:32.070Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:27:32.070Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (18099.217 ms) ======
[2024-09-25T21:27:32.070Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T21:27:32.070Z] GC before operation: completed in 74.646 ms, heap usage 263.211 MB -> 55.299 MB.
[2024-09-25T21:27:35.469Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:27:37.913Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:27:40.368Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:27:43.769Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:27:44.528Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:27:46.108Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:27:47.680Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:27:49.266Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:27:50.026Z] 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.
[2024-09-25T21:27:50.026Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:27:50.026Z] Movies recommended for you:
[2024-09-25T21:27:50.026Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:27:50.026Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:27:50.026Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17490.307 ms) ======
[2024-09-25T21:27:50.026Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T21:27:50.026Z] GC before operation: completed in 92.804 ms, heap usage 2.047 GB -> 57.139 MB.
[2024-09-25T21:27:52.471Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:27:55.915Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:27:58.361Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:28:00.807Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:28:02.379Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:28:03.952Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:28:05.566Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:28:07.509Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:28:07.509Z] 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.
[2024-09-25T21:28:07.509Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:28:07.509Z] Movies recommended for you:
[2024-09-25T21:28:07.509Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:28:07.509Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:28:07.509Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17563.531 ms) ======
[2024-09-25T21:28:07.509Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T21:28:07.509Z] GC before operation: completed in 96.519 ms, heap usage 1.149 GB -> 60.275 MB.
[2024-09-25T21:28:09.969Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:28:12.417Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:28:15.812Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:28:18.280Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:28:19.852Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:28:21.430Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:28:23.016Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:28:23.782Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:28:24.551Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-25T21:28:24.551Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:28:24.551Z] Movies recommended for you:
[2024-09-25T21:28:24.551Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:28:24.551Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:28:24.551Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16873.780 ms) ======
[2024-09-25T21:28:24.551Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T21:28:24.551Z] GC before operation: completed in 92.050 ms, heap usage 2.343 GB -> 57.077 MB.
[2024-09-25T21:28:27.946Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:28:30.395Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:28:32.840Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:28:36.241Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:28:37.822Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:28:39.397Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:28:40.971Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:28:42.544Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:28:42.544Z] 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.
[2024-09-25T21:28:42.544Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:28:42.544Z] Movies recommended for you:
[2024-09-25T21:28:42.544Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:28:42.544Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:28:42.544Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (18166.484 ms) ======
[2024-09-25T21:28:42.544Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T21:28:42.544Z] GC before operation: completed in 86.131 ms, heap usage 77.913 MB -> 55.860 MB.
[2024-09-25T21:28:45.950Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:28:48.397Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:28:50.836Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:28:53.289Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:28:54.877Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:28:56.454Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:28:58.909Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:28:59.675Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:29:00.439Z] 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.
[2024-09-25T21:29:00.439Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:29:00.439Z] Movies recommended for you:
[2024-09-25T21:29:00.439Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:29:00.440Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:29:00.440Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17446.456 ms) ======
[2024-09-25T21:29:00.440Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T21:29:00.440Z] GC before operation: completed in 93.894 ms, heap usage 2.618 GB -> 59.686 MB.
[2024-09-25T21:29:02.895Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:29:05.524Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:29:08.950Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:29:11.407Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:29:12.980Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:29:14.569Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:29:16.173Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:29:17.764Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:29:17.764Z] 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.
[2024-09-25T21:29:17.764Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:29:17.764Z] Movies recommended for you:
[2024-09-25T21:29:17.764Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:29:17.764Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:29:17.764Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17325.952 ms) ======
[2024-09-25T21:29:17.764Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T21:29:17.764Z] GC before operation: completed in 89.626 ms, heap usage 535.860 MB -> 55.647 MB.
[2024-09-25T21:29:20.209Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:29:23.625Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:29:26.070Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:29:28.514Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:29:30.093Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:29:31.667Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:29:34.116Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:29:34.875Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:29:35.637Z] 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.
[2024-09-25T21:29:35.638Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:29:35.638Z] Movies recommended for you:
[2024-09-25T21:29:35.638Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:29:35.638Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:29:35.638Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (17818.311 ms) ======
[2024-09-25T21:29:35.638Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T21:29:35.638Z] GC before operation: completed in 87.295 ms, heap usage 1.031 GB -> 56.432 MB.
[2024-09-25T21:29:38.083Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:29:40.545Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:29:43.941Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:29:46.387Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:29:48.174Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:29:48.935Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:29:50.505Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:29:52.077Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:29:52.843Z] 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.
[2024-09-25T21:29:52.843Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:29:52.843Z] Movies recommended for you:
[2024-09-25T21:29:52.843Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:29:52.843Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:29:52.843Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16948.487 ms) ======
[2024-09-25T21:29:52.843Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T21:29:52.843Z] GC before operation: completed in 91.590 ms, heap usage 622.700 MB -> 58.209 MB.
[2024-09-25T21:29:55.284Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:29:57.732Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:30:00.519Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:30:02.969Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:30:04.545Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:30:06.119Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:30:07.696Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:30:09.268Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:30:09.268Z] 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.
[2024-09-25T21:30:09.268Z] The best model improves the baseline by 14.43%.
[2024-09-25T21:30:09.268Z] Movies recommended for you:
[2024-09-25T21:30:09.268Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:30:09.268Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:30:09.268Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16632.828 ms) ======
[2024-09-25T21:30:10.033Z] -----------------------------------
[2024-09-25T21:30:10.033Z] renaissance-movie-lens_0_PASSED
[2024-09-25T21:30:10.033Z] -----------------------------------
[2024-09-25T21:30:10.033Z]
[2024-09-25T21:30:10.033Z] TEST TEARDOWN:
[2024-09-25T21:30:10.033Z] Nothing to be done for teardown.
[2024-09-25T21:30:10.033Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 21:30:09 2024 Epoch Time (ms): 1727299809746