renaissance-movie-lens_0
[2024-08-15T02:56:39.762Z] Running test renaissance-movie-lens_0 ...
[2024-08-15T02:56:39.762Z] ===============================================
[2024-08-15T02:56:39.762Z] renaissance-movie-lens_0 Start Time: Wed Aug 14 21:56:39 2024 Epoch Time (ms): 1723690599405
[2024-08-15T02:56:39.762Z] variation: NoOptions
[2024-08-15T02:56:39.762Z] JVM_OPTIONS:
[2024-08-15T02:56:39.762Z] { \
[2024-08-15T02:56:39.762Z] echo ""; echo "TEST SETUP:"; \
[2024-08-15T02:56:39.762Z] echo "Nothing to be done for setup."; \
[2024-08-15T02:56:39.762Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_1723689961300/renaissance-movie-lens_0"; \
[2024-08-15T02:56:39.762Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_1723689961300/renaissance-movie-lens_0"; \
[2024-08-15T02:56:39.762Z] echo ""; echo "TESTING:"; \
[2024-08-15T02:56:39.762Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/jdkbinary/j2sdk-image/jdk-17.0.13+3/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_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_1723689961300/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-15T02:56:39.762Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_1723689961300/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-15T02:56:39.762Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-15T02:56:39.762Z] echo "Nothing to be done for teardown."; \
[2024-08-15T02:56:39.762Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux_testList_0/aqa-tests/TKG/../TKG/output_1723689961300/TestTargetResult";
[2024-08-15T02:56:39.762Z]
[2024-08-15T02:56:39.762Z] TEST SETUP:
[2024-08-15T02:56:39.762Z] Nothing to be done for setup.
[2024-08-15T02:56:39.762Z]
[2024-08-15T02:56:39.762Z] TESTING:
[2024-08-15T02:56:42.817Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-15T02:56:44.230Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-08-15T02:56:47.734Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-15T02:56:47.735Z] Training: 60056, validation: 20285, test: 19854
[2024-08-15T02:56:47.735Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-15T02:56:47.735Z] GC before operation: completed in 58.280 ms, heap usage 70.501 MB -> 37.693 MB.
[2024-08-15T02:56:55.544Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:56:58.715Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:57:01.793Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:57:04.980Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:57:06.394Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:57:08.627Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:57:10.846Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:57:12.293Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:57:12.978Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:57:12.978Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:57:12.978Z] Movies recommended for you:
[2024-08-15T02:57:12.978Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:57:12.978Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:57:12.978Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25123.847 ms) ======
[2024-08-15T02:57:12.978Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-15T02:57:12.978Z] GC before operation: completed in 52.726 ms, heap usage 97.065 MB -> 54.775 MB.
[2024-08-15T02:57:16.052Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:57:19.132Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:57:21.345Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:57:23.575Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:57:24.995Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:57:27.210Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:57:28.633Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:57:30.863Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:57:30.863Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:57:30.863Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:57:30.863Z] Movies recommended for you:
[2024-08-15T02:57:30.863Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:57:30.863Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:57:30.863Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17825.039 ms) ======
[2024-08-15T02:57:30.863Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-15T02:57:30.863Z] GC before operation: completed in 72.218 ms, heap usage 442.918 MB -> 51.566 MB.
[2024-08-15T02:57:33.943Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:57:37.069Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:57:39.263Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:57:41.472Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:57:42.899Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:57:44.327Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:57:45.745Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:57:47.167Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:57:47.853Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:57:47.853Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:57:47.853Z] Movies recommended for you:
[2024-08-15T02:57:47.853Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:57:47.853Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:57:47.853Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16914.134 ms) ======
[2024-08-15T02:57:47.853Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-15T02:57:47.853Z] GC before operation: completed in 62.663 ms, heap usage 283.795 MB -> 51.975 MB.
[2024-08-15T02:57:50.943Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:57:53.150Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:57:55.370Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:57:57.578Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:57:58.267Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:57:59.684Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:58:01.118Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:58:02.564Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:58:02.564Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:58:03.245Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:58:03.245Z] Movies recommended for you:
[2024-08-15T02:58:03.245Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:58:03.245Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:58:03.245Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15209.905 ms) ======
[2024-08-15T02:58:03.245Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-15T02:58:03.245Z] GC before operation: completed in 96.132 ms, heap usage 317.633 MB -> 52.316 MB.
[2024-08-15T02:58:05.466Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:58:07.682Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:58:09.895Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:58:13.006Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:58:13.703Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:58:15.134Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:58:16.555Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:58:17.977Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:58:17.977Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:58:17.977Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:58:18.659Z] Movies recommended for you:
[2024-08-15T02:58:18.659Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:58:18.659Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:58:18.659Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15224.882 ms) ======
[2024-08-15T02:58:18.659Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-15T02:58:18.659Z] GC before operation: completed in 59.438 ms, heap usage 530.618 MB -> 55.827 MB.
[2024-08-15T02:58:20.894Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:58:23.134Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:58:25.373Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:58:27.592Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:58:29.017Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:58:30.454Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:58:31.878Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:58:33.305Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:58:33.305Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:58:33.305Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:58:33.984Z] Movies recommended for you:
[2024-08-15T02:58:33.984Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:58:33.984Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:58:33.984Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15237.888 ms) ======
[2024-08-15T02:58:33.984Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-15T02:58:33.984Z] GC before operation: completed in 63.720 ms, heap usage 330.297 MB -> 52.483 MB.
[2024-08-15T02:58:36.203Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:58:38.460Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:58:40.691Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:58:42.932Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:58:44.347Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:58:45.759Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:58:47.173Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:58:48.595Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:58:48.595Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:58:48.595Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:58:48.595Z] Movies recommended for you:
[2024-08-15T02:58:48.595Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:58:48.595Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:58:48.595Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15196.183 ms) ======
[2024-08-15T02:58:48.595Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-15T02:58:49.275Z] GC before operation: completed in 64.798 ms, heap usage 523.707 MB -> 55.961 MB.
[2024-08-15T02:58:51.497Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:58:53.709Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:58:55.914Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:58:58.122Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:58:59.587Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:59:01.010Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:59:02.440Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:59:03.133Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:59:03.828Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:59:03.828Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:59:03.828Z] Movies recommended for you:
[2024-08-15T02:59:03.828Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:59:03.828Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:59:03.828Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14736.809 ms) ======
[2024-08-15T02:59:03.828Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-15T02:59:03.828Z] GC before operation: completed in 58.840 ms, heap usage 335.568 MB -> 56.089 MB.
[2024-08-15T02:59:06.349Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:59:08.562Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:59:10.761Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:59:12.970Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:59:14.380Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:59:15.820Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:59:17.230Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:59:18.643Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:59:18.643Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:59:18.643Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:59:18.643Z] Movies recommended for you:
[2024-08-15T02:59:18.643Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:59:18.643Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:59:18.643Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15045.082 ms) ======
[2024-08-15T02:59:18.643Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-15T02:59:18.643Z] GC before operation: completed in 76.501 ms, heap usage 339.426 MB -> 52.550 MB.
[2024-08-15T02:59:20.892Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:59:23.980Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:59:26.212Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:59:27.631Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:59:29.044Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:59:30.485Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:59:31.925Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:59:33.363Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:59:33.363Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:59:33.363Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:59:33.363Z] Movies recommended for you:
[2024-08-15T02:59:33.363Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:59:33.363Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:59:33.363Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14718.941 ms) ======
[2024-08-15T02:59:33.363Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-15T02:59:34.048Z] GC before operation: completed in 83.599 ms, heap usage 442.373 MB -> 52.836 MB.
[2024-08-15T02:59:36.259Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:59:38.466Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:59:40.682Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:59:42.891Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:59:44.310Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T02:59:44.996Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T02:59:46.415Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T02:59:47.822Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T02:59:48.516Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T02:59:48.517Z] The best model improves the baseline by 14.43%.
[2024-08-15T02:59:48.517Z] Movies recommended for you:
[2024-08-15T02:59:48.517Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T02:59:48.517Z] There is no way to check that no silent failure occurred.
[2024-08-15T02:59:48.517Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14720.727 ms) ======
[2024-08-15T02:59:48.517Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-15T02:59:48.517Z] GC before operation: completed in 60.979 ms, heap usage 77.292 MB -> 55.024 MB.
[2024-08-15T02:59:50.827Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T02:59:53.047Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T02:59:55.259Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T02:59:57.463Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T02:59:58.879Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:00:00.302Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:00:01.719Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:00:03.135Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:00:03.135Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:00:03.135Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:00:03.135Z] Movies recommended for you:
[2024-08-15T03:00:03.135Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:00:03.135Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:00:03.135Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14807.124 ms) ======
[2024-08-15T03:00:03.135Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-15T03:00:03.135Z] GC before operation: completed in 60.410 ms, heap usage 236.419 MB -> 52.691 MB.
[2024-08-15T03:00:06.229Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:00:07.642Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:00:10.698Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:00:12.127Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:00:14.369Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:00:15.056Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:00:16.477Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:00:17.892Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:00:18.583Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:00:18.583Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:00:18.583Z] Movies recommended for you:
[2024-08-15T03:00:18.583Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:00:18.583Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:00:18.583Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15039.970 ms) ======
[2024-08-15T03:00:18.583Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-15T03:00:18.583Z] GC before operation: completed in 78.604 ms, heap usage 317.318 MB -> 52.889 MB.
[2024-08-15T03:00:20.809Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:00:23.033Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:00:25.223Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:00:27.433Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:00:28.866Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:00:30.275Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:00:31.709Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:00:33.121Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:00:33.814Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:00:33.814Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:00:33.814Z] Movies recommended for you:
[2024-08-15T03:00:33.814Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:00:33.814Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:00:33.814Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15116.283 ms) ======
[2024-08-15T03:00:33.814Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-15T03:00:33.814Z] GC before operation: completed in 63.068 ms, heap usage 497.392 MB -> 56.021 MB.
[2024-08-15T03:00:36.024Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:00:38.251Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:00:41.328Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:00:42.743Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:00:44.163Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:00:45.575Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:00:47.007Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:00:48.423Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:00:48.423Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:00:48.423Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:00:48.423Z] Movies recommended for you:
[2024-08-15T03:00:48.423Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:00:48.423Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:00:48.423Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15072.351 ms) ======
[2024-08-15T03:00:48.423Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-15T03:00:49.105Z] GC before operation: completed in 62.311 ms, heap usage 694.788 MB -> 56.384 MB.
[2024-08-15T03:00:51.324Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:00:53.546Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:00:55.757Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:00:57.982Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:00:59.395Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:01:00.808Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:01:02.217Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:01:03.732Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:01:03.732Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:01:03.732Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:01:03.732Z] Movies recommended for you:
[2024-08-15T03:01:03.732Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:01:03.732Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:01:03.732Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15058.277 ms) ======
[2024-08-15T03:01:03.732Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-15T03:01:03.732Z] GC before operation: completed in 96.909 ms, heap usage 258.304 MB -> 52.867 MB.
[2024-08-15T03:01:06.802Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:01:09.056Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:01:10.475Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:01:12.693Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:01:14.097Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:01:15.511Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:01:16.941Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:01:18.368Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:01:18.368Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:01:18.368Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:01:18.368Z] Movies recommended for you:
[2024-08-15T03:01:18.368Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:01:18.368Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:01:18.368Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14668.721 ms) ======
[2024-08-15T03:01:18.368Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-15T03:01:18.368Z] GC before operation: completed in 78.815 ms, heap usage 299.764 MB -> 52.695 MB.
[2024-08-15T03:01:21.461Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:01:23.331Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:01:25.570Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:01:27.792Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:01:29.201Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:01:30.659Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:01:32.081Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:01:33.497Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:01:33.497Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:01:33.497Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:01:33.497Z] Movies recommended for you:
[2024-08-15T03:01:33.497Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:01:33.497Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:01:33.497Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14775.445 ms) ======
[2024-08-15T03:01:33.497Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-15T03:01:33.497Z] GC before operation: completed in 90.136 ms, heap usage 846.777 MB -> 56.743 MB.
[2024-08-15T03:01:35.696Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:01:37.908Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:01:40.995Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:01:42.405Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:01:43.828Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:01:45.254Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:01:46.663Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:01:48.078Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:01:48.078Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:01:48.078Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:01:48.078Z] Movies recommended for you:
[2024-08-15T03:01:48.078Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:01:48.078Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:01:48.078Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14693.615 ms) ======
[2024-08-15T03:01:48.078Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-15T03:01:48.078Z] GC before operation: completed in 59.980 ms, heap usage 413.470 MB -> 53.079 MB.
[2024-08-15T03:01:51.179Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-15T03:01:52.605Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-15T03:01:55.695Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-15T03:01:57.131Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-15T03:01:58.555Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-15T03:01:59.965Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-15T03:02:01.404Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-15T03:02:02.850Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-15T03:02:02.850Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-15T03:02:02.850Z] The best model improves the baseline by 14.43%.
[2024-08-15T03:02:03.528Z] Movies recommended for you:
[2024-08-15T03:02:03.528Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-15T03:02:03.528Z] There is no way to check that no silent failure occurred.
[2024-08-15T03:02:03.528Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14901.745 ms) ======
[2024-08-15T03:02:04.206Z] -----------------------------------
[2024-08-15T03:02:04.206Z] renaissance-movie-lens_0_PASSED
[2024-08-15T03:02:04.206Z] -----------------------------------
[2024-08-15T03:02:04.206Z]
[2024-08-15T03:02:04.206Z] TEST TEARDOWN:
[2024-08-15T03:02:04.206Z] Nothing to be done for teardown.
[2024-08-15T03:02:04.206Z] renaissance-movie-lens_0 Finish Time: Wed Aug 14 22:02:04 2024 Epoch Time (ms): 1723690924058