renaissance-movie-lens_0
[2024-08-22T04:47:05.168Z] Running test renaissance-movie-lens_0 ...
[2024-08-22T04:47:05.168Z] ===============================================
[2024-08-22T04:47:05.168Z] renaissance-movie-lens_0 Start Time: Wed Aug 21 23:47:04 2024 Epoch Time (ms): 1724302024755
[2024-08-22T04:47:05.168Z] variation: NoOptions
[2024-08-22T04:47:05.168Z] JVM_OPTIONS:
[2024-08-22T04:47:05.168Z] { \
[2024-08-22T04:47:05.168Z] echo ""; echo "TEST SETUP:"; \
[2024-08-22T04:47:05.168Z] echo "Nothing to be done for setup."; \
[2024-08-22T04:47:05.168Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17243011117876/renaissance-movie-lens_0"; \
[2024-08-22T04:47:05.168Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17243011117876/renaissance-movie-lens_0"; \
[2024-08-22T04:47:05.168Z] echo ""; echo "TESTING:"; \
[2024-08-22T04:47:05.168Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-11.0.25+4/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_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17243011117876/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-22T04:47:05.168Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17243011117876/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-22T04:47:05.168Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-22T04:47:05.168Z] echo "Nothing to be done for teardown."; \
[2024-08-22T04:47:05.168Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17243011117876/TestTargetResult";
[2024-08-22T04:47:05.168Z]
[2024-08-22T04:47:05.168Z] TEST SETUP:
[2024-08-22T04:47:05.168Z] Nothing to be done for setup.
[2024-08-22T04:47:05.168Z]
[2024-08-22T04:47:05.168Z] TESTING:
[2024-08-22T04:47:08.197Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-22T04:47:10.372Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-08-22T04:47:13.407Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-22T04:47:14.079Z] Training: 60056, validation: 20285, test: 19854
[2024-08-22T04:47:14.079Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-22T04:47:14.079Z] GC before operation: completed in 81.543 ms, heap usage 87.703 MB -> 37.167 MB.
[2024-08-22T04:47:21.599Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:47:25.795Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:47:29.813Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:47:33.823Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:47:35.225Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:47:37.459Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:47:39.650Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:47:41.229Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:47:41.903Z] 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.
[2024-08-22T04:47:41.903Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:47:41.903Z] Movies recommended for you:
[2024-08-22T04:47:41.903Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:47:41.903Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:47:41.903Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (27832.839 ms) ======
[2024-08-22T04:47:41.903Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-22T04:47:41.903Z] GC before operation: completed in 140.811 ms, heap usage 421.070 MB -> 48.373 MB.
[2024-08-22T04:47:44.979Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:47:49.033Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:47:51.233Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:47:54.294Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:47:56.479Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:47:57.917Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:48:00.099Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:48:01.505Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:48:02.178Z] 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.
[2024-08-22T04:48:02.178Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:48:02.178Z] Movies recommended for you:
[2024-08-22T04:48:02.178Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:48:02.178Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:48:02.178Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20277.967 ms) ======
[2024-08-22T04:48:02.178Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-22T04:48:02.178Z] GC before operation: completed in 181.666 ms, heap usage 66.125 MB -> 51.772 MB.
[2024-08-22T04:48:05.216Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:48:09.210Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:48:11.422Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:48:14.479Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:48:15.891Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:48:18.100Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:48:19.495Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:48:21.701Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:48:21.701Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-22T04:48:21.701Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:48:21.701Z] Movies recommended for you:
[2024-08-22T04:48:21.701Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:48:21.701Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:48:21.701Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (19269.540 ms) ======
[2024-08-22T04:48:21.701Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-22T04:48:21.701Z] GC before operation: completed in 131.356 ms, heap usage 390.289 MB -> 51.477 MB.
[2024-08-22T04:48:24.756Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:48:27.828Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:48:30.045Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:48:33.119Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:48:34.505Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:48:35.921Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:48:38.096Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:48:39.513Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:48:40.185Z] 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.
[2024-08-22T04:48:40.185Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:48:40.185Z] Movies recommended for you:
[2024-08-22T04:48:40.185Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:48:40.185Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:48:40.185Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18239.291 ms) ======
[2024-08-22T04:48:40.185Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-22T04:48:40.185Z] GC before operation: completed in 128.948 ms, heap usage 416.474 MB -> 51.814 MB.
[2024-08-22T04:48:43.245Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:48:45.838Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:48:48.888Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:48:51.058Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:48:53.243Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:48:54.662Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:48:56.067Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:48:57.489Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:48:58.164Z] 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.
[2024-08-22T04:48:58.164Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:48:58.164Z] Movies recommended for you:
[2024-08-22T04:48:58.164Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:48:58.164Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:48:58.164Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (18020.350 ms) ======
[2024-08-22T04:48:58.164Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-22T04:48:58.164Z] GC before operation: completed in 125.252 ms, heap usage 301.395 MB -> 51.902 MB.
[2024-08-22T04:49:01.200Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:49:04.251Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:49:07.307Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:49:09.494Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:49:10.909Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:49:13.098Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:49:14.492Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:49:15.889Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:49:16.564Z] 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.
[2024-08-22T04:49:16.564Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:49:16.564Z] Movies recommended for you:
[2024-08-22T04:49:16.564Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:49:16.564Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:49:16.564Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18117.928 ms) ======
[2024-08-22T04:49:16.564Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-22T04:49:16.564Z] GC before operation: completed in 152.172 ms, heap usage 391.283 MB -> 51.952 MB.
[2024-08-22T04:49:19.625Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:49:21.820Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:49:24.857Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:49:27.052Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:49:28.448Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:49:30.635Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:49:32.034Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:49:33.428Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:49:34.103Z] 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.
[2024-08-22T04:49:34.103Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:49:34.103Z] Movies recommended for you:
[2024-08-22T04:49:34.103Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:49:34.103Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:49:34.103Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17448.900 ms) ======
[2024-08-22T04:49:34.103Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-22T04:49:34.103Z] GC before operation: completed in 126.794 ms, heap usage 363.944 MB -> 52.031 MB.
[2024-08-22T04:49:37.164Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:49:39.756Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:49:41.947Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:49:44.998Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:49:46.399Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:49:47.852Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:49:50.080Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:49:51.509Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:49:51.509Z] 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.
[2024-08-22T04:49:51.509Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:49:51.509Z] Movies recommended for you:
[2024-08-22T04:49:51.509Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:49:51.509Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:49:51.509Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17583.081 ms) ======
[2024-08-22T04:49:51.509Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-22T04:49:52.182Z] GC before operation: completed in 135.764 ms, heap usage 167.873 MB -> 52.182 MB.
[2024-08-22T04:49:54.461Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:49:57.532Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:49:59.751Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:50:02.793Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:50:04.187Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:50:05.580Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:50:07.757Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:50:09.166Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:50:09.166Z] 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.
[2024-08-22T04:50:09.166Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:50:09.166Z] Movies recommended for you:
[2024-08-22T04:50:09.166Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:50:09.166Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:50:09.166Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17559.668 ms) ======
[2024-08-22T04:50:09.166Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-22T04:50:09.835Z] GC before operation: completed in 132.870 ms, heap usage 265.081 MB -> 52.022 MB.
[2024-08-22T04:50:12.043Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:50:15.071Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:50:17.263Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:50:20.298Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:50:21.707Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:50:23.114Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:50:24.523Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:50:26.701Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:50:26.701Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-08-22T04:50:26.701Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:50:26.701Z] Movies recommended for you:
[2024-08-22T04:50:26.701Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:50:26.701Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:50:26.701Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17127.602 ms) ======
[2024-08-22T04:50:26.701Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-22T04:50:26.701Z] GC before operation: completed in 153.889 ms, heap usage 154.688 MB -> 52.057 MB.
[2024-08-22T04:50:29.738Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:50:31.919Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:50:34.950Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:50:37.152Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:50:39.502Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:50:40.887Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:50:42.270Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:50:43.675Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:50:44.391Z] 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.
[2024-08-22T04:50:44.391Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:50:44.391Z] Movies recommended for you:
[2024-08-22T04:50:44.391Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:50:44.391Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:50:44.391Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17302.499 ms) ======
[2024-08-22T04:50:44.391Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-22T04:50:44.391Z] GC before operation: completed in 133.097 ms, heap usage 440.872 MB -> 52.039 MB.
[2024-08-22T04:50:47.450Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:50:49.626Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:50:52.646Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:50:54.840Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:50:56.284Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:50:58.468Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:50:59.870Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:51:01.276Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:51:01.952Z] 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.
[2024-08-22T04:51:01.952Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:51:01.952Z] Movies recommended for you:
[2024-08-22T04:51:01.952Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:51:01.952Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:51:01.952Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17622.449 ms) ======
[2024-08-22T04:51:01.952Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-22T04:51:01.952Z] GC before operation: completed in 127.729 ms, heap usage 385.575 MB -> 52.198 MB.
[2024-08-22T04:51:05.008Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:51:07.185Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:51:10.224Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:51:12.460Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:51:13.867Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:51:16.062Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:51:17.456Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:51:18.898Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:51:19.573Z] 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.
[2024-08-22T04:51:19.573Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:51:19.573Z] Movies recommended for you:
[2024-08-22T04:51:19.573Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:51:19.573Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:51:19.573Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17519.380 ms) ======
[2024-08-22T04:51:19.573Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-22T04:51:19.573Z] GC before operation: completed in 124.314 ms, heap usage 225.057 MB -> 52.247 MB.
[2024-08-22T04:51:22.631Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:51:24.813Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:51:27.874Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:51:30.068Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:51:31.467Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:51:34.082Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:51:35.483Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:51:36.880Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:51:36.880Z] 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.
[2024-08-22T04:51:36.880Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:51:37.551Z] Movies recommended for you:
[2024-08-22T04:51:37.551Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:51:37.551Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:51:37.551Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17572.547 ms) ======
[2024-08-22T04:51:37.551Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-22T04:51:37.551Z] GC before operation: completed in 126.012 ms, heap usage 156.829 MB -> 51.880 MB.
[2024-08-22T04:51:40.593Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:51:42.773Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:51:44.956Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:51:48.077Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:51:49.480Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:51:50.878Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:51:53.064Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:51:54.585Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:51:54.585Z] 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.
[2024-08-22T04:51:54.585Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:51:54.585Z] Movies recommended for you:
[2024-08-22T04:51:54.585Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:51:54.585Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:51:54.585Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (17391.824 ms) ======
[2024-08-22T04:51:54.585Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-22T04:51:54.585Z] GC before operation: completed in 128.486 ms, heap usage 202.428 MB -> 52.200 MB.
[2024-08-22T04:51:57.643Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:51:59.837Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:52:02.884Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:52:05.104Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:52:06.499Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:52:08.705Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:52:10.110Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:52:11.543Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:52:12.220Z] 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.
[2024-08-22T04:52:12.220Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:52:12.220Z] Movies recommended for you:
[2024-08-22T04:52:12.220Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:52:12.220Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:52:12.220Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17148.720 ms) ======
[2024-08-22T04:52:12.220Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-22T04:52:12.220Z] GC before operation: completed in 129.888 ms, heap usage 84.289 MB -> 53.586 MB.
[2024-08-22T04:52:15.320Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:52:17.521Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:52:20.567Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:52:22.741Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:52:24.138Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:52:25.560Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:52:27.740Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:52:29.139Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:52:29.139Z] 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.
[2024-08-22T04:52:29.139Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:52:29.808Z] Movies recommended for you:
[2024-08-22T04:52:29.808Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:52:29.808Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:52:29.808Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17343.118 ms) ======
[2024-08-22T04:52:29.808Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-22T04:52:29.808Z] GC before operation: completed in 129.415 ms, heap usage 374.659 MB -> 52.140 MB.
[2024-08-22T04:52:32.525Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:52:34.719Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:52:37.760Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:52:40.015Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:52:41.432Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:52:43.609Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:52:45.040Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:52:46.440Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:52:46.440Z] 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.
[2024-08-22T04:52:46.440Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:52:46.440Z] Movies recommended for you:
[2024-08-22T04:52:46.440Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:52:46.440Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:52:46.440Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (17035.730 ms) ======
[2024-08-22T04:52:46.440Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-22T04:52:47.115Z] GC before operation: completed in 140.058 ms, heap usage 394.402 MB -> 52.236 MB.
[2024-08-22T04:52:50.167Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:52:52.370Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:52:54.556Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:52:57.590Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:52:58.998Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:53:00.414Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:53:01.839Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:53:04.023Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:53:04.023Z] 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.
[2024-08-22T04:53:04.023Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:53:04.023Z] Movies recommended for you:
[2024-08-22T04:53:04.023Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:53:04.023Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:53:04.023Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (17253.410 ms) ======
[2024-08-22T04:53:04.023Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-22T04:53:04.023Z] GC before operation: completed in 129.149 ms, heap usage 435.368 MB -> 52.511 MB.
[2024-08-22T04:53:07.059Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T04:53:10.106Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T04:53:12.283Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T04:53:14.458Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T04:53:15.879Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T04:53:18.058Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T04:53:19.458Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T04:53:20.845Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T04:53:21.514Z] 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.
[2024-08-22T04:53:21.514Z] The best model improves the baseline by 14.43%.
[2024-08-22T04:53:21.514Z] Movies recommended for you:
[2024-08-22T04:53:21.514Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T04:53:21.514Z] There is no way to check that no silent failure occurred.
[2024-08-22T04:53:21.514Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (17202.018 ms) ======
[2024-08-22T04:53:22.184Z] -----------------------------------
[2024-08-22T04:53:22.184Z] renaissance-movie-lens_0_PASSED
[2024-08-22T04:53:22.184Z] -----------------------------------
[2024-08-22T04:53:22.853Z]
[2024-08-22T04:53:22.853Z] TEST TEARDOWN:
[2024-08-22T04:53:22.853Z] Nothing to be done for teardown.
[2024-08-22T04:53:22.853Z] renaissance-movie-lens_0 Finish Time: Wed Aug 21 23:53:22 2024 Epoch Time (ms): 1724302402267