renaissance-movie-lens_0
[2023-04-18T22:08:03.594Z] Running test renaissance-movie-lens_0 ...
[2023-04-18T22:08:03.594Z] ===============================================
[2023-04-18T22:08:03.594Z] renaissance-movie-lens_0 Start Time: Tue Apr 18 22:08:03 2023 Epoch Time (ms): 1681855683376
[2023-04-18T22:08:03.594Z] variation: NoOptions
[2023-04-18T22:08:03.594Z] JVM_OPTIONS:
[2023-04-18T22:08:03.594Z] { \
[2023-04-18T22:08:03.594Z] echo ""; echo "TEST SETUP:"; \
[2023-04-18T22:08:03.594Z] echo "Nothing to be done for setup."; \
[2023-04-18T22:08:03.594Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_16818548411492/renaissance-movie-lens_0"; \
[2023-04-18T22:08:03.594Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_16818548411492/renaissance-movie-lens_0"; \
[2023-04-18T22:08:03.594Z] echo ""; echo "TESTING:"; \
[2023-04-18T22:08:03.594Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/openjdkbinary/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_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_16818548411492/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-18T22:08:03.594Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_16818548411492/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-18T22:08:03.594Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-18T22:08:03.594Z] echo "Nothing to be done for teardown."; \
[2023-04-18T22:08:03.594Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_16818548411492/TestTargetResult";
[2023-04-18T22:08:03.594Z]
[2023-04-18T22:08:03.594Z] TEST SETUP:
[2023-04-18T22:08:03.594Z] Nothing to be done for setup.
[2023-04-18T22:08:03.594Z]
[2023-04-18T22:08:03.594Z] TESTING:
[2023-04-18T22:08:06.670Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-18T22:08:08.392Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2023-04-18T22:08:13.243Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-18T22:08:13.611Z] Training: 60056, validation: 20285, test: 19854
[2023-04-18T22:08:13.611Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-18T22:08:13.611Z] GC before operation: completed in 93.002 ms, heap usage 54.975 MB -> 36.927 MB.
[2023-04-18T22:08:22.692Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:08:27.578Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:08:32.448Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:08:35.528Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:08:38.559Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:08:40.886Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:08:43.213Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:08:44.928Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:08:45.269Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:08:45.636Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:08:45.636Z] Movies recommended for you:
[2023-04-18T22:08:45.636Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:08:45.636Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:08:45.636Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (31980.231 ms) ======
[2023-04-18T22:08:45.636Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-18T22:08:45.978Z] GC before operation: completed in 125.889 ms, heap usage 131.158 MB -> 52.657 MB.
[2023-04-18T22:08:49.872Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:08:52.904Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:08:56.794Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:08:59.115Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:09:00.819Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:09:02.023Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:09:03.729Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:09:05.466Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:09:05.809Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:09:05.809Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:09:05.809Z] Movies recommended for you:
[2023-04-18T22:09:05.809Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:09:05.809Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:09:05.809Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20136.578 ms) ======
[2023-04-18T22:09:05.809Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-18T22:09:06.152Z] GC before operation: completed in 78.402 ms, heap usage 270.969 MB -> 48.925 MB.
[2023-04-18T22:09:08.476Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:09:10.940Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:09:13.333Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:09:15.680Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:09:17.387Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:09:18.572Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:09:20.293Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:09:21.483Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:09:21.828Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:09:21.828Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:09:22.172Z] Movies recommended for you:
[2023-04-18T22:09:22.172Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:09:22.172Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:09:22.172Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16021.095 ms) ======
[2023-04-18T22:09:22.172Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-18T22:09:22.172Z] GC before operation: completed in 81.785 ms, heap usage 206.179 MB -> 49.089 MB.
[2023-04-18T22:09:24.495Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:09:26.822Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:09:29.148Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:09:31.481Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:09:32.670Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:09:33.858Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:09:35.047Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:09:36.756Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:09:36.756Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:09:36.756Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:09:36.756Z] Movies recommended for you:
[2023-04-18T22:09:36.756Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:09:36.756Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:09:36.756Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14743.376 ms) ======
[2023-04-18T22:09:36.756Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-18T22:09:37.097Z] GC before operation: completed in 75.916 ms, heap usage 126.516 MB -> 49.379 MB.
[2023-04-18T22:09:39.419Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:09:41.745Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:09:44.067Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:09:46.391Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:09:47.577Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:09:48.763Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:09:50.470Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:09:51.660Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:09:52.001Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:09:52.001Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:09:52.001Z] Movies recommended for you:
[2023-04-18T22:09:52.001Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:09:52.001Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:09:52.001Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15005.311 ms) ======
[2023-04-18T22:09:52.001Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-18T22:09:52.001Z] GC before operation: completed in 70.729 ms, heap usage 115.308 MB -> 49.525 MB.
[2023-04-18T22:09:54.343Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:09:56.660Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:09:58.983Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:10:01.310Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:10:02.497Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:10:03.678Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:10:05.585Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:10:06.769Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:10:06.769Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:10:06.769Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:10:07.113Z] Movies recommended for you:
[2023-04-18T22:10:07.113Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:10:07.113Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:10:07.113Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14937.361 ms) ======
[2023-04-18T22:10:07.113Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-18T22:10:07.113Z] GC before operation: completed in 75.407 ms, heap usage 287.456 MB -> 49.761 MB.
[2023-04-18T22:10:09.435Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:10:11.746Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:10:14.068Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:10:16.391Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:10:17.123Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:10:18.834Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:10:20.020Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:10:21.209Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:10:21.551Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:10:21.551Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:10:21.551Z] Movies recommended for you:
[2023-04-18T22:10:21.551Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:10:21.551Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:10:21.551Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14560.059 ms) ======
[2023-04-18T22:10:21.551Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-18T22:10:21.551Z] GC before operation: completed in 79.213 ms, heap usage 117.788 MB -> 49.673 MB.
[2023-04-18T22:10:23.873Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:10:26.196Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:10:28.515Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:10:30.229Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:10:31.943Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:10:33.133Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:10:34.323Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:10:35.702Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:10:36.045Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:10:36.045Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:10:36.045Z] Movies recommended for you:
[2023-04-18T22:10:36.045Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:10:36.045Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:10:36.045Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14313.091 ms) ======
[2023-04-18T22:10:36.045Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-18T22:10:36.045Z] GC before operation: completed in 75.954 ms, heap usage 287.063 MB -> 50.199 MB.
[2023-04-18T22:10:38.362Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:10:40.684Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:10:43.006Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:10:45.336Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:10:46.530Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:10:47.717Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:10:49.421Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:10:50.610Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:10:50.953Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:10:50.954Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:10:50.954Z] Movies recommended for you:
[2023-04-18T22:10:50.954Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:10:50.954Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:10:50.954Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14932.338 ms) ======
[2023-04-18T22:10:50.954Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-18T22:10:50.954Z] GC before operation: completed in 66.245 ms, heap usage 73.049 MB -> 49.782 MB.
[2023-04-18T22:10:53.279Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:10:55.639Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:10:57.960Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:10:59.668Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:11:01.377Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:11:02.560Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:11:03.840Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:11:04.607Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:11:04.946Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:11:04.946Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:11:04.946Z] Movies recommended for you:
[2023-04-18T22:11:04.946Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:11:04.946Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:11:04.946Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13982.502 ms) ======
[2023-04-18T22:11:04.946Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-18T22:11:05.287Z] GC before operation: completed in 72.957 ms, heap usage 58.935 MB -> 52.848 MB.
[2023-04-18T22:11:06.991Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:11:09.302Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:11:11.629Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:11:13.939Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:11:15.129Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:11:15.887Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:11:17.601Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:11:18.780Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:11:18.780Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:11:18.780Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:11:18.780Z] Movies recommended for you:
[2023-04-18T22:11:18.780Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:11:18.780Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:11:18.780Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13820.968 ms) ======
[2023-04-18T22:11:18.780Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-18T22:11:19.122Z] GC before operation: completed in 79.822 ms, heap usage 171.870 MB -> 49.817 MB.
[2023-04-18T22:11:21.467Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:11:23.171Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:11:26.229Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:11:27.930Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:11:29.107Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:11:30.286Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:11:31.464Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:11:32.728Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:11:33.068Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:11:33.068Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:11:33.068Z] Movies recommended for you:
[2023-04-18T22:11:33.068Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:11:33.068Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:11:33.068Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14100.503 ms) ======
[2023-04-18T22:11:33.068Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-18T22:11:33.068Z] GC before operation: completed in 72.539 ms, heap usage 72.437 MB -> 49.851 MB.
[2023-04-18T22:11:35.379Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:11:37.693Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:11:40.004Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:11:41.703Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:11:42.893Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:11:44.073Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:11:45.770Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:11:46.948Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:11:46.949Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:11:46.949Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:11:46.949Z] Movies recommended for you:
[2023-04-18T22:11:46.949Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:11:46.949Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:11:46.949Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13823.804 ms) ======
[2023-04-18T22:11:46.949Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-18T22:11:46.949Z] GC before operation: completed in 74.823 ms, heap usage 236.242 MB -> 50.170 MB.
[2023-04-18T22:11:49.329Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:11:51.045Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:11:53.352Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:11:55.087Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:11:56.284Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:11:57.467Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:11:58.644Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:11:59.915Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:11:59.915Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:11:59.915Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:12:00.256Z] Movies recommended for you:
[2023-04-18T22:12:00.256Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:12:00.256Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:12:00.256Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13068.944 ms) ======
[2023-04-18T22:12:00.256Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-18T22:12:00.256Z] GC before operation: completed in 78.436 ms, heap usage 73.828 MB -> 49.639 MB.
[2023-04-18T22:12:02.563Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:12:04.264Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:12:06.576Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:12:08.904Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:12:10.094Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:12:11.796Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:12:12.982Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:12:14.163Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:12:14.504Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:12:14.504Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:12:14.504Z] Movies recommended for you:
[2023-04-18T22:12:14.504Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:12:14.504Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:12:14.504Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14316.862 ms) ======
[2023-04-18T22:12:14.504Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-18T22:12:14.504Z] GC before operation: completed in 79.611 ms, heap usage 239.475 MB -> 50.101 MB.
[2023-04-18T22:12:16.818Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:12:18.523Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:12:20.833Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:12:22.535Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:12:23.724Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:12:24.905Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:12:26.605Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:12:27.337Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:12:27.678Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:12:27.678Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:12:27.678Z] Movies recommended for you:
[2023-04-18T22:12:27.678Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:12:27.678Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:12:27.678Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13087.909 ms) ======
[2023-04-18T22:12:27.678Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-18T22:12:27.678Z] GC before operation: completed in 68.033 ms, heap usage 62.997 MB -> 50.016 MB.
[2023-04-18T22:12:30.014Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:12:31.755Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:12:34.072Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:12:35.770Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:12:36.950Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:12:38.125Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:12:39.827Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:12:41.008Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:12:41.008Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:12:41.008Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:12:41.008Z] Movies recommended for you:
[2023-04-18T22:12:41.008Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:12:41.008Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:12:41.008Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13251.630 ms) ======
[2023-04-18T22:12:41.008Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-18T22:12:41.008Z] GC before operation: completed in 87.796 ms, heap usage 382.294 MB -> 53.314 MB.
[2023-04-18T22:12:43.314Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:12:45.622Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:12:47.932Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:12:50.247Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:12:51.431Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:12:52.162Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:12:53.866Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:12:55.044Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:12:55.044Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:12:55.044Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:12:55.383Z] Movies recommended for you:
[2023-04-18T22:12:55.383Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:12:55.383Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:12:55.383Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14145.606 ms) ======
[2023-04-18T22:12:55.383Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-18T22:12:55.383Z] GC before operation: completed in 67.987 ms, heap usage 182.903 MB -> 50.091 MB.
[2023-04-18T22:12:57.787Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:12:59.498Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:13:01.808Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:13:04.122Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:13:04.855Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:13:06.559Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:13:07.742Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:13:09.450Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:13:09.450Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:13:09.450Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:13:09.450Z] Movies recommended for you:
[2023-04-18T22:13:09.450Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:13:09.450Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:13:09.450Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14081.754 ms) ======
[2023-04-18T22:13:09.450Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-18T22:13:09.450Z] GC before operation: completed in 82.897 ms, heap usage 121.368 MB -> 50.056 MB.
[2023-04-18T22:13:11.764Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:13:13.468Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:13:15.784Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:13:17.487Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:13:18.712Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:13:19.890Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:13:21.589Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:13:22.769Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:13:22.769Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2023-04-18T22:13:22.769Z] The best model improves the baseline by 14.34%.
[2023-04-18T22:13:22.769Z] Movies recommended for you:
[2023-04-18T22:13:22.769Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:13:22.769Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:13:22.769Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13316.507 ms) ======
[2023-04-18T22:13:23.108Z] -----------------------------------
[2023-04-18T22:13:23.108Z] renaissance-movie-lens_0_PASSED
[2023-04-18T22:13:23.108Z] -----------------------------------
[2023-04-18T22:13:23.108Z]
[2023-04-18T22:13:23.108Z] TEST TEARDOWN:
[2023-04-18T22:13:23.108Z] Nothing to be done for teardown.
[2023-04-18T22:13:23.447Z] renaissance-movie-lens_0 Finish Time: Tue Apr 18 22:13:23 2023 Epoch Time (ms): 1681856003104