renaissance-movie-lens_0
[2025-02-26T21:33:23.934Z] Running test renaissance-movie-lens_0 ...
[2025-02-26T21:33:23.934Z] ===============================================
[2025-02-26T21:33:23.934Z] renaissance-movie-lens_0 Start Time: Wed Feb 26 21:33:23 2025 Epoch Time (ms): 1740605603415
[2025-02-26T21:33:23.934Z] variation: NoOptions
[2025-02-26T21:33:23.934Z] JVM_OPTIONS:
[2025-02-26T21:33:23.934Z] { \
[2025-02-26T21:33:23.934Z] echo ""; echo "TEST SETUP:"; \
[2025-02-26T21:33:23.934Z] echo "Nothing to be done for setup."; \
[2025-02-26T21:33:23.934Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406047564400/renaissance-movie-lens_0"; \
[2025-02-26T21:33:23.934Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406047564400/renaissance-movie-lens_0"; \
[2025-02-26T21:33:23.934Z] echo ""; echo "TESTING:"; \
[2025-02-26T21:33:23.934Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406047564400/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-26T21:33:23.934Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406047564400/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-26T21:33:23.934Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-26T21:33:23.934Z] echo "Nothing to be done for teardown."; \
[2025-02-26T21:33:23.934Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17406047564400/TestTargetResult";
[2025-02-26T21:33:23.934Z]
[2025-02-26T21:33:23.934Z] TEST SETUP:
[2025-02-26T21:33:23.934Z] Nothing to be done for setup.
[2025-02-26T21:33:23.934Z]
[2025-02-26T21:33:23.934Z] TESTING:
[2025-02-26T21:33:26.955Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-26T21:33:28.915Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-26T21:33:30.873Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-26T21:33:31.828Z] Training: 60056, validation: 20285, test: 19854
[2025-02-26T21:33:31.828Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-26T21:33:31.828Z] GC before operation: completed in 44.003 ms, heap usage 87.529 MB -> 37.191 MB.
[2025-02-26T21:33:36.012Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:33:39.041Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:33:42.066Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:33:44.024Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:33:45.986Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:33:46.941Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:33:49.043Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:33:49.997Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:33:49.997Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:33:49.997Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:33:49.997Z] Movies recommended for you:
[2025-02-26T21:33:49.997Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:33:49.997Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:33:49.997Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (18769.300 ms) ======
[2025-02-26T21:33:49.997Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-26T21:33:49.997Z] GC before operation: completed in 63.466 ms, heap usage 301.721 MB -> 53.924 MB.
[2025-02-26T21:33:53.093Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:33:55.050Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:33:57.028Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:33:59.819Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:34:00.772Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:34:01.725Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:34:02.683Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:34:05.725Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:34:05.725Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:34:05.725Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:34:05.725Z] Movies recommended for you:
[2025-02-26T21:34:05.725Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:34:05.725Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:34:05.725Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (14398.052 ms) ======
[2025-02-26T21:34:05.725Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-26T21:34:05.725Z] GC before operation: completed in 58.910 ms, heap usage 214.519 MB -> 52.020 MB.
[2025-02-26T21:34:06.680Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:34:08.641Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:34:11.735Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:34:13.692Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:34:14.645Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:34:16.604Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:34:17.558Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:34:18.564Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:34:19.698Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:34:19.698Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:34:19.698Z] Movies recommended for you:
[2025-02-26T21:34:19.698Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:34:19.698Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:34:19.698Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14459.935 ms) ======
[2025-02-26T21:34:19.698Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-26T21:34:19.698Z] GC before operation: completed in 58.569 ms, heap usage 73.387 MB -> 50.004 MB.
[2025-02-26T21:34:21.660Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:34:23.623Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:34:25.583Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:34:28.605Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:34:29.563Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:34:30.516Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:34:32.474Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:34:33.428Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:34:33.428Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:34:33.428Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:34:33.428Z] Movies recommended for you:
[2025-02-26T21:34:33.428Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:34:33.428Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:34:33.428Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14272.619 ms) ======
[2025-02-26T21:34:33.428Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-26T21:34:33.428Z] GC before operation: completed in 67.881 ms, heap usage 131.004 MB -> 53.712 MB.
[2025-02-26T21:34:35.389Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:34:37.364Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:34:39.327Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:34:41.289Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:34:43.247Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:34:44.202Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:34:45.173Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:34:46.134Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:34:47.092Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:34:47.092Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:34:47.092Z] Movies recommended for you:
[2025-02-26T21:34:47.092Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:34:47.092Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:34:47.092Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13103.820 ms) ======
[2025-02-26T21:34:47.092Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-26T21:34:47.092Z] GC before operation: completed in 61.448 ms, heap usage 189.990 MB -> 50.654 MB.
[2025-02-26T21:34:49.051Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:34:51.010Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:34:52.989Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:34:53.949Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:34:55.909Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:34:56.865Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:34:57.819Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:34:58.801Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:34:59.755Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:34:59.755Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:34:59.755Z] Movies recommended for you:
[2025-02-26T21:34:59.755Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:34:59.755Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:34:59.755Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (12676.960 ms) ======
[2025-02-26T21:34:59.755Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-26T21:34:59.755Z] GC before operation: completed in 101.166 ms, heap usage 258.337 MB -> 50.746 MB.
[2025-02-26T21:35:02.587Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:35:03.886Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:35:04.842Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:35:06.802Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:35:07.757Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:35:09.717Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:35:10.674Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:35:11.628Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:35:11.628Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:35:11.628Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:35:11.628Z] Movies recommended for you:
[2025-02-26T21:35:11.628Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:35:11.628Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:35:11.628Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (12429.765 ms) ======
[2025-02-26T21:35:11.628Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-26T21:35:11.628Z] GC before operation: completed in 65.639 ms, heap usage 193.975 MB -> 50.835 MB.
[2025-02-26T21:35:13.585Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:35:15.544Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:35:18.569Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:35:20.530Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:35:21.484Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:35:22.439Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:35:23.395Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:35:24.350Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:35:25.304Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:35:25.305Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:35:25.305Z] Movies recommended for you:
[2025-02-26T21:35:25.305Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:35:25.305Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:35:25.305Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12895.548 ms) ======
[2025-02-26T21:35:25.305Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-26T21:35:25.305Z] GC before operation: completed in 64.534 ms, heap usage 460.406 MB -> 57.831 MB.
[2025-02-26T21:35:27.269Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:35:29.233Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:35:31.193Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:35:32.149Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:35:34.109Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:35:35.062Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:35:36.017Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:35:36.971Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:35:36.971Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:35:36.971Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:35:37.926Z] Movies recommended for you:
[2025-02-26T21:35:37.926Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:35:37.926Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:35:37.926Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12493.086 ms) ======
[2025-02-26T21:35:37.926Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-26T21:35:37.926Z] GC before operation: completed in 68.766 ms, heap usage 232.157 MB -> 54.923 MB.
[2025-02-26T21:35:39.887Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:35:41.850Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:35:43.811Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:35:44.778Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:35:46.738Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:35:47.694Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:35:48.650Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:35:49.605Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:35:50.561Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:35:50.562Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:35:50.562Z] Movies recommended for you:
[2025-02-26T21:35:50.562Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:35:50.562Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:35:50.562Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12663.397 ms) ======
[2025-02-26T21:35:50.562Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-26T21:35:50.562Z] GC before operation: completed in 68.518 ms, heap usage 197.694 MB -> 51.101 MB.
[2025-02-26T21:35:52.524Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:35:54.487Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:35:56.481Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:35:58.490Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:35:59.451Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:36:00.405Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:36:01.361Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:36:02.317Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:36:02.317Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:36:02.317Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:36:03.272Z] Movies recommended for you:
[2025-02-26T21:36:03.272Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:36:03.272Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:36:03.272Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12477.366 ms) ======
[2025-02-26T21:36:03.272Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-26T21:36:03.272Z] GC before operation: completed in 80.936 ms, heap usage 615.907 MB -> 54.303 MB.
[2025-02-26T21:36:05.234Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:36:07.194Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:36:09.154Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:36:10.110Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:36:12.072Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:36:13.027Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:36:13.983Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:36:14.939Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:36:14.939Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:36:14.939Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:36:15.894Z] Movies recommended for you:
[2025-02-26T21:36:15.894Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:36:15.894Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:36:15.895Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12584.757 ms) ======
[2025-02-26T21:36:15.895Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-26T21:36:15.895Z] GC before operation: completed in 73.043 ms, heap usage 444.732 MB -> 54.363 MB.
[2025-02-26T21:36:17.854Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:36:19.814Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:36:21.773Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:36:23.734Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:36:24.689Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:36:25.643Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:36:27.646Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:36:28.600Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:36:28.600Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:36:28.600Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:36:28.600Z] Movies recommended for you:
[2025-02-26T21:36:28.600Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:36:28.600Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:36:28.600Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13194.246 ms) ======
[2025-02-26T21:36:28.600Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-26T21:36:28.600Z] GC before operation: completed in 73.695 ms, heap usage 419.532 MB -> 54.611 MB.
[2025-02-26T21:36:30.565Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:36:32.525Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:36:35.548Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:36:37.521Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:36:38.476Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:36:39.432Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:36:40.394Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:36:42.352Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:36:42.352Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:36:42.352Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:36:42.352Z] Movies recommended for you:
[2025-02-26T21:36:42.352Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:36:42.352Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:36:42.352Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13455.910 ms) ======
[2025-02-26T21:36:42.352Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-26T21:36:42.352Z] GC before operation: completed in 67.883 ms, heap usage 404.543 MB -> 54.450 MB.
[2025-02-26T21:36:44.312Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:36:46.392Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:36:48.350Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:36:50.311Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:36:51.266Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:36:52.228Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:36:54.058Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:36:55.369Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:36:55.369Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:36:55.369Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:36:55.369Z] Movies recommended for you:
[2025-02-26T21:36:55.369Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:36:55.369Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:36:55.369Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12656.112 ms) ======
[2025-02-26T21:36:55.369Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-26T21:36:55.369Z] GC before operation: completed in 66.499 ms, heap usage 343.846 MB -> 51.277 MB.
[2025-02-26T21:36:57.333Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:36:59.291Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:37:01.256Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:37:03.215Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:37:04.169Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:37:05.124Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:37:06.078Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:37:08.042Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:37:08.042Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:37:08.042Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:37:08.042Z] Movies recommended for you:
[2025-02-26T21:37:08.042Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:37:08.042Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:37:08.042Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12936.339 ms) ======
[2025-02-26T21:37:08.042Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-26T21:37:08.042Z] GC before operation: completed in 66.773 ms, heap usage 220.802 MB -> 51.199 MB.
[2025-02-26T21:37:10.003Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:37:11.961Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:37:14.985Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:37:15.942Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:37:17.900Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:37:18.857Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:37:19.812Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:37:20.767Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:37:21.728Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:37:21.728Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:37:21.728Z] Movies recommended for you:
[2025-02-26T21:37:21.728Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:37:21.728Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:37:21.728Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13400.377 ms) ======
[2025-02-26T21:37:21.728Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-26T21:37:21.728Z] GC before operation: completed in 61.040 ms, heap usage 402.377 MB -> 53.455 MB.
[2025-02-26T21:37:23.691Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:37:25.650Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:37:27.612Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:37:29.572Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:37:30.526Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:37:31.481Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:37:33.441Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:37:34.394Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:37:34.394Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:37:34.394Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:37:34.394Z] Movies recommended for you:
[2025-02-26T21:37:34.394Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:37:34.394Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:37:34.394Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12785.808 ms) ======
[2025-02-26T21:37:34.394Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-26T21:37:34.394Z] GC before operation: completed in 63.823 ms, heap usage 575.581 MB -> 56.940 MB.
[2025-02-26T21:37:36.352Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:37:38.309Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:37:40.275Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:37:41.232Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:37:43.190Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:37:44.145Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:37:45.098Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:37:47.060Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:37:47.060Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:37:47.060Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:37:47.060Z] Movies recommended for you:
[2025-02-26T21:37:47.061Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:37:47.061Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:37:47.061Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12709.545 ms) ======
[2025-02-26T21:37:47.061Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-26T21:37:47.061Z] GC before operation: completed in 65.026 ms, heap usage 215.028 MB -> 54.511 MB.
[2025-02-26T21:37:49.021Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:37:51.196Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:37:53.156Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:37:55.118Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:37:56.073Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:37:57.028Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:37:57.983Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:37:59.944Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:37:59.944Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-26T21:37:59.944Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:37:59.945Z] Movies recommended for you:
[2025-02-26T21:37:59.945Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:37:59.945Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:37:59.945Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12711.795 ms) ======
[2025-02-26T21:38:00.902Z] -----------------------------------
[2025-02-26T21:38:00.903Z] renaissance-movie-lens_0_PASSED
[2025-02-26T21:38:00.903Z] -----------------------------------
[2025-02-26T21:38:00.903Z]
[2025-02-26T21:38:00.903Z] TEST TEARDOWN:
[2025-02-26T21:38:00.903Z] Nothing to be done for teardown.
[2025-02-26T21:38:00.903Z] renaissance-movie-lens_0 Finish Time: Wed Feb 26 21:37:59 2025 Epoch Time (ms): 1740605879891