renaissance-movie-lens_0
[2025-02-05T22:13:03.251Z] Running test renaissance-movie-lens_0 ...
[2025-02-05T22:13:03.251Z] ===============================================
[2025-02-05T22:13:03.251Z] renaissance-movie-lens_0 Start Time: Wed Feb 5 22:13:02 2025 Epoch Time (ms): 1738793582836
[2025-02-05T22:13:03.251Z] variation: NoOptions
[2025-02-05T22:13:03.251Z] JVM_OPTIONS:
[2025-02-05T22:13:03.251Z] { \
[2025-02-05T22:13:03.251Z] echo ""; echo "TEST SETUP:"; \
[2025-02-05T22:13:03.251Z] echo "Nothing to be done for setup."; \
[2025-02-05T22:13:03.251Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17387927367138/renaissance-movie-lens_0"; \
[2025-02-05T22:13:03.251Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17387927367138/renaissance-movie-lens_0"; \
[2025-02-05T22:13:03.251Z] echo ""; echo "TESTING:"; \
[2025-02-05T22:13:03.252Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17387927367138/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-05T22:13:03.252Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17387927367138/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-05T22:13:03.252Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-05T22:13:03.252Z] echo "Nothing to be done for teardown."; \
[2025-02-05T22:13:03.252Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17387927367138/TestTargetResult";
[2025-02-05T22:13:03.252Z]
[2025-02-05T22:13:03.252Z] TEST SETUP:
[2025-02-05T22:13:03.252Z] Nothing to be done for setup.
[2025-02-05T22:13:03.252Z]
[2025-02-05T22:13:03.252Z] TESTING:
[2025-02-05T22:13:07.730Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-05T22:13:09.335Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2025-02-05T22:13:12.777Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-05T22:13:12.777Z] Training: 60056, validation: 20285, test: 19854
[2025-02-05T22:13:12.777Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-05T22:13:12.777Z] GC before operation: completed in 55.309 ms, heap usage 191.814 MB -> 37.404 MB.
[2025-02-05T22:13:17.274Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:13:20.716Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:13:23.219Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:13:26.686Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:13:27.461Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:13:29.063Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:13:31.589Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:13:33.193Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:13:33.193Z] 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.
[2025-02-05T22:13:33.193Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:13:33.193Z] Movies recommended for you:
[2025-02-05T22:13:33.193Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:13:33.193Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:13:33.193Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20458.299 ms) ======
[2025-02-05T22:13:33.193Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-05T22:13:33.193Z] GC before operation: completed in 92.181 ms, heap usage 2.962 GB -> 55.727 MB.
[2025-02-05T22:13:36.647Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:13:39.143Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:13:41.637Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:13:44.129Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:13:45.731Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:13:47.614Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:13:49.215Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:13:50.814Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:13:50.814Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-05T22:13:51.594Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:13:51.594Z] Movies recommended for you:
[2025-02-05T22:13:51.594Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:13:51.594Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:13:51.594Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17899.019 ms) ======
[2025-02-05T22:13:51.594Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-05T22:13:51.594Z] GC before operation: completed in 78.236 ms, heap usage 184.420 MB -> 57.105 MB.
[2025-02-05T22:13:54.078Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:13:56.571Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:13:59.236Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:14:01.726Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:14:03.332Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:14:04.939Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:14:06.543Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:14:08.149Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:14:08.149Z] 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.
[2025-02-05T22:14:08.149Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:14:08.149Z] Movies recommended for you:
[2025-02-05T22:14:08.149Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:14:08.149Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:14:08.149Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17010.495 ms) ======
[2025-02-05T22:14:08.149Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-05T22:14:08.149Z] GC before operation: completed in 68.961 ms, heap usage 176.894 MB -> 51.567 MB.
[2025-02-05T22:14:11.593Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:14:14.090Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:14:16.589Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:14:19.087Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:14:20.693Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:14:21.481Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:14:23.974Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:14:24.750Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:14:25.528Z] 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.
[2025-02-05T22:14:25.528Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:14:25.528Z] Movies recommended for you:
[2025-02-05T22:14:25.528Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:14:25.528Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:14:25.528Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16988.453 ms) ======
[2025-02-05T22:14:25.528Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-05T22:14:25.528Z] GC before operation: completed in 91.986 ms, heap usage 1.349 GB -> 56.565 MB.
[2025-02-05T22:14:28.015Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:14:30.508Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:14:32.997Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:14:35.494Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:14:37.120Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:14:38.746Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:14:40.344Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:14:41.947Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:14:42.726Z] 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.
[2025-02-05T22:14:42.726Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:14:42.726Z] Movies recommended for you:
[2025-02-05T22:14:42.726Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:14:42.726Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:14:42.726Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16892.561 ms) ======
[2025-02-05T22:14:42.726Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-05T22:14:42.726Z] GC before operation: completed in 85.623 ms, heap usage 3.507 GB -> 61.466 MB.
[2025-02-05T22:14:45.214Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:14:47.721Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:14:50.225Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:14:52.712Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:14:54.318Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:14:55.934Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:14:57.539Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:14:58.332Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:14:59.113Z] 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.
[2025-02-05T22:14:59.113Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:14:59.113Z] Movies recommended for you:
[2025-02-05T22:14:59.113Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:14:59.113Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:14:59.113Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16491.616 ms) ======
[2025-02-05T22:14:59.113Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-05T22:14:59.113Z] GC before operation: completed in 73.651 ms, heap usage 2.325 GB -> 56.971 MB.
[2025-02-05T22:15:01.615Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:15:04.108Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:15:06.595Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:15:09.082Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:15:10.681Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:15:12.287Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:15:13.899Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:15:15.515Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:15:15.515Z] 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.
[2025-02-05T22:15:15.515Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:15:15.515Z] Movies recommended for you:
[2025-02-05T22:15:15.515Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:15:15.515Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:15:15.515Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16545.473 ms) ======
[2025-02-05T22:15:15.515Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-05T22:15:15.515Z] GC before operation: completed in 83.155 ms, heap usage 841.724 MB -> 55.851 MB.
[2025-02-05T22:15:18.016Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:15:20.506Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:15:23.973Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:15:26.463Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:15:27.242Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:15:28.847Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:15:30.445Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:15:32.049Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:15:32.839Z] 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.
[2025-02-05T22:15:32.839Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:15:32.839Z] Movies recommended for you:
[2025-02-05T22:15:32.839Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:15:32.839Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:15:32.839Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16822.809 ms) ======
[2025-02-05T22:15:32.839Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-05T22:15:32.839Z] GC before operation: completed in 86.373 ms, heap usage 1.732 GB -> 57.241 MB.
[2025-02-05T22:15:35.343Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:15:37.832Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:15:40.326Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:15:42.823Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:15:44.485Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:15:46.087Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:15:47.694Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:15:48.469Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:15:49.243Z] 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.
[2025-02-05T22:15:49.243Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:15:49.243Z] Movies recommended for you:
[2025-02-05T22:15:49.243Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:15:49.243Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:15:49.243Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16499.758 ms) ======
[2025-02-05T22:15:49.243Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-05T22:15:49.243Z] GC before operation: completed in 75.329 ms, heap usage 1.418 GB -> 57.012 MB.
[2025-02-05T22:15:51.728Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:15:54.224Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:15:56.720Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:15:59.223Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:16:00.836Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:16:02.458Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:16:04.080Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:16:05.692Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:16:05.692Z] 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.
[2025-02-05T22:16:05.692Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:16:05.692Z] Movies recommended for you:
[2025-02-05T22:16:05.692Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:16:05.692Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:16:05.692Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16442.041 ms) ======
[2025-02-05T22:16:05.692Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-05T22:16:05.692Z] GC before operation: completed in 81.378 ms, heap usage 93.004 MB -> 55.747 MB.
[2025-02-05T22:16:08.180Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:16:10.670Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:16:13.154Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:16:15.665Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:16:17.264Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:16:18.879Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:16:20.502Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:16:22.103Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:16:22.103Z] 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.
[2025-02-05T22:16:22.103Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:16:22.103Z] Movies recommended for you:
[2025-02-05T22:16:22.103Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:16:22.103Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:16:22.103Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16471.653 ms) ======
[2025-02-05T22:16:22.103Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-05T22:16:22.103Z] GC before operation: completed in 73.932 ms, heap usage 238.400 MB -> 52.134 MB.
[2025-02-05T22:16:24.592Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:16:27.213Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:16:29.695Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:16:32.185Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:16:33.785Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:16:35.390Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:16:37.001Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:16:38.615Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:16:38.615Z] 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.
[2025-02-05T22:16:38.615Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:16:38.615Z] Movies recommended for you:
[2025-02-05T22:16:38.615Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:16:38.615Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:16:38.615Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16465.663 ms) ======
[2025-02-05T22:16:38.615Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-05T22:16:38.615Z] GC before operation: completed in 80.796 ms, heap usage 356.689 MB -> 52.449 MB.
[2025-02-05T22:16:41.116Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:16:43.619Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:16:46.109Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:16:48.592Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:16:50.196Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:16:51.803Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:16:53.405Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:16:55.034Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:16:55.034Z] 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.
[2025-02-05T22:16:55.034Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:16:55.034Z] Movies recommended for you:
[2025-02-05T22:16:55.034Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:16:55.034Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:16:55.034Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16351.031 ms) ======
[2025-02-05T22:16:55.034Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-05T22:16:55.034Z] GC before operation: completed in 75.983 ms, heap usage 104.099 MB -> 56.981 MB.
[2025-02-05T22:16:57.547Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:17:00.033Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:17:02.567Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:17:05.057Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:17:06.658Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:17:08.269Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:17:09.880Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:17:11.486Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:17:11.486Z] 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.
[2025-02-05T22:17:11.486Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:17:11.486Z] Movies recommended for you:
[2025-02-05T22:17:11.486Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:17:11.486Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:17:11.486Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16380.237 ms) ======
[2025-02-05T22:17:11.486Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-05T22:17:11.486Z] GC before operation: completed in 76.832 ms, heap usage 108.867 MB -> 55.975 MB.
[2025-02-05T22:17:13.983Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:17:16.477Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:17:19.177Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:17:21.665Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:17:23.278Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:17:24.957Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:17:26.563Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:17:28.176Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:17:28.176Z] 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.
[2025-02-05T22:17:28.176Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:17:28.176Z] Movies recommended for you:
[2025-02-05T22:17:28.176Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:17:28.176Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:17:28.176Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16404.438 ms) ======
[2025-02-05T22:17:28.176Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-05T22:17:28.176Z] GC before operation: completed in 78.876 ms, heap usage 166.416 MB -> 52.403 MB.
[2025-02-05T22:17:30.676Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:17:33.188Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:17:35.673Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:17:38.165Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:17:39.763Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:17:41.364Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:17:42.976Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:17:44.574Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:17:44.574Z] 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.
[2025-02-05T22:17:44.574Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:17:44.574Z] Movies recommended for you:
[2025-02-05T22:17:44.574Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:17:44.574Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:17:44.574Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16664.506 ms) ======
[2025-02-05T22:17:44.574Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-05T22:17:45.354Z] GC before operation: completed in 87.741 ms, heap usage 2.291 GB -> 57.453 MB.
[2025-02-05T22:17:47.846Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:17:50.332Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:17:52.815Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:17:55.306Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:17:56.910Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:17:57.685Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:17:59.292Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:18:00.902Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:18:01.675Z] 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.
[2025-02-05T22:18:01.675Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:18:01.675Z] Movies recommended for you:
[2025-02-05T22:18:01.675Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:18:01.675Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:18:01.675Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16441.552 ms) ======
[2025-02-05T22:18:01.675Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-05T22:18:01.675Z] GC before operation: completed in 88.700 ms, heap usage 2.156 GB -> 57.274 MB.
[2025-02-05T22:18:04.165Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:18:06.660Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:18:09.174Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:18:11.668Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:18:13.286Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:18:14.061Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:18:15.679Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:18:17.291Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:18:18.068Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-05T22:18:18.068Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:18:18.068Z] Movies recommended for you:
[2025-02-05T22:18:18.068Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:18:18.068Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:18:18.068Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16305.283 ms) ======
[2025-02-05T22:18:18.068Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-05T22:18:18.068Z] GC before operation: completed in 74.483 ms, heap usage 2.432 GB -> 57.426 MB.
[2025-02-05T22:18:20.577Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:18:23.063Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:18:25.566Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:18:28.066Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:18:29.675Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:18:30.449Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:18:32.053Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:18:33.669Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:18:34.446Z] 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.
[2025-02-05T22:18:34.446Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:18:34.446Z] Movies recommended for you:
[2025-02-05T22:18:34.446Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:18:34.446Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:18:34.446Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16355.758 ms) ======
[2025-02-05T22:18:34.446Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-05T22:18:34.446Z] GC before operation: completed in 82.862 ms, heap usage 439.287 MB -> 52.745 MB.
[2025-02-05T22:18:36.933Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:18:39.415Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:18:41.915Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:18:44.401Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:18:46.000Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:18:46.774Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:18:48.379Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:18:49.977Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:18:50.754Z] 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.
[2025-02-05T22:18:50.754Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:18:50.754Z] Movies recommended for you:
[2025-02-05T22:18:50.754Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:18:50.754Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:18:50.754Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16221.262 ms) ======
[2025-02-05T22:18:51.529Z] -----------------------------------
[2025-02-05T22:18:51.529Z] renaissance-movie-lens_0_PASSED
[2025-02-05T22:18:51.529Z] -----------------------------------
[2025-02-05T22:18:51.529Z]
[2025-02-05T22:18:51.529Z] TEST TEARDOWN:
[2025-02-05T22:18:51.529Z] Nothing to be done for teardown.
[2025-02-05T22:18:51.529Z] renaissance-movie-lens_0 Finish Time: Wed Feb 5 22:18:50 2025 Epoch Time (ms): 1738793930710