renaissance-movie-lens_0
[2024-10-15T23:10:40.653Z] Running test renaissance-movie-lens_0 ...
[2024-10-15T23:10:40.653Z] ===============================================
[2024-10-15T23:10:40.653Z] renaissance-movie-lens_0 Start Time: Tue Oct 15 18:10:39 2024 Epoch Time (ms): 1729033839934
[2024-10-15T23:10:40.653Z] variation: NoOptions
[2024-10-15T23:10:40.653Z] JVM_OPTIONS:
[2024-10-15T23:10:40.654Z] { \
[2024-10-15T23:10:40.654Z] echo ""; echo "TEST SETUP:"; \
[2024-10-15T23:10:40.654Z] echo "Nothing to be done for setup."; \
[2024-10-15T23:10:40.654Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17290329119781/renaissance-movie-lens_0"; \
[2024-10-15T23:10:40.654Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17290329119781/renaissance-movie-lens_0"; \
[2024-10-15T23:10:40.654Z] echo ""; echo "TESTING:"; \
[2024-10-15T23:10:40.654Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-11.0.25+9/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17290329119781/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-15T23:10:40.654Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17290329119781/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-15T23:10:40.654Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-15T23:10:40.654Z] echo "Nothing to be done for teardown."; \
[2024-10-15T23:10:40.654Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17290329119781/TestTargetResult";
[2024-10-15T23:10:40.654Z]
[2024-10-15T23:10:40.654Z] TEST SETUP:
[2024-10-15T23:10:40.654Z] Nothing to be done for setup.
[2024-10-15T23:10:40.654Z]
[2024-10-15T23:10:40.654Z] TESTING:
[2024-10-15T23:10:43.708Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-15T23:10:45.867Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-10-15T23:10:48.956Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-15T23:10:48.956Z] Training: 60056, validation: 20285, test: 19854
[2024-10-15T23:10:48.956Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-15T23:10:49.753Z] GC before operation: completed in 61.637 ms, heap usage 58.840 MB -> 37.157 MB.
[2024-10-15T23:10:57.385Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:11:01.405Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:11:05.428Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:11:08.546Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:11:10.772Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:11:12.224Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:11:14.435Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:11:16.650Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:11:16.650Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:11:16.650Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:11:17.332Z] Movies recommended for you:
[2024-10-15T23:11:17.332Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:11:17.332Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:11:17.332Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (27695.299 ms) ======
[2024-10-15T23:11:17.332Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-15T23:11:17.332Z] GC before operation: completed in 139.004 ms, heap usage 514.483 MB -> 53.433 MB.
[2024-10-15T23:11:20.411Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:11:23.518Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:11:26.600Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:11:29.683Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:11:31.108Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:11:33.359Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:11:34.770Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:11:36.994Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:11:37.681Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:11:37.681Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:11:37.681Z] Movies recommended for you:
[2024-10-15T23:11:37.681Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:11:37.681Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:11:37.681Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20383.257 ms) ======
[2024-10-15T23:11:37.681Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-15T23:11:37.681Z] GC before operation: completed in 124.791 ms, heap usage 75.841 MB -> 54.303 MB.
[2024-10-15T23:11:40.772Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:11:43.838Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:11:46.904Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:11:49.971Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:11:51.380Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:11:52.809Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:11:55.011Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:11:56.464Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:11:57.160Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:11:57.161Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:11:57.161Z] Movies recommended for you:
[2024-10-15T23:11:57.161Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:11:57.161Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:11:57.161Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (19312.566 ms) ======
[2024-10-15T23:11:57.161Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-15T23:11:57.161Z] GC before operation: completed in 119.762 ms, heap usage 383.452 MB -> 54.771 MB.
[2024-10-15T23:12:00.243Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:12:02.490Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:12:05.637Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:12:07.849Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:12:10.036Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:12:11.479Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:12:13.676Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:12:15.097Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:12:15.773Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:12:15.773Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:12:15.773Z] Movies recommended for you:
[2024-10-15T23:12:15.773Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:12:15.773Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:12:15.773Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18591.244 ms) ======
[2024-10-15T23:12:15.773Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-15T23:12:15.773Z] GC before operation: completed in 114.019 ms, heap usage 104.354 MB -> 54.321 MB.
[2024-10-15T23:12:18.862Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:12:21.118Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:12:24.180Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:12:26.399Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:12:28.614Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:12:30.064Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:12:31.505Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:12:32.938Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:12:33.626Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:12:33.626Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:12:33.626Z] Movies recommended for you:
[2024-10-15T23:12:33.626Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:12:33.626Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:12:33.626Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17842.528 ms) ======
[2024-10-15T23:12:33.626Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-15T23:12:33.626Z] GC before operation: completed in 125.719 ms, heap usage 99.403 MB -> 53.875 MB.
[2024-10-15T23:12:36.681Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:12:39.770Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:12:42.020Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:12:45.174Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:12:46.580Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:12:47.992Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:12:49.421Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:12:51.636Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:12:51.636Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:12:51.636Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:12:51.636Z] Movies recommended for you:
[2024-10-15T23:12:51.636Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:12:51.636Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:12:51.636Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (17860.269 ms) ======
[2024-10-15T23:12:51.636Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-15T23:12:51.636Z] GC before operation: completed in 131.883 ms, heap usage 480.073 MB -> 55.261 MB.
[2024-10-15T23:12:54.697Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:12:56.952Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:13:00.029Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:13:02.257Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:13:03.867Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:13:05.282Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:13:07.005Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:13:08.449Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:13:08.449Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:13:08.449Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:13:09.154Z] Movies recommended for you:
[2024-10-15T23:13:09.154Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:13:09.154Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:13:09.154Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17121.496 ms) ======
[2024-10-15T23:13:09.154Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-15T23:13:09.154Z] GC before operation: completed in 128.691 ms, heap usage 297.423 MB -> 52.011 MB.
[2024-10-15T23:13:12.232Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:13:14.445Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:13:16.658Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:13:19.761Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:13:21.170Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:13:23.270Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:13:23.957Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:13:26.177Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:13:26.177Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:13:26.177Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:13:26.177Z] Movies recommended for you:
[2024-10-15T23:13:26.177Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:13:26.177Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:13:26.177Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17367.127 ms) ======
[2024-10-15T23:13:26.177Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-15T23:13:26.177Z] GC before operation: completed in 128.546 ms, heap usage 307.482 MB -> 52.346 MB.
[2024-10-15T23:13:29.262Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:13:32.348Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:13:34.563Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:13:38.605Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:13:40.019Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:13:41.514Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:13:42.917Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:13:44.319Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:13:45.020Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:13:45.020Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:13:45.020Z] Movies recommended for you:
[2024-10-15T23:13:45.020Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:13:45.020Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:13:45.020Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (18343.650 ms) ======
[2024-10-15T23:13:45.020Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-15T23:13:45.020Z] GC before operation: completed in 132.282 ms, heap usage 285.047 MB -> 52.152 MB.
[2024-10-15T23:13:48.084Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:13:50.302Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:13:53.369Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:13:55.641Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:13:57.062Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:13:59.280Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:14:00.688Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:14:02.107Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:14:02.107Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:14:02.107Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:14:02.787Z] Movies recommended for you:
[2024-10-15T23:14:02.787Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:14:02.787Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:14:02.787Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17572.220 ms) ======
[2024-10-15T23:14:02.787Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-15T23:14:02.787Z] GC before operation: completed in 130.938 ms, heap usage 128.626 MB -> 52.104 MB.
[2024-10-15T23:14:05.870Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:14:08.099Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:14:11.183Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:14:13.383Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:14:14.818Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:14:17.033Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:14:18.448Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:14:19.860Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:14:19.860Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:14:19.860Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:14:19.860Z] Movies recommended for you:
[2024-10-15T23:14:19.860Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:14:19.860Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:14:19.861Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17466.254 ms) ======
[2024-10-15T23:14:19.861Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-15T23:14:20.544Z] GC before operation: completed in 131.650 ms, heap usage 410.238 MB -> 52.012 MB.
[2024-10-15T23:14:22.764Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:14:25.852Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:14:28.063Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:14:31.140Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:14:32.554Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:14:33.965Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:14:35.391Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:14:37.590Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:14:37.590Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:14:37.590Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:14:37.590Z] Movies recommended for you:
[2024-10-15T23:14:37.590Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:14:37.590Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:14:37.590Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17351.184 ms) ======
[2024-10-15T23:14:37.590Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-15T23:14:37.590Z] GC before operation: completed in 128.139 ms, heap usage 424.443 MB -> 52.262 MB.
[2024-10-15T23:14:40.534Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:14:43.598Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:14:46.647Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:14:48.877Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:14:50.315Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:14:52.528Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:14:54.023Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:14:55.448Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:14:55.448Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:14:55.448Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:14:55.448Z] Movies recommended for you:
[2024-10-15T23:14:55.448Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:14:55.448Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:14:55.448Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (18000.255 ms) ======
[2024-10-15T23:14:55.448Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-15T23:14:56.134Z] GC before operation: completed in 129.706 ms, heap usage 151.853 MB -> 52.230 MB.
[2024-10-15T23:14:58.369Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:15:01.453Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:15:04.545Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:15:06.747Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:15:08.178Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:15:10.390Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:15:11.800Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:15:13.216Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:15:13.216Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:15:13.216Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:15:13.892Z] Movies recommended for you:
[2024-10-15T23:15:13.892Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:15:13.892Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:15:13.892Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17755.483 ms) ======
[2024-10-15T23:15:13.892Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-15T23:15:13.892Z] GC before operation: completed in 129.312 ms, heap usage 466.358 MB -> 52.191 MB.
[2024-10-15T23:15:17.054Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:15:19.289Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:15:21.495Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:15:24.572Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:15:25.984Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:15:27.413Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:15:29.621Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:15:31.029Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:15:31.029Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:15:31.029Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:15:31.029Z] Movies recommended for you:
[2024-10-15T23:15:31.029Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:15:31.029Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:15:31.029Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (17422.496 ms) ======
[2024-10-15T23:15:31.029Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-15T23:15:31.029Z] GC before operation: completed in 127.803 ms, heap usage 144.917 MB -> 52.116 MB.
[2024-10-15T23:15:34.107Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:15:37.195Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:15:39.493Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:15:41.708Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:15:43.898Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:15:45.316Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:15:47.539Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:15:48.948Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:15:48.948Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:15:48.948Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:15:48.948Z] Movies recommended for you:
[2024-10-15T23:15:48.948Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:15:48.948Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:15:48.948Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17948.844 ms) ======
[2024-10-15T23:15:48.948Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-15T23:15:49.629Z] GC before operation: completed in 121.614 ms, heap usage 98.835 MB -> 53.502 MB.
[2024-10-15T23:15:51.847Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:15:54.923Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:15:57.724Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:15:59.480Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:16:01.689Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:16:03.118Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:16:04.529Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:16:05.951Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:16:05.951Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:16:05.951Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:16:06.632Z] Movies recommended for you:
[2024-10-15T23:16:06.632Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:16:06.632Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:16:06.632Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17008.129 ms) ======
[2024-10-15T23:16:06.632Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-15T23:16:06.632Z] GC before operation: completed in 124.348 ms, heap usage 479.082 MB -> 55.595 MB.
[2024-10-15T23:16:08.828Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:16:11.893Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:16:14.131Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:16:17.221Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:16:18.668Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:16:20.125Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:16:22.336Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:16:23.786Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:16:23.786Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:16:23.786Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:16:23.786Z] Movies recommended for you:
[2024-10-15T23:16:23.786Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:16:23.786Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:16:23.786Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (17482.900 ms) ======
[2024-10-15T23:16:23.786Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-15T23:16:24.464Z] GC before operation: completed in 117.215 ms, heap usage 180.011 MB -> 52.141 MB.
[2024-10-15T23:16:26.657Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:16:29.733Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:16:32.807Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:16:35.063Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:16:37.307Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:16:38.737Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:16:40.161Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:16:41.577Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:16:41.577Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:16:41.577Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:16:41.577Z] Movies recommended for you:
[2024-10-15T23:16:41.577Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:16:41.577Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:16:41.577Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (17671.856 ms) ======
[2024-10-15T23:16:41.577Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-15T23:16:42.256Z] GC before operation: completed in 115.297 ms, heap usage 478.557 MB -> 55.808 MB.
[2024-10-15T23:16:44.490Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:16:47.565Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:16:50.622Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:16:52.823Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:16:54.242Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:16:55.666Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:16:57.882Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:16:59.325Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:16:59.325Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-15T23:16:59.325Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:16:59.325Z] Movies recommended for you:
[2024-10-15T23:16:59.325Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:16:59.325Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:16:59.325Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (17695.277 ms) ======
[2024-10-15T23:17:00.760Z] -----------------------------------
[2024-10-15T23:17:00.760Z] renaissance-movie-lens_0_PASSED
[2024-10-15T23:17:00.760Z] -----------------------------------
[2024-10-15T23:17:00.760Z]
[2024-10-15T23:17:00.760Z] TEST TEARDOWN:
[2024-10-15T23:17:00.760Z] Nothing to be done for teardown.
[2024-10-15T23:17:00.760Z] renaissance-movie-lens_0 Finish Time: Tue Oct 15 18:17:00 2024 Epoch Time (ms): 1729034220363