renaissance-movie-lens_0
[2024-09-25T20:51:17.715Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T20:51:17.715Z] ===============================================
[2024-09-25T20:51:17.715Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 20:51:16 2024 Epoch Time (ms): 1727297476812
[2024-09-25T20:51:17.715Z] variation: NoOptions
[2024-09-25T20:51:17.715Z] JVM_OPTIONS:
[2024-09-25T20:51:17.715Z] { \
[2024-09-25T20:51:17.715Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T20:51:17.715Z] echo "Nothing to be done for setup."; \
[2024-09-25T20:51:17.715Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17272966738568/renaissance-movie-lens_0"; \
[2024-09-25T20:51:17.715Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17272966738568/renaissance-movie-lens_0"; \
[2024-09-25T20:51:17.715Z] echo ""; echo "TESTING:"; \
[2024-09-25T20:51:17.715Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_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_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17272966738568/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T20:51:17.715Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17272966738568/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T20:51:17.715Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T20:51:17.715Z] echo "Nothing to be done for teardown."; \
[2024-09-25T20:51:17.715Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17272966738568/TestTargetResult";
[2024-09-25T20:51:17.715Z]
[2024-09-25T20:51:17.715Z] TEST SETUP:
[2024-09-25T20:51:17.715Z] Nothing to be done for setup.
[2024-09-25T20:51:17.715Z]
[2024-09-25T20:51:17.715Z] TESTING:
[2024-09-25T20:51:19.646Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T20:51:22.643Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-09-25T20:51:24.612Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T20:51:26.349Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T20:51:26.349Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T20:51:26.349Z] GC before operation: completed in 35.470 ms, heap usage 66.046 MB -> 39.374 MB.
[2024-09-25T20:51:30.542Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:51:33.525Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:51:36.514Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:51:38.444Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:51:40.375Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:51:42.451Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:51:43.391Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:51:44.342Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:51:44.342Z] 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.
[2024-09-25T20:51:45.288Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:51:45.288Z] Movies recommended for you:
[2024-09-25T20:51:45.288Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:51:45.288Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:51:45.288Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (19802.543 ms) ======
[2024-09-25T20:51:45.288Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T20:51:45.288Z] GC before operation: completed in 77.738 ms, heap usage 542.647 MB -> 62.143 MB.
[2024-09-25T20:51:47.268Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:51:49.208Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:51:51.138Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:51:54.121Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:51:55.061Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:51:56.001Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:51:56.944Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:51:58.884Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:51:58.884Z] 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.
[2024-09-25T20:51:58.884Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:51:58.884Z] Movies recommended for you:
[2024-09-25T20:51:58.884Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:51:58.884Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:51:58.884Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (13789.082 ms) ======
[2024-09-25T20:51:58.884Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T20:51:58.884Z] GC before operation: completed in 70.942 ms, heap usage 533.502 MB -> 60.209 MB.
[2024-09-25T20:52:00.815Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:52:02.748Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:52:05.740Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:52:07.674Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:52:08.615Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:52:09.554Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:52:11.485Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:52:12.430Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:52:12.430Z] 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.
[2024-09-25T20:52:12.430Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:52:12.430Z] Movies recommended for you:
[2024-09-25T20:52:12.430Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:52:12.430Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:52:12.430Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (13901.908 ms) ======
[2024-09-25T20:52:12.430Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T20:52:12.430Z] GC before operation: completed in 71.061 ms, heap usage 548.608 MB -> 57.046 MB.
[2024-09-25T20:52:15.458Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:52:17.419Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:52:19.351Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:52:21.297Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:52:22.237Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:52:23.179Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:52:24.123Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:52:25.065Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:52:26.005Z] 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.
[2024-09-25T20:52:26.005Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:52:26.005Z] Movies recommended for you:
[2024-09-25T20:52:26.005Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:52:26.005Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:52:26.005Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (12893.104 ms) ======
[2024-09-25T20:52:26.005Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T20:52:26.005Z] GC before operation: completed in 76.379 ms, heap usage 255.816 MB -> 60.699 MB.
[2024-09-25T20:52:27.938Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:52:29.869Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:52:31.799Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:52:33.732Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:52:34.672Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:52:35.613Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:52:36.555Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:52:38.485Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:52:38.485Z] 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.
[2024-09-25T20:52:38.485Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:52:38.485Z] Movies recommended for you:
[2024-09-25T20:52:38.485Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:52:38.485Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:52:38.485Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (12512.738 ms) ======
[2024-09-25T20:52:38.485Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T20:52:38.485Z] GC before operation: completed in 71.840 ms, heap usage 200.998 MB -> 55.258 MB.
[2024-09-25T20:52:40.411Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:52:42.339Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:52:44.269Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:52:46.235Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:52:47.175Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:52:48.114Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:52:49.079Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:52:50.019Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:52:50.019Z] 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.
[2024-09-25T20:52:50.019Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:52:50.019Z] Movies recommended for you:
[2024-09-25T20:52:50.019Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:52:50.019Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:52:50.019Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (11659.515 ms) ======
[2024-09-25T20:52:50.019Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T20:52:50.019Z] GC before operation: completed in 74.847 ms, heap usage 374.996 MB -> 55.879 MB.
[2024-09-25T20:52:51.952Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:52:53.887Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:52:55.990Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:52:57.919Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:52:58.859Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:52:59.804Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:53:00.747Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:53:01.687Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:53:02.627Z] 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.
[2024-09-25T20:53:02.627Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:53:02.627Z] Movies recommended for you:
[2024-09-25T20:53:02.627Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:53:02.627Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:53:02.627Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (12075.216 ms) ======
[2024-09-25T20:53:02.627Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T20:53:02.627Z] GC before operation: completed in 82.653 ms, heap usage 1.441 GB -> 60.829 MB.
[2024-09-25T20:53:04.562Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:53:05.509Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:53:07.438Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:53:09.369Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:53:10.310Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:53:11.253Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:53:12.262Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:53:13.202Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:53:13.202Z] 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.
[2024-09-25T20:53:13.202Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:53:14.142Z] Movies recommended for you:
[2024-09-25T20:53:14.142Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:53:14.142Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:53:14.142Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (11400.710 ms) ======
[2024-09-25T20:53:14.142Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T20:53:14.142Z] GC before operation: completed in 72.389 ms, heap usage 164.337 MB -> 58.209 MB.
[2024-09-25T20:53:16.074Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:53:17.013Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:53:18.944Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:53:20.875Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:53:21.818Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:53:22.757Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:53:23.710Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:53:24.648Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:53:24.648Z] 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.
[2024-09-25T20:53:24.648Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:53:24.648Z] Movies recommended for you:
[2024-09-25T20:53:24.648Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:53:24.648Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:53:24.648Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (11092.022 ms) ======
[2024-09-25T20:53:24.648Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T20:53:24.648Z] GC before operation: completed in 82.561 ms, heap usage 1.217 GB -> 58.928 MB.
[2024-09-25T20:53:26.610Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:53:28.551Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:53:30.478Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:53:32.411Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:53:33.363Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:53:34.302Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:53:35.240Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:53:36.179Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:53:36.179Z] 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.
[2024-09-25T20:53:36.179Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:53:36.179Z] Movies recommended for you:
[2024-09-25T20:53:36.179Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:53:36.179Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:53:36.179Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (11288.542 ms) ======
[2024-09-25T20:53:36.179Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T20:53:36.179Z] GC before operation: completed in 82.762 ms, heap usage 1.278 GB -> 60.914 MB.
[2024-09-25T20:53:38.105Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:53:40.033Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:53:42.042Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:53:42.983Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:53:43.924Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:53:44.879Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:53:45.818Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:53:47.748Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:53:47.748Z] 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.
[2024-09-25T20:53:47.749Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:53:47.749Z] Movies recommended for you:
[2024-09-25T20:53:47.749Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:53:47.749Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:53:47.749Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (11148.073 ms) ======
[2024-09-25T20:53:47.749Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T20:53:47.749Z] GC before operation: completed in 77.890 ms, heap usage 207.469 MB -> 56.090 MB.
[2024-09-25T20:53:49.704Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:53:51.632Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:53:53.561Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:53:55.497Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:53:56.435Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:53:58.362Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:53:59.302Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:54:00.241Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:54:00.241Z] 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.
[2024-09-25T20:54:00.241Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:54:00.241Z] Movies recommended for you:
[2024-09-25T20:54:00.241Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:54:00.241Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:54:00.241Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12496.063 ms) ======
[2024-09-25T20:54:00.241Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T20:54:00.241Z] GC before operation: completed in 84.349 ms, heap usage 951.168 MB -> 62.401 MB.
[2024-09-25T20:54:02.168Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:54:04.099Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:54:06.035Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:54:07.963Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:54:08.903Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:54:09.841Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:54:10.780Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:54:11.719Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:54:11.719Z] 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.
[2024-09-25T20:54:11.719Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:54:11.719Z] Movies recommended for you:
[2024-09-25T20:54:11.719Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:54:11.719Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:54:11.719Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (11735.402 ms) ======
[2024-09-25T20:54:11.719Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T20:54:11.719Z] GC before operation: completed in 83.153 ms, heap usage 1.799 GB -> 64.227 MB.
[2024-09-25T20:54:13.757Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:54:15.683Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:54:17.626Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:54:19.553Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:54:20.503Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:54:21.443Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:54:22.389Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:54:23.331Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:54:23.331Z] 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.
[2024-09-25T20:54:23.331Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:54:23.331Z] Movies recommended for you:
[2024-09-25T20:54:23.331Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:54:23.331Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:54:23.331Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (11434.020 ms) ======
[2024-09-25T20:54:23.331Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T20:54:23.331Z] GC before operation: completed in 85.938 ms, heap usage 2.038 GB -> 61.684 MB.
[2024-09-25T20:54:25.293Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:54:27.269Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:54:29.288Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:54:30.235Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:54:31.177Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:54:33.114Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:54:34.054Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:54:34.995Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:54:34.995Z] 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.
[2024-09-25T20:54:34.995Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:54:34.995Z] Movies recommended for you:
[2024-09-25T20:54:34.995Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:54:34.995Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:54:34.995Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (11367.422 ms) ======
[2024-09-25T20:54:34.995Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T20:54:34.995Z] GC before operation: completed in 79.742 ms, heap usage 2.118 GB -> 60.649 MB.
[2024-09-25T20:54:36.926Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:54:38.880Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:54:40.829Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:54:41.768Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:54:42.711Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:54:43.652Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:54:45.592Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:54:46.531Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:54:46.531Z] 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.
[2024-09-25T20:54:46.531Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:54:46.531Z] Movies recommended for you:
[2024-09-25T20:54:46.531Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:54:46.531Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:54:46.531Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (11421.300 ms) ======
[2024-09-25T20:54:46.531Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T20:54:46.531Z] GC before operation: completed in 81.791 ms, heap usage 1.678 GB -> 61.896 MB.
[2024-09-25T20:54:48.464Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:54:50.445Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:54:51.384Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:54:53.313Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:54:54.260Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:54:55.200Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:54:56.139Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:54:57.079Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:54:57.079Z] 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.
[2024-09-25T20:54:57.079Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:54:57.079Z] Movies recommended for you:
[2024-09-25T20:54:57.079Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:54:57.079Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:54:57.079Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (10906.455 ms) ======
[2024-09-25T20:54:57.079Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T20:54:57.079Z] GC before operation: completed in 78.663 ms, heap usage 424.028 MB -> 55.799 MB.
[2024-09-25T20:55:00.079Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:55:01.021Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:55:02.949Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:55:04.880Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:55:05.826Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:55:06.772Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:55:07.711Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:55:08.650Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:55:08.650Z] 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.
[2024-09-25T20:55:08.650Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:55:08.650Z] Movies recommended for you:
[2024-09-25T20:55:08.650Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:55:08.650Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:55:08.650Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (11499.976 ms) ======
[2024-09-25T20:55:08.650Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T20:55:08.650Z] GC before operation: completed in 80.997 ms, heap usage 976.849 MB -> 58.399 MB.
[2024-09-25T20:55:10.579Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:55:12.507Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:55:14.559Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:55:16.490Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:55:17.433Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:55:18.376Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:55:19.322Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:55:20.267Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:55:20.267Z] 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.
[2024-09-25T20:55:20.267Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:55:20.267Z] Movies recommended for you:
[2024-09-25T20:55:20.267Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:55:20.267Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:55:20.267Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (11119.259 ms) ======
[2024-09-25T20:55:20.267Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T20:55:20.267Z] GC before operation: completed in 77.319 ms, heap usage 832.976 MB -> 61.571 MB.
[2024-09-25T20:55:22.199Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:55:24.133Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:55:25.084Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:55:27.037Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:55:27.977Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:55:28.919Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:55:29.857Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:55:31.395Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:55:31.395Z] 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.
[2024-09-25T20:55:31.395Z] The best model improves the baseline by 14.43%.
[2024-09-25T20:55:31.395Z] Movies recommended for you:
[2024-09-25T20:55:31.395Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:55:31.395Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:55:31.395Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (10823.405 ms) ======
[2024-09-25T20:55:32.533Z] -----------------------------------
[2024-09-25T20:55:32.533Z] renaissance-movie-lens_0_PASSED
[2024-09-25T20:55:32.533Z] -----------------------------------
[2024-09-25T20:55:32.533Z]
[2024-09-25T20:55:32.533Z] TEST TEARDOWN:
[2024-09-25T20:55:32.533Z] Nothing to be done for teardown.
[2024-09-25T20:55:32.533Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 20:55:31 2024 Epoch Time (ms): 1727297731694