renaissance-movie-lens_0
[2024-11-23T01:37:25.445Z] Running test renaissance-movie-lens_0 ...
[2024-11-23T01:37:25.445Z] ===============================================
[2024-11-23T01:37:25.445Z] renaissance-movie-lens_0 Start Time: Fri Nov 22 19:37:24 2024 Epoch Time (ms): 1732325844959
[2024-11-23T01:37:25.445Z] variation: NoOptions
[2024-11-23T01:37:25.445Z] JVM_OPTIONS:
[2024-11-23T01:37:25.445Z] { \
[2024-11-23T01:37:25.445Z] echo ""; echo "TEST SETUP:"; \
[2024-11-23T01:37:25.445Z] echo "Nothing to be done for setup."; \
[2024-11-23T01:37:25.445Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17323252272945/renaissance-movie-lens_0"; \
[2024-11-23T01:37:25.445Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17323252272945/renaissance-movie-lens_0"; \
[2024-11-23T01:37:25.445Z] echo ""; echo "TESTING:"; \
[2024-11-23T01:37:25.445Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_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_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17323252272945/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-23T01:37:25.445Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17323252272945/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-23T01:37:25.445Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-23T01:37:25.445Z] echo "Nothing to be done for teardown."; \
[2024-11-23T01:37:25.445Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17323252272945/TestTargetResult";
[2024-11-23T01:37:25.445Z]
[2024-11-23T01:37:25.445Z] TEST SETUP:
[2024-11-23T01:37:25.445Z] Nothing to be done for setup.
[2024-11-23T01:37:25.445Z]
[2024-11-23T01:37:25.445Z] TESTING:
[2024-11-23T01:37:27.719Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-23T01:37:29.947Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-23T01:37:32.185Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-23T01:37:32.905Z] Training: 60056, validation: 20285, test: 19854
[2024-11-23T01:37:32.905Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-23T01:37:32.905Z] GC before operation: completed in 41.379 ms, heap usage 76.163 MB -> 37.737 MB.
[2024-11-23T01:37:40.600Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:37:43.741Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:37:47.800Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:37:50.044Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:37:51.484Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:37:53.740Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:37:55.170Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:37:56.592Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:37:57.282Z] 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.
[2024-11-23T01:37:57.282Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:37:57.282Z] Movies recommended for you:
[2024-11-23T01:37:57.282Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:37:57.282Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:37:57.282Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24595.482 ms) ======
[2024-11-23T01:37:57.282Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-23T01:37:57.282Z] GC before operation: completed in 68.019 ms, heap usage 486.540 MB -> 56.764 MB.
[2024-11-23T01:38:00.391Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:38:03.483Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:38:05.739Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:38:07.967Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:38:09.397Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:38:10.823Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:38:13.050Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:38:14.490Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:38:14.490Z] 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.
[2024-11-23T01:38:14.490Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:38:14.490Z] Movies recommended for you:
[2024-11-23T01:38:14.490Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:38:14.490Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:38:14.490Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17195.750 ms) ======
[2024-11-23T01:38:14.490Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-23T01:38:14.490Z] GC before operation: completed in 58.939 ms, heap usage 531.898 MB -> 54.949 MB.
[2024-11-23T01:38:17.597Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:38:20.698Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:38:22.625Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:38:24.870Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:38:26.306Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:38:27.728Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:38:29.174Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:38:30.604Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:38:30.604Z] 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.
[2024-11-23T01:38:30.604Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:38:31.301Z] Movies recommended for you:
[2024-11-23T01:38:31.301Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:38:31.301Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:38:31.301Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16299.284 ms) ======
[2024-11-23T01:38:31.301Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-23T01:38:31.301Z] GC before operation: completed in 68.232 ms, heap usage 248.874 MB -> 51.937 MB.
[2024-11-23T01:38:33.534Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:38:35.793Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:38:38.031Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:38:40.267Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:38:41.710Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:38:43.139Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:38:44.563Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:38:46.004Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:38:46.691Z] 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.
[2024-11-23T01:38:46.691Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:38:46.691Z] Movies recommended for you:
[2024-11-23T01:38:46.691Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:38:46.691Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:38:46.691Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15373.314 ms) ======
[2024-11-23T01:38:46.691Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-23T01:38:46.691Z] GC before operation: completed in 66.122 ms, heap usage 333.656 MB -> 52.350 MB.
[2024-11-23T01:38:48.904Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:38:51.186Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:38:53.424Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:38:55.638Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:38:57.073Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:38:57.780Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:38:59.257Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:39:00.724Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:39:01.409Z] 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.
[2024-11-23T01:39:01.409Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:39:01.409Z] Movies recommended for you:
[2024-11-23T01:39:01.409Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:39:01.409Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:39:01.409Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14695.761 ms) ======
[2024-11-23T01:39:01.409Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-23T01:39:01.409Z] GC before operation: completed in 62.478 ms, heap usage 536.467 MB -> 55.985 MB.
[2024-11-23T01:39:03.656Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:39:05.904Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:39:08.124Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:39:10.342Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:39:11.789Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:39:13.250Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:39:14.690Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:39:15.385Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:39:16.073Z] 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.
[2024-11-23T01:39:16.073Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:39:16.073Z] Movies recommended for you:
[2024-11-23T01:39:16.073Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:39:16.073Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:39:16.073Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14678.689 ms) ======
[2024-11-23T01:39:16.073Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-23T01:39:16.073Z] GC before operation: completed in 65.476 ms, heap usage 234.101 MB -> 52.411 MB.
[2024-11-23T01:39:18.294Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:39:20.556Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:39:22.799Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:39:25.045Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:39:26.489Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:39:27.910Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:39:29.336Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:39:30.770Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:39:30.770Z] 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.
[2024-11-23T01:39:30.770Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:39:30.770Z] Movies recommended for you:
[2024-11-23T01:39:30.770Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:39:30.770Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:39:30.770Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14911.942 ms) ======
[2024-11-23T01:39:30.770Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-23T01:39:30.770Z] GC before operation: completed in 57.720 ms, heap usage 289.489 MB -> 52.660 MB.
[2024-11-23T01:39:33.015Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:39:35.271Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:39:37.501Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:39:39.738Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:39:41.173Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:39:42.618Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:39:44.243Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:39:44.935Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:39:45.631Z] 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.
[2024-11-23T01:39:45.631Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:39:45.632Z] Movies recommended for you:
[2024-11-23T01:39:45.632Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:39:45.632Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:39:45.632Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14531.661 ms) ======
[2024-11-23T01:39:45.632Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-23T01:39:45.632Z] GC before operation: completed in 56.402 ms, heap usage 439.560 MB -> 53.087 MB.
[2024-11-23T01:39:47.859Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:39:50.093Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:39:51.611Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:39:53.828Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:39:55.249Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:39:56.675Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:39:57.371Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:39:58.816Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:39:58.816Z] 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.
[2024-11-23T01:39:58.816Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:39:59.519Z] Movies recommended for you:
[2024-11-23T01:39:59.519Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:39:59.519Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:39:59.519Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13640.803 ms) ======
[2024-11-23T01:39:59.519Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-23T01:39:59.519Z] GC before operation: completed in 65.951 ms, heap usage 482.100 MB -> 56.214 MB.
[2024-11-23T01:40:01.740Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:40:03.955Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:40:06.224Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:40:07.649Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:40:09.095Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:40:10.528Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:40:11.965Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:40:13.411Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:40:13.411Z] 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.
[2024-11-23T01:40:13.411Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:40:13.411Z] Movies recommended for you:
[2024-11-23T01:40:13.411Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:40:13.411Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:40:13.411Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14242.715 ms) ======
[2024-11-23T01:40:13.411Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-23T01:40:13.411Z] GC before operation: completed in 54.257 ms, heap usage 246.425 MB -> 52.864 MB.
[2024-11-23T01:40:16.569Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:40:18.003Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:40:20.252Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:40:22.487Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:40:23.917Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:40:25.360Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:40:26.813Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:40:27.496Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:40:28.187Z] 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.
[2024-11-23T01:40:28.188Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:40:28.188Z] Movies recommended for you:
[2024-11-23T01:40:28.188Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:40:28.188Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:40:28.188Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14560.689 ms) ======
[2024-11-23T01:40:28.188Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-23T01:40:28.188Z] GC before operation: completed in 60.927 ms, heap usage 96.822 MB -> 54.819 MB.
[2024-11-23T01:40:30.430Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:40:32.682Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:40:34.926Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:40:37.175Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:40:38.614Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:40:39.316Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:40:40.768Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:40:42.227Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:40:42.227Z] 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.
[2024-11-23T01:40:42.227Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:40:42.913Z] Movies recommended for you:
[2024-11-23T01:40:42.913Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:40:42.913Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:40:42.913Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14362.625 ms) ======
[2024-11-23T01:40:42.913Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-23T01:40:42.913Z] GC before operation: completed in 54.207 ms, heap usage 88.955 MB -> 55.113 MB.
[2024-11-23T01:40:45.144Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:40:47.362Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:40:49.616Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:40:51.176Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:40:52.597Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:40:54.071Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:40:55.509Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:40:56.939Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:40:56.939Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T01:40:56.939Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:40:57.630Z] Movies recommended for you:
[2024-11-23T01:40:57.630Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:40:57.630Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:40:57.630Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14730.185 ms) ======
[2024-11-23T01:40:57.630Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-23T01:40:57.630Z] GC before operation: completed in 56.272 ms, heap usage 386.053 MB -> 53.041 MB.
[2024-11-23T01:40:59.870Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:41:02.112Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:41:04.350Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:41:06.579Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:41:08.017Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:41:09.443Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:41:10.871Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:41:11.583Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:41:12.273Z] 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.
[2024-11-23T01:41:12.273Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:41:12.273Z] Movies recommended for you:
[2024-11-23T01:41:12.273Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:41:12.273Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:41:12.273Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14731.030 ms) ======
[2024-11-23T01:41:12.273Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-23T01:41:12.273Z] GC before operation: completed in 63.959 ms, heap usage 277.231 MB -> 52.732 MB.
[2024-11-23T01:41:14.489Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:41:16.719Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:41:18.979Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:41:21.209Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:41:22.635Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:41:24.069Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:41:25.495Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:41:26.201Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:41:26.895Z] 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.
[2024-11-23T01:41:26.895Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:41:26.895Z] Movies recommended for you:
[2024-11-23T01:41:26.895Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:41:26.895Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:41:26.895Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14564.214 ms) ======
[2024-11-23T01:41:26.895Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-23T01:41:26.895Z] GC before operation: completed in 55.324 ms, heap usage 207.714 MB -> 52.874 MB.
[2024-11-23T01:41:29.117Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:41:31.339Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:41:33.552Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:41:35.791Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:41:37.247Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:41:38.683Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:41:40.116Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:41:40.815Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:41:41.511Z] 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.
[2024-11-23T01:41:41.511Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:41:41.512Z] Movies recommended for you:
[2024-11-23T01:41:41.512Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:41:41.512Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:41:41.512Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14533.757 ms) ======
[2024-11-23T01:41:41.512Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-23T01:41:41.512Z] GC before operation: completed in 58.694 ms, heap usage 261.758 MB -> 52.990 MB.
[2024-11-23T01:41:43.737Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:41:45.983Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:41:48.214Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:41:50.487Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:41:51.201Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:41:52.625Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:41:54.049Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:41:55.480Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:41:55.480Z] 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.
[2024-11-23T01:41:55.480Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:41:55.480Z] Movies recommended for you:
[2024-11-23T01:41:55.480Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:41:55.480Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:41:55.480Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14366.237 ms) ======
[2024-11-23T01:41:55.480Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-23T01:41:56.168Z] GC before operation: completed in 64.050 ms, heap usage 126.937 MB -> 55.286 MB.
[2024-11-23T01:41:58.426Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:42:00.660Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:42:02.877Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:42:05.108Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:42:06.240Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:42:07.667Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:42:08.486Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:42:09.928Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:42:09.928Z] 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.
[2024-11-23T01:42:09.928Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:42:09.928Z] Movies recommended for you:
[2024-11-23T01:42:09.928Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:42:09.928Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:42:09.928Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14341.296 ms) ======
[2024-11-23T01:42:09.928Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-23T01:42:09.928Z] GC before operation: completed in 57.134 ms, heap usage 125.777 MB -> 52.820 MB.
[2024-11-23T01:42:13.020Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:42:14.460Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:42:16.722Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:42:18.942Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:42:20.409Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:42:21.091Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:42:22.544Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:42:23.967Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:42:23.967Z] 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.
[2024-11-23T01:42:23.967Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:42:24.652Z] Movies recommended for you:
[2024-11-23T01:42:24.652Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:42:24.652Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:42:24.652Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14140.893 ms) ======
[2024-11-23T01:42:24.652Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-23T01:42:24.652Z] GC before operation: completed in 57.058 ms, heap usage 303.642 MB -> 53.186 MB.
[2024-11-23T01:42:26.884Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T01:42:29.106Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T01:42:31.415Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T01:42:32.842Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T01:42:34.265Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T01:42:35.687Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T01:42:37.111Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T01:42:38.539Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T01:42:38.539Z] 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.
[2024-11-23T01:42:38.539Z] The best model improves the baseline by 14.43%.
[2024-11-23T01:42:38.539Z] Movies recommended for you:
[2024-11-23T01:42:38.539Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T01:42:38.539Z] There is no way to check that no silent failure occurred.
[2024-11-23T01:42:38.539Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14075.943 ms) ======
[2024-11-23T01:42:40.026Z] -----------------------------------
[2024-11-23T01:42:40.026Z] renaissance-movie-lens_0_PASSED
[2024-11-23T01:42:40.026Z] -----------------------------------
[2024-11-23T01:42:40.026Z]
[2024-11-23T01:42:40.026Z] TEST TEARDOWN:
[2024-11-23T01:42:40.026Z] Nothing to be done for teardown.
[2024-11-23T01:42:40.026Z] renaissance-movie-lens_0 Finish Time: Fri Nov 22 19:42:39 2024 Epoch Time (ms): 1732326159280