renaissance-movie-lens_0
[2024-08-23T21:28:36.432Z] Running test renaissance-movie-lens_0 ...
[2024-08-23T21:28:36.432Z] ===============================================
[2024-08-23T21:28:36.432Z] renaissance-movie-lens_0 Start Time: Fri Aug 23 21:28:35 2024 Epoch Time (ms): 1724448515489
[2024-08-23T21:28:36.432Z] variation: NoOptions
[2024-08-23T21:28:36.432Z] JVM_OPTIONS:
[2024-08-23T21:28:36.432Z] { \
[2024-08-23T21:28:36.432Z] echo ""; echo "TEST SETUP:"; \
[2024-08-23T21:28:36.432Z] echo "Nothing to be done for setup."; \
[2024-08-23T21:28:36.432Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17244476067135/renaissance-movie-lens_0"; \
[2024-08-23T21:28:36.432Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17244476067135/renaissance-movie-lens_0"; \
[2024-08-23T21:28:36.432Z] echo ""; echo "TESTING:"; \
[2024-08-23T21:28:36.432Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17244476067135/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-23T21:28:36.432Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17244476067135/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-23T21:28:36.432Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-23T21:28:36.432Z] echo "Nothing to be done for teardown."; \
[2024-08-23T21:28:36.432Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17244476067135/TestTargetResult";
[2024-08-23T21:28:36.432Z]
[2024-08-23T21:28:36.432Z] TEST SETUP:
[2024-08-23T21:28:36.432Z] Nothing to be done for setup.
[2024-08-23T21:28:36.432Z]
[2024-08-23T21:28:36.432Z] TESTING:
[2024-08-23T21:28:39.419Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-23T21:28:42.404Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-08-23T21:28:45.391Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-23T21:28:46.335Z] Training: 60056, validation: 20285, test: 19854
[2024-08-23T21:28:46.335Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-23T21:28:46.335Z] GC before operation: completed in 76.982 ms, heap usage 163.171 MB -> 37.180 MB.
[2024-08-23T21:28:52.972Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:28:55.957Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:28:58.952Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:29:01.940Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:29:03.892Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:29:05.827Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:29:06.770Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:29:08.704Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:29:08.704Z] 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.
[2024-08-23T21:29:08.704Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:29:09.647Z] Movies recommended for you:
[2024-08-23T21:29:09.647Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:29:09.647Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:29:09.647Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23187.034 ms) ======
[2024-08-23T21:29:09.647Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-23T21:29:09.647Z] GC before operation: completed in 124.250 ms, heap usage 324.442 MB -> 50.576 MB.
[2024-08-23T21:29:11.582Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:29:14.574Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:29:16.515Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:29:19.508Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:29:20.448Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:29:22.389Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:29:23.329Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:29:25.266Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:29:25.266Z] 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.
[2024-08-23T21:29:25.266Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:29:25.266Z] Movies recommended for you:
[2024-08-23T21:29:25.266Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:29:25.266Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:29:25.266Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16314.685 ms) ======
[2024-08-23T21:29:25.266Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-23T21:29:26.211Z] GC before operation: completed in 129.255 ms, heap usage 206.068 MB -> 50.943 MB.
[2024-08-23T21:29:28.142Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:29:31.468Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:29:33.403Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:29:35.330Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:29:36.271Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:29:38.208Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:29:39.148Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:29:40.088Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:29:41.027Z] 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.
[2024-08-23T21:29:41.027Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:29:41.027Z] Movies recommended for you:
[2024-08-23T21:29:41.027Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:29:41.027Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:29:41.027Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14968.916 ms) ======
[2024-08-23T21:29:41.027Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-23T21:29:41.027Z] GC before operation: completed in 126.491 ms, heap usage 294.958 MB -> 54.622 MB.
[2024-08-23T21:29:42.956Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:29:44.888Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:29:46.816Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:29:48.755Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:29:50.687Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:29:51.627Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:29:52.568Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:29:54.502Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:29:54.502Z] 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.
[2024-08-23T21:29:54.502Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:29:54.502Z] Movies recommended for you:
[2024-08-23T21:29:54.502Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:29:54.502Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:29:54.502Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13663.541 ms) ======
[2024-08-23T21:29:54.502Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-23T21:29:54.502Z] GC before operation: completed in 120.983 ms, heap usage 304.829 MB -> 51.701 MB.
[2024-08-23T21:29:57.485Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:29:59.416Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:30:01.346Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:30:03.276Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:30:04.217Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:30:06.149Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:30:07.090Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:30:08.032Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:30:08.032Z] 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.
[2024-08-23T21:30:08.032Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:30:08.973Z] Movies recommended for you:
[2024-08-23T21:30:08.973Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:30:08.973Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:30:08.973Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13932.000 ms) ======
[2024-08-23T21:30:08.973Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-23T21:30:08.973Z] GC before operation: completed in 125.548 ms, heap usage 204.774 MB -> 51.840 MB.
[2024-08-23T21:30:10.903Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:30:12.881Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:30:14.817Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:30:16.747Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:30:18.678Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:30:19.618Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:30:20.559Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:30:21.501Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:30:22.441Z] 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.
[2024-08-23T21:30:22.441Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:30:22.441Z] Movies recommended for you:
[2024-08-23T21:30:22.441Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:30:22.441Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:30:22.441Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13519.759 ms) ======
[2024-08-23T21:30:22.441Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-23T21:30:22.441Z] GC before operation: completed in 118.510 ms, heap usage 305.767 MB -> 51.833 MB.
[2024-08-23T21:30:24.372Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:30:26.304Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:30:28.237Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:30:30.167Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:30:31.108Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:30:33.039Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:30:33.979Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:30:34.919Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:30:34.919Z] 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.
[2024-08-23T21:30:34.919Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:30:34.919Z] Movies recommended for you:
[2024-08-23T21:30:34.919Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:30:34.919Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:30:34.919Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13034.442 ms) ======
[2024-08-23T21:30:34.919Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-23T21:30:35.859Z] GC before operation: completed in 124.101 ms, heap usage 289.318 MB -> 52.021 MB.
[2024-08-23T21:30:37.790Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:30:40.415Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:30:41.356Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:30:43.290Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:30:45.232Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:30:46.172Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:30:47.113Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:30:49.048Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:30:49.048Z] 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.
[2024-08-23T21:30:49.048Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:30:49.048Z] Movies recommended for you:
[2024-08-23T21:30:49.048Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:30:49.048Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:30:49.048Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13406.827 ms) ======
[2024-08-23T21:30:49.048Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-23T21:30:49.048Z] GC before operation: completed in 119.463 ms, heap usage 319.299 MB -> 52.332 MB.
[2024-08-23T21:30:50.978Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:30:52.910Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:30:54.842Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:30:56.787Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:30:58.754Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:30:59.696Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:31:00.636Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:31:01.578Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:31:02.518Z] 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.
[2024-08-23T21:31:02.518Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:31:02.518Z] Movies recommended for you:
[2024-08-23T21:31:02.518Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:31:02.518Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:31:02.518Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13205.540 ms) ======
[2024-08-23T21:31:02.518Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-23T21:31:02.518Z] GC before operation: completed in 141.344 ms, heap usage 203.643 MB -> 52.017 MB.
[2024-08-23T21:31:04.453Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:31:06.384Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:31:08.315Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:31:10.250Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:31:11.190Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:31:12.167Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:31:13.108Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:31:14.051Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:31:14.051Z] 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.
[2024-08-23T21:31:14.991Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:31:14.991Z] Movies recommended for you:
[2024-08-23T21:31:14.991Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:31:14.991Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:31:14.991Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12247.187 ms) ======
[2024-08-23T21:31:14.991Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-23T21:31:14.991Z] GC before operation: completed in 133.174 ms, heap usage 220.093 MB -> 52.169 MB.
[2024-08-23T21:31:16.924Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:31:18.854Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:31:19.796Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:31:21.731Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:31:23.663Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:31:24.604Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:31:25.553Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:31:26.494Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:31:26.494Z] 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.
[2024-08-23T21:31:26.494Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:31:26.494Z] Movies recommended for you:
[2024-08-23T21:31:26.494Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:31:26.494Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:31:26.494Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12054.204 ms) ======
[2024-08-23T21:31:26.494Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-23T21:31:26.494Z] GC before operation: completed in 120.119 ms, heap usage 233.665 MB -> 51.902 MB.
[2024-08-23T21:31:28.425Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:31:30.355Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:31:32.287Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:31:34.221Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:31:35.297Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:31:36.235Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:31:37.176Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:31:39.109Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:31:39.109Z] 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.
[2024-08-23T21:31:39.109Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:31:39.109Z] Movies recommended for you:
[2024-08-23T21:31:39.109Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:31:39.109Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:31:39.109Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12008.079 ms) ======
[2024-08-23T21:31:39.109Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-23T21:31:39.109Z] GC before operation: completed in 122.937 ms, heap usage 152.865 MB -> 52.042 MB.
[2024-08-23T21:31:41.044Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:31:42.983Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:31:44.917Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:31:46.856Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:31:47.797Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:31:48.736Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:31:49.677Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:31:51.306Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:31:51.306Z] 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.
[2024-08-23T21:31:51.306Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:31:51.306Z] Movies recommended for you:
[2024-08-23T21:31:51.306Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:31:51.306Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:31:51.306Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (12255.851 ms) ======
[2024-08-23T21:31:51.306Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-23T21:31:51.306Z] GC before operation: completed in 123.912 ms, heap usage 331.357 MB -> 52.290 MB.
[2024-08-23T21:31:53.239Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:31:55.174Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:31:57.107Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:31:59.042Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:31:59.984Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:32:00.927Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:32:01.866Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:32:03.798Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:32:03.798Z] 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.
[2024-08-23T21:32:03.798Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:32:03.798Z] Movies recommended for you:
[2024-08-23T21:32:03.799Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:32:03.799Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:32:03.799Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12126.817 ms) ======
[2024-08-23T21:32:03.799Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-23T21:32:03.799Z] GC before operation: completed in 131.193 ms, heap usage 195.760 MB -> 51.976 MB.
[2024-08-23T21:32:05.728Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:32:07.673Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:32:09.605Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:32:11.535Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:32:12.475Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:32:13.417Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:32:14.357Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:32:15.300Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:32:15.300Z] 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.
[2024-08-23T21:32:15.300Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:32:16.241Z] Movies recommended for you:
[2024-08-23T21:32:16.241Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:32:16.241Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:32:16.241Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12080.642 ms) ======
[2024-08-23T21:32:16.241Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-23T21:32:16.241Z] GC before operation: completed in 136.752 ms, heap usage 144.402 MB -> 52.110 MB.
[2024-08-23T21:32:18.174Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:32:20.107Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:32:22.045Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:32:22.989Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:32:24.927Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:32:25.870Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:32:26.811Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:32:27.752Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:32:27.752Z] 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.
[2024-08-23T21:32:27.752Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:32:27.752Z] Movies recommended for you:
[2024-08-23T21:32:27.752Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:32:27.752Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:32:27.752Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12295.037 ms) ======
[2024-08-23T21:32:27.752Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-23T21:32:28.694Z] GC before operation: completed in 137.188 ms, heap usage 117.989 MB -> 52.194 MB.
[2024-08-23T21:32:30.626Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:32:32.559Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:32:34.508Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:32:36.476Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:32:38.410Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:32:40.344Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:32:41.286Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:32:43.227Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:32:43.227Z] 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.
[2024-08-23T21:32:43.227Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:32:43.227Z] Movies recommended for you:
[2024-08-23T21:32:43.227Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:32:43.227Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:32:43.227Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14893.812 ms) ======
[2024-08-23T21:32:43.227Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-23T21:32:43.227Z] GC before operation: completed in 137.643 ms, heap usage 324.066 MB -> 52.133 MB.
[2024-08-23T21:32:46.218Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:32:48.157Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:32:51.145Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:32:54.130Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:32:55.072Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:32:57.007Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:32:57.949Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:32:59.882Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:32:59.882Z] 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.
[2024-08-23T21:32:59.882Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:32:59.882Z] Movies recommended for you:
[2024-08-23T21:32:59.882Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:32:59.882Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:32:59.882Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16488.593 ms) ======
[2024-08-23T21:32:59.882Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-23T21:32:59.882Z] GC before operation: completed in 133.851 ms, heap usage 410.639 MB -> 52.293 MB.
[2024-08-23T21:33:02.545Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:33:05.531Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:33:07.465Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:33:09.398Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:33:11.333Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:33:13.264Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:33:14.204Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:33:16.132Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:33:16.132Z] 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.
[2024-08-23T21:33:16.132Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:33:16.132Z] Movies recommended for you:
[2024-08-23T21:33:16.132Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:33:16.132Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:33:16.132Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16297.782 ms) ======
[2024-08-23T21:33:16.132Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-23T21:33:16.132Z] GC before operation: completed in 139.136 ms, heap usage 231.701 MB -> 52.345 MB.
[2024-08-23T21:33:19.112Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-23T21:33:21.041Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-23T21:33:24.194Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-23T21:33:26.122Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-23T21:33:28.068Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-23T21:33:29.008Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-23T21:33:30.941Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-23T21:33:31.883Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-23T21:33:32.828Z] 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.
[2024-08-23T21:33:32.828Z] The best model improves the baseline by 14.43%.
[2024-08-23T21:33:32.828Z] Movies recommended for you:
[2024-08-23T21:33:32.828Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-23T21:33:32.828Z] There is no way to check that no silent failure occurred.
[2024-08-23T21:33:32.828Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16089.744 ms) ======
[2024-08-23T21:33:33.770Z] -----------------------------------
[2024-08-23T21:33:33.770Z] renaissance-movie-lens_0_PASSED
[2024-08-23T21:33:33.770Z] -----------------------------------
[2024-08-23T21:33:33.770Z]
[2024-08-23T21:33:33.770Z] TEST TEARDOWN:
[2024-08-23T21:33:33.770Z] Nothing to be done for teardown.
[2024-08-23T21:33:33.770Z] renaissance-movie-lens_0 Finish Time: Fri Aug 23 21:33:33 2024 Epoch Time (ms): 1724448813228