renaissance-movie-lens_0
[2024-09-26T01:06:38.517Z] Running test renaissance-movie-lens_0 ...
[2024-09-26T01:06:38.517Z] ===============================================
[2024-09-26T01:06:38.517Z] renaissance-movie-lens_0 Start Time: Thu Sep 26 01:06:38 2024 Epoch Time (ms): 1727312798377
[2024-09-26T01:06:38.517Z] variation: NoOptions
[2024-09-26T01:06:38.517Z] JVM_OPTIONS:
[2024-09-26T01:06:38.517Z] { \
[2024-09-26T01:06:38.517Z] echo ""; echo "TEST SETUP:"; \
[2024-09-26T01:06:38.517Z] echo "Nothing to be done for setup."; \
[2024-09-26T01:06:38.517Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17273117559386/renaissance-movie-lens_0"; \
[2024-09-26T01:06:38.517Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17273117559386/renaissance-movie-lens_0"; \
[2024-09-26T01:06:38.517Z] echo ""; echo "TESTING:"; \
[2024-09-26T01:06:38.517Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17273117559386/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-26T01:06:38.517Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17273117559386/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-26T01:06:38.517Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-26T01:06:38.517Z] echo "Nothing to be done for teardown."; \
[2024-09-26T01:06:38.517Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17273117559386/TestTargetResult";
[2024-09-26T01:06:38.517Z]
[2024-09-26T01:06:38.517Z] TEST SETUP:
[2024-09-26T01:06:38.517Z] Nothing to be done for setup.
[2024-09-26T01:06:38.517Z]
[2024-09-26T01:06:38.517Z] TESTING:
[2024-09-26T01:06:44.238Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-26T01:06:46.687Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-09-26T01:06:50.195Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-26T01:06:50.989Z] Training: 60056, validation: 20285, test: 19854
[2024-09-26T01:06:50.989Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-26T01:06:50.989Z] GC before operation: completed in 56.371 ms, heap usage 192.951 MB -> 38.017 MB.
[2024-09-26T01:06:56.556Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:07:00.983Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:07:04.376Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:07:07.876Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:07:10.324Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:07:11.896Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:07:14.349Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:07:15.920Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:07:16.687Z] 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.
[2024-09-26T01:07:16.687Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:07:16.687Z] Movies recommended for you:
[2024-09-26T01:07:16.687Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:07:16.687Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:07:16.687Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26057.789 ms) ======
[2024-09-26T01:07:16.687Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-26T01:07:16.687Z] GC before operation: completed in 109.059 ms, heap usage 709.265 MB -> 52.977 MB.
[2024-09-26T01:07:20.075Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:07:23.462Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:07:26.853Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:07:30.238Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:07:31.808Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:07:34.256Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:07:35.836Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:07:38.283Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:07:38.283Z] 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.
[2024-09-26T01:07:38.283Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:07:38.283Z] Movies recommended for you:
[2024-09-26T01:07:38.283Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:07:38.283Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:07:38.283Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21503.619 ms) ======
[2024-09-26T01:07:38.283Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-26T01:07:38.283Z] GC before operation: completed in 116.243 ms, heap usage 161.049 MB -> 51.682 MB.
[2024-09-26T01:07:41.668Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:07:45.059Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:07:48.451Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:07:51.862Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:07:53.436Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:07:55.011Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:07:56.585Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:07:59.055Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:07:59.055Z] 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.
[2024-09-26T01:07:59.055Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:07:59.055Z] Movies recommended for you:
[2024-09-26T01:07:59.055Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:07:59.055Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:07:59.055Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20636.668 ms) ======
[2024-09-26T01:07:59.055Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-26T01:07:59.055Z] GC before operation: completed in 112.346 ms, heap usage 572.373 MB -> 55.650 MB.
[2024-09-26T01:08:02.442Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:08:05.871Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:08:09.277Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:08:11.722Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:08:14.186Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:08:15.759Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:08:17.327Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:08:19.776Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:08:19.776Z] 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.
[2024-09-26T01:08:19.776Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:08:19.776Z] Movies recommended for you:
[2024-09-26T01:08:19.776Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:08:19.776Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:08:19.776Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20603.703 ms) ======
[2024-09-26T01:08:19.776Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-26T01:08:19.776Z] GC before operation: completed in 110.688 ms, heap usage 742.310 MB -> 56.028 MB.
[2024-09-26T01:08:23.166Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:08:26.562Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:08:29.015Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:08:32.401Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:08:33.975Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:08:36.423Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:08:38.010Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:08:39.583Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:08:40.344Z] 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.
[2024-09-26T01:08:40.344Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:08:40.344Z] Movies recommended for you:
[2024-09-26T01:08:40.344Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:08:40.344Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:08:40.344Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20065.815 ms) ======
[2024-09-26T01:08:40.344Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-26T01:08:40.344Z] GC before operation: completed in 121.756 ms, heap usage 347.596 MB -> 52.666 MB.
[2024-09-26T01:08:43.737Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:08:46.193Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:08:49.588Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:08:53.000Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:08:54.581Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:08:56.153Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:08:58.606Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:09:00.193Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:09:00.193Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-26T01:09:00.193Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:09:00.193Z] Movies recommended for you:
[2024-09-26T01:09:00.193Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:09:00.193Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:09:00.193Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20252.346 ms) ======
[2024-09-26T01:09:00.193Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-26T01:09:00.953Z] GC before operation: completed in 113.525 ms, heap usage 261.147 MB -> 52.545 MB.
[2024-09-26T01:09:03.394Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:09:06.826Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:09:10.222Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:09:12.667Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:09:15.132Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:09:16.713Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:09:18.463Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:09:20.041Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:09:20.802Z] 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.
[2024-09-26T01:09:20.802Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:09:20.802Z] Movies recommended for you:
[2024-09-26T01:09:20.802Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:09:20.802Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:09:20.802Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20124.941 ms) ======
[2024-09-26T01:09:20.802Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-26T01:09:20.802Z] GC before operation: completed in 110.915 ms, heap usage 298.017 MB -> 52.817 MB.
[2024-09-26T01:09:24.195Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:09:26.643Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:09:30.034Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:09:33.438Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:09:35.011Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:09:36.592Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:09:38.289Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:09:39.871Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:09:40.630Z] 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.
[2024-09-26T01:09:40.630Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:09:40.630Z] Movies recommended for you:
[2024-09-26T01:09:40.630Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:09:40.630Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:09:40.630Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19769.023 ms) ======
[2024-09-26T01:09:40.630Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-26T01:09:40.630Z] GC before operation: completed in 119.606 ms, heap usage 279.572 MB -> 53.022 MB.
[2024-09-26T01:09:44.029Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:09:46.480Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:09:49.865Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:09:53.261Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:09:54.841Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:09:56.419Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:09:58.871Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:10:00.442Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:10:00.442Z] 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.
[2024-09-26T01:10:00.442Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:10:00.442Z] Movies recommended for you:
[2024-09-26T01:10:00.442Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:10:00.442Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:10:00.442Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19965.016 ms) ======
[2024-09-26T01:10:00.442Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-26T01:10:01.209Z] GC before operation: completed in 113.739 ms, heap usage 373.858 MB -> 52.891 MB.
[2024-09-26T01:10:03.645Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:10:07.046Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:10:10.448Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:10:12.890Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:10:14.465Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:10:16.907Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:10:18.477Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:10:20.051Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:10:20.818Z] 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.
[2024-09-26T01:10:20.818Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:10:20.818Z] Movies recommended for you:
[2024-09-26T01:10:20.818Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:10:20.818Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:10:20.818Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19947.639 ms) ======
[2024-09-26T01:10:20.818Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-26T01:10:20.818Z] GC before operation: completed in 119.659 ms, heap usage 230.891 MB -> 52.957 MB.
[2024-09-26T01:10:24.218Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:10:26.661Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:10:30.054Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:10:33.438Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:10:35.018Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:10:36.588Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:10:39.030Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:10:40.613Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:10:40.614Z] 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.
[2024-09-26T01:10:40.614Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:10:41.377Z] Movies recommended for you:
[2024-09-26T01:10:41.377Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:10:41.377Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:10:41.377Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20070.229 ms) ======
[2024-09-26T01:10:41.377Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-26T01:10:41.377Z] GC before operation: completed in 116.665 ms, heap usage 261.342 MB -> 52.670 MB.
[2024-09-26T01:10:43.819Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:10:47.238Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:10:50.639Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:10:53.085Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:10:55.533Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:10:57.111Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:10:58.685Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:11:00.258Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:11:01.020Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-26T01:11:01.020Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:11:01.020Z] Movies recommended for you:
[2024-09-26T01:11:01.020Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:11:01.020Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:11:01.020Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19913.464 ms) ======
[2024-09-26T01:11:01.020Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-26T01:11:01.020Z] GC before operation: completed in 117.085 ms, heap usage 298.534 MB -> 52.927 MB.
[2024-09-26T01:11:04.412Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:11:06.921Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:11:10.315Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:11:13.710Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:11:15.290Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:11:16.865Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:11:19.324Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:11:20.896Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:11:20.897Z] 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.
[2024-09-26T01:11:20.897Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:11:20.897Z] Movies recommended for you:
[2024-09-26T01:11:20.897Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:11:20.897Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:11:20.897Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20098.121 ms) ======
[2024-09-26T01:11:20.897Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-26T01:11:21.657Z] GC before operation: completed in 111.272 ms, heap usage 461.903 MB -> 53.202 MB.
[2024-09-26T01:11:24.098Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:11:27.498Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:11:30.889Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:11:33.334Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:11:34.912Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:11:37.414Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:11:38.987Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:11:40.561Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:11:41.322Z] 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.
[2024-09-26T01:11:41.322Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:11:41.322Z] Movies recommended for you:
[2024-09-26T01:11:41.322Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:11:41.322Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:11:41.322Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19905.765 ms) ======
[2024-09-26T01:11:41.322Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-26T01:11:41.322Z] GC before operation: completed in 115.379 ms, heap usage 229.576 MB -> 52.776 MB.
[2024-09-26T01:11:44.728Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:11:47.675Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:11:50.672Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:11:53.419Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:11:55.866Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:11:57.459Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:11:59.041Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:12:01.501Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:12:01.501Z] 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.
[2024-09-26T01:12:01.501Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:12:01.501Z] Movies recommended for you:
[2024-09-26T01:12:01.501Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:12:01.501Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:12:01.501Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20307.632 ms) ======
[2024-09-26T01:12:01.501Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-26T01:12:01.501Z] GC before operation: completed in 117.831 ms, heap usage 535.898 MB -> 56.416 MB.
[2024-09-26T01:12:04.899Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:12:08.357Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:12:10.809Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:12:14.203Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:12:15.774Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:12:17.351Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:12:19.795Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:12:21.367Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:12:22.129Z] 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.
[2024-09-26T01:12:22.129Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:12:22.129Z] Movies recommended for you:
[2024-09-26T01:12:22.129Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:12:22.129Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:12:22.129Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (20222.931 ms) ======
[2024-09-26T01:12:22.129Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-26T01:12:22.129Z] GC before operation: completed in 157.304 ms, heap usage 285.845 MB -> 53.040 MB.
[2024-09-26T01:12:25.519Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:12:27.964Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:12:31.363Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:12:34.757Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:12:36.334Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:12:37.923Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:12:39.496Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:12:41.956Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:12:41.956Z] 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.
[2024-09-26T01:12:41.956Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:12:41.956Z] Movies recommended for you:
[2024-09-26T01:12:41.956Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:12:41.956Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:12:41.956Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19962.177 ms) ======
[2024-09-26T01:12:41.956Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-26T01:12:41.956Z] GC before operation: completed in 118.746 ms, heap usage 143.031 MB -> 52.805 MB.
[2024-09-26T01:12:45.347Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:12:48.759Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:12:51.205Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:12:54.594Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:12:56.183Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:12:57.755Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:12:59.334Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:13:01.779Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:13:01.779Z] 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.
[2024-09-26T01:13:01.779Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:13:01.779Z] Movies recommended for you:
[2024-09-26T01:13:01.779Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:13:01.779Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:13:01.779Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19711.961 ms) ======
[2024-09-26T01:13:01.779Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-26T01:13:01.779Z] GC before operation: completed in 112.754 ms, heap usage 390.107 MB -> 53.062 MB.
[2024-09-26T01:13:05.170Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:13:07.733Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:13:11.123Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:13:14.516Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:13:16.094Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:13:17.668Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:13:19.244Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:13:21.703Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:13:21.703Z] 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.
[2024-09-26T01:13:21.703Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:13:21.703Z] Movies recommended for you:
[2024-09-26T01:13:21.703Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:13:21.703Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:13:21.704Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19731.935 ms) ======
[2024-09-26T01:13:21.704Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-26T01:13:21.704Z] GC before operation: completed in 114.417 ms, heap usage 273.709 MB -> 53.137 MB.
[2024-09-26T01:13:25.093Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T01:13:28.496Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T01:13:30.950Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T01:13:34.346Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T01:13:35.931Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T01:13:37.513Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T01:13:39.087Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T01:13:41.534Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T01:13:41.534Z] 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.
[2024-09-26T01:13:41.534Z] The best model improves the baseline by 14.43%.
[2024-09-26T01:13:41.534Z] Movies recommended for you:
[2024-09-26T01:13:41.534Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T01:13:41.534Z] There is no way to check that no silent failure occurred.
[2024-09-26T01:13:41.534Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19733.835 ms) ======
[2024-09-26T01:13:42.300Z] -----------------------------------
[2024-09-26T01:13:42.300Z] renaissance-movie-lens_0_PASSED
[2024-09-26T01:13:42.300Z] -----------------------------------
[2024-09-26T01:13:42.300Z]
[2024-09-26T01:13:42.300Z] TEST TEARDOWN:
[2024-09-26T01:13:42.300Z] Nothing to be done for teardown.
[2024-09-26T01:13:42.300Z] renaissance-movie-lens_0 Finish Time: Thu Sep 26 01:13:41 2024 Epoch Time (ms): 1727313221867