renaissance-movie-lens_0
[2024-08-28T21:00:03.248Z] Running test renaissance-movie-lens_0 ...
[2024-08-28T21:00:03.248Z] ===============================================
[2024-08-28T21:00:03.248Z] renaissance-movie-lens_0 Start Time: Wed Aug 28 21:00:02 2024 Epoch Time (ms): 1724878802782
[2024-08-28T21:00:03.248Z] variation: NoOptions
[2024-08-28T21:00:03.248Z] JVM_OPTIONS:
[2024-08-28T21:00:03.248Z] { \
[2024-08-28T21:00:03.248Z] echo ""; echo "TEST SETUP:"; \
[2024-08-28T21:00:03.248Z] echo "Nothing to be done for setup."; \
[2024-08-28T21:00:03.248Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17248779586869/renaissance-movie-lens_0"; \
[2024-08-28T21:00:03.248Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17248779586869/renaissance-movie-lens_0"; \
[2024-08-28T21:00:03.248Z] echo ""; echo "TESTING:"; \
[2024-08-28T21:00:03.248Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17248779586869/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-28T21:00:03.248Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17248779586869/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-28T21:00:03.248Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-28T21:00:03.248Z] echo "Nothing to be done for teardown."; \
[2024-08-28T21:00:03.248Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17248779586869/TestTargetResult";
[2024-08-28T21:00:03.248Z]
[2024-08-28T21:00:03.248Z] TEST SETUP:
[2024-08-28T21:00:03.248Z] Nothing to be done for setup.
[2024-08-28T21:00:03.248Z]
[2024-08-28T21:00:03.248Z] TESTING:
[2024-08-28T21:00:07.330Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-28T21:00:09.250Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-08-28T21:00:13.349Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-28T21:00:13.349Z] Training: 60056, validation: 20285, test: 19854
[2024-08-28T21:00:13.349Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-28T21:00:13.349Z] GC before operation: completed in 48.319 ms, heap usage 100.123 MB -> 39.423 MB.
[2024-08-28T21:00:19.945Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:24.028Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:28.119Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:00:31.083Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:00:32.019Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:00:33.944Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:00:35.863Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:00:37.784Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:00:37.784Z] 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.
[2024-08-28T21:00:37.784Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:00:37.784Z] Movies recommended for you:
[2024-08-28T21:00:37.784Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:00:37.784Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:00:37.784Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24521.515 ms) ======
[2024-08-28T21:00:37.784Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-28T21:00:38.724Z] GC before operation: completed in 93.851 ms, heap usage 130.791 MB -> 52.466 MB.
[2024-08-28T21:00:41.702Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:00:44.670Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:00:46.591Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:00:49.556Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:00:50.491Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:00:52.410Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:00:53.344Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:00:55.265Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:00:55.265Z] 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.
[2024-08-28T21:00:55.265Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:00:55.265Z] Movies recommended for you:
[2024-08-28T21:00:55.265Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:00:55.265Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:00:55.265Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17108.604 ms) ======
[2024-08-28T21:00:55.265Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-28T21:00:55.265Z] GC before operation: completed in 94.012 ms, heap usage 1.315 GB -> 57.993 MB.
[2024-08-28T21:00:58.232Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:01.373Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:03.294Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:05.216Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:07.140Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:08.075Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:09.996Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:10.932Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:10.932Z] 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.
[2024-08-28T21:01:10.932Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:01:10.932Z] Movies recommended for you:
[2024-08-28T21:01:10.932Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:10.932Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:10.932Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (15727.943 ms) ======
[2024-08-28T21:01:10.932Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-28T21:01:10.932Z] GC before operation: completed in 92.153 ms, heap usage 354.517 MB -> 53.671 MB.
[2024-08-28T21:01:13.902Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:15.825Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:17.745Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:20.714Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:21.649Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:22.584Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:24.507Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:25.442Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:25.442Z] 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.
[2024-08-28T21:01:25.442Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:01:25.442Z] Movies recommended for you:
[2024-08-28T21:01:25.442Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:25.442Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:25.442Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14379.358 ms) ======
[2024-08-28T21:01:25.442Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-28T21:01:25.442Z] GC before operation: completed in 89.975 ms, heap usage 472.025 MB -> 54.070 MB.
[2024-08-28T21:01:28.407Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:30.331Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:32.429Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:34.349Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:35.284Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:37.206Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:38.143Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:39.078Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:40.015Z] 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.
[2024-08-28T21:01:40.015Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:01:40.015Z] Movies recommended for you:
[2024-08-28T21:01:40.015Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:40.015Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:40.015Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14103.655 ms) ======
[2024-08-28T21:01:40.015Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-28T21:01:40.015Z] GC before operation: completed in 92.644 ms, heap usage 379.218 MB -> 54.202 MB.
[2024-08-28T21:01:42.981Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:44.904Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:01:46.827Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:01:48.749Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:01:49.690Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:01:51.615Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:01:52.551Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:01:53.489Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:01:53.489Z] 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.
[2024-08-28T21:01:53.489Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:01:54.426Z] Movies recommended for you:
[2024-08-28T21:01:54.426Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:01:54.426Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:01:54.426Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14011.526 ms) ======
[2024-08-28T21:01:54.426Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-28T21:01:54.426Z] GC before operation: completed in 95.622 ms, heap usage 355.402 MB -> 57.330 MB.
[2024-08-28T21:01:56.361Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:01:58.283Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:02:00.205Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:02:02.127Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:02:04.054Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:02:04.990Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:02:05.926Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:02:07.852Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:02:07.852Z] 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.
[2024-08-28T21:02:07.852Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:02:07.852Z] Movies recommended for you:
[2024-08-28T21:02:07.852Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:02:07.852Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:02:07.852Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13701.694 ms) ======
[2024-08-28T21:02:07.852Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-28T21:02:07.852Z] GC before operation: completed in 94.334 ms, heap usage 353.052 MB -> 54.188 MB.
[2024-08-28T21:02:10.817Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:02:12.774Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:02:14.697Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:02:16.620Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:02:17.557Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:02:19.479Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:02:20.415Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:02:21.350Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:02:22.287Z] 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.
[2024-08-28T21:02:22.287Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:02:22.287Z] Movies recommended for you:
[2024-08-28T21:02:22.287Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:02:22.287Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:02:22.287Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14230.179 ms) ======
[2024-08-28T21:02:22.287Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-28T21:02:22.287Z] GC before operation: completed in 125.546 ms, heap usage 994.501 MB -> 58.476 MB.
[2024-08-28T21:02:25.251Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:02:27.174Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:02:29.095Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:02:31.016Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:02:31.952Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:02:33.880Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:02:34.816Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:02:35.750Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:02:36.688Z] 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.
[2024-08-28T21:02:36.688Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:02:36.688Z] Movies recommended for you:
[2024-08-28T21:02:36.688Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:02:36.688Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:02:36.688Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14272.635 ms) ======
[2024-08-28T21:02:36.688Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-28T21:02:36.688Z] GC before operation: completed in 94.565 ms, heap usage 362.726 MB -> 54.447 MB.
[2024-08-28T21:02:38.674Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:02:41.644Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:02:43.564Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:02:45.485Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:02:47.406Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:02:48.344Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:02:49.281Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:02:51.204Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:02:51.204Z] 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.
[2024-08-28T21:02:51.204Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:02:51.204Z] Movies recommended for you:
[2024-08-28T21:02:51.204Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:02:51.204Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:02:51.204Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14395.016 ms) ======
[2024-08-28T21:02:51.204Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-28T21:02:51.204Z] GC before operation: completed in 92.990 ms, heap usage 380.443 MB -> 54.598 MB.
[2024-08-28T21:02:53.134Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:02:55.102Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:02:58.071Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:02:59.999Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:03:00.936Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:03:01.872Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:03:03.796Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:03:04.749Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:03:04.750Z] 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.
[2024-08-28T21:03:04.750Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:03:04.750Z] Movies recommended for you:
[2024-08-28T21:03:04.750Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:03:04.750Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:03:04.750Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13753.561 ms) ======
[2024-08-28T21:03:04.750Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-28T21:03:04.750Z] GC before operation: completed in 93.208 ms, heap usage 470.923 MB -> 54.315 MB.
[2024-08-28T21:03:07.724Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:03:09.647Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:03:11.571Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:03:13.493Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:03:14.430Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:03:15.372Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:03:17.297Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:03:18.235Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:03:18.235Z] 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.
[2024-08-28T21:03:18.235Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:03:18.235Z] Movies recommended for you:
[2024-08-28T21:03:18.235Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:03:18.235Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:03:18.235Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13646.371 ms) ======
[2024-08-28T21:03:18.235Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-28T21:03:19.190Z] GC before operation: completed in 100.002 ms, heap usage 1.262 GB -> 59.095 MB.
[2024-08-28T21:03:21.113Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:03:23.037Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:03:24.958Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:03:26.880Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:03:28.803Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:03:29.741Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:03:31.691Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:03:32.628Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:03:32.628Z] 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.
[2024-08-28T21:03:32.628Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:03:32.628Z] Movies recommended for you:
[2024-08-28T21:03:32.628Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:03:32.628Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:03:32.628Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13953.550 ms) ======
[2024-08-28T21:03:32.628Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-28T21:03:32.628Z] GC before operation: completed in 100.412 ms, heap usage 361.631 MB -> 54.609 MB.
[2024-08-28T21:03:34.552Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:03:37.524Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:03:39.443Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:03:41.365Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:03:42.302Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:03:43.241Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:03:45.163Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:03:46.102Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:03:46.102Z] 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.
[2024-08-28T21:03:46.102Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:03:46.102Z] Movies recommended for you:
[2024-08-28T21:03:46.102Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:03:46.102Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:03:46.102Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13595.231 ms) ======
[2024-08-28T21:03:46.102Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-28T21:03:46.102Z] GC before operation: completed in 94.712 ms, heap usage 995.565 MB -> 58.322 MB.
[2024-08-28T21:03:49.072Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:03:51.062Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:03:52.997Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:03:54.973Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:03:55.909Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:03:56.845Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:03:58.768Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:03:59.707Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:03:59.707Z] 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.
[2024-08-28T21:03:59.707Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:03:59.707Z] Movies recommended for you:
[2024-08-28T21:03:59.707Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:03:59.707Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:03:59.707Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13356.697 ms) ======
[2024-08-28T21:03:59.707Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-28T21:03:59.707Z] GC before operation: completed in 95.649 ms, heap usage 388.300 MB -> 54.564 MB.
[2024-08-28T21:04:02.682Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:04:04.604Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:04:06.526Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:04:08.453Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:04:09.390Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:04:10.327Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:04:12.250Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:04:13.188Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:04:13.188Z] 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.
[2024-08-28T21:04:13.188Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:04:13.188Z] Movies recommended for you:
[2024-08-28T21:04:13.188Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:04:13.188Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:04:13.188Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13611.413 ms) ======
[2024-08-28T21:04:13.188Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-28T21:04:13.188Z] GC before operation: completed in 98.866 ms, heap usage 1.090 GB -> 58.794 MB.
[2024-08-28T21:04:16.159Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:04:18.083Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:04:20.009Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:04:21.934Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:04:22.870Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:04:24.796Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:04:25.734Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:04:26.679Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:04:26.679Z] 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.
[2024-08-28T21:04:26.679Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:04:26.679Z] Movies recommended for you:
[2024-08-28T21:04:26.679Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:04:26.679Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:04:26.679Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13446.068 ms) ======
[2024-08-28T21:04:26.679Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-28T21:04:27.621Z] GC before operation: completed in 152.303 ms, heap usage 355.105 MB -> 54.523 MB.
[2024-08-28T21:04:29.549Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:04:31.661Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:04:33.584Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:04:35.510Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:04:36.445Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:04:37.381Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:04:39.303Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:04:40.239Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:04:40.239Z] 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.
[2024-08-28T21:04:40.239Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:04:40.239Z] Movies recommended for you:
[2024-08-28T21:04:40.239Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:04:40.239Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:04:40.239Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13345.277 ms) ======
[2024-08-28T21:04:40.239Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-28T21:04:40.239Z] GC before operation: completed in 91.163 ms, heap usage 789.207 MB -> 58.118 MB.
[2024-08-28T21:04:43.209Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:04:45.129Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:04:47.049Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:04:48.969Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:04:49.906Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:04:50.841Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:04:51.776Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:04:53.697Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:04:53.697Z] 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.
[2024-08-28T21:04:53.697Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:04:53.697Z] Movies recommended for you:
[2024-08-28T21:04:53.697Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:04:53.697Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:04:53.697Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12879.006 ms) ======
[2024-08-28T21:04:53.697Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-28T21:04:53.697Z] GC before operation: completed in 91.457 ms, heap usage 378.153 MB -> 54.735 MB.
[2024-08-28T21:04:55.616Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T21:04:57.535Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T21:05:00.179Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T21:05:01.145Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T21:05:03.095Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T21:05:04.030Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T21:05:04.965Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T21:05:06.886Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T21:05:06.886Z] 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.
[2024-08-28T21:05:06.886Z] The best model improves the baseline by 14.43%.
[2024-08-28T21:05:06.886Z] Movies recommended for you:
[2024-08-28T21:05:06.886Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T21:05:06.886Z] There is no way to check that no silent failure occurred.
[2024-08-28T21:05:06.886Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13089.235 ms) ======
[2024-08-28T21:05:07.821Z] -----------------------------------
[2024-08-28T21:05:07.821Z] renaissance-movie-lens_0_PASSED
[2024-08-28T21:05:07.821Z] -----------------------------------
[2024-08-28T21:05:07.821Z]
[2024-08-28T21:05:07.821Z] TEST TEARDOWN:
[2024-08-28T21:05:07.821Z] Nothing to be done for teardown.
[2024-08-28T21:05:07.821Z] renaissance-movie-lens_0 Finish Time: Wed Aug 28 21:05:07 2024 Epoch Time (ms): 1724879107681