renaissance-movie-lens_0
[2025-02-05T22:03:21.268Z] Running test renaissance-movie-lens_0 ...
[2025-02-05T22:03:21.268Z] ===============================================
[2025-02-05T22:03:21.268Z] renaissance-movie-lens_0 Start Time: Wed Feb 5 22:03:20 2025 Epoch Time (ms): 1738793000548
[2025-02-05T22:03:21.268Z] variation: NoOptions
[2025-02-05T22:03:21.268Z] JVM_OPTIONS:
[2025-02-05T22:03:21.268Z] { \
[2025-02-05T22:03:21.268Z] echo ""; echo "TEST SETUP:"; \
[2025-02-05T22:03:21.268Z] echo "Nothing to be done for setup."; \
[2025-02-05T22:03:21.268Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17387921454995/renaissance-movie-lens_0"; \
[2025-02-05T22:03:21.268Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17387921454995/renaissance-movie-lens_0"; \
[2025-02-05T22:03:21.268Z] echo ""; echo "TESTING:"; \
[2025-02-05T22:03:21.268Z] "/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_17387921454995/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-05T22:03:21.268Z] 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_17387921454995/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-05T22:03:21.268Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-05T22:03:21.268Z] echo "Nothing to be done for teardown."; \
[2025-02-05T22:03:21.268Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17387921454995/TestTargetResult";
[2025-02-05T22:03:21.268Z]
[2025-02-05T22:03:21.268Z] TEST SETUP:
[2025-02-05T22:03:21.268Z] Nothing to be done for setup.
[2025-02-05T22:03:21.268Z]
[2025-02-05T22:03:21.268Z] TESTING:
[2025-02-05T22:03:24.306Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-05T22:03:27.346Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2025-02-05T22:03:31.511Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-05T22:03:31.511Z] Training: 60056, validation: 20285, test: 19854
[2025-02-05T22:03:31.511Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-05T22:03:31.511Z] GC before operation: completed in 52.149 ms, heap usage 78.008 MB -> 39.412 MB.
[2025-02-05T22:03:39.689Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:03:43.906Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:03:48.081Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:03:51.114Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:03:52.803Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:03:54.770Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:03:57.802Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:03:59.768Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:03:59.768Z] 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.
[2025-02-05T22:03:59.768Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:04:00.725Z] Movies recommended for you:
[2025-02-05T22:04:00.725Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:04:00.725Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:04:00.725Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (28489.137 ms) ======
[2025-02-05T22:04:00.725Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-05T22:04:00.725Z] GC before operation: completed in 127.171 ms, heap usage 549.544 MB -> 56.194 MB.
[2025-02-05T22:04:06.142Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:04:09.356Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:04:12.394Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:04:16.681Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:04:17.640Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:04:19.603Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:04:21.565Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:04:23.531Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:04:24.487Z] 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.
[2025-02-05T22:04:24.487Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:04:24.487Z] Movies recommended for you:
[2025-02-05T22:04:24.487Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:04:24.487Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:04:24.487Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (23697.009 ms) ======
[2025-02-05T22:04:24.487Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-05T22:04:24.487Z] GC before operation: completed in 91.032 ms, heap usage 545.200 MB -> 56.771 MB.
[2025-02-05T22:04:27.515Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:04:29.480Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:04:31.444Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:04:34.488Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:04:35.444Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:04:36.402Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:04:37.366Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:04:39.331Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:04:39.331Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-05T22:04:39.331Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:04:39.331Z] Movies recommended for you:
[2025-02-05T22:04:39.331Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:04:39.331Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:04:39.331Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14964.243 ms) ======
[2025-02-05T22:04:39.331Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-05T22:04:39.331Z] GC before operation: completed in 112.719 ms, heap usage 714.487 MB -> 57.188 MB.
[2025-02-05T22:04:41.392Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:04:43.356Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:04:45.323Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:04:47.287Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:04:49.249Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:04:50.209Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:04:51.166Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:04:52.124Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:04:53.081Z] 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.
[2025-02-05T22:04:53.081Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:04:53.081Z] Movies recommended for you:
[2025-02-05T22:04:53.081Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:04:53.081Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:04:53.081Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13433.444 ms) ======
[2025-02-05T22:04:53.081Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-05T22:04:53.081Z] GC before operation: completed in 77.443 ms, heap usage 381.525 MB -> 54.104 MB.
[2025-02-05T22:04:55.084Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:04:57.048Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:04:59.011Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:05:00.978Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:05:01.936Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:05:02.895Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:05:04.861Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:05:05.823Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:05:05.823Z] 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.
[2025-02-05T22:05:05.823Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:05:05.823Z] Movies recommended for you:
[2025-02-05T22:05:05.823Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:05:05.823Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:05:05.823Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (12974.505 ms) ======
[2025-02-05T22:05:05.823Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-05T22:05:05.823Z] GC before operation: completed in 80.485 ms, heap usage 376.654 MB -> 54.204 MB.
[2025-02-05T22:05:07.786Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:05:11.619Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:05:12.578Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:05:14.541Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:05:16.502Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:05:17.458Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:05:18.413Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:05:20.378Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:05:20.378Z] 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.
[2025-02-05T22:05:20.378Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:05:20.378Z] Movies recommended for you:
[2025-02-05T22:05:20.378Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:05:20.378Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:05:20.378Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14702.475 ms) ======
[2025-02-05T22:05:20.378Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-05T22:05:20.378Z] GC before operation: completed in 90.400 ms, heap usage 375.459 MB -> 54.220 MB.
[2025-02-05T22:05:23.408Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:05:25.372Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:05:27.335Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:05:29.300Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:05:31.270Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:05:32.228Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:05:33.184Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:05:35.153Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:05:35.153Z] 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.
[2025-02-05T22:05:35.153Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:05:35.153Z] Movies recommended for you:
[2025-02-05T22:05:35.153Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:05:35.153Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:05:35.153Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14274.792 ms) ======
[2025-02-05T22:05:35.153Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-05T22:05:35.153Z] GC before operation: completed in 92.858 ms, heap usage 634.717 MB -> 57.695 MB.
[2025-02-05T22:05:37.123Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:05:40.154Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:05:42.192Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:05:44.158Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:05:45.115Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:05:46.075Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:05:47.031Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:05:47.989Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:05:48.946Z] 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.
[2025-02-05T22:05:48.946Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:05:48.946Z] Movies recommended for you:
[2025-02-05T22:05:48.946Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:05:48.946Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:05:48.946Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13614.146 ms) ======
[2025-02-05T22:05:48.946Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-05T22:05:48.946Z] GC before operation: completed in 115.604 ms, heap usage 1.448 GB -> 59.260 MB.
[2025-02-05T22:05:50.924Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:05:52.888Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:05:54.894Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:05:56.860Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:05:57.817Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:05:59.779Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:06:00.739Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:06:01.697Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:06:01.697Z] 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.
[2025-02-05T22:06:01.697Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:06:01.697Z] Movies recommended for you:
[2025-02-05T22:06:01.697Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:06:01.697Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:06:01.697Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13169.517 ms) ======
[2025-02-05T22:06:01.697Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-05T22:06:01.697Z] GC before operation: completed in 89.098 ms, heap usage 274.955 MB -> 54.441 MB.
[2025-02-05T22:06:04.737Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:06:06.703Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:06:08.667Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:06:10.630Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:06:12.593Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:06:13.550Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:06:14.507Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:06:16.473Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:06:16.473Z] 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.
[2025-02-05T22:06:16.473Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:06:16.473Z] Movies recommended for you:
[2025-02-05T22:06:16.473Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:06:16.473Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:06:16.473Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14256.248 ms) ======
[2025-02-05T22:06:16.473Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-05T22:06:16.473Z] GC before operation: completed in 88.331 ms, heap usage 374.570 MB -> 54.565 MB.
[2025-02-05T22:06:18.436Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:06:20.398Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:06:22.361Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:06:24.329Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:06:26.044Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:06:26.999Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:06:27.965Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:06:28.921Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:06:28.921Z] 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.
[2025-02-05T22:06:28.921Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:06:28.921Z] Movies recommended for you:
[2025-02-05T22:06:28.921Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:06:28.921Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:06:28.921Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12823.208 ms) ======
[2025-02-05T22:06:28.921Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-05T22:06:29.876Z] GC before operation: completed in 127.776 ms, heap usage 603.698 MB -> 57.670 MB.
[2025-02-05T22:06:31.844Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:06:33.808Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:06:35.774Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:06:38.853Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:06:39.810Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:06:40.768Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:06:42.734Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:06:43.692Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:06:44.648Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-05T22:06:44.648Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:06:44.648Z] Movies recommended for you:
[2025-02-05T22:06:44.648Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:06:44.648Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:06:44.648Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15038.335 ms) ======
[2025-02-05T22:06:44.648Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-05T22:06:44.648Z] GC before operation: completed in 109.756 ms, heap usage 357.836 MB -> 54.371 MB.
[2025-02-05T22:06:46.622Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:06:49.687Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:06:51.647Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:06:54.682Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:06:55.646Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:06:57.609Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:06:58.573Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:06:59.532Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:07:00.487Z] 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.
[2025-02-05T22:07:00.487Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:07:00.487Z] Movies recommended for you:
[2025-02-05T22:07:00.487Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:07:00.487Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:07:00.487Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15693.609 ms) ======
[2025-02-05T22:07:00.487Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-05T22:07:00.487Z] GC before operation: completed in 89.219 ms, heap usage 457.736 MB -> 54.699 MB.
[2025-02-05T22:07:02.464Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:07:04.476Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:07:07.678Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:07:09.640Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:07:10.596Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:07:11.553Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:07:13.516Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:07:14.502Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:07:14.502Z] 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.
[2025-02-05T22:07:14.502Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:07:15.458Z] Movies recommended for you:
[2025-02-05T22:07:15.458Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:07:15.458Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:07:15.458Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14708.297 ms) ======
[2025-02-05T22:07:15.458Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-05T22:07:15.458Z] GC before operation: completed in 90.302 ms, heap usage 835.197 MB -> 58.006 MB.
[2025-02-05T22:07:17.419Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:07:19.384Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:07:22.416Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:07:24.379Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:07:25.334Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:07:27.303Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:07:28.259Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:07:29.214Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:07:30.169Z] 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.
[2025-02-05T22:07:30.169Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:07:30.169Z] Movies recommended for you:
[2025-02-05T22:07:30.169Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:07:30.169Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:07:30.169Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14883.543 ms) ======
[2025-02-05T22:07:30.169Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-05T22:07:30.169Z] GC before operation: completed in 98.386 ms, heap usage 361.033 MB -> 54.602 MB.
[2025-02-05T22:07:33.201Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:07:35.168Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:07:36.894Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:07:39.922Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:07:40.882Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:07:41.841Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:07:43.807Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:07:44.768Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:07:45.751Z] 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.
[2025-02-05T22:07:45.751Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:07:45.751Z] Movies recommended for you:
[2025-02-05T22:07:45.751Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:07:45.751Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:07:45.751Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15305.660 ms) ======
[2025-02-05T22:07:45.751Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-05T22:07:45.751Z] GC before operation: completed in 95.067 ms, heap usage 373.485 MB -> 54.659 MB.
[2025-02-05T22:07:47.714Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:07:49.687Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:07:52.736Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:07:53.693Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:07:55.655Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:07:56.611Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:07:57.572Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:07:58.528Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:07:58.528Z] 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.
[2025-02-05T22:07:58.528Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:07:59.483Z] Movies recommended for you:
[2025-02-05T22:07:59.483Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:07:59.483Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:07:59.483Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13568.852 ms) ======
[2025-02-05T22:07:59.483Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-05T22:07:59.483Z] GC before operation: completed in 83.084 ms, heap usage 352.238 MB -> 54.455 MB.
[2025-02-05T22:08:01.444Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:08:03.477Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:08:05.438Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:08:07.398Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:08:08.354Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:08:09.310Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:08:10.265Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:08:12.232Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:08:12.232Z] 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.
[2025-02-05T22:08:12.232Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:08:12.232Z] Movies recommended for you:
[2025-02-05T22:08:12.232Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:08:12.232Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:08:12.232Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12983.835 ms) ======
[2025-02-05T22:08:12.232Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-05T22:08:12.232Z] GC before operation: completed in 85.589 ms, heap usage 353.824 MB -> 54.553 MB.
[2025-02-05T22:08:14.197Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:08:16.161Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:08:18.128Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:08:20.128Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:08:21.090Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:08:22.053Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:08:24.024Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:08:24.980Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:08:24.980Z] 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.
[2025-02-05T22:08:24.980Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:08:24.980Z] Movies recommended for you:
[2025-02-05T22:08:24.980Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:08:24.980Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:08:24.980Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12890.141 ms) ======
[2025-02-05T22:08:24.980Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-05T22:08:24.980Z] GC before operation: completed in 89.129 ms, heap usage 378.657 MB -> 54.660 MB.
[2025-02-05T22:08:26.945Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:08:28.912Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:08:30.874Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:08:32.834Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:08:33.790Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:08:35.751Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:08:36.706Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:08:37.661Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:08:37.661Z] 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.
[2025-02-05T22:08:37.661Z] The best model improves the baseline by 14.43%.
[2025-02-05T22:08:37.661Z] Movies recommended for you:
[2025-02-05T22:08:37.661Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:08:37.661Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:08:37.661Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12900.536 ms) ======
[2025-02-05T22:08:39.626Z] -----------------------------------
[2025-02-05T22:08:39.626Z] renaissance-movie-lens_0_PASSED
[2025-02-05T22:08:39.626Z] -----------------------------------
[2025-02-05T22:08:39.626Z]
[2025-02-05T22:08:39.626Z] TEST TEARDOWN:
[2025-02-05T22:08:39.626Z] Nothing to be done for teardown.
[2025-02-05T22:08:39.626Z] renaissance-movie-lens_0 Finish Time: Wed Feb 5 22:08:39 2025 Epoch Time (ms): 1738793319195