renaissance-movie-lens_0
[2025-02-19T22:32:03.511Z] Running test renaissance-movie-lens_0 ...
[2025-02-19T22:32:03.511Z] ===============================================
[2025-02-19T22:32:03.511Z] renaissance-movie-lens_0 Start Time: Wed Feb 19 17:32:03 2025 Epoch Time (ms): 1740004323197
[2025-02-19T22:32:03.511Z] variation: NoOptions
[2025-02-19T22:32:03.511Z] JVM_OPTIONS:
[2025-02-19T22:32:03.511Z] { \
[2025-02-19T22:32:03.511Z] echo ""; echo "TEST SETUP:"; \
[2025-02-19T22:32:03.511Z] echo "Nothing to be done for setup."; \
[2025-02-19T22:32:03.511Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17400039292661/renaissance-movie-lens_0"; \
[2025-02-19T22:32:03.511Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17400039292661/renaissance-movie-lens_0"; \
[2025-02-19T22:32:03.511Z] echo ""; echo "TESTING:"; \
[2025-02-19T22:32:03.511Z] "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/jdkbinary/j2sdk-image/Contents/Home/bin/..//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 "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17400039292661/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-19T22:32:03.511Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17400039292661/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-19T22:32:03.511Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-19T22:32:03.511Z] echo "Nothing to be done for teardown."; \
[2025-02-19T22:32:03.511Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17400039292661/TestTargetResult";
[2025-02-19T22:32:03.511Z]
[2025-02-19T22:32:03.511Z] TEST SETUP:
[2025-02-19T22:32:03.511Z] Nothing to be done for setup.
[2025-02-19T22:32:03.511Z]
[2025-02-19T22:32:03.511Z] TESTING:
[2025-02-19T22:32:05.345Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-19T22:32:06.587Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-02-19T22:32:07.843Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-19T22:32:08.225Z] Training: 60056, validation: 20285, test: 19854
[2025-02-19T22:32:08.225Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-19T22:32:08.225Z] GC before operation: completed in 37.217 ms, heap usage 101.718 MB -> 36.566 MB.
[2025-02-19T22:32:11.395Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:32:13.175Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:32:14.425Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:32:16.222Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:32:17.001Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:32:17.832Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:32:18.665Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:32:19.951Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:32:19.951Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:32:19.951Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:32:19.951Z] Movies recommended for you:
[2025-02-19T22:32:19.951Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:32:19.951Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:32:19.951Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (11770.172 ms) ======
[2025-02-19T22:32:19.951Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-19T22:32:19.951Z] GC before operation: completed in 56.463 ms, heap usage 349.497 MB -> 48.904 MB.
[2025-02-19T22:32:21.816Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:32:23.062Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:32:24.367Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:32:25.621Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:32:26.899Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:32:27.690Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:32:28.459Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:32:29.236Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:32:29.595Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:32:29.595Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:32:29.595Z] Movies recommended for you:
[2025-02-19T22:32:29.595Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:32:29.595Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:32:29.595Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (9520.614 ms) ======
[2025-02-19T22:32:29.595Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-19T22:32:29.595Z] GC before operation: completed in 55.259 ms, heap usage 166.303 MB -> 48.975 MB.
[2025-02-19T22:32:31.412Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:32:32.648Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:32:33.891Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:32:35.186Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:32:36.049Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:32:37.339Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:32:38.159Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:32:38.953Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:32:38.953Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:32:38.953Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:32:38.953Z] Movies recommended for you:
[2025-02-19T22:32:38.953Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:32:38.953Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:32:38.953Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (9379.588 ms) ======
[2025-02-19T22:32:38.953Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-19T22:32:38.953Z] GC before operation: completed in 53.662 ms, heap usage 296.530 MB -> 49.370 MB.
[2025-02-19T22:32:40.793Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:32:44.467Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:32:44.467Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:32:44.891Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:32:46.169Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:32:46.946Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:32:47.718Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:32:48.502Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:32:48.502Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:32:48.502Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:32:48.502Z] Movies recommended for you:
[2025-02-19T22:32:48.502Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:32:48.502Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:32:48.502Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (9680.571 ms) ======
[2025-02-19T22:32:48.502Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-19T22:32:48.861Z] GC before operation: completed in 64.296 ms, heap usage 210.790 MB -> 49.587 MB.
[2025-02-19T22:32:50.121Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:32:51.955Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:32:53.195Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:32:54.476Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:32:55.250Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:32:56.035Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:32:57.305Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:32:58.098Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:32:58.098Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:32:58.098Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:32:58.098Z] Movies recommended for you:
[2025-02-19T22:32:58.098Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:32:58.098Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:32:58.098Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9431.681 ms) ======
[2025-02-19T22:32:58.098Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-19T22:32:58.098Z] GC before operation: completed in 55.559 ms, heap usage 221.627 MB -> 49.771 MB.
[2025-02-19T22:32:59.885Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:33:01.176Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:33:02.433Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:33:03.701Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:33:04.486Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:33:05.299Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:33:06.082Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:33:06.875Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:33:06.875Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:33:06.875Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:33:06.875Z] Movies recommended for you:
[2025-02-19T22:33:06.875Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:33:06.875Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:33:06.875Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (8812.444 ms) ======
[2025-02-19T22:33:06.875Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-19T22:33:07.238Z] GC before operation: completed in 57.276 ms, heap usage 76.964 MB -> 49.556 MB.
[2025-02-19T22:33:08.481Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:33:09.763Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:33:11.654Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:33:12.918Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:33:13.277Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:33:14.053Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:33:14.829Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:33:15.608Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:33:15.608Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:33:15.608Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:33:15.984Z] Movies recommended for you:
[2025-02-19T22:33:15.984Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:33:15.984Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:33:15.984Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (8702.233 ms) ======
[2025-02-19T22:33:15.984Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-19T22:33:15.984Z] GC before operation: completed in 52.157 ms, heap usage 266.932 MB -> 49.926 MB.
[2025-02-19T22:33:17.291Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:33:19.100Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:33:20.383Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:33:21.658Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:33:22.448Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:33:23.239Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:33:24.032Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:33:24.819Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:33:24.819Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:33:24.819Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:33:24.819Z] Movies recommended for you:
[2025-02-19T22:33:24.819Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:33:24.819Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:33:24.819Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (9090.021 ms) ======
[2025-02-19T22:33:24.819Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-19T22:33:24.819Z] GC before operation: completed in 52.149 ms, heap usage 71.869 MB -> 49.969 MB.
[2025-02-19T22:33:26.624Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:33:27.882Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:33:29.157Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:33:30.422Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:33:31.213Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:33:31.985Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:33:32.782Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:33:34.026Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:33:34.026Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:33:34.026Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:33:34.026Z] Movies recommended for you:
[2025-02-19T22:33:34.026Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:33:34.026Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:33:34.026Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (9045.678 ms) ======
[2025-02-19T22:33:34.026Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-19T22:33:34.026Z] GC before operation: completed in 43.498 ms, heap usage 83.940 MB -> 49.855 MB.
[2025-02-19T22:33:35.269Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:33:37.091Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:33:38.341Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:33:39.600Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:33:40.385Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:33:41.168Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:33:42.005Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:33:42.829Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:33:42.829Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:33:43.187Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:33:43.187Z] Movies recommended for you:
[2025-02-19T22:33:43.187Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:33:43.187Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:33:43.187Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (8993.215 ms) ======
[2025-02-19T22:33:43.187Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-19T22:33:43.187Z] GC before operation: completed in 48.975 ms, heap usage 177.467 MB -> 50.039 MB.
[2025-02-19T22:33:44.444Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:33:46.235Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:33:47.472Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:33:48.720Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:33:49.494Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:33:50.273Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:33:51.051Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:33:51.837Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:33:52.202Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:33:52.203Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:33:52.203Z] Movies recommended for you:
[2025-02-19T22:33:52.203Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:33:52.203Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:33:52.203Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (9021.513 ms) ======
[2025-02-19T22:33:52.203Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-19T22:33:52.203Z] GC before operation: completed in 46.454 ms, heap usage 121.033 MB -> 49.691 MB.
[2025-02-19T22:33:53.478Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:33:55.328Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:33:56.584Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:33:57.832Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:33:58.610Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:33:59.380Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:34:00.167Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:34:00.966Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:34:00.966Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:34:00.966Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:34:00.966Z] Movies recommended for you:
[2025-02-19T22:34:00.966Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:34:00.966Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:34:00.966Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (8909.487 ms) ======
[2025-02-19T22:34:00.966Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-19T22:34:01.333Z] GC before operation: completed in 43.723 ms, heap usage 83.366 MB -> 49.834 MB.
[2025-02-19T22:34:02.625Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:34:04.441Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:34:05.700Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:34:06.954Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:34:07.741Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:34:08.532Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:34:09.306Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:34:10.100Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:34:10.100Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:34:10.100Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:34:10.462Z] Movies recommended for you:
[2025-02-19T22:34:10.462Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:34:10.462Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:34:10.462Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (9163.506 ms) ======
[2025-02-19T22:34:10.462Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-19T22:34:10.462Z] GC before operation: completed in 42.033 ms, heap usage 78.912 MB -> 50.062 MB.
[2025-02-19T22:34:11.769Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:34:13.047Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:34:14.847Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:34:16.115Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:34:16.884Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:34:17.663Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:34:18.446Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:34:19.230Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:34:19.594Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:34:19.594Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:34:19.594Z] Movies recommended for you:
[2025-02-19T22:34:19.594Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:34:19.594Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:34:19.594Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (9207.897 ms) ======
[2025-02-19T22:34:19.594Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-19T22:34:19.594Z] GC before operation: completed in 48.373 ms, heap usage 169.139 MB -> 49.943 MB.
[2025-02-19T22:34:20.844Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:34:22.651Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:34:23.900Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:34:25.138Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:34:25.997Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:34:26.770Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:34:27.550Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:34:28.330Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:34:28.689Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:34:28.689Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:34:28.689Z] Movies recommended for you:
[2025-02-19T22:34:28.689Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:34:28.689Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:34:28.689Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (9046.732 ms) ======
[2025-02-19T22:34:28.689Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-19T22:34:28.689Z] GC before operation: completed in 59.136 ms, heap usage 170.185 MB -> 50.101 MB.
[2025-02-19T22:34:29.957Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:34:31.211Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:34:32.458Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:34:34.250Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:34:35.053Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:34:35.821Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:34:36.590Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:34:37.358Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:34:37.358Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:34:37.358Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:34:37.736Z] Movies recommended for you:
[2025-02-19T22:34:37.736Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:34:37.736Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:34:37.736Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (8828.484 ms) ======
[2025-02-19T22:34:37.736Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-19T22:34:37.736Z] GC before operation: completed in 52.538 ms, heap usage 145.643 MB -> 50.138 MB.
[2025-02-19T22:34:38.980Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:34:40.237Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:34:42.023Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:34:43.288Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:34:44.159Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:34:44.935Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:34:45.714Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:34:46.520Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:34:46.520Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:34:46.520Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:34:46.520Z] Movies recommended for you:
[2025-02-19T22:34:46.520Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:34:46.520Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:34:46.520Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (8988.318 ms) ======
[2025-02-19T22:34:46.520Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-19T22:34:46.520Z] GC before operation: completed in 56.531 ms, heap usage 127.823 MB -> 49.936 MB.
[2025-02-19T22:34:47.773Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:34:49.569Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:34:50.826Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:34:52.083Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:34:52.451Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:34:53.237Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:34:54.040Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:34:54.819Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:34:54.819Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:34:54.819Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:34:55.183Z] Movies recommended for you:
[2025-02-19T22:34:55.183Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:34:55.183Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:34:55.183Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (8357.120 ms) ======
[2025-02-19T22:34:55.183Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-19T22:34:55.183Z] GC before operation: completed in 57.275 ms, heap usage 121.440 MB -> 49.974 MB.
[2025-02-19T22:34:56.433Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:34:57.699Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:34:59.511Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:35:00.299Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:35:01.088Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:35:01.876Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:35:02.669Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:35:03.471Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:35:03.853Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:35:03.853Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:35:03.853Z] Movies recommended for you:
[2025-02-19T22:35:03.853Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:35:03.853Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:35:03.853Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (8747.302 ms) ======
[2025-02-19T22:35:03.853Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-19T22:35:03.853Z] GC before operation: completed in 53.673 ms, heap usage 126.724 MB -> 50.140 MB.
[2025-02-19T22:35:05.127Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-19T22:35:06.941Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-19T22:35:08.186Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-19T22:35:09.431Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-19T22:35:10.200Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-19T22:35:10.967Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-19T22:35:11.744Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-19T22:35:12.523Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-19T22:35:12.887Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-19T22:35:12.887Z] The best model improves the baseline by 14.52%.
[2025-02-19T22:35:12.887Z] Movies recommended for you:
[2025-02-19T22:35:12.887Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-19T22:35:12.887Z] There is no way to check that no silent failure occurred.
[2025-02-19T22:35:12.887Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (8935.104 ms) ======
[2025-02-19T22:35:12.887Z] -----------------------------------
[2025-02-19T22:35:12.887Z] renaissance-movie-lens_0_PASSED
[2025-02-19T22:35:12.887Z] -----------------------------------
[2025-02-19T22:35:12.887Z]
[2025-02-19T22:35:12.887Z] TEST TEARDOWN:
[2025-02-19T22:35:12.887Z] Nothing to be done for teardown.
[2025-02-19T22:35:12.887Z] renaissance-movie-lens_0 Finish Time: Wed Feb 19 17:35:12 2025 Epoch Time (ms): 1740004512798