renaissance-movie-lens_0
[2024-12-06T22:32:29.223Z] Running test renaissance-movie-lens_0 ...
[2024-12-06T22:32:29.223Z] ===============================================
[2024-12-06T22:32:29.223Z] renaissance-movie-lens_0 Start Time: Fri Dec 6 22:32:28 2024 Epoch Time (ms): 1733524348936
[2024-12-06T22:32:29.223Z] variation: NoOptions
[2024-12-06T22:32:29.223Z] JVM_OPTIONS:
[2024-12-06T22:32:29.223Z] { \
[2024-12-06T22:32:29.223Z] echo ""; echo "TEST SETUP:"; \
[2024-12-06T22:32:29.223Z] echo "Nothing to be done for setup."; \
[2024-12-06T22:32:29.223Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17335231462955/renaissance-movie-lens_0"; \
[2024-12-06T22:32:29.223Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17335231462955/renaissance-movie-lens_0"; \
[2024-12-06T22:32:29.223Z] echo ""; echo "TESTING:"; \
[2024-12-06T22:32:29.223Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17335231462955/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-06T22:32:29.223Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17335231462955/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-06T22:32:29.223Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-06T22:32:29.223Z] echo "Nothing to be done for teardown."; \
[2024-12-06T22:32:29.223Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17335231462955/TestTargetResult";
[2024-12-06T22:32:29.223Z]
[2024-12-06T22:32:29.223Z] TEST SETUP:
[2024-12-06T22:32:29.223Z] Nothing to be done for setup.
[2024-12-06T22:32:29.223Z]
[2024-12-06T22:32:29.223Z] TESTING:
[2024-12-06T22:32:33.394Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-06T22:32:36.133Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-12-06T22:32:41.470Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-06T22:32:41.470Z] Training: 60056, validation: 20285, test: 19854
[2024-12-06T22:32:41.470Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-06T22:32:41.858Z] GC before operation: completed in 91.492 ms, heap usage 105.881 MB -> 36.431 MB.
[2024-12-06T22:32:51.560Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:32:58.179Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:33:02.467Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:33:06.774Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:33:09.382Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:33:11.284Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:33:13.898Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:33:15.860Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:33:16.260Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:33:16.260Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:33:16.661Z] Movies recommended for you:
[2024-12-06T22:33:16.661Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:33:16.661Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:33:16.661Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (34879.290 ms) ======
[2024-12-06T22:33:16.661Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-06T22:33:16.661Z] GC before operation: completed in 101.174 ms, heap usage 155.684 MB -> 49.898 MB.
[2024-12-06T22:33:20.884Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:33:25.089Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:33:28.538Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:33:31.870Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:33:33.982Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:33:35.874Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:33:37.780Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:33:40.368Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:33:40.368Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:33:40.368Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:33:40.368Z] Movies recommended for you:
[2024-12-06T22:33:40.368Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:33:40.368Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:33:40.368Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (23822.740 ms) ======
[2024-12-06T22:33:40.368Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-06T22:33:40.754Z] GC before operation: completed in 89.148 ms, heap usage 94.727 MB -> 48.925 MB.
[2024-12-06T22:33:44.100Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:33:47.462Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:33:50.820Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:33:54.151Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:33:56.090Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:33:58.665Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:34:00.542Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:34:02.454Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:34:02.838Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:34:02.838Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:34:02.838Z] Movies recommended for you:
[2024-12-06T22:34:02.838Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:34:02.838Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:34:02.838Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (22303.105 ms) ======
[2024-12-06T22:34:02.838Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-06T22:34:02.838Z] GC before operation: completed in 92.969 ms, heap usage 198.985 MB -> 49.307 MB.
[2024-12-06T22:34:07.034Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:34:10.373Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:34:13.732Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:34:16.292Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:34:18.196Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:34:20.110Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:34:22.131Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:34:24.100Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:34:24.100Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:34:24.486Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:34:24.486Z] Movies recommended for you:
[2024-12-06T22:34:24.486Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:34:24.486Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:34:24.486Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (21471.147 ms) ======
[2024-12-06T22:34:24.486Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-06T22:34:24.486Z] GC before operation: completed in 94.366 ms, heap usage 172.988 MB -> 49.654 MB.
[2024-12-06T22:34:27.814Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:34:31.136Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:34:35.382Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:34:37.958Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:34:39.296Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:34:41.266Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:34:43.854Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:34:45.198Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:34:45.589Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:34:45.589Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:34:45.994Z] Movies recommended for you:
[2024-12-06T22:34:45.994Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:34:45.994Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:34:45.994Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21340.253 ms) ======
[2024-12-06T22:34:45.994Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-06T22:34:45.994Z] GC before operation: completed in 119.386 ms, heap usage 93.466 MB -> 51.339 MB.
[2024-12-06T22:34:49.362Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:34:51.975Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:34:55.333Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:34:58.650Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:35:00.583Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:35:02.513Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:35:04.442Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:35:05.819Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:35:06.231Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:35:06.231Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:35:06.631Z] Movies recommended for you:
[2024-12-06T22:35:06.631Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:35:06.631Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:35:06.631Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20463.409 ms) ======
[2024-12-06T22:35:06.631Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-06T22:35:06.631Z] GC before operation: completed in 95.543 ms, heap usage 74.973 MB -> 49.631 MB.
[2024-12-06T22:35:09.942Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:35:12.625Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:35:15.969Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:35:18.524Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:35:20.425Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:35:22.354Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:35:24.259Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:35:26.176Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:35:26.176Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:35:26.558Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:35:26.558Z] Movies recommended for you:
[2024-12-06T22:35:26.558Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:35:26.558Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:35:26.558Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (19951.973 ms) ======
[2024-12-06T22:35:26.558Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-06T22:35:26.558Z] GC before operation: completed in 90.976 ms, heap usage 208.793 MB -> 49.959 MB.
[2024-12-06T22:35:29.882Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:35:32.428Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:35:35.756Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:35:38.347Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:35:40.246Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:35:42.161Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:35:44.069Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:35:45.484Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:35:45.888Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:35:45.888Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:35:45.888Z] Movies recommended for you:
[2024-12-06T22:35:45.888Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:35:45.888Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:35:45.888Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19352.250 ms) ======
[2024-12-06T22:35:45.888Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-06T22:35:45.888Z] GC before operation: completed in 92.047 ms, heap usage 106.414 MB -> 50.652 MB.
[2024-12-06T22:35:49.205Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:35:51.804Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:35:55.210Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:35:57.763Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:35:59.653Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:36:01.549Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:36:03.500Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:36:05.448Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:36:05.448Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:36:05.448Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:36:05.448Z] Movies recommended for you:
[2024-12-06T22:36:05.448Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:36:05.448Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:36:05.448Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19525.105 ms) ======
[2024-12-06T22:36:05.448Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-06T22:36:05.831Z] GC before operation: completed in 99.637 ms, heap usage 201.477 MB -> 50.040 MB.
[2024-12-06T22:36:09.227Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:36:11.766Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:36:15.071Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:36:17.656Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:36:19.597Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:36:20.934Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:36:22.843Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:36:24.751Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:36:25.140Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:36:25.140Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:36:25.140Z] Movies recommended for you:
[2024-12-06T22:36:25.140Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:36:25.140Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:36:25.140Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19503.440 ms) ======
[2024-12-06T22:36:25.140Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-06T22:36:25.140Z] GC before operation: completed in 93.059 ms, heap usage 122.538 MB -> 50.058 MB.
[2024-12-06T22:36:28.452Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:36:31.790Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:36:34.379Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:36:36.936Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:36:39.509Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:36:40.863Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:36:42.760Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:36:44.690Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:36:44.690Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:36:44.690Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:36:45.080Z] Movies recommended for you:
[2024-12-06T22:36:45.080Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:36:45.080Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:36:45.080Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19711.498 ms) ======
[2024-12-06T22:36:45.080Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-06T22:36:45.080Z] GC before operation: completed in 94.146 ms, heap usage 173.533 MB -> 49.885 MB.
[2024-12-06T22:36:48.397Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:36:50.972Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:36:54.453Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:36:57.036Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:36:58.954Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:37:00.889Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:37:02.843Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:37:04.186Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:37:04.571Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:37:04.571Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:37:04.957Z] Movies recommended for you:
[2024-12-06T22:37:04.957Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:37:04.957Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:37:04.957Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19766.869 ms) ======
[2024-12-06T22:37:04.957Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-06T22:37:04.957Z] GC before operation: completed in 94.845 ms, heap usage 152.897 MB -> 50.024 MB.
[2024-12-06T22:37:08.292Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:37:10.931Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:37:14.310Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:37:16.887Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:37:18.791Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:37:20.722Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:37:22.654Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:37:24.607Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:37:24.607Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:37:24.607Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:37:24.996Z] Movies recommended for you:
[2024-12-06T22:37:24.996Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:37:24.996Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:37:24.996Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (19966.436 ms) ======
[2024-12-06T22:37:24.996Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-06T22:37:24.997Z] GC before operation: completed in 91.463 ms, heap usage 192.592 MB -> 50.225 MB.
[2024-12-06T22:37:28.351Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:37:30.976Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:37:34.359Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:37:36.916Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:37:38.832Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:37:40.743Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:37:42.698Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:37:44.614Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:37:44.614Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:37:44.614Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:37:44.614Z] Movies recommended for you:
[2024-12-06T22:37:44.614Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:37:44.614Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:37:44.614Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19750.593 ms) ======
[2024-12-06T22:37:44.614Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-06T22:37:44.998Z] GC before operation: completed in 94.386 ms, heap usage 153.551 MB -> 49.942 MB.
[2024-12-06T22:37:48.290Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:37:50.830Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:37:54.310Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:37:56.913Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:37:58.812Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:38:00.701Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:38:02.659Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:38:04.002Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:38:04.399Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:38:04.399Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:38:04.399Z] Movies recommended for you:
[2024-12-06T22:38:04.399Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:38:04.399Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:38:04.399Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19724.194 ms) ======
[2024-12-06T22:38:04.399Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-06T22:38:04.780Z] GC before operation: completed in 92.657 ms, heap usage 174.575 MB -> 50.155 MB.
[2024-12-06T22:38:08.108Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:38:10.699Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:38:14.110Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:38:16.725Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:38:18.075Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:38:20.009Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:38:22.032Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:38:23.947Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:38:23.948Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:38:23.948Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:38:23.948Z] Movies recommended for you:
[2024-12-06T22:38:23.948Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:38:23.948Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:38:23.948Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19417.655 ms) ======
[2024-12-06T22:38:23.948Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-06T22:38:24.329Z] GC before operation: completed in 92.661 ms, heap usage 179.140 MB -> 50.227 MB.
[2024-12-06T22:38:27.640Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:38:30.227Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:38:33.602Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:38:36.223Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:38:37.554Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:38:39.540Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:38:41.443Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:38:42.813Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:38:43.218Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:38:43.218Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:38:43.218Z] Movies recommended for you:
[2024-12-06T22:38:43.218Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:38:43.218Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:38:43.218Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19097.790 ms) ======
[2024-12-06T22:38:43.218Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-06T22:38:43.218Z] GC before operation: completed in 105.603 ms, heap usage 124.197 MB -> 49.984 MB.
[2024-12-06T22:38:46.511Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:38:49.868Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:38:52.424Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:38:55.113Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:38:57.000Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:38:58.956Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:39:00.884Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:39:02.260Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:39:02.669Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:39:02.669Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:39:02.669Z] Movies recommended for you:
[2024-12-06T22:39:02.669Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:39:02.669Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:39:02.669Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19454.902 ms) ======
[2024-12-06T22:39:02.669Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-06T22:39:03.057Z] GC before operation: completed in 99.794 ms, heap usage 140.401 MB -> 50.059 MB.
[2024-12-06T22:39:06.358Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:39:08.907Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:39:12.257Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:39:14.840Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:39:16.802Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:39:18.132Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:39:20.036Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:39:22.093Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:39:22.093Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:39:22.478Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:39:22.478Z] Movies recommended for you:
[2024-12-06T22:39:22.478Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:39:22.478Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:39:22.478Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19521.499 ms) ======
[2024-12-06T22:39:22.478Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-06T22:39:22.478Z] GC before operation: completed in 92.022 ms, heap usage 202.689 MB -> 50.302 MB.
[2024-12-06T22:39:25.815Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T22:39:28.368Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T22:39:31.712Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T22:39:34.272Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T22:39:36.172Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T22:39:38.075Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T22:39:39.409Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T22:39:41.340Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T22:39:41.721Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T22:39:41.721Z] The best model improves the baseline by 14.52%.
[2024-12-06T22:39:41.721Z] Movies recommended for you:
[2024-12-06T22:39:41.721Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T22:39:41.721Z] There is no way to check that no silent failure occurred.
[2024-12-06T22:39:41.721Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19288.950 ms) ======
[2024-12-06T22:39:42.549Z] -----------------------------------
[2024-12-06T22:39:42.549Z] renaissance-movie-lens_0_PASSED
[2024-12-06T22:39:42.549Z] -----------------------------------
[2024-12-06T22:39:42.549Z]
[2024-12-06T22:39:42.549Z] TEST TEARDOWN:
[2024-12-06T22:39:42.549Z] Nothing to be done for teardown.
[2024-12-06T22:39:42.549Z] renaissance-movie-lens_0 Finish Time: Fri Dec 6 22:39:42 2024 Epoch Time (ms): 1733524782346