renaissance-movie-lens_0
[2024-11-17T07:49:03.386Z] Running test renaissance-movie-lens_0 ...
[2024-11-17T07:49:03.386Z] ===============================================
[2024-11-17T07:49:03.386Z] renaissance-movie-lens_0 Start Time: Sun Nov 17 07:49:03 2024 Epoch Time (ms): 1731829743043
[2024-11-17T07:49:03.386Z] variation: NoOptions
[2024-11-17T07:49:03.386Z] JVM_OPTIONS:
[2024-11-17T07:49:03.386Z] { \
[2024-11-17T07:49:03.386Z] echo ""; echo "TEST SETUP:"; \
[2024-11-17T07:49:03.386Z] echo "Nothing to be done for setup."; \
[2024-11-17T07:49:03.386Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318272656887/renaissance-movie-lens_0"; \
[2024-11-17T07:49:03.386Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318272656887/renaissance-movie-lens_0"; \
[2024-11-17T07:49:03.386Z] echo ""; echo "TESTING:"; \
[2024-11-17T07:49:03.386Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-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_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318272656887/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-17T07:49:03.386Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318272656887/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-17T07:49:03.386Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-17T07:49:03.386Z] echo "Nothing to be done for teardown."; \
[2024-11-17T07:49:03.386Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318272656887/TestTargetResult";
[2024-11-17T07:49:03.386Z]
[2024-11-17T07:49:03.386Z] TEST SETUP:
[2024-11-17T07:49:03.386Z] Nothing to be done for setup.
[2024-11-17T07:49:03.386Z]
[2024-11-17T07:49:03.386Z] TESTING:
[2024-11-17T07:49:13.196Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-17T07:49:21.404Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-17T07:49:37.553Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-17T07:49:37.553Z] Training: 60056, validation: 20285, test: 19854
[2024-11-17T07:49:37.553Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-17T07:49:38.315Z] GC before operation: completed in 344.765 ms, heap usage 58.349 MB -> 36.440 MB.
[2024-11-17T07:50:09.098Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:50:28.751Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:50:44.913Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:50:58.785Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:51:07.009Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:51:15.415Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:51:22.243Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:51:29.124Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:51:30.761Z] 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.
[2024-11-17T07:51:30.761Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:51:31.534Z] Movies recommended for you:
[2024-11-17T07:51:31.534Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:51:31.534Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:51:31.534Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (113237.086 ms) ======
[2024-11-17T07:51:31.534Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-17T07:51:32.314Z] GC before operation: completed in 639.535 ms, heap usage 232.710 MB -> 49.907 MB.
[2024-11-17T07:51:44.040Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:51:55.817Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:52:07.446Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:52:15.650Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:52:22.492Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:52:28.110Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:52:33.762Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:52:39.368Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:52:40.148Z] 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.
[2024-11-17T07:52:40.149Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:52:40.149Z] Movies recommended for you:
[2024-11-17T07:52:40.149Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:52:40.149Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:52:40.149Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (68351.092 ms) ======
[2024-11-17T07:52:40.149Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-17T07:52:40.927Z] GC before operation: completed in 320.551 ms, heap usage 270.434 MB -> 49.074 MB.
[2024-11-17T07:52:49.263Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:53:01.146Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:53:11.024Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:53:19.321Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:53:25.452Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:53:31.100Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:53:36.689Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:53:43.587Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:53:43.587Z] 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.
[2024-11-17T07:53:44.364Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:53:44.364Z] Movies recommended for you:
[2024-11-17T07:53:44.364Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:53:44.364Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:53:44.364Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (63594.364 ms) ======
[2024-11-17T07:53:44.364Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-17T07:53:45.149Z] GC before operation: completed in 402.606 ms, heap usage 187.281 MB -> 49.304 MB.
[2024-11-17T07:53:55.058Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:54:04.891Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:54:13.088Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:54:21.285Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:54:28.067Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:54:32.500Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:54:38.056Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:54:43.698Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:54:44.475Z] 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.
[2024-11-17T07:54:45.257Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:54:45.257Z] Movies recommended for you:
[2024-11-17T07:54:45.257Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:54:45.257Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:54:45.257Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (60427.389 ms) ======
[2024-11-17T07:54:45.257Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-17T07:54:45.257Z] GC before operation: completed in 366.110 ms, heap usage 176.009 MB -> 49.643 MB.
[2024-11-17T07:54:54.071Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:55:04.010Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:55:15.712Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:55:27.583Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:55:32.439Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:55:37.318Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:55:47.861Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:55:55.212Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:55:56.085Z] 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.
[2024-11-17T07:55:56.085Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:55:56.940Z] Movies recommended for you:
[2024-11-17T07:55:56.940Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:55:56.940Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:55:56.940Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (71074.195 ms) ======
[2024-11-17T07:55:56.940Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-17T07:55:56.940Z] GC before operation: completed in 470.569 ms, heap usage 221.097 MB -> 49.815 MB.
[2024-11-17T07:56:07.572Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:56:18.081Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:56:26.067Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:56:35.497Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:56:40.380Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:56:46.496Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:56:51.372Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:56:57.503Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:56:58.354Z] 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.
[2024-11-17T07:56:58.354Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:56:59.205Z] Movies recommended for you:
[2024-11-17T07:56:59.205Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:56:59.205Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:56:59.206Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (61897.545 ms) ======
[2024-11-17T07:56:59.206Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-17T07:56:59.206Z] GC before operation: completed in 364.715 ms, heap usage 175.665 MB -> 49.740 MB.
[2024-11-17T07:57:09.845Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:57:17.246Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:57:26.164Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:57:35.146Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:57:41.288Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:57:47.388Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:57:53.697Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:57:59.157Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:57:59.157Z] 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.
[2024-11-17T07:58:00.000Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:58:00.000Z] Movies recommended for you:
[2024-11-17T07:58:00.000Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:58:00.000Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:58:00.000Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (60420.138 ms) ======
[2024-11-17T07:58:00.000Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-17T07:58:00.000Z] GC before operation: completed in 407.257 ms, heap usage 193.291 MB -> 49.906 MB.
[2024-11-17T07:58:10.877Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:58:18.506Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:58:29.360Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:58:38.465Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:58:43.461Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:58:49.654Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:58:55.253Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T07:59:00.284Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T07:59:01.147Z] 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.
[2024-11-17T07:59:01.147Z] The best model improves the baseline by 14.52%.
[2024-11-17T07:59:01.147Z] Movies recommended for you:
[2024-11-17T07:59:01.147Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T07:59:01.147Z] There is no way to check that no silent failure occurred.
[2024-11-17T07:59:01.147Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (61045.388 ms) ======
[2024-11-17T07:59:01.147Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-17T07:59:02.018Z] GC before operation: completed in 374.615 ms, heap usage 223.840 MB -> 50.225 MB.
[2024-11-17T07:59:11.144Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T07:59:22.019Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T07:59:32.778Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T07:59:40.424Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T07:59:45.466Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T07:59:50.522Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T07:59:56.827Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:00:01.876Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:00:04.308Z] 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.
[2024-11-17T08:00:04.308Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:00:04.308Z] Movies recommended for you:
[2024-11-17T08:00:04.308Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:00:04.308Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:00:04.308Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (62097.897 ms) ======
[2024-11-17T08:00:04.308Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-17T08:00:04.308Z] GC before operation: completed in 493.415 ms, heap usage 258.839 MB -> 50.079 MB.
[2024-11-17T08:00:15.199Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:00:22.838Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:00:33.646Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:00:39.934Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:00:44.903Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:00:48.750Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:00:53.724Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:00:58.666Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:00:58.666Z] 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.
[2024-11-17T08:00:58.666Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:00:59.533Z] Movies recommended for you:
[2024-11-17T08:00:59.533Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:00:59.533Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:00:59.533Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (54882.947 ms) ======
[2024-11-17T08:00:59.533Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-17T08:00:59.533Z] GC before operation: completed in 344.201 ms, heap usage 237.380 MB -> 50.109 MB.
[2024-11-17T08:01:11.015Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:01:21.815Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:01:32.605Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:01:43.507Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:01:49.767Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:01:55.996Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:02:03.560Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:02:09.813Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:02:10.677Z] 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.
[2024-11-17T08:02:10.677Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:02:10.677Z] Movies recommended for you:
[2024-11-17T08:02:10.677Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:02:10.677Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:02:10.677Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (71321.460 ms) ======
[2024-11-17T08:02:10.677Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-17T08:02:11.542Z] GC before operation: completed in 434.435 ms, heap usage 174.918 MB -> 49.911 MB.
[2024-11-17T08:02:22.700Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:02:33.590Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:02:44.548Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:02:55.561Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:03:01.865Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:03:08.144Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:03:15.817Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:03:22.797Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:03:23.677Z] 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.
[2024-11-17T08:03:23.677Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:03:23.677Z] Movies recommended for you:
[2024-11-17T08:03:23.677Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:03:23.677Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:03:23.677Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (72039.677 ms) ======
[2024-11-17T08:03:23.677Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-17T08:03:23.677Z] GC before operation: completed in 440.825 ms, heap usage 208.831 MB -> 50.032 MB.
[2024-11-17T08:03:34.598Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:03:45.627Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:03:58.507Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:04:09.375Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:04:15.714Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:04:22.013Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:04:29.703Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:04:36.616Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:04:37.501Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-17T08:04:37.501Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:04:38.376Z] Movies recommended for you:
[2024-11-17T08:04:38.377Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:04:38.377Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:04:38.377Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (74150.817 ms) ======
[2024-11-17T08:04:38.377Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-17T08:04:38.377Z] GC before operation: completed in 559.505 ms, heap usage 148.312 MB -> 50.372 MB.
[2024-11-17T08:04:49.393Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:05:00.376Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:05:11.328Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:05:22.216Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:05:28.539Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:05:34.839Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:05:41.763Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:05:48.046Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:05:48.916Z] 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.
[2024-11-17T08:05:48.916Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:05:49.782Z] Movies recommended for you:
[2024-11-17T08:05:49.782Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:05:49.782Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:05:49.782Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (71215.752 ms) ======
[2024-11-17T08:05:49.782Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-17T08:05:49.782Z] GC before operation: completed in 372.569 ms, heap usage 242.144 MB -> 49.940 MB.
[2024-11-17T08:06:00.654Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:06:09.915Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:06:22.756Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:06:30.443Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:06:38.210Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:06:45.247Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:06:52.877Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:06:57.883Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:06:59.688Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-17T08:06:59.688Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:06:59.688Z] Movies recommended for you:
[2024-11-17T08:06:59.688Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:06:59.688Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:06:59.688Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (69653.458 ms) ======
[2024-11-17T08:06:59.688Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-17T08:07:00.558Z] GC before operation: completed in 518.862 ms, heap usage 178.385 MB -> 50.229 MB.
[2024-11-17T08:07:11.410Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:07:24.305Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:07:35.023Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:07:45.662Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:07:51.758Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:07:58.330Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:08:05.826Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:08:11.902Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:08:13.691Z] 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.
[2024-11-17T08:08:13.691Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:08:13.691Z] Movies recommended for you:
[2024-11-17T08:08:13.691Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:08:13.691Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:08:13.691Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (73516.767 ms) ======
[2024-11-17T08:08:13.691Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-17T08:08:14.534Z] GC before operation: completed in 393.678 ms, heap usage 212.071 MB -> 50.688 MB.
[2024-11-17T08:08:26.981Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:08:35.796Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:08:48.255Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:08:58.859Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:09:05.249Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:09:11.383Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:09:18.779Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:09:26.212Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:09:27.054Z] 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.
[2024-11-17T08:09:27.054Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:09:27.054Z] Movies recommended for you:
[2024-11-17T08:09:27.054Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:09:27.054Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:09:27.054Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (73005.321 ms) ======
[2024-11-17T08:09:27.054Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-17T08:09:27.908Z] GC before operation: completed in 425.804 ms, heap usage 205.621 MB -> 50.132 MB.
[2024-11-17T08:09:40.526Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:09:51.042Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:10:01.526Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:10:12.763Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:10:20.202Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:10:27.678Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:10:35.169Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:10:41.306Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:10:42.165Z] 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.
[2024-11-17T08:10:42.165Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:10:43.004Z] Movies recommended for you:
[2024-11-17T08:10:43.004Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:10:43.004Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:10:43.004Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (75123.724 ms) ======
[2024-11-17T08:10:43.004Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-17T08:10:43.004Z] GC before operation: completed in 625.101 ms, heap usage 194.282 MB -> 50.113 MB.
[2024-11-17T08:10:55.548Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:11:06.129Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:11:18.702Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:11:27.646Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:11:33.719Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:11:39.940Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:11:45.987Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:11:53.514Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:11:54.359Z] 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.
[2024-11-17T08:11:55.230Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:11:55.230Z] Movies recommended for you:
[2024-11-17T08:11:55.230Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:11:55.230Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:11:55.230Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (71872.668 ms) ======
[2024-11-17T08:11:55.230Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-17T08:11:56.104Z] GC before operation: completed in 511.208 ms, heap usage 190.849 MB -> 50.336 MB.
[2024-11-17T08:12:08.640Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T08:12:19.311Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T08:12:32.210Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T08:12:41.276Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T08:12:48.822Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T08:12:54.971Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T08:13:02.556Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T08:13:10.039Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T08:13:10.900Z] 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.
[2024-11-17T08:13:10.900Z] The best model improves the baseline by 14.52%.
[2024-11-17T08:13:11.737Z] Movies recommended for you:
[2024-11-17T08:13:11.737Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T08:13:11.737Z] There is no way to check that no silent failure occurred.
[2024-11-17T08:13:11.737Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (75567.865 ms) ======
[2024-11-17T08:13:12.578Z] -----------------------------------
[2024-11-17T08:13:12.578Z] renaissance-movie-lens_0_PASSED
[2024-11-17T08:13:12.578Z] -----------------------------------
[2024-11-17T08:13:12.578Z]
[2024-11-17T08:13:12.578Z] TEST TEARDOWN:
[2024-11-17T08:13:12.578Z] Nothing to be done for teardown.
[2024-11-17T08:13:12.578Z] renaissance-movie-lens_0 Finish Time: Sun Nov 17 08:13:12 2024 Epoch Time (ms): 1731831192369