renaissance-movie-lens_0
[2024-11-29T15:26:11.609Z] Running test renaissance-movie-lens_0 ...
[2024-11-29T15:26:11.609Z] ===============================================
[2024-11-29T15:26:11.609Z] renaissance-movie-lens_0 Start Time: Fri Nov 29 15:26:11 2024 Epoch Time (ms): 1732893971229
[2024-11-29T15:26:11.609Z] variation: NoOptions
[2024-11-29T15:26:11.609Z] JVM_OPTIONS:
[2024-11-29T15:26:11.609Z] { \
[2024-11-29T15:26:11.609Z] echo ""; echo "TEST SETUP:"; \
[2024-11-29T15:26:11.609Z] echo "Nothing to be done for setup."; \
[2024-11-29T15:26:11.609Z] mkdir -p "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_17328918013078/renaissance-movie-lens_0"; \
[2024-11-29T15:26:11.609Z] cd "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_17328918013078/renaissance-movie-lens_0"; \
[2024-11-29T15:26:11.609Z] echo ""; echo "TESTING:"; \
[2024-11-29T15:26:11.609Z] "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/jdkbinary/j2sdk-image/bin/java" -jar "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_17328918013078/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-29T15:26:11.609Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/..; rm -f -r "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_17328918013078/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-29T15:26:11.609Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-29T15:26:11.609Z] echo "Nothing to be done for teardown."; \
[2024-11-29T15:26:11.609Z] } 2>&1 | tee -a "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_solaris/aqa-tests/TKG/../TKG/output_17328918013078/TestTargetResult";
[2024-11-29T15:26:11.609Z]
[2024-11-29T15:26:11.609Z] TEST SETUP:
[2024-11-29T15:26:11.609Z] Nothing to be done for setup.
[2024-11-29T15:26:11.609Z]
[2024-11-29T15:26:11.609Z] TESTING:
[2024-11-29T15:26:22.035Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-29T15:26:28.119Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 1 (out of 1) threads.
[2024-11-29T15:26:39.084Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-29T15:26:39.813Z] Training: 60056, validation: 20285, test: 19854
[2024-11-29T15:26:39.813Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-29T15:26:39.813Z] GC before operation: completed in 143.522 ms, heap usage 47.472 MB -> 26.280 MB.
[2024-11-29T15:26:55.809Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:27:03.719Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:27:11.180Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:27:17.256Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:27:21.185Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:27:26.206Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:27:30.071Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:27:32.997Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:27:33.633Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:27:34.293Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:27:34.293Z] Movies recommended for you:
[2024-11-29T15:27:34.293Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:27:34.293Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:27:34.293Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (54853.665 ms) ======
[2024-11-29T15:27:34.293Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-29T15:27:34.954Z] GC before operation: completed in 217.335 ms, heap usage 112.290 MB -> 61.931 MB.
[2024-11-29T15:27:42.888Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:27:50.250Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:27:54.235Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:27:59.312Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:28:03.353Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:28:06.408Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:28:09.395Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:28:12.608Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:28:12.608Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:28:12.608Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:28:13.263Z] Movies recommended for you:
[2024-11-29T15:28:13.263Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:28:13.263Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:28:13.263Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (38402.823 ms) ======
[2024-11-29T15:28:13.263Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-29T15:28:13.263Z] GC before operation: completed in 204.646 ms, heap usage 89.220 MB -> 49.124 MB.
[2024-11-29T15:28:19.286Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:28:24.214Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:28:29.102Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:28:35.626Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:28:39.599Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:28:42.542Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:28:45.518Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:28:48.652Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:28:49.293Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:28:49.293Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:28:49.293Z] Movies recommended for you:
[2024-11-29T15:28:49.293Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:28:49.293Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:28:49.293Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (36122.610 ms) ======
[2024-11-29T15:28:49.293Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-29T15:28:49.942Z] GC before operation: completed in 245.311 ms, heap usage 95.274 MB -> 65.986 MB.
[2024-11-29T15:28:54.006Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:28:58.913Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:29:03.934Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:29:08.996Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:29:11.132Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:29:14.224Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:29:17.175Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:29:20.127Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:29:20.127Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:29:20.787Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:29:20.787Z] Movies recommended for you:
[2024-11-29T15:29:20.787Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:29:20.787Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:29:20.787Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (31080.746 ms) ======
[2024-11-29T15:29:20.787Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-29T15:29:21.435Z] GC before operation: completed in 465.810 ms, heap usage 128.301 MB -> 75.771 MB.
[2024-11-29T15:29:25.508Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:29:30.462Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:29:35.342Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:29:40.255Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:29:43.183Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:29:45.282Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:29:48.242Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:29:51.395Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:29:51.395Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:29:51.395Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:29:52.058Z] Movies recommended for you:
[2024-11-29T15:29:52.058Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:29:52.058Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:29:52.058Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (30659.758 ms) ======
[2024-11-29T15:29:52.058Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-29T15:29:52.058Z] GC before operation: completed in 241.016 ms, heap usage 126.387 MB -> 72.154 MB.
[2024-11-29T15:29:56.934Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:30:01.870Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:30:06.786Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:30:10.729Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:30:13.349Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:30:16.279Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:30:19.254Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:30:22.209Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:30:22.856Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:30:22.856Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:30:22.856Z] Movies recommended for you:
[2024-11-29T15:30:22.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:30:22.856Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:30:22.856Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (30823.268 ms) ======
[2024-11-29T15:30:22.856Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-29T15:30:22.856Z] GC before operation: completed in 261.697 ms, heap usage 127.398 MB -> 67.989 MB.
[2024-11-29T15:30:27.748Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:30:32.672Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:30:37.627Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:30:42.542Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:30:44.804Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:30:47.730Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:30:50.823Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:30:53.817Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:30:53.817Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:30:53.817Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:30:54.466Z] Movies recommended for you:
[2024-11-29T15:30:54.466Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:30:54.466Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:30:54.466Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (31088.337 ms) ======
[2024-11-29T15:30:54.466Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-29T15:30:54.466Z] GC before operation: completed in 242.645 ms, heap usage 109.276 MB -> 64.110 MB.
[2024-11-29T15:30:59.928Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:31:04.766Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:31:08.666Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:31:13.734Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:31:16.690Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:31:19.629Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:31:22.546Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:31:24.752Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:31:25.394Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:31:26.068Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:31:26.068Z] Movies recommended for you:
[2024-11-29T15:31:26.068Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:31:26.068Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:31:26.068Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (31589.127 ms) ======
[2024-11-29T15:31:26.068Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-29T15:31:26.068Z] GC before operation: completed in 394.169 ms, heap usage 104.809 MB -> 70.953 MB.
[2024-11-29T15:31:30.973Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:31:35.973Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:31:40.835Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:31:44.657Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:31:48.275Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:31:50.526Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:31:53.488Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:31:56.504Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:31:57.150Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:31:57.150Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:31:57.150Z] Movies recommended for you:
[2024-11-29T15:31:57.150Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:31:57.150Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:31:57.150Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (30851.093 ms) ======
[2024-11-29T15:31:57.150Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-29T15:31:57.803Z] GC before operation: completed in 338.911 ms, heap usage 109.465 MB -> 71.026 MB.
[2024-11-29T15:32:01.624Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:32:07.675Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:32:11.516Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:32:16.436Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:32:19.368Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:32:22.293Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:32:25.201Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:32:27.301Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:32:27.945Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:32:27.945Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:32:28.582Z] Movies recommended for you:
[2024-11-29T15:32:28.582Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:32:28.582Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:32:28.583Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (30760.122 ms) ======
[2024-11-29T15:32:28.583Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-29T15:32:28.583Z] GC before operation: completed in 228.121 ms, heap usage 110.486 MB -> 69.776 MB.
[2024-11-29T15:32:33.758Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:32:38.131Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:32:42.983Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:32:46.848Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:32:49.783Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:32:52.719Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:32:54.815Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:32:57.771Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:32:58.407Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:32:58.407Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:32:58.407Z] Movies recommended for you:
[2024-11-29T15:32:58.407Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:32:58.407Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:32:58.407Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (29965.578 ms) ======
[2024-11-29T15:32:58.407Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-29T15:32:59.043Z] GC before operation: completed in 401.920 ms, heap usage 124.216 MB -> 64.773 MB.
[2024-11-29T15:33:02.952Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:33:07.896Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:33:12.873Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:33:16.767Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:33:19.723Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:33:22.766Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:33:25.712Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:33:28.689Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:33:28.689Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:33:28.689Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:33:29.451Z] Movies recommended for you:
[2024-11-29T15:33:29.451Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:33:29.451Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:33:29.451Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (30324.172 ms) ======
[2024-11-29T15:33:29.451Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-29T15:33:30.109Z] GC before operation: completed in 617.234 ms, heap usage 105.399 MB -> 73.122 MB.
[2024-11-29T15:33:34.025Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:33:40.229Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:33:45.234Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:33:50.290Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:33:53.259Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:33:55.391Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:33:59.311Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:34:01.689Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:34:02.337Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:34:02.337Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:34:02.337Z] Movies recommended for you:
[2024-11-29T15:34:02.337Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:34:02.337Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:34:02.337Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (32690.841 ms) ======
[2024-11-29T15:34:02.337Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-29T15:34:03.043Z] GC before operation: completed in 250.174 ms, heap usage 155.094 MB -> 71.487 MB.
[2024-11-29T15:34:08.227Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:34:12.214Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:34:17.775Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:34:21.763Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:34:24.656Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:34:27.629Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:34:29.789Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:34:32.761Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:34:32.761Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:34:32.761Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:34:32.761Z] Movies recommended for you:
[2024-11-29T15:34:32.761Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:34:32.761Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:34:32.761Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (30390.528 ms) ======
[2024-11-29T15:34:32.761Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-29T15:34:33.396Z] GC before operation: completed in 235.907 ms, heap usage 129.303 MB -> 68.249 MB.
[2024-11-29T15:34:38.490Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:34:42.416Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:34:47.302Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:34:52.180Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:34:54.349Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:34:57.381Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:34:59.557Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:35:02.134Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:35:02.769Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:35:02.769Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:35:02.769Z] Movies recommended for you:
[2024-11-29T15:35:02.769Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:35:02.769Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:35:02.769Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (29618.769 ms) ======
[2024-11-29T15:35:02.769Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-29T15:35:03.404Z] GC before operation: completed in 419.644 ms, heap usage 130.428 MB -> 65.399 MB.
[2024-11-29T15:35:08.302Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:35:12.586Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:35:18.601Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:35:22.432Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:35:25.342Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:35:29.187Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:35:32.079Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:35:34.993Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:35:35.688Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:35:35.688Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:35:35.688Z] Movies recommended for you:
[2024-11-29T15:35:35.688Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:35:35.688Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:35:35.688Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (32289.125 ms) ======
[2024-11-29T15:35:35.688Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-29T15:35:35.688Z] GC before operation: completed in 244.496 ms, heap usage 102.368 MB -> 69.285 MB.
[2024-11-29T15:35:41.896Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:35:46.873Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:35:52.329Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:35:57.422Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:35:59.518Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:36:02.475Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:36:05.579Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:36:08.537Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:36:09.176Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:36:09.176Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:36:09.825Z] Movies recommended for you:
[2024-11-29T15:36:09.825Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:36:09.825Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:36:09.825Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (33701.673 ms) ======
[2024-11-29T15:36:09.825Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-29T15:36:09.825Z] GC before operation: completed in 251.828 ms, heap usage 117.974 MB -> 70.318 MB.
[2024-11-29T15:36:14.825Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:36:19.723Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:36:24.667Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:36:29.758Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:36:31.907Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:36:34.862Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:36:38.701Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:36:40.697Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:36:41.336Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:36:41.336Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:36:41.969Z] Movies recommended for you:
[2024-11-29T15:36:41.969Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:36:41.969Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:36:41.969Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (31945.041 ms) ======
[2024-11-29T15:36:41.969Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-29T15:36:41.969Z] GC before operation: completed in 241.000 ms, heap usage 125.428 MB -> 69.186 MB.
[2024-11-29T15:36:46.862Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:36:50.742Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:36:55.637Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:37:00.507Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:37:02.628Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:37:05.549Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:37:08.601Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:37:10.882Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:37:11.518Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:37:11.518Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:37:11.518Z] Movies recommended for you:
[2024-11-29T15:37:11.518Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:37:11.518Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:37:11.518Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (29717.054 ms) ======
[2024-11-29T15:37:11.518Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-29T15:37:12.162Z] GC before operation: completed in 256.238 ms, heap usage 112.227 MB -> 63.263 MB.
[2024-11-29T15:37:17.057Z] RMSE (validation) = 3.6219689545487626 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T15:37:21.947Z] RMSE (validation) = 2.1340923188651915 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T15:37:27.037Z] RMSE (validation) = 1.3105186849113732 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T15:37:31.376Z] RMSE (validation) = 1.0067105965077905 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T15:37:34.295Z] RMSE (validation) = 1.2280678781652192 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T15:37:37.237Z] RMSE (validation) = 1.1218153401857944 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T15:37:40.300Z] RMSE (validation) = 0.9269352310919159 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T15:37:42.690Z] RMSE (validation) = 0.9005755986883522 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T15:37:43.360Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9083924152149858.
[2024-11-29T15:37:43.360Z] The best model improves the baseline by 14.33%.
[2024-11-29T15:37:43.360Z] Movies recommended for you:
[2024-11-29T15:37:43.360Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T15:37:43.360Z] There is no way to check that no silent failure occurred.
[2024-11-29T15:37:43.360Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (31754.455 ms) ======
[2024-11-29T15:37:45.502Z] -----------------------------------
[2024-11-29T15:37:45.502Z] renaissance-movie-lens_0_PASSED
[2024-11-29T15:37:45.502Z] -----------------------------------
[2024-11-29T15:37:45.502Z]
[2024-11-29T15:37:45.502Z] TEST TEARDOWN:
[2024-11-29T15:37:45.502Z] Nothing to be done for teardown.
[2024-11-29T15:37:45.502Z] renaissance-movie-lens_0 Finish Time: Fri Nov 29 15:37:44 2024 Epoch Time (ms): 1732894664873