Large bagging is very slow

Bagging is very slow. I am not sure what is causing it. See https://github.com/Microsoft/LightGBM/issues/562 for the dataset. I am using 0.40 subsampling to have this issue, it is not reproducible when subsampling is 0.60. I think bagging uses only 1 core, but I don't see this issue when using 0.60.
Using DLL compiled with Visual Studio 2017.

can you change 0.5 in this line : https://github.com/Microsoft/LightGBM/blob/master/src/boosting/gbdt.cpp#L150, to 1e-6 , and try again?

can you also try the 'bagging' branch ?

@guolinke Switching to 1e-6 seems to fix the issue.
On bagging branch, it was the same (branch is gone now?).

@Laurae2 can you try the latest master branch?

@Laurae2
I add some timing output in the "bagging" branch. Can you run with it and paste the logs (enable verbose) ?

Here some logs. I think some logs are out of place, no idea why. I'll retry with CLI.
> Laurae::timer_func_print({model <- lgb.train(params = list(objective = "binary",
+                                                            metric = "auc",
+                                                            bin_construct_sample_cnt = 2250000L,
+                                                            early_stopping_round = 25),
+                                              train,
+                                              5,
+                                              list(test = test),
+                                              verbose = 2)})
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=11
[1]:	test's auc:0.501172
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=11
[2]:	test's auc:0.501379
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=8
[3]:	test's auc:0.502558
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=12
[4]:	test's auc:0.502981
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=10
[5]:	test's auc:0.50398
The function ran in 37827.132 milliseconds.
[1] 37827.13
> rm(model)
> gc()
[LightGBM] [Info] GBDT::boosting costs 0.027171
[LightGBM] [Info] GBDT::train_score costs 0.012051
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.000001
[LightGBM] [Info] GBDT::valid_score costs 0.006769
[LightGBM] [Info] GBDT::metric costs 0.000000
[LightGBM] [Info] GBDT::bagging costs 0.000003
[LightGBM] [Info] GBDT::bagging_subset_time costs 0.000000
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 0.000000
[LightGBM] [Info] GBDT::sub_gradient costs 0.000000
[LightGBM] [Info] GBDT::tree costs 27.997018
[LightGBM] [Info] SerialTreeLearner::init_train costs 2.393088
[LightGBM] [Info] SerialTreeLearner::init_split costs 12.377513
[LightGBM] [Info] SerialTreeLearner::hist_build costs 10.837631
[LightGBM] [Info] SerialTreeLearner::find_split costs 2.226329
[LightGBM] [Info] SerialTreeLearner::split costs 0.070301
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 14.763519
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  692175 37.0    1168576 62.5  1168576 62.5
Vcells 3516642 26.9    5133766 39.2  4078954 31.2
> Laurae::timer_func_print({model <- lgb.train(params = list(objective = "binary",
+                                                            metric = "auc",
+                                                            bin_construct_sample_cnt = 2250000L,
+                                                            early_stopping_round = 25,
+                                                            bagging_freq = 1,
+                                                            bagging_seed = 1,
+                                                            bagging_fraction = 0.6),
+                                              train,
+                                              5,
+                                              list(test = test),
+                                              verbose = 2)})
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Debug] Re-bagging, using 1350000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=12
[1]:	test's auc:0.500272
[LightGBM] [Debug] Re-bagging, using 1350000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=11
[2]:	test's auc:0.500702
[LightGBM] [Debug] Re-bagging, using 1350000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=11
[3]:	test's auc:0.501856
[LightGBM] [Debug] Re-bagging, using 1350000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=11
[4]:	test's auc:0.503777
[LightGBM] [Debug] Re-bagging, using 1350000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=10
[5]:	test's auc:0.50587
The function ran in 24566.072 milliseconds.
[1] 24566.07
>
>
> rm(model)
> gc()
[LightGBM] [Info] GBDT::boosting costs 0.079639
[LightGBM] [Info] GBDT::train_score costs 0.025451
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.065390
[LightGBM] [Info] GBDT::valid_score costs 0.016837
[LightGBM] [Info] GBDT::metric costs 0.000000
[LightGBM] [Info] GBDT::bagging costs 0.013739
[LightGBM] [Info] GBDT::bagging_subset_time costs 0.000000
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 0.000000
[LightGBM] [Info] GBDT::sub_gradient costs 0.000000
[LightGBM] [Info] GBDT::tree costs 50.325836
[LightGBM] [Info] SerialTreeLearner::init_train costs 6.186171
[LightGBM] [Info] SerialTreeLearner::init_split costs 20.463570
[LightGBM] [Info] SerialTreeLearner::hist_build costs 18.954661
[LightGBM] [Info] SerialTreeLearner::find_split costs 4.454569
[LightGBM] [Info] SerialTreeLearner::split costs 0.111960
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 26.636777
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  694113 37.1    1168576 62.5  1168576 62.5
Vcells 3522613 26.9    6240519 47.7  4383248 33.5
> Laurae::timer_func_print({model <- lgb.train(params = list(objective = "binary",
+                                                            metric = "auc",
+                                                            bin_construct_sample_cnt = 2250000L,
+                                                            early_stopping_round = 25,
+                                                            bagging_freq = 1,
+                                                            bagging_seed = 1,
+                                                            bagging_fraction = 0.4),
+                                              train,
+                                              5,
+                                              list(test = test),
+                                              verbose = 2)})
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Debug] use subset for bagging
[LightGBM] [Debug] Re-bagging, using 900000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=11
[1]:	test's auc:0.501405
[LightGBM] [Debug] Re-bagging, using 900000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=13
[2]:	test's auc:0.502849
[LightGBM] [Debug] Re-bagging, using 900000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=10
[3]:	test's auc:0.504528
[LightGBM] [Debug] Re-bagging, using 900000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=9
[4]:	test's auc:0.506207
[LightGBM] [Debug] Re-bagging, using 900000 data to train
[LightGBM] [Info] Trained a tree with leaves=31 and max_depth=13
[5]:	test's auc:0.506727
The function ran in 90240.890 milliseconds.
[1] 90240.89
> rm(model)
> gc()
[LightGBM] [Info] GBDT::boosting costs 0.165529
[LightGBM] [Info] GBDT::train_score costs 0.124710
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.065391
[LightGBM] [Info] GBDT::valid_score costs 0.023227
[LightGBM] [Info] GBDT::metric costs 0.000000
[LightGBM] [Info] GBDT::bagging costs 76.685486
[LightGBM] [Info] GBDT::bagging_subset_time costs 28.801937
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 47.856741
[LightGBM] [Info] GBDT::sub_gradient costs 0.007842
[LightGBM] [Info] GBDT::tree costs 61.569484
[LightGBM] [Info] SerialTreeLearner::init_train costs 7.088206
[LightGBM] [Info] SerialTreeLearner::init_split costs 26.233300
[LightGBM] [Info] SerialTreeLearner::hist_build costs 21.110779
[LightGBM] [Info] SerialTreeLearner::find_split costs 6.784831
[LightGBM] [Info] SerialTreeLearner::split costs 0.141303
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 33.290067
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  694853 37.2    1168576 62.5  1168576 62.5
Vcells 3523226 26.9    6240519 47.7  4389355 33.5

@guolinke Better logs below:
Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=1.0 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.466967 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:1, valid_1 binary_logloss : 0.669789
[LightGBM] [Info] 3.083114 seconds elapsed, finished iteration 1
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:2, valid_1 binary_logloss : 0.65686
[LightGBM] [Info] 6.534374 seconds elapsed, finished iteration 2
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:3, valid_1 binary_logloss : 0.649763
[LightGBM] [Info] 9.767502 seconds elapsed, finished iteration 3
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:4, valid_1 binary_logloss : 0.645923
[LightGBM] [Info] 12.838336 seconds elapsed, finished iteration 4
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:5, valid_1 binary_logloss : 0.643904
[LightGBM] [Info] 15.916650 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training
[LightGBM] [Info] GBDT::boosting costs 0.028511
[LightGBM] [Info] GBDT::train_score costs 0.009519
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.000001
[LightGBM] [Info] GBDT::valid_score costs 0.002431
[LightGBM] [Info] GBDT::metric costs 0.005669
[LightGBM] [Info] GBDT::bagging costs 0.000002
[LightGBM] [Info] GBDT::bagging_subset_time costs 0.000000
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 0.000000
[LightGBM] [Info] GBDT::sub_gradient costs 0.000000
[LightGBM] [Info] GBDT::tree costs 15.870458
[LightGBM] [Info] SerialTreeLearner::init_train costs 2.178705
[LightGBM] [Info] SerialTreeLearner::init_split costs 3.308739
[LightGBM] [Info] SerialTreeLearner::hist_build costs 9.902303
[LightGBM] [Info] SerialTreeLearner::find_split costs 0.456796
[LightGBM] [Info] SerialTreeLearner::split costs 0.021500
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 5.481398

Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=0.6 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 6.990656 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:1, valid_1 binary_logloss : 0.669724
[LightGBM] [Info] 3.145600 seconds elapsed, finished iteration 1
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:2, valid_1 binary_logloss : 0.656882
[LightGBM] [Info] 5.911323 seconds elapsed, finished iteration 2
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:3, valid_1 binary_logloss : 0.649771
[LightGBM] [Info] 8.587378 seconds elapsed, finished iteration 3
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:4, valid_1 binary_logloss : 0.645973
[LightGBM] [Info] 11.349179 seconds elapsed, finished iteration 4
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:5, valid_1 binary_logloss : 0.64391
[LightGBM] [Info] 14.342477 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training
[LightGBM] [Info] GBDT::boosting costs 0.027847
[LightGBM] [Info] GBDT::train_score costs 0.008080
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.015252
[LightGBM] [Info] GBDT::valid_score costs 0.002572
[LightGBM] [Info] GBDT::metric costs 0.003098
[LightGBM] [Info] GBDT::bagging costs 0.013149
[LightGBM] [Info] GBDT::bagging_subset_time costs 0.000000
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 0.000000
[LightGBM] [Info] GBDT::sub_gradient costs 0.000000
[LightGBM] [Info] GBDT::tree costs 14.272429
[LightGBM] [Info] SerialTreeLearner::init_train costs 3.820590
[LightGBM] [Info] SerialTreeLearner::init_split costs 2.218611
[LightGBM] [Info] SerialTreeLearner::hist_build costs 7.742144
[LightGBM] [Info] SerialTreeLearner::find_split costs 0.451989
[LightGBM] [Info] SerialTreeLearner::split costs 0.017599
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 6.033382

Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=0.4 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.311082 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:1, valid_1 binary_logloss : 0.669779
[LightGBM] [Info] 15.066362 seconds elapsed, finished iteration 1
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:2, valid_1 binary_logloss : 0.65695
[LightGBM] [Info] 32.950422 seconds elapsed, finished iteration 2
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:3, valid_1 binary_logloss : 0.649819
[LightGBM] [Info] 47.337700 seconds elapsed, finished iteration 3
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:4, valid_1 binary_logloss : 0.645965
[LightGBM] [Info] 60.975131 seconds elapsed, finished iteration 4
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:5, valid_1 binary_logloss : 0.64389
[LightGBM] [Info] 73.995944 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training
[LightGBM] [Info] GBDT::boosting costs 0.027556
[LightGBM] [Info] GBDT::train_score costs 0.034221
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.000001
[LightGBM] [Info] GBDT::valid_score costs 0.002442
[LightGBM] [Info] GBDT::metric costs 0.003057
[LightGBM] [Info] GBDT::bagging costs 68.762191
[LightGBM] [Info] GBDT::bagging_subset_time costs 28.349320
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 40.400013
[LightGBM] [Info] GBDT::sub_gradient costs 0.007574
[LightGBM] [Info] GBDT::tree costs 5.158859
[LightGBM] [Info] SerialTreeLearner::init_train costs 0.882581
[LightGBM] [Info] SerialTreeLearner::init_split costs 1.307824
[LightGBM] [Info] SerialTreeLearner::hist_build costs 2.537724
[LightGBM] [Info] SerialTreeLearner::find_split costs 0.419427
[LightGBM] [Info] SerialTreeLearner::split costs 0.008666
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 2.188091

@guolinke This is with 1e-6 fix:
Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=1.0 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.291155 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:1, valid_1 binary_logloss : 0.669789
[LightGBM] [Info] 3.088217 seconds elapsed, finished iteration 1
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:2, valid_1 binary_logloss : 0.65686
[LightGBM] [Info] 6.512720 seconds elapsed, finished iteration 2
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:3, valid_1 binary_logloss : 0.649763
[LightGBM] [Info] 9.766369 seconds elapsed, finished iteration 3
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:4, valid_1 binary_logloss : 0.645923
[LightGBM] [Info] 12.856576 seconds elapsed, finished iteration 4
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:5, valid_1 binary_logloss : 0.643904
[LightGBM] [Info] 15.938305 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training
[LightGBM] [Info] GBDT::boosting costs 0.031661
[LightGBM] [Info] GBDT::train_score costs 0.009531
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.000001
[LightGBM] [Info] GBDT::valid_score costs 0.002449
[LightGBM] [Info] GBDT::metric costs 0.003105
[LightGBM] [Info] GBDT::bagging costs 0.000002
[LightGBM] [Info] GBDT::bagging_subset_time costs 0.000000
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 0.000000
[LightGBM] [Info] GBDT::sub_gradient costs 0.000000
[LightGBM] [Info] GBDT::tree costs 15.891500
[LightGBM] [Info] SerialTreeLearner::init_train costs 2.169989
[LightGBM] [Info] SerialTreeLearner::init_split costs 3.359920
[LightGBM] [Info] SerialTreeLearner::hist_build costs 9.847964
[LightGBM] [Info] SerialTreeLearner::find_split costs 0.481699
[LightGBM] [Info] SerialTreeLearner::split costs 0.029584
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 5.522944

Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=0.6 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.777340 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:1, valid_1 binary_logloss : 0.669724
[LightGBM] [Info] 2.760088 seconds elapsed, finished iteration 1
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:2, valid_1 binary_logloss : 0.656882
[LightGBM] [Info] 5.420121 seconds elapsed, finished iteration 2
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:3, valid_1 binary_logloss : 0.649771
[LightGBM] [Info] 7.992326 seconds elapsed, finished iteration 3
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:4, valid_1 binary_logloss : 0.645973
[LightGBM] [Info] 10.777746 seconds elapsed, finished iteration 4
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:5, valid_1 binary_logloss : 0.64391
[LightGBM] [Info] 13.426416 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training
[LightGBM] [Info] GBDT::boosting costs 0.026495
[LightGBM] [Info] GBDT::train_score costs 0.008024
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.015815
[LightGBM] [Info] GBDT::valid_score costs 0.002523
[LightGBM] [Info] GBDT::metric costs 0.003107
[LightGBM] [Info] GBDT::bagging costs 0.012842
[LightGBM] [Info] GBDT::bagging_subset_time costs 0.000000
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 0.000000
[LightGBM] [Info] GBDT::sub_gradient costs 0.000000
[LightGBM] [Info] GBDT::tree costs 13.357567
[LightGBM] [Info] SerialTreeLearner::init_train costs 3.719762
[LightGBM] [Info] SerialTreeLearner::init_split costs 1.805211
[LightGBM] [Info] SerialTreeLearner::hist_build costs 7.380577
[LightGBM] [Info] SerialTreeLearner::find_split costs 0.435972
[LightGBM] [Info] SerialTreeLearner::split costs 0.014171
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 5.518769

Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=0.4 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.265347 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:1, valid_1 binary_logloss : 0.669779
[LightGBM] [Info] 2.088153 seconds elapsed, finished iteration 1
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:2, valid_1 binary_logloss : 0.65695
[LightGBM] [Info] 4.460803 seconds elapsed, finished iteration 2
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:3, valid_1 binary_logloss : 0.649819
[LightGBM] [Info] 6.536185 seconds elapsed, finished iteration 3
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:4, valid_1 binary_logloss : 0.645965
[LightGBM] [Info] 8.671731 seconds elapsed, finished iteration 4
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:5, valid_1 binary_logloss : 0.64389
[LightGBM] [Info] 10.821296 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training
[LightGBM] [Info] GBDT::boosting costs 0.030278
[LightGBM] [Info] GBDT::train_score costs 0.006794
[LightGBM] [Info] GBDT::out_of_bag_score costs 0.022166
[LightGBM] [Info] GBDT::valid_score costs 0.002548
[LightGBM] [Info] GBDT::metric costs 0.003119
[LightGBM] [Info] GBDT::bagging costs 0.013182
[LightGBM] [Info] GBDT::bagging_subset_time costs 0.000000
[LightGBM] [Info] GBDT::reset_tree_learner_time costs 0.000000
[LightGBM] [Info] GBDT::sub_gradient costs 0.000000
[LightGBM] [Info] GBDT::tree costs 10.743162
[LightGBM] [Info] SerialTreeLearner::init_train costs 3.490598
[LightGBM] [Info] SerialTreeLearner::init_split costs 1.424444
[LightGBM] [Info] SerialTreeLearner::hist_build costs 5.369412
[LightGBM] [Info] SerialTreeLearner::find_split costs 0.439387
[LightGBM] [Info] SerialTreeLearner::split costs 0.016834
[LightGBM] [Info] SerialTreeLearner::ordered_bin costs 4.910205

@Laurae2 Thanks for the help.
I think the latest master branch have fixed this.

@guolinke I am getting segmentation fault instead now.
Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=1.0 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.356559 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:1, valid_1 binary_logloss : 0.669789
[LightGBM] [Info] 3.090586 seconds elapsed, finished iteration 1
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:2, valid_1 binary_logloss : 0.65686
[LightGBM] [Info] 6.522369 seconds elapsed, finished iteration 2
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:3, valid_1 binary_logloss : 0.649763
[LightGBM] [Info] 9.768736 seconds elapsed, finished iteration 3
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:4, valid_1 binary_logloss : 0.645923
[LightGBM] [Info] 12.862355 seconds elapsed, finished iteration 4
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=3
[LightGBM] [Info] Iteration:5, valid_1 binary_logloss : 0.643904
[LightGBM] [Info] 15.948677 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training

Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=0.6 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.292828 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
Segmentation fault

Lolo@Laurae MINGW64 /e/lightgbm
$ E:/lightgbm/lightgbm.exe data=../benchmark_lot/data/reput_train_lgb_na.data num_threads=8 learning_rate=0.25 max_depth=3 num_leaves=7 min_gain_to_split=1 min_sum_hessian_in_leaf=1 min_data_in_leaf=1 num_trees=5 metric=binary_logloss bagging_freq=1 bagging_seed=1 bagging_fraction=0.4 test=../benchmark_lot/data/reput_test_lgb_na.data app=binary
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Finished loading data in 7.078148 seconds
[LightGBM] [Info] Number of positive: 742198, number of negative: 1507802
[LightGBM] [Info] Total Bins 6027180
[LightGBM] [Info] Number of data: 2250000, number of used features: 23636
Segmentation fault

@Laurae2 sorry, it has a bug. I just use "push -f" to fix it.


rpc for cloud protos

@glimchb @sandersms  - the RPC (grpc/http) definitions for cloud protos

The changes here look good.  Awaiting the comment above to approve and merge.

@glimchb - documented all bgp apis optional in the API comments. if this looks good, I can submit another commit with generated code on top of this.
thanks @sandersms for the review.


Insight track function called multiple times

🐛 Current behavior
Insights track called multiple times
Insights track function is called as many times as the useHits hook or Hit component are used in the InstantSearch component.
🔍 Steps to reproduce
Go to the live reproduction
See the multiple logs in the console
Remove one component using the useHits hook
See the number of logs decreasing
Live reproduction
https://codesandbox.io/s/relaxed-james-x92y0k?file=/src/App.tsx
💭 Expected behavior
the 'view Hits' event is supposed to be called juste one time, regardless of how many times you used the useHits hook.
Package version
algoliasearch 4.14.3, instantsearch.js 4.43.1, react-instantsearch-hooks-web 6.38.1, search-insights 2.2.3-hooks-web
Operating system
No response
Browser
No response
Code of Conduct

[X] I agree to follow this project's Code of Conduct

This is a good point. One solution I'm seeing is memoizing sendEvent somewhat, maybe only for the view case, as you could have clicks that happen multiple times on the same element, but viewing it multiple times still counts as viewing it once.

I don't understand your solution. On my side can I do anything to avoid this behavior beside not using the hook more than one time ?
Can you edit my codesandbox maybe ?

Sorry, the solution would be in the source code, I'm not sure what an user land solution could be for this issue.
As a temporary workaround, I guess you could divide the stats by the number of hits components you have

In addition to the statistics analysis deffect, it provide a big dump of performance, maybe to link with this ticket :
https://github.com/algolia/instantsearch/issues/5237
For example on our side, we were using useHits in every HitComponent and we are displaying 100 hits by page.
That was producing nearly 500 to 1000 HTTP requests only to send viewed events when page display. 😆

Does this has a solution? we have a similar problem but is affecting our limits in Algolia because are receiving 6 times the same view event

Any updates on this @Haroenv ?

No updates for this yet, as nobody else as brought up this limitation. You can filter out the secondary view events in the onEvent hook of the insights option too.

Do you have an example of how to do that?

@Haroenv we are experiencing this issue as well

+1


Support multiple IDE prefixes, ignore versions without a recent projects file

This PR fixes two issues I have experienced with this extension:

When using the IntelliJ IDEA Community Edition, projects do not show up because the config prefix is different from the ultimate edition.
To fix this, I introduced the option of having multiple prefixes for an IDE. This means that you can't use both community and ultimate edition at the same time, but matches the behavior with the PyCharm Community vs. Professional Edition.

When you update an IDE, the recentProjects file is not copied until you start it, but the folder already exists. This causes the extension to show no projects for the IDE.
To fix this, I ignored all IDE folders that don't include the recent projects file.

I also just noticed that PyCharm community edition didn't actually work because the regex does not accept additional characters between the IDE prefix and the version, so my initial statement isn't entirely correct.
It might also be an option to individually list community and professional editions, but then we should have separate icons for them and unfortunately the JetBrains toolbox doesn't generate unique script names for them.


Improve docs around scaling UCP up and down

We already have docs on how to add/remove nodes from a UCP cluster. But some users have been running into problems because they're breaking the swarm consensus group by removing too many nodes at a time and doing other operations like that.
Improve these docs with things that are missing and might lead users into making mistakes.

@alexmavr I think this is mostly addressed by #1792. Do you think that one is enough to close this issue?

I also want to add docker swarm join-token information in joining a node and document the expectations of UCP being installed as a secondary step, so let's leave this open for now

This seems to be mostly solved by now.


Are there any plans to port puppeteer to Python?

No plans for the foreseeable future. We want to nail the Node experience first and foster a JS API that works really well with the DevTools protocol.
FWIW, there are several community projects that allow you to work with the protocol using Python:
https://github.com/ChromeDevTools/awesome-chrome-devtools#protocol-driver-libraries-in-various-languages

Thanks! I did see some good Python modules in there.
I'm new to open source, so I apologize for this potentially unnecessary question: is it "not okay" to independently start an open source port (assuming that further reading of the Python implementations don't have all the functionality I need), and attempt to keep it in line with the Puppeteer API?
In my day job, there are immediately tangible benefits to using Puppeteer over PhantomJS, and maintaining the project may end up being more efficient. However, I don't want to step on anyone's toes!

it's totally OK to write a python library that exposes the puppeteer API in python. It would make sense to depend on chromote or pychromedevtools for interfacing with the protocol.
go for it!

is it "not okay" to independently start an open source port

Nothing stopping you from starting something new and basing the API off another project. In fact, Puppeteer's API was inspired by projects like NightmareJS, PhantomJS, and others.

keep it in line with the Puppeteer API

I think is the challenge right now. Puppeteer's API will most certainly evolve as we work towards a 1.0. IMO, it may be too soon to try to port code. I'd take a look at those other projects though.
Another thing you could try is calling into Node/puppeteer from Python. Obviously wouldn't be ideal if you're trying to avoid Node altogether :)

https://pypi.python.org/pypi/pyppeteer ;-) FYI

@zyv Unfortunately (and I can't emphasize "unfortunately" enough), the module must run on Python 2.7 with no external dependencies outside a websocket-client and the standard library for my use case.
So, again unfortunately, that completely rules out Python 3.x+, asyncio, gevent, etc.
I have an implementation that's completely usable in production for my own use cases at the moment, but it's pretty big, and there are some latent synchronization issues that I haven't resolved yet (mostly because they only come up during super high traffic periods, like when screencasting etc. none of the triggers are in any way required for my use case, so I haven't had the motivation to resolve them yet), which is preventing me from publishing. I can be a bit of a perfectionist :P

FWIW: If anyone else has an interest in the module, please let me know. I've been under the impression that the constraints and use case are so narrow that it wasn't worth publishing, but I will if I learn otherwise.

@michaelfward I'd be interested to see that! For us I don't think the py3 + deps is necessarily an issue but would be interested to see your approach!

@michaelfward this would be of interest to us as well as sadly we still require python2 support


CrossValidationReporter: Display results on each split on the fly

As suggested by @glemaitre
Problem
Suppose I have a long cross-validation on 5 splits and that I made a mistake in my preprocessing. As a result, I get a very bad score on my 5 splits. But I could have saved time because if the score is too bad on my first split, I already know that I made a mistake. No need to wait for the 5 splits to be computed.
Solution
Display the results of each split progressively, as soon as they are finished, and not only all splits at the end.
When I do cross-validation that takes a few hours, I would like to know as early as possible if there is a problem in my first split so that I can do an early stop.

Also related to #https://github.com/probabl-ai/skore/issues/949


InitAccessors: Parse initializer exprs associated with computed prope…

…rties that have init accessor
Initialization expressions are not allowed on computed properties but
if a property has init accessor it should be allowed because it could
be used by a memberwise initializer.

@swift-ci please test

@swift-ci please test

@swift-ci please test Windows platform

@swift-ci please test Windows platform


Added multiple filters in Web Image Filter project

Added several filters for Web Image Filter python project

I've added the following filters in the project -

Gaussian Blur
Duo Tone
Pencil Sketch
Colored Sketch
Cartoon(light)
Cartoon(dark)
Emboss
Splash
Canny

Close #3457

Checklist:

[x] I have mentioned the issue number in my Pull Request.
[x] I have commented my code, particularly in hard-to-understand areas
[x] I have created a helpful and easy to understand README.md

Hi @TusharKesarwani @MohitGupta121 please review this PR


kie-issues#372: DMN Editor: Removing GWT-based autocompletion feature

Closes https://github.com/kiegroup/kie-issues/issues/372
All modules and related code to the GWT-based autocompletion feature have been removed.
Potentially impacted point (already checked)

Documentation tab
new BEE (Function FEEL, PMML, JAVA expressions)

Related PR on drools repo: https://github.com/kiegroup/drools/pull/5362

The (build) kie-tools-stunner-editors check has failed. Please check the logs.


Suggestion/Feedback

String class and its corresponding methods should also be included.

@underscoreanuj good idea ,I will update this soon

Closing this, since this is being added in #441


WIP: Adds example createToken mutation and helper functions

This is more a proof of concept so we can get everything working
At the moment it's hardcoded to user id 1

Bleh merge conflicts  Token union

@Neitsch just adding to this here and there, definitely going to clean up

Hi @ncrmro, would you like to adapt this PR to latest code version or would it be better to close it and create a new PR?

At the moment I'm going to say just close and create anew for now.


Del env var doc cleanup

Harness Developer Pull Request
Thanks for helping us make the Developer Hub better. The PR will be looked at
by the maintainers.
What Type of PR is This?

[ ] Issue
[ ] Feature
[ x] Maintenance/Chore

If tied to an Issue, list the Issue(s) here:
Changed  tags inserted in post-migration cleanup to  because of JSX errors. I was misled by my research into whether  was usable with markdown.
House Keeping
Some items to keep track of. Screen shots of changes are optional but would help the maintainers review quicker.

[ x] Tested Locally
[ ] Optional Screen Shoot.

Preview environment: https://ci.pr.harness.io/pr-326


Changes in Toml not always picked up

Hi,
I think i've run into a bug when changing the key in a config.toml file.
Reproduction steps:
Suppose i have the following content in the config.toml file:
    [params.header]
        image = "profile.png"
        name = "todo change name"

I run Hugo server from the cmd line. If I make a change in the config.toml file by changing the value of the key 'name' then the preview shows the new, updated value in the browser after save. Everything is fine.
Now suppose I do not like the key 'name', so I change it into 'description' and I do the same in the html (partial) file and save both files. Now I expect the change to be reflected in the preview. However this is this not the case. If I restart hugo from the cmd line, the change is shown.
Restarting the server is cumbersome and should not be necessary for this particular kind of change.
Version:
$ hugo version
Hugo Static Site Generator v0.15 BuildDate: 2015-11-26T07:29:07+01:00

I'm new to Hugo, so apologies if i'm making wrong assumptions here.
thank you,
Stephan

Thanks for the report. This is fixed in the upcoming Hugo 0.16.


Fix svg icons for callouts https://github.com/KosmosisDire/obsidian-webpage-export/issues/229

Potential fix for https://github.com/KosmosisDire/obsidian-webpage-export/issues/229

Hi this is already fixed in the 1.7.4 branch


Questions about DAGNet?

when run googlenet,  two SimpleNet are created. My question is  caffe2 source code contains SimpleNet and DAGNet and many differences between them.  Could you give me more information about them and which net(googlenet is not) uses DAGNet, thank you!!!

They are different ways to execute a net. SimpleNet uses a single thread and runs operators sequentially. The DAGNet uses multiple threads to run operators in parallel, where possible. You can set the "num_workers" property on the DAGNet to set the number of threads to use.
You can modify any net to use either SimpleNet or DAGNet. It doesn't depend on the model you use.

thank you very much for your answer.


Apply #12576 to top right reaction picker

I'll just open this since I cannot find a reason for the bug below.
After this PR, the reaction emoji is slightly shifted toward right in the top right drop-down. Seeing this in both Chromium (81) and Safari, but not in Firefox. Bottom left picker is unaffected.

Edit: confirmed to be Apple-specific. Should have something to do with the rendering engine.

Codecov Report

Merging #12623 into master will increase coverage by 0.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #12623      +/-   ##
==========================================
+ Coverage   43.40%   43.42%   +0.01%
==========================================
  Files         645      645
  Lines       71288    71288
==========================================
+ Hits        30944    30955      +11
+ Misses      35327    35319       -8
+ Partials     5017     5014       -3

Impacted Files
Coverage Δ

services/pull/check.go
47.69% <0.00%> (-2.31%)
:arrow_down:

modules/queue/unique_queue_disk_channel.go
53.84% <0.00%> (-1.54%)
:arrow_down:

modules/git/commit.go
54.05% <0.00%> (-0.68%)
:arrow_down:

models/error.go
34.81% <0.00%> (-0.52%)
:arrow_down:

models/issue_comment.go
53.75% <0.00%> (-0.46%)
:arrow_down:

services/pull/pull.go
42.03% <0.00%> (+0.46%)
:arrow_up:

modules/git/repo.go
49.74% <0.00%> (+0.50%)
:arrow_up:

modules/queue/workerpool.go
60.00% <0.00%> (+1.22%)
:arrow_up:

modules/log/event.go
59.43% <0.00%> (+1.88%)
:arrow_up:

modules/git/utils.go
77.04% <0.00%> (+3.27%)
:arrow_up:

... and 4 more

Continue to review full report at Codecov.

Legend - Click here to learn more
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Powered by Codecov. Last update e25d486...4369c73. Read the comment docs.

how does it look in dark theme?

In Arc Green theme the only change is the rule for overriding highlight color on hover. The two popups would match exactly except for the bug I mentioned earlier.

I'm not sure what this change exactly fixes from the screenshots. Can you showcase a before/after comparison?
Last time I worked at that picker, I had the idea of rewriting it to flexbox because it's quite a hack currently, layout-wise.

As the title suggests, this PR adds hover effect to the picker in the top right corner.
For comparison, goto try.gitea.io and click on any of the top right picker icons.
The previous :hover styling targeted .select-reaction .item in  .segment.reactions only, leaving out the top ones.

Ah I see. Totally missed the fact that there's two ways to trigger that menu.


Basic benchmark of flask uncovers costly line in urls.py

This benchmark script:
from flask import Flask

def create_app():
    """Create and configure an instance of the Flask application."""
    app = Flask(__name__, instance_relative_config=True)

    @app.route("/hello")
    def index():
        return "Hello, World!"

    # make url_for('index') == url_for('blog.index')
    # in another app, you might define a separate main index here with
    # app.route, while giving the blog blueprint a url_prefix, but for
    # the tutorial the blog will be the main index
    app.add_url_rule("/", endpoint="index")

    return app

app = create_app()

def bench(n = 10_000):
    for _ in range(n):
        app.test_client().get("/hello")
        app.test_client().get("/")

bench()

Run with cProfile
python -m cProfile -s time bench.py
returns
        26003364 function calls (25977275 primitive calls) in 29.408 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
   340012    2.902    0.000    3.460    0.000 urls.py:547(url_quote)
   140000    1.483    0.000    2.629    0.000 urls.py:457(url_parse)
   140000    0.794    0.000    1.003    0.000 urls.py:387(_unquote_to_bytes)
5147167/5147129    0.771    0.000    0.771    0.000 {built-in method builtins.isinstance}

10% of the execution time is spent running url_quote()
url_quote is run 34 times per request
url_parse is run 14 times per request
_unquote_to_bytes is run 14 times per request.
isinstance() is run multiple times from each of those functions.

Running this on a line profiler shows that this line is particularly expensive. Around 6% of the overall benchmark CPU time.
https://github.com/pallets/werkzeug/blob/main/src/werkzeug/urls.py#L572
if this function could be optimized it would have a knock-on effect to Flask performance.

This code seems to come back to a 9 year old commit for Python 3. It might need refactoring
https://github.com/pallets/werkzeug/commit/aee68cfa9a761e711f80c0011d8dff6bff52b32d

The execution time for these functions is down to 3 things:

The API accepting str and bytes, then having to check and encode decode
The continuous encode/decode cycle between those functions of the same data
_make_encode_wrapper is inefficiently using a lambda, it's also called 144 times per request

This is related to a longer term goal I have. Much of the urls module is a copy of Python 2's urlparse and Python 3's urllib.parse. Due to compatibility when this first happened, there are tons of redundant parsing/unparsing and encoding/decoding. I would like to remove as much reliance on our own url functions as possible and go back to Python's implementation now that we only support Python 3.

I want to try to make a branch of Werkzeug that replaces all use of our urls with Python's urllib. It would be interesting to see the performance results there, I wonder if urllib has similar issues. At least it would reduce the maintenance surface of Werkzeug, and maybe allow us to focus on reimplementing specific functions.

This is related to a longer term goal I have. Much of the urls module is a copy of Python 2's urlparse and Python 3's urllib.parse. Due to compatibility when this first happened, there are tons of redundant parsing/unparsing and encoding/decoding. I would like to remove as much reliance on our own url functions as possible and go back to Python's implementation now that we only support Python 3.

That would be frustrating as the interface of the Python module is pretty shit: the *Result objects remain barebones named tuples, which lack all the utility and convenience of werkzeug.urls.BaseURL.
Now I can only speak for myself, but I routinely add werkzeug dependencies to projects just so I can use werkzeug.urls. The only annoyance I can remember with it is that "simple" querystring manipulations on existing urls are not completely straightforward (BaseURL has no dedicated shortcuts so it requires decoding the QS, updating it, re-encoding it, then replacing it). But overall it's an absolute joy to work with in all the ways urllib.parse isn't.

Have you provides that feedback as issues or pull requests on the Python tracker? I'm not sure what I'm supposed to do about that, I don't want to maintain a parallel implementation of something that is basically in the standard library.

I looked at the line-by-line profile for url_quote and found the following line in urls.py to be the heavy one:
safe = (frozenset(bytearray(safe)) | _always_safe) - frozenset(bytearray(unsafe))

Using the same benchmark script provided above, you can shave off .6-.7 second from the total by replacing both instances of this construct with a function that doesn't use frozenset or bytearray objects but rather plain bytes objects along the lines of the following:
def mksafe(safe, unsafe):
       safe = safe if type(safe) is bytes else bytes(safe)
       unsafe = unsafe if type(unsafe) is bytes else bytes(unsafe)
       safe = safe + _always_safe3
       for b in unsafe:
           try:
               bi = safe.index(b)
           except ValueError:
               bi = None
           while bi:
               safe = safe[:bi] + safe[bi+1:]
               try:
                   bi = safe.index(b)
               except ValueError:
                   bi = None
       return safe

This is ugly, I guess, and it doesn't check for strings and handle encoding -- but the code in relevant functions seems to take care of these concerns apart from the given construct. Of course, these byte sequences are not unique (as with the sets), but they are immutable. Even so, it appears to be faster than the present.
I mention this only to say that these functions could probably be optimized to speed them up, possibly considerably given these initial results. But if the general sense is that they should be moved over to the urllib family of functions instead of doing any further tinkering with what's there now, then I guess it's moot. I could, of course, also be missing some regression in functionality here, too (and would be happy for somebody to note any).
But if it's the case that it'd be preferred to just make the move to urllib instead, I'm happy to take a shot at it, if it'd be helpful.

Since removing our own implementation would be a pretty big project, you're welcome to add an improvement before that. I don't know when the removal would happen, I could have time tomorrow, or in a year.

Yes, I agree it would be a considerable amount of work to make the move to urllib.parse functions. And I'd imagine it'd be better for that to be done by or at least with the direct involvement of somebody or some people with more background on how the current code came to be.
I'll likely roll in some (much) smaller changes to see if the performance concerns might be allayed without such massive alterations and let you/others take a look.

I've been using the following benchmark to run with cprofile and timeit.
from werkzeug import Request, Response, Client
from werkzeug.exceptions import HTTPException
from werkzeug.routing import Map, Rule

@Request.application
def app(request):
    router = url_map.bind_to_environ(request.environ)
    router.match()
    return Response("Hello, World!")

url_map = Map([Rule("/hello", endpoint="index"), Rule("/", endpoint="index")])
client = Client(app)

def bench(n=10_000):
    for _ in range(n):
        client.get("/hello")
        client.get("/")

if __name__ == "__main__":
    bench()

$ python -m cProfile -s time -o bench.prof bench.py
$ pipx run snakeviz bench.prof

$ python -m timeit -s 'from bench import bench' 'bench(1000)'


Feature/unr 4255 test expected log

Description
Added AddExpectedError API that allows you to flag warnings / errors as expected to appear, hence not making tests fail when they do.
Tests
Include SpatialTestNetOwnership fix to be able to always pass with green instead of "passing with warnings"

Is there a reason on adding this over just calling UAutomationBlueprintFunctionLibrary::AddExpectedLogError directly?

Is there a reason on adding this over just calling UAutomationBlueprintFunctionLibrary::AddExpectedLogError directly?

The same reason that we thought that there was no functionality for it, because there's no API in the FunctionalTests. I found out about it because it shows up in the BP when you search.

The same reason that we thought that there was no functionality for it, because there's no API in the FunctionalTests. I found out about it because it shows up in the BP when you search.

Okay, expose it for blueprint access then?

The same reason that we thought that there was no functionality for it, because there's no API in the FunctionalTests. I found out about it because it shows up in the BP when you search.

Okay, expose it for blueprint access then?

If I do, then they'll have the same function exposed twice, so I opted to let the blueprint library handle it for BPs and this one doing it for cpp.


Float/Double Support for Formatter

Currently, something like String.format("%f", 1.0f) leads to TFormatter#formatValue:
throw new UnknownFormatConversionException(String.valueOf(specifier));

Right, String.format has only limited support, and I have no plans to support it fully.

@MeFisto94 , I ran into this recently too.  I needed just 1 decimal place so I found the fractional part via Math.floor(doubleValue * 10.0d) % 10.

Is there any interest in a PR for this? My library formic currently works on GWT by using a JS library, format4js, but I don't know how to do something like that when it's used by TeaVM. Because formic knows it isn't running on GWT, it thinks it's running on a platform with complete String.format(), and so calling formic's Stringf.format() falls back to normal String.format(). I recently worked with some code that converts doubles and floats to Strings at a low level in pure Java. I used Ryu, which has some printf() support for C but not Java. It wouldn't help with dates or times, but it could handle variable lengths of precision, I think, with some adjustment. My code that modifies Ryu is here.

@tommyettinger I don't think TeaVM should use format4js for this purpose. If you want to help with it, you can contribute your code to Formatter implementation.

I'm certainly interested, and yeah I would probably use Formatter (I was just looking there!). My concern is that it would probably need a fairly substantial amount of code from Ryu, which I believe is compatibly licensed (Apache 2.0). I could take RyuFloat and RyuDouble in as package-private for java.util, which might actually be what the JDK does in recent versions; I know they changed their float-printing algorithm recently. I could also use other number-printing code if there's some here.
Related to that last point, I noticed that String.valueOf(Integer.MIN_VALUE) and String.valueOf(Long.MIN_VALUE) both return "-0" instead of a large negative number.

@tommyettinger you don't need code for formatting numbers, dates, etc, it's already there. See java.text implementation with all formatters available. The only thing necessary is to properly parse format spec and call corresponding code. If you want to create a PR, please don't forget to add tests and run them properly (./gradlew test). Also, don't forget to run ./gradlew check to check if code follows conventions.

Very nice, I'm glad there's so much already here! I'll look around java.text and see which additional formatting modes can be supported right away, and which ones might be less immediate (%A is probably less important).


FRDM-K64F + telnet-sample

Describe the bug
I have created the example Telnet console application for the board FRDM-K64F under Windows.
Although I get a connection to the board with a telnet client, but there are no inputs and outputs on the client possible.
To Reproduce
Steps to reproduce the behavior:

cd samples/net/telnet
mkdir build; cd build
cmake -GNinja -DBOARD=frdm_k64f ..
ninja
Put the result zephyr/zephyr.bin into the board
wait until dhcpv4 has finished
connect with a Telnet-client

Expected behavior
Input/Output to the Shell via Telnet-console
console output
uart:~$ ***** Booting Zephyr OS zephyr-v1.13.0-2584-g8ec7c73d2e *****
[00:00:00.008,451]  net_config.net_config_init: Initializing network
[00:00:00.008,474]  net_config.setup_ipv4: IPv4 address: <IP_ADDRESS>
[00:00:00.008,489]  net_config.setup_dhcpv4: Running dhcpv4 client...
[00:00:06.021,795]  net_dhcpv4.dhcpv4_handle_msg_ack: Received: <IP_ADDRESS>
[00:00:06.021,829]  net_config.ipv4_addr_add_handler: IPv4 address: <IP_ADDRESS>
[00:00:06.021,832]  net_config.ipv4_addr_add_handler: Lease time: 86400 seconds
[00:00:06.021,846]  net_config.ipv4_addr_add_handler: Subnet: <IP_ADDRESS>
[00:00:06.021,859]  net_config.ipv4_addr_add_handler: Router: <IP_ADDRESS>
[00:00:08.020,315]  net_telnet.telnet_setup_server: Telnet console enabled on AF_INET
[00:00:08.020,341]  net_telnet.telnet_console_init: Telnet console initialized
[00:00:08.020,346]  net_telnet_sample.main: Starting Telnet sample
[00:00:19.963,597]  net_telnet.telnet_accept: Telnet client connected (family AF_INET)
Environment:

OS: Windows
Toolchain: gccarmemb Version 7.3.1

This issue look more like a feature than a bug @galak .
I've tested in qemu and posix, but there is not shell in telnet console. It's not restricted to k64f.

Yes, the telnet cannot be used atm as the shell is missing support for network transport.
Originally posted by @jukkar in https://github.com/zephyrproject-rtos/zephyr/issues/14691#issuecomment-474730959

This is generic shell issue, fixing title.

Could we reassign to someone else? Adding shell network backend is not in my plans and scope.


Update checkout for tensorflow and swift-apis.

https://github.com/tensorflow/tensorflow/commit/f23a914b52d1935602d0a94c426ccab15c8fed69
https://github.com/tensorflow/swift-apis/commit/c09de96f739aa8bfce0c41e19213bd2d7fa9a4ba

@swift-ci Please clean test tensorflow

CI failed:
Please upgrade your bazel installation to version 0.27.1 or higher to build TensorFlow!

I'll upgrade bazel on the CI machines and retrigger.

@swift-ci Please test tensorflow

CI failed on Linux while building TensorFlow:
+ bazel build -c opt --define framework_shared_object=false //tensorflow:libtensorflow.so
...
 - /home/swiftci/jenkins/workspace/swift-PR-TensorFlow-Linux/tensorflow/WORKSPACE:19:1
ERROR: An error occurred during the fetch of repository 'local_config_python':
   Traceback (most recent call last):
	File "/home/swiftci/jenkins/workspace/swift-PR-TensorFlow-Linux/tensorflow/third_party/py/python_configure.bzl", line 345
		_create_local_python_repository(<1 more arguments>)
	File "/home/swiftci/jenkins/workspace/swift-PR-TensorFlow-Linux/tensorflow/third_party/py/python_configure.bzl", line 296, in _create_local_python_repository
		_get_numpy_include(<2 more arguments>)
	File "/home/swiftci/jenkins/workspace/swift-PR-TensorFlow-Linux/tensorflow/third_party/py/python_configure.bzl", line 276, in _get_numpy_include
		_execute(repository_ctx, <3 more arguments>)
	File "/home/swiftci/jenkins/workspace/swift-PR-TensorFlow-Linux/tensorflow/third_party/py/python_configure.bzl", line 56, in _execute
		_fail(<1 more arguments>)
	File "/home/swiftci/jenkins/workspace/swift-PR-TensorFlow-Linux/tensorflow/third_party/py/python_configure.bzl", line 27, in _fail
		fail(<1 more arguments>)
Python Configuration Error: Problem getting numpy include path.
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ImportError: No module named numpy

I verified that installing numpy (sudo apt-get install python-pip and sudo pip install numpy) fixes the issue.

@swift-ci Please test tensorflow linux

@swift-ci Please clean test tensorflow linux

There's a new linker error for libswiftTensorFlow.dylib:
[1341/1829][ 73%][1580.361s] Linking CXX shared library lib/swift/macosx/x86_64/libswiftTensorFlow.dylib
FAILED: lib/swift/macosx/x86_64/libswiftTensorFlow.dylib
: && /Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/Ninja-ReleaseAssert+stdlib-Release/llvm-macosx-x86_64/./bin/clang++ -Wno-unknown-warning-option -Werror=unguarded-availability-new -fno-stack-protector -fPIC -fvisibility-inlines-hidden -Werror=date-time -Werror=unguarded-availability-new -std=c++14 -Wall -Wextra -Wno-unused-parameter -Wwrite-strings -Wcast-qual -Wmissing-field-initializers -Wimplicit-fallthrough -Wcovered-switch-default -Wno-class-memaccess -Wno-noexcept-type -Wnon-virtual-dtor -Wdelete-non-virtual-dtor -Wstring-conversion -fdiagnostics-color -Werror=switch -Wdocumentation -Wimplicit-fallthrough -Wunreachable-code -Woverloaded-virtual -DOBJC_OLD_DISPATCH_PROTOTYPES=0 -O3 -isysroot /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.15.sdk -dynamiclib -Wl,-headerpad_max_install_names -dynamiclib\ -Wl,-headerpad_max_install_names -Xlinker -compatibility_version -Xlinker 1 -target x86_64-apple-macosx10.9 -isysroot /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.15.sdk -arch x86_64 -F /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.15.sdk/../../../Developer/Library/Frameworks -mmacosx-version-min=10.9 -Wl,-dead_strip -Wl,-sectcreate,__TEXT,__info_plist,/Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/Ninja-ReleaseAssert+stdlib-Release/swift-macosx-x86_64/stdlib/public/TensorFlow/Info.plist -Wl,-application_extension  "-L/Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/Ninja-ReleaseAssert+stdlib-Release/swift-macosx-x86_64/./lib/swift/macosx/x86_64" "-L/Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/Ninja-ReleaseAssert+stdlib-Release/swift-macosx-x86_64/./bin/../lib/swift/macosx/x86_64" "-L/Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/Ninja-ReleaseAssert+stdlib-Release/swift-macosx-x86_64/./bin/../lib/swift/macosx" -o lib/swift/macosx/x86_64/libswiftTensorFlow.dylib -install_name @rpath/libswiftTensorFlow.dylib stdlib/public/TensorFlow/OSX/x86_64/TensorFlow.o stdlib/public/CTensorFlow/CMakeFiles/swiftCTensorFlow-macosx-x86_64.dir/ctensorflow_init.cpp.o -L/Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/Ninja-ReleaseAssert+stdlib-Release/llvm-macosx-x86_64/./lib -Wl,-rpath,/Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/tensorflow/bazel-bin/tensorflow lib/swift/macosx/x86_64/libswiftSwiftOnoneSupport.dylib lib/swift/macosx/x86_64/libswiftPython.dylib lib/swift/macosx/x86_64/libswiftCore.dylib lib/swift/macosx/x86_64/libswiftDarwin.dylib /Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/tensorflow/bazel-bin/tensorflow/libtensorflow.so && :
ld: warning: linking against a dylib which is not safe for use in application extensions: /Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/tensorflow/bazel-bin/tensorflow/libtensorflow.so
ld: warning: linking against a dylib which is not safe for use in application extensions: /Users/swiftci/jenkins/workspace/swift-PR-TensorFlow-macOS/Ninja-ReleaseAssert+stdlib-Release/swift-macosx-x86_64/lib/swift/macosx/libtensorflow.so
Undefined symbols for architecture x86_64:
  "_TF_GraphToFunction", referenced from:
      closure #1 (Swift.UnsafeBufferPointer<Swift.OpaquePointer?>) -> Swift.OpaquePointer in TensorFlow.TFFunction.init(trace: TensorFlow.LazyTensorTrace, name: Swift.String?) -> TensorFlow.TFFunction in TensorFlow.o
  "_TF_FunctionName", referenced from:
      TensorFlow.TFFunction.name.getter : Swift.String in TensorFlow.o
      TensorFlow.TFFunction.execute(_: [TensorFlow.TFETensorHandle], usingXLA: Swift.Bool) -> [TensorFlow.TFETensorHandle] in TensorFlow.o
ld: symbol(s) not found for architecture x86_64

@bgogul: do you have any idea why this error occurs?
TF_GraphToFunction is still defined in tensorflow/c/c_api.h and doesn't appear to have changed.

This patch is obsolete now. Both tensorflow and tensorflow-swift-apis have been updated in other PRs.


Sort fastcgi params to have stable ordering

The hash sorting was started in #532 and this fixes it for fastcgi as well.
/cc @s0enke

:cool:

Thanks, looking forward to the next release. Any plans on when that's going to be?


Miscellaneous bug fixes

Combined PR for a few small bug fixes:

Fixes #41491: Clarified the diamond problem with default interface methods. Add an image to illustrate the problem, and provide additional text explanation.
Fixes #40808: Remove the (now) obsolete paragraph.
Fixes #41507: Clarify how additional { and } characters are parsed in interpolated raw string literals.
Fixes #41538: Mention collection expressions in the member access article. Include links to the collection expressions article.
Fixes #30876: Mention that when a static field initializer creates an instance of a type, that instance constructor is invoked before the static constructor.
Fixes #41629: Note that the execution order of static constructors isn't specified. While here, perform an edit pass.

The fixes are in the order of the commits, so reviewing commit-by-commit may be more useful.

Internal previews

📄 File
🔗 Preview link

docs/csharp/advanced-topics/interface-implementation/mixins-with-default-interface-methods.md
Tutorial: Mix functionality in when creating classes using interfaces with default interface methods

docs/csharp/language-reference/operators/member-access-operators.md
Member access operators and expressions - the dot, indexer, and invocation operators.

docs/csharp/language-reference/tokens/interpolated.md
"$ - string interpolation - format string output"

docs/csharp/programming-guide/strings/index.md
Strings and string literals

ping @adegeo for review


Strong naming... yay!!! ... booo!!  (╯°□°）╯︵ ┻━┻

Integrated MediatR as a submodule

it won't ever be strong named (see https://github.com/jbogard/MediatR/issues/86)
Namespaces are updated on build to differ, so consumes can continue to use MediatR in their related code if they want.

TODOs:

[ ] Add strong name key (.NET Foundation key?)
[ ] Sign assemblies
[ ] Ensure version is pinned for 0.Minor.0.0 (eventually we'll move to Major.0.0.0)

cc @NTaylorMullen @tintoy

This is awesome @david-driscoll! Definitely a pre-req to getting good signing support. I'll ask around about the .NET Foundation key.

Fixed #94

Appveyor failure is transient sadly.


Monkey Jumpsuits and Jumpskirts

Adds monkey sprites for everything in the outfits folder, this includes all jumpsuits and jumpskirts. Outfits do NOT have pockets currently and only PDA slot. Adds monkey sprites for EVA suits ( EVA, prisoner EVA and Syndicate EVA). Does NOT contain sprites for all hardsuits. Monkeys can now equip uniforms in innerclothing and evasuits in outerclothing (only EVA suits mentioned). This uses a monkey species id and does not create new clothing prototypes.
Monkeys can now be cuffed and creampied.
previous pr was borked and my repository completely screwed so i remade repository losing my branches in process, was no reviews on previous pr though.

[x] I have added screenshots/videos to this PR showcasing its changes ingame, or this PR does not require an ingame showcase

Changelog

:cl:

add: Monkeys can now wear uniforms and EVA suits (i sprited all 130 by hand)
add: Security is now authorized to handcuff troublesome monkeys.
add: Clowns are now encouraged to creampie as many monkeys as possible.

Use https://github.com/space-wizards/space-station-14/pull/15969


New rule: require space after comment

" This is a comment
instead of
"This is a comment

yea, perhaps add to existing https://rules.abaplint.org/check_comments/ rule

Maybe it would be better if you could specify forbidden comment patterns (quickfix removes entire comment), then you could also get rid of things like "use this source file for abab unit classes".

sounds good


Fix KubevirtVmHighMemoryUsage alert

Signed-off-by: Shirly Radco<EMAIL_ADDRESS>
What this PR does / why we need it:
Added namespace to the KubevirtVmHighMemoryUsage
alert so that the pod will be correctly identified.
Which issue(s) this PR fixes (optional, in fixes #<issue number>(, fixes #<issue_number>, ...) format, will close the issue(s) when PR gets merged):
Fixes #
Special notes for your reviewer:
Release note:

NONE

/retest-required

/assign @jean-edouard

Hi @vasiliy-ul, will you please review my PR?

Hi @marceloamaral can you please review my PR?

Hi @vasiliy-ul, will you please review my PR?

/lgtm

/cherry-pick release-0.54

@sradco for future reference: to enable bug tracking, please attach bug information for PRs like this in the future by either pasting the full bug URL to the PR description or using this syntax: RHBZ#2088464.


GSE3 user Interface wont load[BUG]

🔵 Describe the bug:
Typing /gse results in the error, the GSE3 UI wont load, though GSE3 macros continue to work.
I can get GSE3 UI to load if I disable the Addon Elvui.
🔵 To reproduce:
/gse
🔵 The error:
Paste the error message in this blockquote.
"
Message: Interface\AddOns\GSE_GUI\Editor.lua:434: attempt to call method 'SetBackdrop' (a nil value)
Time: Fri Jul  9 14:44:15 2021
Count: 1
Stack: Interface\AddOns\GSE_GUI\Editor.lua:434: attempt to call method 'SetBackdrop' (a nil value)
[string "=(tail call)"]: ?
[string "@Interface\AddOns\GSE_GUI\Editor.lua"]:434: in function GUIDrawMetadataEditor' [string "@Interface\AddOns\GSE_GUI\Editor.lua"]:3213: in function GUISelectEditorTab'
[string "@Interface\AddOns\GSE_GUI\Editor.lua"]:280: in function <Interface\AddOns\GSE_GUI\Editor.lua:279>
[string "=[C]"]: ?
[string "@Interface\AddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\AceGUI-3.0.lua"]:72: in function <...ddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\AceGUI-3.0.lua:70>
[string "@Interface\AddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\AceGUI-3.0.lua"]:306: in function Fire' [string "@Interface\AddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\widgets\AceGUIContainer-TabGroup.lua"]:156: in function SelectTab'
[string "@Interface\AddOns\GSE_GUI\Editor.lua"]:287: in function GUIEditorPerformLayout' [string "@Interface\AddOns\GSE_GUI\GUIFunctions.lua"]:95: in function GUILoadEditor'
[string "@Interface\AddOns\GSE_GUI\Viewer.lua"]:265: in function <Interface\AddOns\GSE_GUI\Viewer.lua:264>
[string "=[C]"]: ?
[string "@Interface\AddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\AceGUI-3.0.lua"]:72: in function <...ddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\AceGUI-3.0.lua:70>
[string "@Interface\AddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\AceGUI-3.0.lua"]:306: in function `Fire'
[string "@Interface\AddOns\AddOnSkins\Libs\Ace3\AceGUI-3.0\widgets\AceGUIWidget-Button.lua"]:22: in function <...Libs\Ace3\AceGUI-3.0\widgets\AceGUIWidget-Button.lua:19>
Locals:
"
🔵
/ Screenshots:
If applicable, add screenshots to help explain your problem.
🔵 Expected behavior:
GSE3 UI to load and be able to create/edit GSE3 Macros.
🔵 GSE.lua file:
Please provide your GSE.lua file or the export string for the specific macro that is causing an issue.
GSE.lua.txt
🔵 Desktop (please complete the following information):

OS: Windows 10 64bit
Game Version bcc

🔵 GSE Version:

Version: 3.0.26  (Though all versions since the swap to version 3 have the problem.
Downloaded From: [e.g. https://www.curseforge.com]

🔵 Additional context:
GSE3 is much better  :-)

Hi stint-D,
min GSE’s options (Escape - interface - Addon - GSE) there is a setting to fix Backdrop nil.  If you turn that on does your problem persist?

That indeed fixed the problem Tim, thanks for your time.
I did first raise a forum post asking the community in case it was a simple fix like this.
I was curtly shown the door to raise a bug here, so apologies for wasting your time with a simple click of a known button.
Love the new GSE3, thanks for your effort.

You’re not wasting my time at all.  This is an ongoing problem #831 has  a link where I need information from the Elv guys so that I can fix this properly.
TimothyLuke

On 10 Jul 2021, at 4:44 pm, Tiny-D @.***> wrote:

That indeed fixed the problem Tim, thanks for your time.
I did first raise a forum post asking the community in case it was a simple fix like this.
I was curtly shown the door to raise a bug here, so apologies for wasting your time with a simple click of a known button.
Love the new GSE3, thanks for your effort.
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or unsubscribe.


add infrastructure-components make fmt

[APPROVALNOTIFIER] This PR is NOT APPROVED
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Hi @sadysnaat. Thanks for your PR.
I'm waiting for a spectrocloud member to verify that this patch is reasonable to test. If it is, they should reply with /ok-to-test on its own line. Until that is done, I will not automatically test new commits in this PR, but the usual testing commands by org members will still work. Regular contributors should join the org to skip this step.
Once the patch is verified, the new status will be reflected by the ok-to-test label.
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/test all

/test ?

/test cluster-api-provider-maas_image_build

/test cluster-api-provider-maas_image_build

/test cluster-api-provider-maas_image_build

[APPROVALNOTIFIER] This PR is APPROVED
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[AIRFLOW-2187] Fix Broken Travis CI due to AIRFLOW-2123

Make sure you have checked all steps below.
JIRA

[x] My PR addresses the following Airflow JIRA issues and references them in the PR title. For example, "[AIRFLOW-XXX] My Airflow PR"

https://issues.apache.org/jira/browse/AIRFLOW-2187

Description

[x] Here are some details about my PR, including screenshots of any UI changes:
Added the packages to ignore for Python 3 which fixes the broken Travis CI

Tests

[x] My PR adds the following unit tests OR does not need testing for this extremely good reason:
NA

Commits

[x] My commits all reference JIRA issues in their subject lines, and I have squashed multiple commits if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":

Subject is separated from body by a blank line
Subject is limited to 50 characters
Subject does not end with a period
Subject uses the imperative mood ("add", not "adding")
Body wraps at 72 characters
Body explains "what" and "why", not "how"

[x] Passes git diff upstream/master -u -- "*.py" | flake8 --diff

cc @Fokko PTAL. This should fix the broken Travis CI

cc @Fokko @jgao54 @ashb  PTAL. This fixes the failing Travis CI.

Codecov Report

Merging #3108 into master will increase coverage by 0.13%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #3108      +/-   ##
==========================================
+ Coverage   72.92%   73.06%   +0.13%
==========================================
  Files         180      180
  Lines       12578    12578
==========================================
+ Hits         9173     9190      +17
+ Misses       3405     3388      -17

Impacted Files
Coverage Δ

airflow/jobs.py
80.08% <0%> (+0.44%)
:arrow_up:

airflow/configuration.py
84.97% <0%> (+0.85%)
:arrow_up:

airflow/www/app.py
98.95% <0%> (+2.08%)
:arrow_up:

airflow/utils/helpers.py
54.02% <0%> (+2.87%)
:arrow_up:

airflow/hooks/hdfs_hook.py
92.5% <0%> (+5%)
:arrow_up:

airflow/task/task_runner/bash_task_runner.py
100% <0%> (+6.66%)
:arrow_up:

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Automatic app reconnection when node restarts

Title
When a connection is established to a CockroachDB node and that node goes away for planned or un-planned reasons, then the client needs to re-establish the connection.
Description
Connection can be re-established automatically by some load balancer and connection pool managers.  If not, then application should re-establish the connection.
Outline

provide example of load balancer that has automatic reconnection
provide example of connection pool manager that has automation reconnection

Expected Audience
Application developers

Fixed with #6706.


stretchly: 0.21.0 -> 0.21.1, use system electron, simplify

Motivation for this change
Cleanup and prefer our electron.
Works well here, but let's check with maintainer for approval ;).
Neat app, noticed after electron refactoring PR and am giving a whirl
starting today! :)
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Ensured that relevant documentation is up to date
[ ] Fits CONTRIBUTING.md.

Merging based on maintainer approval.


Add finding.yaml and matches.json formats to documentation

Signed-off-by: Gary O'Neall<EMAIL_ADDRESS>

@swinslow If you get a chance, can you review this and make sure I got the format right?

@goneall I looked at the remainder and added a few more comments -- hope this is helpful!

I looked at the remainder and added a few more comments -- hope this is helpful!

Thanks @swinslow ! - very helpful.
I've updated the docs a bit more and will merge the PR.


cmd/go: Sorry, host unauthorized.

Please answer these questions before submitting your issue. Thanks!
What version of Go are you using (go version)?
1.8.1
What operating system and processor architecture are you using (go env)?
GOARCH="amd64"
GOBIN=""
GOEXE=""
GOHOSTARCH="amd64"
GOHOSTOS="darwin"
GOOS="darwin"
GOPATH="/Users/liam/go"
GORACE=""
GOROOT="/usr/local/Cellar/go/1.8.1/libexec"
GOTOOLDIR="/usr/local/Cellar/go/1.8.1/libexec/pkg/tool/darwin_amd64"
GCCGO="gccgo"
CC="clang"
GOGCCFLAGS="-fPIC -m64 -pthread -fno-caret-diagnostics -Qunused-arguments -fmessage-length=0 -fdebug-prefix-map=/var/folders/hq/v7wjj6rx23ncpg2bp_3zrms80000gn/T/go-build210902941=/tmp/go-build -gno-record-gcc-switches -fno-common"
CXX="clang++"
CGO_ENABLED="1"
PKG_CONFIG="pkg-config"
CGO_CFLAGS="-g -O2"
CGO_CPPFLAGS=""
CGO_CXXFLAGS="-g -O2"
CGO_FFLAGS="-g -O2"
CGO_LDFLAGS="-g -O2"
What did you do?
Compiled a binary
The error is on a program I am not allowed to share unfortunately
What did you expect to see?
I don't know since I cannot compile the binary
What did you see instead?
Sorry, host (HARDWAREIDOFSOMESORT) unauthorized.
I am not sure if I should post the hardware Id for security reasons. I installed golang via brew
My OS is a Macbook Pro 13inch.(on apple hardware)
The problem might be with my goarch, but I am not sure, and I do not know how to set it to the right value.

Can you at least show us the invocation (optionally mildly redacted) of the go command that you are using? And also the output if you run it with the -x flag? (go build -x ...)

UPDATE: I am using the go-sqlite3 lib

I am trying to, the errors in the code have changed when I set GOARCH to 386

I think this is a problem with the go-sqlite3 lib which uses CGO

Here is the output when I type go build -x
WORK=/var/folders/hq/v7wjj6rx23ncpg2bp_3zrms80000gn/T/go-build951901880
mkdir -p $WORK/github.com/skilstak/sk/cmd/sk/_obj/
mkdir -p $WORK/github.com/skilstak/sk/cmd/sk/_obj/exe/
cd /Users/liam/go/src/github.com/skilstak/sk/cmd/sk
/usr/local/Cellar/go/1.8.1/libexec/pkg/tool/darwin_amd64/compile -o $WORK/github.com/skilstak/sk/cmd/sk.a -trimpath $WORK -p main -complete -buildid e09c9ed37e74b8a78957d7fd1ba044970c8ce92a -D _/Users/liam/go/src/github.com/skilstak/sk/cmd/sk -I $WORK -I /Users/liam/go/pkg/darwin_amd64 -pack ./main.go
cd .
/usr/local/Cellar/go/1.8.1/libexec/pkg/tool/darwin_amd64/link -o $WORK/github.com/skilstak/sk/cmd/sk/_obj/exe/a.out -L $WORK -L /Users/liam/go/pkg/darwin_amd64 -extld=clang -buildmode=exe -buildid=e09c9ed37e74b8a78957d7fd1ba044970c8ce92a $WORK/github.com/skilstak/sk/cmd/sk.a
mv $WORK/github.com/skilstak/sk/cmd/sk/_obj/exe/a.out sk

And here is the output with GOARCH set to 386
WORK=/var/folders/hq/v7wjj6rx23ncpg2bp_3zrms80000gn/T/go-build361352098
mkdir -p $WORK/github.com/mattn/go-sqlite3/_obj/
mkdir -p $WORK/github.com/mattn/
cd /Users/liam/go/src/github.com/mattn/go-sqlite3
/usr/local/Cellar/go/1.8.1/libexec/pkg/tool/darwin_amd64/compile -o $WORK/github.com/mattn/go-sqlite3.a -trimpath $WORK -p github.com/mattn/go-sqlite3 -complete -buildid 243eef54ca2810e6cd70d3740496e335b4080a24 -D _/Users/liam/go/src/github.com/mattn/go-sqlite3 -I $WORK -I /Users/liam/go/pkg/darwin_386 -pack ./doc.go ./sqlite3_go18.go
# github.com/mattn/go-sqlite3
../../../../mattn/go-sqlite3/sqlite3_go18.go:18: undefined: SQLiteConn
../../../../mattn/go-sqlite3/sqlite3_go18.go:26: undefined: SQLiteConn
../../../../mattn/go-sqlite3/sqlite3_go18.go:27: undefined: namedValue
../../../../mattn/go-sqlite3/sqlite3_go18.go:29: undefined: namedValue
../../../../mattn/go-sqlite3/sqlite3_go18.go:35: undefined: SQLiteConn
../../../../mattn/go-sqlite3/sqlite3_go18.go:36: undefined: namedValue
../../../../mattn/go-sqlite3/sqlite3_go18.go:44: undefined: SQLiteConn
../../../../mattn/go-sqlite3/sqlite3_go18.go:49: undefined: SQLiteConn
../../../../mattn/go-sqlite3/sqlite3_go18.go:54: undefined: SQLiteStmt
../../../../mattn/go-sqlite3/sqlite3_go18.go:63: undefined: SQLiteStmt
../../../../mattn/go-sqlite3/sqlite3_go18.go:36: too many errors

I don't see the host unauthorized message in the output you provided.
Please provide the full output for a single run with no GOARCH set, including the error message. You can redact individual bits (like the hardware id, if that's what it is), but the more complete a picture we have, the better we can help. I've never seen a "host unauthorized" message before.

The host unauthorized part comes when you run the binary

In go 1.8 the message was Killed:9

export GOARCH="" does not change the goarch when I run go env

Maybe you have some antivirus software on your machine preventing which binaries you're allowed to run. This might also be an sqlite package problem. In any case, I don't see a Go issue here.
Let's move debugging to the mailing list. See https://golang.org/wiki/Questions.

Thank you guys so much

This is not an issue with the sqlite package, I changed the lib, also I have no firewall installed on this system. I reinstalled golang using the official .pkg file, and no luck


Tensorflow 2.2.0 import error - DLL load failed

Please make sure that this is a build/installation issue. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template
System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10 Pro [Version 10.0.19041.264]
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: N/A
TensorFlow installed from (source or binary): pip install
TensorFlow version: 2.2.0
Python version: 3.6.8 (issue also occurs with 3.7.7 and 3.8.3)
Installed using virtualenv? pip? conda?: pip
Bazel version (if compiling from source): N/A
GCC/Compiler version (if compiling from source): N/A
CUDA/cuDNN version: N/A
GPU model and memory: N/A

Describe the problem
I get the error that I'm missing some dynamic libraries when I try to import tensorflow after installing it. In the error message it is suggested that I download certain C++ dlls from visual studio. I did that and it still didn't work. I also downloaded visual studio code and the same problem persisted. Please note: the error message attached below also occurred with only tensorflow installed, with only tensorflow and tensorflow-gpu installed and finally, with all 3 (tensorflow, tensorflow-gpu, and tensorflow-cpu) installed.
Provide the exact sequence of commands / steps that you executed before running into the problem
pip install tensorflow
pip install tensorflow-gpu
pip install tensorflow-cpu
python

import tensorflow

Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
tensorflow import error log.txt

@saharatech
Please go through this comment and this issue and let us know if it helps.
Link

@Saduf2019
Yes, that helped, thank you very much. My CPU does not support AVX2. I will try building TensorFlow from source. Please feel free to close this issue.

@saharatech
Glad you were able to resolve the issue, hence moving it to closed status.


Add support for date on lock screen

Feature Request
Just be able to add date on top of time (for example)
Implementation
Just add all options like time but for date and use options given by i3lock(-color)
Have you planned working on this feature?
Yes

I already done modifications and commits locally.
I don't know how to open MR for this issue, or push my feature branch.

As I just made an MR, you can, if you want, label it wiht 'has-mr'

Any updates on this?

i have the date on my lockscreen. Do you want to know how?

Any updates on this?

Sorry I totally lost track of the remaining problems to be fixed before merge.
If anyone passing by, please feel free to make a list and I will try to fix ASAP


Set minimum OS required version in pubspec

Use case
With the recent bugs in flutterfire, and having to downgrade versions to avoid bugs, I've had to blow away my Podfile a few times to have it rebuild by flutter. I'd like to specify an OS version in the pubspec and have it propagate to Podfile, build.gradle, etc... That way when I need to regenerate the various OS, it already has my min OS requirements.

Proposal
Include OS version requirements in Pubspec.yaml

This would also solve https://github.com/flutter/flutter/issues/78862.


Clarify documentation for supported encodings in AFJSONResponseSerializer

This clarify the data encoding needed for AFJSONResponseSerializer to successfully parsing the response as JSON object.

@skyline75489 Can you merge in master or rebase off of it to get the build to pass on Travis?

Codecov Report

Merging #3750 into master will increase coverage by 4.15%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #3750      +/-   ##
==========================================
+ Coverage    82.8%   86.95%   +4.15%
==========================================
  Files          46       45       -1
  Lines        5304     5505     +201
  Branches      439      439
==========================================
+ Hits         4392     4787     +395
- Misses        671      715      +44
+ Partials      241        3     -238

Impacted Files
Coverage Δ

AFNetworking/AFURLResponseSerialization.h
90% <ø> (ø)
:arrow_up:

UIKit+AFNetworking/UIProgressView+AFNetworking.m
0% <0%> (ø)
:arrow_up:

Tests/Tests/AFXMLParserResponseSerializerTests.m

UIKit+AFNetworking/AFAutoPurgingImageCache.m
96.29% <0%> (+2.9%)
:arrow_up:

AFNetworking/AFNetworkReachabilityManager.m
71.9% <0%> (+4.91%)
:arrow_up:

UIKit+AFNetworking/AFImageDownloader.m
97.53% <0%> (+6.1%)
:arrow_up:

...t+AFNetworking/AFNetworkActivityIndicatorManager.m
83.33% <0%> (+6.34%)
:arrow_up:

AFNetworking/AFHTTPSessionManager.m
88.96% <0%> (+6.96%)
:arrow_up:

UIKit+AFNetworking/UIImageView+AFNetworking.m
96.92% <0%> (+7.86%)
:arrow_up:

UIKit+AFNetworking/UIButton+AFNetworking.m
77.14% <0%> (+8.39%)
:arrow_up:

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@SlaunchaMan Done.


Remove version: DeaDBeeF.DeaDBeeF version 1.9.3

[Automated] It returns code over 400 in all urls
Microsoft Reviewers: Open in CodeFlow

Close with reason: Removal appears to be automated and in error;


Give cloud-controller-manager permission to watch secrets

What this PR does / why we need it:
In kubernetes/kubernetes/pkg/controller/client_builder.go it attempts to start a watch for new secrets if the secret containing a token for a service account does not exist the first time it checks. Without the watch permission for secrets this crashes the cloud-controller-manager and the pod restarts until the secret does exist.
Which issue this PR fixes (optional, in fixes #<issue number>(, fixes #<issue_number>, ...) format, will close that issue when PR gets merged): fixes #545
Special notes for your reviewer:
Release note:

/assign @nickrenren

/test cloud-provider-openstack-acceptance-test-e2e-conformance-stable-branch-v1.14

/ok-to-test

/lgtm
/approve


Redirect test output when we are running on the CI

The idea of this PR is just redirecting to dev/null only if we are running the deployer on the CI and get the full
output when running locally.
Accordingly, to GitHub docs about environment variables, the CI is always set to true. I think that should be enough to "detect" the CI environment.
Closes #694

Picked some reviewers but everyone is invited to review :wink:


add a option to open file in browser with web-url on starting

i think there should be a option   "web-root"  in the plugin setting
such as web-root:localhost/../xxx/
so that i can open a file in browser width web-url on starting like this:

based on this idea php and jsp file can be previewed  easily

Yes there is settings for that and it is called home page.check it out.

yes it's exist,but can't open file using web-path on starting

check out the open-in-browsers package(https://atom.io/packages/open-in-browsers) with pp package(preview-plus https://atom.io/packages/pp)


HBASE-27033 Backport "HBASE-27013 Introduce read all bytes when using…

… pread for prefetch"

introduce optional flag hfile.pread.all.bytes.enabled for pread that must read full bytes with the next block header

please see the original commit from branch-2 https://github.com/apache/hbase/commit/43e8c11989712875a61e584c5557a0263056b009

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32m 54s

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Docker
ClientAPI=1.41 ServerAPI=1.41 base: https://ci-hbase.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-4429/1/artifact/yetus-general-check/output/Dockerfile

GITHUB PR
https://github.com/apache/hbase/pull/4429

Optional Tests
dupname asflicense javac spotbugs hadoopcheck hbaseanti spotless checkstyle compile

uname
Linux 26475f694390 5.4.0-90-generic #101-Ubuntu SMP Fri Oct 15 20:00:55 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux

Build tool
maven

Personality
dev-support/hbase-personality.sh

git revision
branch-2.4 / aa7f98275f

Default Java
AdoptOpenJDK-1.8.0_282-b08

Max. process+thread count
65 (vs. ulimit of 12500)

modules
C: hbase-common hbase-server U: .

Console output
https://ci-hbase.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-4429/1/console

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4m 13s
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0m 33s
branch-2.4 passed

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0m 43s
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163m 10s
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183m 36s

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Docker
ClientAPI=1.41 ServerAPI=1.41 base: https://ci-hbase.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-4429/1/artifact/yetus-jdk8-hadoop2-check/output/Dockerfile

GITHUB PR
https://github.com/apache/hbase/pull/4429

Optional Tests
javac javadoc unit shadedjars compile

uname
Linux fe4fe894e9e0 5.4.0-1071-aws #76~18.04.1-Ubuntu SMP Mon Mar 28 17:49:57 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

Build tool
maven

Personality
dev-support/hbase-personality.sh

git revision
branch-2.4 / aa7f98275f

Default Java
AdoptOpenJDK-1.8.0_282-b08

Test Results
https://ci-hbase.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-4429/1/testReport/

Max. process+thread count
2596 (vs. ulimit of 12500)

modules
C: hbase-common hbase-server U: .

Console output
https://ci-hbase.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-4429/1/console

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4m 47s
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-0 :warning:
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hbase-common generated 1 new + 15 unchanged - 0 fixed = 16 total (was 15)

_ Other Tests _

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196m 21s

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GITHUB PR
https://github.com/apache/hbase/pull/4429

Optional Tests
javac javadoc unit shadedjars compile

uname
Linux 8ca541518d0c 5.4.0-1068-aws #72~18.04.1-Ubuntu SMP Thu Mar 3 08:49:49 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

Build tool
maven

Personality
dev-support/hbase-personality.sh

git revision
branch-2.4 / aa7f98275f

Default Java
AdoptOpenJDK-11.0.10+9

javadoc
https://ci-hbase.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-4429/1/artifact/yetus-jdk11-hadoop3-check/output/diff-javadoc-javadoc-hbase-common.txt

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modules
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feat: add runtimeClassName

To enhance the scheduling of Ollama on nodes within my cluster, which features a variety of node types, the ability to specify the runtimeClassName would be greatly beneficial

Added.