Unable to train object detection model

hi,
I need help.
I'm following the video.
After downloading the image from the open image dataset, I tried to train train_ssd.py and when I ran the command I got an error like this:
I think 8GB of memory is enough.
Why do I get an error?
root@abc-desktop:/jetson-inference/python/training/detection/ssd# python3 train_ssd.py --data=data/fruit --model-dir=models/fruit --batch-size=4 --epochs=1
2020-12-03 08:48:52 - Using CUDA...
2020-12-03 08:48:52 - Namespace(balance_data=False, base_net=None, base_net_lr=0.001, batch_size=4, checkpoint_folder='models/fruit', dataset_type='open_images', datasets=['data/fruit'], debug_steps=10, extra_layers_lr=None, freeze_base_net=False, freeze_net=False, gamma=0.1, lr=0.01, mb2_width_mult=1.0, milestones='80,100', momentum=0.9, net='mb1-ssd', num_epochs=1, num_workers=2, pretrained_ssd='models/mobilenet-v1-ssd-mp-0_675.pth', resume=None, scheduler='cosine', t_max=100, use_cuda=True, validation_epochs=1, weight_decay=0.0005)
2020-12-03 08:48:52 - Prepare training datasets.
2020-12-03 08:48:52 - loading annotations from: data/fruit/sub-train-annotations-bbox.csv
2020-12-03 08:48:52 - annotations loaded from:  data/fruit/sub-train-annotations-bbox.csv
num images:  2022
2020-12-03 08:48:58 - Dataset Summary:Number of Images: 2022
Minimum Number of Images for a Class: -1
Label Distribution:
Apple: 1466
Banana: 604
Grape: 904
Orange: 2734
Pear: 356
Pineapple: 201
Strawberry: 3402
Watermelon: 297
2020-12-03 08:48:58 - Stored labels into file models/fruit/labels.txt.
2020-12-03 08:48:58 - Train dataset size: 2022
2020-12-03 08:48:58 - Prepare Validation datasets.
2020-12-03 08:48:58 - loading annotations from: data/fruit/sub-test-annotations-bbox.csv
2020-12-03 08:48:58 - annotations loaded from:  data/fruit/sub-test-annotations-bbox.csv
num images:  365
2020-12-03 08:48:59 - Dataset Summary:Number of Images: 365
Minimum Number of Images for a Class: -1
Label Distribution:
Apple: 133
Banana: 50
Grape: 151
Orange: 382
Pear: 36
Pineapple: 42
Strawberry: 343
Watermelon: 51
2020-12-03 08:48:59 - Validation dataset size: 365
2020-12-03 08:48:59 - Build network.
2020-12-03 08:49:00 - Init from pretrained ssd models/mobilenet-v1-ssd-mp-0_675.pth
Traceback (most recent call last):
File "train_ssd.py", line 309, in
net.init_from_pretrained_ssd(args.pretrained_ssd)
File "/jetson-inference/python/training/detection/ssd/vision/ssd/ssd.py", line 119, in init_from_pretrained_ssd
state_dict = torch.load(model, map_location=lambda storage, loc: storage)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 585, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 772, in _legacy_load
deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly)
RuntimeError: unexpected EOF, expected 257966 more bytes. The file might be corrupted.

It seems like your models/mobilenet-v1-ssd-mp-0_675.pth file may be corrupt.
Can you try running these commands?
$ cd jetson-inference/python/training/detection/ssd
$ wget https://nvidia.box.com/shared/static/djf5w54rjvpqocsiztzaandq1m3avr7c.pth -O models/mobilenet-v1-ssd-mp-0_675.pth

Thank you
I didn't get any errors during training on train_ssd.py.
but, after exporting, detect failed. In other words, I tried to detect only one image, but the box did not appear.
Why did detect fail?
//This is a state of learning.
root@abc-desktop:/jetson-inference/python/training/detection/ssd# python3 train_ssd.py --data=data/fruit/ --model-dir=models/fruit/ --batch-size=2 --workers=1 --epoch=1
2020-12-08 14:35:00 - Using CUDA...
2020-12-08 14:35:00 - Namespace(balance_data=False, base_net=None, base_net_lr=0.001, batch_size=2, checkpoint_folder='models/fruit/', dataset_type='open_images', datasets=['data/fruit/'], debug_steps=10, extra_layers_lr=None, freeze_base_net=False, freeze_net=False, gamma=0.1, lr=0.01, mb2_width_mult=1.0, milestones='80,100', momentum=0.9, net='mb1-ssd', num_epochs=1, num_workers=1, pretrained_ssd='models/mobilenet-v1-ssd-mp-0_675.pth', resume=None, scheduler='cosine', t_max=100, use_cuda=True, validation_epochs=1, weight_decay=0.0005)
2020-12-08 14:35:00 - Prepare training datasets.
2020-12-08 14:35:00 - loading annotations from: data/fruit/sub-train-annotations-bbox.csv
2020-12-08 14:35:00 - annotations loaded from:  data/fruit/sub-train-annotations-bbox.csv
num images:  2022
2020-12-08 14:35:06 - Dataset Summary:Number of Images: 2022
Minimum Number of Images for a Class: -1
Label Distribution:
Apple: 1552
Banana: 610
Grape: 1130
Orange: 2452
Pear: 396
Pineapple: 219
Strawberry: 3275
Watermelon: 265
2020-12-08 14:35:06 - Stored labels into file models/fruit/labels.txt.
2020-12-08 14:35:06 - Train dataset size: 2022
2020-12-08 14:35:06 - Prepare Validation datasets.
2020-12-08 14:35:06 - loading annotations from: data/fruit/sub-test-annotations-bbox.csv
2020-12-08 14:35:06 - annotations loaded from:  data/fruit/sub-test-annotations-bbox.csv
num images:  365
2020-12-08 14:35:08 - Dataset Summary:Number of Images: 365
Minimum Number of Images for a Class: -1
Label Distribution:
Apple: 135
Banana: 43
Grape: 163
Orange: 260
Pear: 44
Pineapple: 39
Strawberry: 303
Watermelon: 61
2020-12-08 14:35:08 - Validation dataset size: 365
2020-12-08 14:35:08 - Build network.
2020-12-08 14:35:08 - Init from pretrained ssd models/mobilenet-v1-ssd-mp-0_675.pth
2020-12-08 14:35:10 - Took 1.77 seconds to load the model.
2020-12-08 14:35:39 - Learning rate: 0.01, Base net learning rate: 0.001, Extra Layers learning rate: 0.01.
2020-12-08 14:35:39 - Uses CosineAnnealingLR scheduler.
2020-12-08 14:35:39 - Start training from epoch 0.
/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step().  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
warnings.warn(warning.format(ret))
2020-12-08 14:36:25 - Epoch: 0, Step: 10/1011, Avg Loss: 14.5199, Avg Regression Loss 4.4924, Avg Classification Loss: 10.0275
2020-12-08 14:36:30 - Epoch: 0, Step: 20/1011, Avg Loss: 10.1059, Avg Regression Loss 3.9718, Avg Classification Loss: 6.1341
2020-12-08 14:36:37 - Epoch: 0, Step: 30/1011, Avg Loss: 8.9187, Avg Regression Loss 3.1364, Avg Classification Loss: 5.7823
2020-12-08 14:36:47 - Epoch: 0, Step: 40/1011, Avg Loss: 9.7260, Avg Regression Loss 4.2638, Avg Classification Loss: 5.4623
2020-12-08 14:36:53 - Epoch: 0, Step: 50/1011, Avg Loss: 10.9533, Avg Regression Loss 4.6289, Avg Classification Loss: 6.3244
2020-12-08 14:37:00 - Epoch: 0, Step: 60/1011, Avg Loss: 9.1247, Avg Regression Loss 3.5626, Avg Classification Loss: 5.5621
2020-12-08 14:37:05 - Epoch: 0, Step: 70/1011, Avg Loss: 8.5219, Avg Regression Loss 3.3023, Avg Classification Loss: 5.2197
2020-12-08 14:37:09 - Epoch: 0, Step: 80/1011, Avg Loss: 8.5600, Avg Regression Loss 2.8761, Avg Classification Loss: 5.6838
2020-12-08 14:37:14 - Epoch: 0, Step: 90/1011, Avg Loss: 7.5302, Avg Regression Loss 2.6558, Avg Classification Loss: 4.8744
2020-12-08 14:37:18 - Epoch: 0, Step: 100/1011, Avg Loss: 8.6010, Avg Regression Loss 3.5954, Avg Classification Loss: 5.0056
2020-12-08 14:37:28 - Epoch: 0, Step: 110/1011, Avg Loss: 8.1808, Avg Regression Loss 3.0599, Avg Classification Loss: 5.1209
2020-12-08 14:37:33 - Epoch: 0, Step: 120/1011, Avg Loss: 8.0501, Avg Regression Loss 3.0772, Avg Classification Loss: 4.9729
2020-12-08 14:37:37 - Epoch: 0, Step: 130/1011, Avg Loss: 8.1948, Avg Regression Loss 3.1437, Avg Classification Loss: 5.0511
2020-12-08 14:37:47 - Epoch: 0, Step: 140/1011, Avg Loss: 7.4560, Avg Regression Loss 2.6546, Avg Classification Loss: 4.8014
2020-12-08 14:37:52 - Epoch: 0, Step: 150/1011, Avg Loss: 7.7333, Avg Regression Loss 2.9388, Avg Classification Loss: 4.7945
2020-12-08 14:37:57 - Epoch: 0, Step: 160/1011, Avg Loss: 8.2852, Avg Regression Loss 3.2806, Avg Classification Loss: 5.0046
2020-12-08 14:38:01 - Epoch: 0, Step: 170/1011, Avg Loss: 7.5026, Avg Regression Loss 2.6710, Avg Classification Loss: 4.8316
2020-12-08 14:38:09 - Epoch: 0, Step: 180/1011, Avg Loss: 7.7154, Avg Regression Loss 2.4192, Avg Classification Loss: 5.2962
2020-12-08 14:38:14 - Epoch: 0, Step: 190/1011, Avg Loss: 9.9042, Avg Regression Loss 4.5449, Avg Classification Loss: 5.3593
2020-12-08 14:38:21 - Epoch: 0, Step: 200/1011, Avg Loss: 9.4489, Avg Regression Loss 4.2731, Avg Classification Loss: 5.1758
2020-12-08 14:38:26 - Epoch: 0, Step: 210/1011, Avg Loss: 8.6582, Avg Regression Loss 3.6028, Avg Classification Loss: 5.0554
2020-12-08 14:38:30 - Epoch: 0, Step: 220/1011, Avg Loss: 7.9419, Avg Regression Loss 2.9801, Avg Classification Loss: 4.9618
2020-12-08 14:38:35 - Epoch: 0, Step: 230/1011, Avg Loss: 7.7724, Avg Regression Loss 2.8450, Avg Classification Loss: 4.9274
2020-12-08 14:38:40 - Epoch: 0, Step: 240/1011, Avg Loss: 6.8938, Avg Regression Loss 2.2602, Avg Classification Loss: 4.6337
2020-12-08 14:38:48 - Epoch: 0, Step: 250/1011, Avg Loss: 6.9706, Avg Regression Loss 2.1466, Avg Classification Loss: 4.8240
2020-12-08 14:38:53 - Epoch: 0, Step: 260/1011, Avg Loss: 6.5615, Avg Regression Loss 2.0407, Avg Classification Loss: 4.5207
2020-12-08 14:38:57 - Epoch: 0, Step: 270/1011, Avg Loss: 7.5310, Avg Regression Loss 2.5796, Avg Classification Loss: 4.9514
2020-12-08 14:39:05 - Epoch: 0, Step: 280/1011, Avg Loss: 7.4665, Avg Regression Loss 2.6466, Avg Classification Loss: 4.8199
2020-12-08 14:39:10 - Epoch: 0, Step: 290/1011, Avg Loss: 7.7696, Avg Regression Loss 3.2720, Avg Classification Loss: 4.4976
2020-12-08 14:39:17 - Epoch: 0, Step: 300/1011, Avg Loss: 7.8625, Avg Regression Loss 3.1431, Avg Classification Loss: 4.7194
2020-12-08 14:39:22 - Epoch: 0, Step: 310/1011, Avg Loss: 7.3450, Avg Regression Loss 2.3516, Avg Classification Loss: 4.9934
2020-12-08 14:39:26 - Epoch: 0, Step: 320/1011, Avg Loss: 7.2802, Avg Regression Loss 2.6770, Avg Classification Loss: 4.6032
2020-12-08 14:39:31 - Epoch: 0, Step: 330/1011, Avg Loss: 6.4982, Avg Regression Loss 1.8892, Avg Classification Loss: 4.6090
2020-12-08 14:39:39 - Epoch: 0, Step: 340/1011, Avg Loss: 7.1250, Avg Regression Loss 2.5867, Avg Classification Loss: 4.5383
2020-12-08 14:39:44 - Epoch: 0, Step: 350/1011, Avg Loss: 7.8499, Avg Regression Loss 2.9168, Avg Classification Loss: 4.9332
2020-12-08 14:39:50 - Epoch: 0, Step: 360/1011, Avg Loss: 7.6063, Avg Regression Loss 2.5247, Avg Classification Loss: 5.0817
2020-12-08 14:39:56 - Epoch: 0, Step: 370/1011, Avg Loss: 8.8012, Avg Regression Loss 3.7026, Avg Classification Loss: 5.0986
2020-12-08 14:40:01 - Epoch: 0, Step: 380/1011, Avg Loss: 6.9447, Avg Regression Loss 2.1803, Avg Classification Loss: 4.7644
2020-12-08 14:40:05 - Epoch: 0, Step: 390/1011, Avg Loss: 7.3531, Avg Regression Loss 2.6368, Avg Classification Loss: 4.7163
2020-12-08 14:40:10 - Epoch: 0, Step: 400/1011, Avg Loss: 8.6836, Avg Regression Loss 4.0794, Avg Classification Loss: 4.6042
2020-12-08 14:40:15 - Epoch: 0, Step: 410/1011, Avg Loss: 7.8766, Avg Regression Loss 3.2878, Avg Classification Loss: 4.5888
2020-12-08 14:40:20 - Epoch: 0, Step: 420/1011, Avg Loss: 7.4941, Avg Regression Loss 2.5234, Avg Classification Loss: 4.9707
2020-12-08 14:40:24 - Epoch: 0, Step: 430/1011, Avg Loss: 6.8185, Avg Regression Loss 2.4050, Avg Classification Loss: 4.4135
2020-12-08 14:40:29 - Epoch: 0, Step: 440/1011, Avg Loss: 7.5844, Avg Regression Loss 3.0289, Avg Classification Loss: 4.5554
2020-12-08 14:40:39 - Epoch: 0, Step: 450/1011, Avg Loss: 7.6584, Avg Regression Loss 2.8568, Avg Classification Loss: 4.8016
2020-12-08 14:40:44 - Epoch: 0, Step: 460/1011, Avg Loss: 7.3348, Avg Regression Loss 2.7907, Avg Classification Loss: 4.5441
2020-12-08 14:40:48 - Epoch: 0, Step: 470/1011, Avg Loss: 6.2670, Avg Regression Loss 1.8812, Avg Classification Loss: 4.3857
2020-12-08 14:40:55 - Epoch: 0, Step: 480/1011, Avg Loss: 7.0250, Avg Regression Loss 2.5542, Avg Classification Loss: 4.4708
2020-12-08 14:40:59 - Epoch: 0, Step: 490/1011, Avg Loss: 8.0442, Avg Regression Loss 3.0904, Avg Classification Loss: 4.9538
2020-12-08 14:41:08 - Epoch: 0, Step: 500/1011, Avg Loss: 7.5016, Avg Regression Loss 2.6675, Avg Classification Loss: 4.8340
2020-12-08 14:41:13 - Epoch: 0, Step: 510/1011, Avg Loss: 6.4084, Avg Regression Loss 2.1852, Avg Classification Loss: 4.2232
2020-12-08 14:41:18 - Epoch: 0, Step: 520/1011, Avg Loss: 8.7560, Avg Regression Loss 4.1758, Avg Classification Loss: 4.5802
2020-12-08 14:41:23 - Epoch: 0, Step: 530/1011, Avg Loss: 7.0062, Avg Regression Loss 2.5292, Avg Classification Loss: 4.4770
2020-12-08 14:41:29 - Epoch: 0, Step: 540/1011, Avg Loss: 7.8892, Avg Regression Loss 3.3556, Avg Classification Loss: 4.5336
2020-12-08 14:41:33 - Epoch: 0, Step: 550/1011, Avg Loss: 7.5412, Avg Regression Loss 2.9724, Avg Classification Loss: 4.5688
2020-12-08 14:41:38 - Epoch: 0, Step: 560/1011, Avg Loss: 7.6099, Avg Regression Loss 3.1637, Avg Classification Loss: 4.4461
2020-12-08 14:41:48 - Epoch: 0, Step: 570/1011, Avg Loss: 6.9136, Avg Regression Loss 2.3119, Avg Classification Loss: 4.6017
2020-12-08 14:41:55 - Epoch: 0, Step: 580/1011, Avg Loss: 9.0745, Avg Regression Loss 4.4068, Avg Classification Loss: 4.6677
2020-12-08 14:41:59 - Epoch: 0, Step: 590/1011, Avg Loss: 10.0077, Avg Regression Loss 5.4643, Avg Classification Loss: 4.5434
2020-12-08 14:42:04 - Epoch: 0, Step: 600/1011, Avg Loss: 6.2361, Avg Regression Loss 1.9596, Avg Classification Loss: 4.2765
2020-12-08 14:42:09 - Epoch: 0, Step: 610/1011, Avg Loss: 7.4510, Avg Regression Loss 2.8949, Avg Classification Loss: 4.5561
2020-12-08 14:42:16 - Epoch: 0, Step: 620/1011, Avg Loss: 7.4226, Avg Regression Loss 2.7658, Avg Classification Loss: 4.6568
2020-12-08 14:42:21 - Epoch: 0, Step: 630/1011, Avg Loss: 7.6734, Avg Regression Loss 2.9830, Avg Classification Loss: 4.6904
2020-12-08 14:42:25 - Epoch: 0, Step: 640/1011, Avg Loss: 7.1532, Avg Regression Loss 2.3490, Avg Classification Loss: 4.8042
2020-12-08 14:42:30 - Epoch: 0, Step: 650/1011, Avg Loss: 6.6333, Avg Regression Loss 2.4285, Avg Classification Loss: 4.2048
2020-12-08 14:42:35 - Epoch: 0, Step: 660/1011, Avg Loss: 6.4980, Avg Regression Loss 2.2451, Avg Classification Loss: 4.2529
2020-12-08 14:42:44 - Epoch: 0, Step: 670/1011, Avg Loss: 7.4537, Avg Regression Loss 3.0762, Avg Classification Loss: 4.3775
2020-12-08 14:42:49 - Epoch: 0, Step: 680/1011, Avg Loss: 7.9041, Avg Regression Loss 3.5037, Avg Classification Loss: 4.4004
2020-12-08 14:42:53 - Epoch: 0, Step: 690/1011, Avg Loss: 7.8822, Avg Regression Loss 3.4341, Avg Classification Loss: 4.4482
2020-12-08 14:42:58 - Epoch: 0, Step: 700/1011, Avg Loss: 8.1162, Avg Regression Loss 3.7871, Avg Classification Loss: 4.3291
2020-12-08 14:43:06 - Epoch: 0, Step: 710/1011, Avg Loss: 8.1627, Avg Regression Loss 3.3867, Avg Classification Loss: 4.7759
2020-12-08 14:43:11 - Epoch: 0, Step: 720/1011, Avg Loss: 6.0715, Avg Regression Loss 1.6442, Avg Classification Loss: 4.4273
2020-12-08 14:43:16 - Epoch: 0, Step: 730/1011, Avg Loss: 6.7726, Avg Regression Loss 2.1483, Avg Classification Loss: 4.6243
2020-12-08 14:43:20 - Epoch: 0, Step: 740/1011, Avg Loss: 6.3247, Avg Regression Loss 1.8859, Avg Classification Loss: 4.4388
2020-12-08 14:43:29 - Epoch: 0, Step: 750/1011, Avg Loss: 6.9105, Avg Regression Loss 2.3605, Avg Classification Loss: 4.5500
2020-12-08 14:43:33 - Epoch: 0, Step: 760/1011, Avg Loss: 9.4562, Avg Regression Loss 4.9078, Avg Classification Loss: 4.5484
2020-12-08 14:43:38 - Epoch: 0, Step: 770/1011, Avg Loss: 7.1738, Avg Regression Loss 2.7943, Avg Classification Loss: 4.3795
2020-12-08 14:43:43 - Epoch: 0, Step: 780/1011, Avg Loss: 7.3370, Avg Regression Loss 2.5424, Avg Classification Loss: 4.7945
2020-12-08 14:43:48 - Epoch: 0, Step: 790/1011, Avg Loss: 6.3570, Avg Regression Loss 1.9268, Avg Classification Loss: 4.4302
2020-12-08 14:43:52 - Epoch: 0, Step: 800/1011, Avg Loss: 7.4442, Avg Regression Loss 2.7927, Avg Classification Loss: 4.6515
2020-12-08 14:43:57 - Epoch: 0, Step: 810/1011, Avg Loss: 6.9505, Avg Regression Loss 2.5166, Avg Classification Loss: 4.4339
2020-12-08 14:44:02 - Epoch: 0, Step: 820/1011, Avg Loss: 6.5706, Avg Regression Loss 2.2807, Avg Classification Loss: 4.2899
2020-12-08 14:44:08 - Epoch: 0, Step: 830/1011, Avg Loss: 7.6370, Avg Regression Loss 2.9771, Avg Classification Loss: 4.6599
2020-12-08 14:44:13 - Epoch: 0, Step: 840/1011, Avg Loss: 6.9682, Avg Regression Loss 2.5986, Avg Classification Loss: 4.3696
2020-12-08 14:44:17 - Epoch: 0, Step: 850/1011, Avg Loss: 7.6184, Avg Regression Loss 2.7529, Avg Classification Loss: 4.8656
2020-12-08 14:44:22 - Epoch: 0, Step: 860/1011, Avg Loss: 8.2485, Avg Regression Loss 3.6630, Avg Classification Loss: 4.5855
2020-12-08 14:44:27 - Epoch: 0, Step: 870/1011, Avg Loss: 7.5299, Avg Regression Loss 3.0305, Avg Classification Loss: 4.4994
2020-12-08 14:44:36 - Epoch: 0, Step: 880/1011, Avg Loss: 6.2379, Avg Regression Loss 1.7530, Avg Classification Loss: 4.4849
2020-12-08 14:44:41 - Epoch: 0, Step: 890/1011, Avg Loss: 7.1604, Avg Regression Loss 2.5130, Avg Classification Loss: 4.6473
2020-12-08 14:44:46 - Epoch: 0, Step: 900/1011, Avg Loss: 6.2334, Avg Regression Loss 1.7023, Avg Classification Loss: 4.5312
2020-12-08 14:44:53 - Epoch: 0, Step: 910/1011, Avg Loss: 6.1479, Avg Regression Loss 1.7785, Avg Classification Loss: 4.3693
2020-12-08 14:44:58 - Epoch: 0, Step: 920/1011, Avg Loss: 6.7641, Avg Regression Loss 2.4480, Avg Classification Loss: 4.3160
2020-12-08 14:45:05 - Epoch: 0, Step: 930/1011, Avg Loss: 6.5744, Avg Regression Loss 2.2958, Avg Classification Loss: 4.2786
2020-12-08 14:45:10 - Epoch: 0, Step: 940/1011, Avg Loss: 7.2514, Avg Regression Loss 2.7078, Avg Classification Loss: 4.5436
2020-12-08 14:45:14 - Epoch: 0, Step: 950/1011, Avg Loss: 6.8946, Avg Regression Loss 2.5979, Avg Classification Loss: 4.2967
2020-12-08 14:45:19 - Epoch: 0, Step: 960/1011, Avg Loss: 7.1061, Avg Regression Loss 2.8859, Avg Classification Loss: 4.2202
2020-12-08 14:45:24 - Epoch: 0, Step: 970/1011, Avg Loss: 7.4662, Avg Regression Loss 3.1387, Avg Classification Loss: 4.3275
2020-12-08 14:45:29 - Epoch: 0, Step: 980/1011, Avg Loss: 6.8864, Avg Regression Loss 2.5813, Avg Classification Loss: 4.3052
2020-12-08 14:45:33 - Epoch: 0, Step: 990/1011, Avg Loss: 7.2122, Avg Regression Loss 2.8819, Avg Classification Loss: 4.3302
2020-12-08 14:45:38 - Epoch: 0, Step: 1000/1011, Avg Loss: 6.9349, Avg Regression Loss 2.7818, Avg Classification Loss: 4.1531
2020-12-08 14:45:43 - Epoch: 0, Step: 1010/1011, Avg Loss: 6.9592, Avg Regression Loss 2.4485, Avg Classification Loss: 4.5106
2020-12-08 14:46:16 - Epoch: 0, Validation Loss: 5.8151, Validation Regression Loss 1.5735, Validation Classification Loss: 4.2416
2020-12-08 14:46:16 - Saved model models/fruit/mb1-ssd-Epoch-0-Loss-5.815091200865032.pth
2020-12-08 14:46:16 - Task done, exiting program.
root@abc-desktop:/jetson-inference/python/training/detection/ssd#
//This is the state of detect.
root@abc-desktop:/jetson-inference/python/training/detection/ssd# detectnet --model=models/fruit/ssd-mobilenet.onnx --labels=models/fruit/labels.txt --input-blob=input_0 --output-cvg=scores --output-bbox=boxes "/jetson-inference/data/images/fruit_0.jpg" /jetson-inference/data/images/test/fruit_0.jpg*
[video]  created imageLoader from file:///jetson-inference/data/images/fruit_0.jpg
imageLoader video options:
-- URI: file:///jetson-inference/data/images/fruit_0.jpg
- protocol:  file
- location:  /jetson-inference/data/images/fruit_0.jpg
- extension: jpg
-- deviceType: file
-- ioType:     input
-- codec:      unknown
-- width:      0
-- height:     0
-- frameRate:  0.000000
-- bitRate:    0
-- numBuffers: 4
-- zeroCopy:   true
-- flipMethod: none
-- loop:       0
[video]  created imageWriter from file:///jetson-inference/data/images/test/fruit_0.jpg
imageWriter video options:
-- URI: file:///jetson-inference/data/images/test/fruit_0.jpg
- protocol:  file
- location:  /jetson-inference/data/images/test/fruit_0.jpg
- extension: jpg
-- deviceType: file
-- ioType:     output
-- codec:      unknown
-- width:      0
-- height:     0
-- frameRate:  0.000000
-- bitRate:    0
-- numBuffers: 4
-- zeroCopy:   true
-- flipMethod: none
-- loop:       0
[OpenGL] glDisplay -- X screen 0 resolution:  1920x1080
[OpenGL] glDisplay -- X window resolution:    1920x1080
[OpenGL] glDisplay -- display device initialized (1920x1080)
[video]  created glDisplay from display://0
glDisplay video options:
-- URI: display://0
- protocol:  display
- location:  0
-- deviceType: display
-- ioType:     output
-- codec:      raw
-- width:      1920
-- height:     1080
-- frameRate:  0.000000
-- bitRate:    0
-- numBuffers: 4
-- zeroCopy:   true
-- flipMethod: none
-- loop:       0
detectNet -- loading detection network model from:
-- prototxt     NULL
-- model        models/fruit/ssd-mobilenet.onnx
-- input_blob   'input_0'
-- output_cvg   'scores'
-- output_bbox  'boxes'
-- mean_pixel   0.000000
-- mean_binary  NULL
-- class_labels models/fruit/labels.txt
-- threshold    0.500000
-- batch_size   1
[TRT]    TensorRT version 7.1.3
[TRT]    loading NVIDIA plugins...
[TRT]    Registered plugin creator - ::GridAnchor_TRT version 1
[TRT]    Registered plugin creator - ::NMS_TRT version 1
[TRT]    Registered plugin creator - ::Reorg_TRT version 1
[TRT]    Registered plugin creator - ::Region_TRT version 1
[TRT]    Registered plugin creator - ::Clip_TRT version 1
[TRT]    Registered plugin creator - ::LReLU_TRT version 1
[TRT]    Registered plugin creator - ::PriorBox_TRT version 1
[TRT]    Registered plugin creator - ::Normalize_TRT version 1
[TRT]    Registered plugin creator - ::RPROI_TRT version 1
[TRT]    Registered plugin creator - ::BatchedNMS_TRT version 1
[TRT]    Could not register plugin creator -  ::FlattenConcat_TRT version 1
[TRT]    Registered plugin creator - ::CropAndResize version 1
[TRT]    Registered plugin creator - ::DetectionLayer_TRT version 1
[TRT]    Registered plugin creator - ::Proposal version 1
[TRT]    Registered plugin creator - ::ProposalLayer_TRT version 1
[TRT]    Registered plugin creator - ::PyramidROIAlign_TRT version 1
[TRT]    Registered plugin creator - ::ResizeNearest_TRT version 1
[TRT]    Registered plugin creator - ::Split version 1
[TRT]    Registered plugin creator - ::SpecialSlice_TRT version 1
[TRT]    Registered plugin creator - ::InstanceNormalization_TRT version 1
[TRT]    detected model format - ONNX  (extension '.onnx')
[TRT]    desired precision specified for GPU: FASTEST
[TRT]    requested fasted precision for device GPU without providing valid calibrator, disabling INT8
[TRT]    native precisions detected for GPU:  FP32, FP16
[TRT]    selecting fastest native precision for GPU:  FP16
[TRT]    attempting to open engine cache file models/fruit/ssd-mobilenet.onnx.1.1.7103.GPU.FP16.engine
[TRT]    loading network plan from engine cache... models/fruit/ssd-mobilenet.onnx.1.1.7103.GPU.FP16.engine
[TRT]    device GPU, loaded models/fruit/ssd-mobilenet.onnx
[TRT]    Deserialize required 11521252 microseconds.
[TRT]
[TRT]    CUDA engine context initialized on device GPU:
[TRT]       -- layers       104
[TRT]       -- maxBatchSize 1
[TRT]       -- workspace    0
[TRT]       -- deviceMemory 23254528
[TRT]       -- bindings     3
[TRT]       binding 0
-- index   0
-- name    'input_0'
-- type    FP32
-- in/out  INPUT
-- # dims  4
-- dim #0  1 (SPATIAL)
-- dim #1  3 (SPATIAL)
-- dim #2  300 (SPATIAL)
-- dim #3  300 (SPATIAL)
[TRT]       binding 1
-- index   1
-- name    'scores'
-- type    FP32
-- in/out  OUTPUT
-- # dims  3
-- dim #0  1 (SPATIAL)
-- dim #1  3000 (SPATIAL)
-- dim #2  9 (SPATIAL)
[TRT]       binding 2
-- index   2
-- name    'boxes'
-- type    FP32
-- in/out  OUTPUT
-- # dims  3
-- dim #0  1 (SPATIAL)
-- dim #1  3000 (SPATIAL)
-- dim #2  4 (SPATIAL)
[TRT]
[TRT]    binding to input 0 input_0  binding index:  0
[TRT]    binding to input 0 input_0  dims (b=1 c=3 h=300 w=300) size=1080000
[TRT]    binding to output 0 scores  binding index:  1
[TRT]    binding to output 0 scores  dims (b=1 c=3000 h=9 w=1) size=108000
[TRT]    binding to output 1 boxes  binding index:  2
[TRT]    binding to output 1 boxes  dims (b=1 c=3000 h=4 w=1) size=48000
[TRT]
[TRT]    device GPU, models/fruit/ssd-mobilenet.onnx initialized.
[TRT]    detectNet -- number object classes:  9
[TRT]    detectNet -- maximum bounding boxes:  3000
[TRT]    detectNet -- loaded 9 class info entries
[TRT]    detectNet -- number of object classes:  9
[image] loaded '/jetson-inference/data/images/fruit_0.jpg'  (688x1024, 3 channels)
[OpenGL] glDisplay -- set the window size to 688x1024
[OpenGL] creating 688x1024 texture (GL_RGB8 format, 2113536 bytes)
[cuda]   registered openGL texture for interop access (688x1024, GL_RGB8, 2113536 bytes)
[image] saved '/jetson-inference/data/images/test/fruit_0.jpg'  (688x1024, 3 channels)
[TRT]    ------------------------------------------------
[TRT]    Timing Report models/fruit/ssd-mobilenet.onnx
[TRT]    ------------------------------------------------
[TRT]    Pre-Process   CPU   0.08453ms  CUDA   2.06687ms
[TRT]    Network       CPU 136.43465ms  CUDA 133.99661ms
[TRT]    Post-Process  CPU   5.87615ms  CUDA   6.10953ms
[TRT]    Total         CPU 142.39532ms  CUDA 142.17302ms
[TRT]    ------------------------------------------------
[TRT]    note -- when processing a single image, run 'sudo jetson_clocks' before
to disable DVFS for more accurate profiling/timing measurements
[image] imageLoader -- End of Stream (EOS) has been reached, stream has been closed
detectnet:  shutting down...
detectnet:  shutdown complete.
root@abc-desktop:/jetson-inference/python/training/detection/ssd#

It looks like you only trained it for 1 epoch - you would probably need to train it for more epochs (30 is a good start)
BTW here is my fruit model that I trained for 100 epochs:   https://nvidia.box.com/shared/static/gq0zlf0g2r258g3ldabl9o7vch18cxmi.gz
You can try that first and see if it works on your Jetson.

Thank you. It worked very well. The box output nicely.


Show hierarchy information

Given a decomposition and information about member classes we can show which parts of the decomposition follow the class hierarchy, e.g.
   7----- Künste und Unterhaltung
↳ 70---- Künste
↳ 700--- Künste
↳ 700.2- Standardschlüssel für die Künste
↳ 700.2- Verschiedenes
   -0---- Facettenindikator
   --0--- Hilfstafel 1. Standardschlüssel
↳ --0.2- Verschiedenes
↳ --0.23 Das Thema als Beruf, Erwerbstätigkeit, Freizeitbeschäftigung

requires #8

We could also highlight the last concept in a hierarchy as bold because it is the most specific and therefore probably the most relevant one, right?

We could also highlight the last concept in a hierarchy as bold because it is the most specific and therefore probably the most relevant one, right?

Yes, actually how it was designed at the example https://coli-conc.gbv.de/coli-ana/


Create first public npm release 0.0.1

I'm submitting a ...
[ ] bug report
[x] feature request
[ ] question about the decisions made in the repository
[ ] question about how to use this project

Summary

Other information (e.g. detailed explanation, stacktraces, related issues, suggestions how to fix, links for us to have context, eg. StackOverflow, personal fork, etc.)

Done https://www.npmjs.com/package/@fridgefm/inverter


Need Help as I am beignner in Audio classification

I am unable to get what I should return for Custom audio data forn CRNN model...
Like for image dataset class we return image array in numpy and its label through the get_item function in custom dataset class..
Likewise what should I return in Custom dataset class with label of audio for my custom audio dataset.

audio torch datasets can also return a numpy array and a label. The convention used here and in torchaudio is (channel, signal). See here: https://github.com/ksanjeevan/crnn-audio-classification/blob/f09449b87ed61d28b82931c7055d033a7b69160b/data/data_sets.py#L24


[EPIC] Add CLI interface for Gravitino

Describe the proposal
Currently, Gravitino provides web UI, Java SDK, Python SDK to manipulate the metadata. The current web UI is not a full functionality UI, user still needs to write Java, Python codes or directly use REST APIs to communicate with Gravitino, which makes user hard to use Gravitino at the first stage.
Instead of adding more and more features in web UI, we think that adding a simple CLI interface will significantly help the users, especially the developers. So in this EPIC issue, we are planning to the CLI support for Gravitino.
Task list
The design doc and subtasks will be broken down here.

@justinmclean , can you please post the design doc here, so we can we have a discussion on the issue.

Background
Other catalogs provide a CLI to interact with the catalog’s metadata. A Gravitino API would also be useful to be able to script Gravitino, automate Gravitino setup, put metadata under version control, for demos or a variety of other uses.
Goals

A simple wrapper around the Gravitino REST service
Provide help on CLI use to user
Supports listing of simple information  about  metalakes, catalogs, schema and tables
Can show simple text output or raw JSON text from REST call as an option
Create and delete metalakes, catalogs and schema
To be a useful simple utility
Simple user input validation
Simple error handling
Debug/log option

Not Goals

Not to implement the full REST API
Not to display all information about a know entity
Initial versions assumes no authentication

Design
The CLI would be implemented in Python using requests library for the REST interface and click for the CLI. The command consists of options to specify the metalake, catalog, schema etc etc, a way to specify the format and a command to perform an action.
Suggested Usage:
Usage: GravCLI.py [OPTIONS] [COMMAND]
Options:
  --host TEXT      Gravitino REST host and port (host:port)
  --metalake TEXT  Metalake name
  --catalog TEXT   Catalog name
  --schema TEXT    Schema name
  --table TEXT     Table name
  --format TEXT    "simple" or “JSON"
  --help           Show this message and exit.

Optionally consider having the commands as options i.e add –list –details –delete –create instead of using a command.
Commands

Details (default) displays details about the specified entity.
List displays details about the children of a specified entity. e.g. list all often tables in a catalog.
Delete deletes an entity.
Create creates an entity. Extra options than those listed above will be needed for this based on each entity.

Example Usage
List info about a metalake:
python3 GravCLI.py --metalake metalake_demo
List info about tables:
python3 GravCLI.py --metalake metalake_demo --catalog catalog_mysql --schema demo_llamaindex --table city_stats list
List info about a metalake’s catalogs:
python3 GravCLI.py --metalake metalake_demo list
Create info a new metalake:
python3 GravCLI.py --metalake metalake_new –comment “a new metalake”  create
Delete a metalake:
python3 GravCLI.py --metalake metalake_demo delete
Note command can come first and doesn’t have to be at the end.

I think it is simple to use the current Java Gravitino to create a CLI tool. Current Python client only supports Fileset, if you want to use Python, then you have to write all the rest protocols from ground.
Also, for the CLI arguments, can you please investigate some other similar projects to see how they design and how we can refer?
Besides, I think the design doc should list all the commands about list, create, get, alter, and delete for all the entities, also should include access control operations and tag operations, to achieve them in CLI also.

Also please put the design doc in the gdoc, so we can comment inline.

The above isn't using the Python client. It's using the REST interface - this is what most other catalogs do.

Then you have to implement all the REST interfaces including metalake, catalog, schema, table..., also including authorization, this is a huge work, why can't we use the existing Java client and do a simple wrapper to achieve the CLI?

The entire basic REST interface in Python is about 100 lines of code, much less than the equilivant Java code.

I don't think so, once you implement some complicated "create" or "alter" command, you will have to handle complicated cases including serialization and deserialization. Also, in Java client we already achieve some authorization methods like Kerberos, oauth, etc. If you want to write them in plain using Python, you will have to deal with them. It's not as simple as you think.

I have also looked at several other CLIs and the design above is similar to how they do it. My initial thought was not to implement the entire API/REST interface, but it can be extended once we we have something that useful.

If you're not doing a complete CLI, then there's no difference compared to the current web UI, users still cannot fully experience the whole features easily.

The initial objective (as described above) is not to implement the full API, but we can expand on the initial API to eventually cover everything. If you want me to break it up into stages and put what is developed in each, I can do that.

My feeling is that if we use Java client for CLI, then we already handle most of the of the JSON serde and security things, so we can only focus on CLI implementation. But if we choose to use Python, we will need to implement from the ground, this may take lots of work, especially since we have several data structures like "type", and "expression", they're nested and complicated to serialize/deserialize. If we choose Java, the current Java client already did it for us, so we don't have to do it again. The key thing is that for CLI, we don't have to do the JSON/REST thing again, we can leverage the current client and focus on the CLI thing only. It's not a problem of choosing languages, it's a problem of not doing duplicated work. If there's a full-functionality Python client, then I'm also fine with a simple wrapper of that Python client to achieve CLI in Python.
It's just my comment, I think we should discuss this in the community and involve others' opinions, I will also post this on the issue.

The initial objective (as described above) is not to implement the full API, but we can expand on the initial API to eventually cover everything. If you want me to break it up into stages and put what is developed in each, I can do that.

This is a huge issue, it should be broken into multiple small tasks to achieve them step by step.

Link to design doc: https://docs.google.com/document/d/19CXHeg_5iphO8D3UD16rexVE23M4TMZXOU3X0lBqL6A/edit?usp=sharing

IMO, the current design doesn't seem to be very different from using curl directly.
As a CLI, I think it's more about user interaction, and results presentation, but the current CLI seems to be more of a simplification of the use of the curl tool, and doesn't bring a lot of convenience to the user.
Feel free to point out if I've misunderstood anything!

It is easier to use thancurl as the user doesn't need to supply correctly format JSON, it fills in many of the default parameters, and its output is more user-friendly.

Everything has been done and merged


Provide clear option for TextInput component.

[x] I have searched the Issues to see if this bug has already been reported
[x] I have tested the latest version

Summary
Describe how it should work, and provide examples of the solution, which might include screenshots or code snippets.
Context
What are you trying to accomplish? How is your use case affected by not having this feature?

A bit more info please.

I think what he may have meant is that the TextInput component exists but there is no documentation on the flowbite-react.com site

It is documented: https://flowbite-react.com/docs/components/forms

Thank you so much. I was blind. I saw the "Forms" heading on the left-side navbar with only FileInput and Floating Label and thought where did all the inputs go


PHP Warning: Undefined array key 1 in src/services.php on line 294

Initial use of slic (previously I had used tric) and got this error when I ran this command:
slic site-cli cli info
Errors:
PHP Warning:  Undefined array key 1 in ~/slic/src/services.php on line 294
Warning: Undefined array key 1 in ~/slic/src/services.php on line 294

Possibly related but didn't see the issue happen at the previous command run - and running it again, got this error:

PHP Warning:  rmdir(/Users/cliff/Documents/git/GitHub/slic/_wordpress/wp-content/plugins): Permission denied in ~/slic/src/utils.php on line 526
Warning: rmdir(/Users/cliff/Documents/git/GitHub/slic/_wordpress/wp-content/plugins): Permission denied in ~/slic/src/utils.php on line 526

Another possibly related, even after recursively assigning everyone read/write permission:
PHP Warning: rmdir(~/slic/_wordpress): Directory not empty in ~/slic/src/utils.php on line 526
Warning: rmdir(~/slic/_wordpress): Directory not empty in ~/slic/src/utils.php on line 526

Another possibly related error upon initializing slic:
service "slic" is not running container #1

@cliffordp the main issue here is fixed in https://github.com/stellarwp/slic/releases/tag/1.2.4
If you're still experiencing the other issues here, please open up separate issues for each one.


Connectivity tests fail when run in non cilium-test namespace

The connectivity tests consistently fail when run in a namespace other than cilium-test:
$ ./cilium connectivity test --test-namespace latest
[...]
📋 Test Report
❌ 6/31 tests failed (16/233 actions), 0 tests skipped, 1 scenarios skipped:
Test [client-egress]:
  ❌ client-egress/pod-to-pod/curl-0: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-egress/pod-to-pod/curl-1: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
  ❌ client-egress/pod-to-pod/curl-2: latest/client2-8484df99d-cbwj6 (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-egress/pod-to-pod/curl-3: latest/client2-8484df99d-cbwj6 (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
Test [client-egress-expression]:
  ❌ client-egress-expression/pod-to-pod/curl-2: latest/client2-8484df99d-cbwj6 (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-egress-expression/pod-to-pod/curl-3: latest/client2-8484df99d-cbwj6 (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
Test [client-egress-to-echo-service-account]:
  ❌ client-egress-to-echo-service-account/pod-to-pod/curl-0: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-egress-to-echo-service-account/pod-to-pod/curl-1: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
Test [client-egress-to-echo-deny]:
  ❌ client-egress-to-echo-deny/pod-to-pod/curl-0: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-egress-to-echo-deny/pod-to-pod/curl-1: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
  ❌ client-egress-to-echo-deny/pod-to-pod/curl-2: latest/client2-8484df99d-cbwj6 (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-egress-to-echo-deny/pod-to-pod/curl-3: latest/client2-8484df99d-cbwj6 (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
Test [client-ingress-to-echo-named-port-deny]:
  ❌ client-ingress-to-echo-named-port-deny/pod-to-pod/curl-0: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-ingress-to-echo-named-port-deny/pod-to-pod/curl-1: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
Test [client-egress-to-echo-service-account-deny]:
  ❌ client-egress-to-echo-service-account-deny/pod-to-pod/curl-0: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-other-node-848c6d48fb-2hx2n (<IP_ADDRESS>:8080)
  ❌ client-egress-to-echo-service-account-deny/pod-to-pod/curl-1: latest/client-594cbbdf44-dt5fj (<IP_ADDRESS>) -> latest/echo-same-node-6d78d7f79b-j94j7 (<IP_ADDRESS>:8080)
connectivity test failed: 6 tests failed

I've rerun with -p to pause on the first failure and collected a sysdump:
cilium-sysdump-20220926-205342.zip
This is with Cilium CLI d9371d3dc6dbe9f8d8437bd34ee848d7358ff4e0.
Investigation
cilium monitor shows this is caused by policy drops:
xx drop (Policy denied) flow 0x76b6c553 to endpoint 0, ifindex 25, file 2:1188, , identity 4620->34411: <IP_ADDRESS>:52440 -> <IP_ADDRESS>:8080 tcp SYN

Looking at the BPF policy map for that endpoint, it is indeed missing a rule to allow on that port:
$ ks exec cilium-x6rtv -- cilium bpf policy get -n 705
POLICY   DIRECTION   IDENTITY   PORT/PROTO   PROXY PORT   BYTES   PACKETS
Allow    Ingress     0          ANY          NONE         0       0
Allow    Ingress     1          ANY          NONE         392     4
Allow    Egress      15025      53/TCP       NONE         0       0
Allow    Egress      15025      53/UDP       NONE         0       0
Allow    Egress      15025      53/SCTP      NONE         0       0

Dumping the only CNP in that namespace reveals the issue:
W0926 20:54:10.346854  299992 gcp.go:119] WARNING: the gcp auth plugin is deprecated in v1.22+, unavailable in v1.26+; use gcloud instead.
To learn more, consult https://cloud.google.com/blog/products/containers-kubernetes/kubectl-auth-changes-in-gke
apiVersion: v1
items:
- apiVersion: cilium.io/v2
  kind: CiliumNetworkPolicy
  metadata:
    creationTimestamp: "2022-09-26T18:50:44Z"
    generation: 1
    name: client-egress-to-echo
    namespace: c1
    resourceVersion: "37680"
    uid: f4a95476-ed39-473e-bb00-bec11882b02b
  spec:
    egress:
    - toEndpoints:
      - matchLabels:
          any:io.kubernetes.pod.namespace: cilium-test
          any:kind: echo
      toPorts:
      - ports:
        - port: "8080"
          protocol: TCP
    - toEndpoints:
      - matchExpressions:
        - key: any:k8s-app
          operator: In
          values:
          - kube-dns
          - coredns
          - node-local-dns
        - key: any:io.kubernetes.pod.namespace
          operator: In
          values:
          - kube-system
      toPorts:
      - ports:
        - port: "53"
          protocol: ANY
    endpointSelector:
      matchLabels:
        any:kind: client
kind: List
metadata:
  resourceVersion: ""

Root Cause
It seems commit https://github.com/cilium/cilium-cli/commit/fc192b0b62694f362a40eef73df56bb1ccf1895b (and maybe others) added policies with the cilium-test hardcoded. I'm not sure what the intent was here given they are CNPs anyway so the namespace is implicit.
cc @sayboras

thanks for highlighting this, I don't think it's intended 💯

@sayboras I think you missed some policies as it's still failing with the same policy drops. And I still have the same policy loaded with cilium-test hardcoded. I checked out latest:
$ git log -n1
commit e206203241b7e8164195bbc16bf46617ef3928a6 (HEAD -> master, origin/master, origin/HEAD)
Author: Tam Mach<EMAIL_ADDRESS>Date:   Tue Sep 27 13:07:55 2022 +1000

    test/connectivity: Remove explicit namespace in policy manifest

    As the connectivity test can be from another namespace, which is passed
    by --test-namespace CLI flag, we should not have any hard coded namespace
    in all related policy manifests.

    Signed-off-by: Tam Mach<EMAIL_ADDRESS>
and I ran make already.

Those maybe?
$ git grep cilium-test connectivity/manifests/
connectivity/manifests/client-egress-to-echo-deny.yaml:        io.kubernetes.pod.namespace: cilium-test
connectivity/manifests/client-egress-to-echo-expression.yaml:        io.kubernetes.pod.namespace: cilium-test
connectivity/manifests/client-egress-to-echo-named-port-deny.yaml:        io.kubernetes.pod.namespace: cilium-test
connectivity/manifests/client-egress-to-echo-service-account-deny.yaml:        io.kubernetes.pod.namespace: cilium-test
connectivity/manifests/client-egress-to-echo-service-account.yaml:        io.kubernetes.pod.namespace: cilium-test
connectivity/manifests/client-egress-to-echo.yaml:        io.kubernetes.pod.namespace: cilium-test

Maybe we should convert one of the CI tests to use a test namespace name other than cilium-test, so we'll be more likely to catch regressions like this in the future?

sorry all, I thought I removed the label in match selector before
https://github.com/cilium/cilium-cli/pull/1113

Actually, we need those dummy labels, so that the value can be updated dynamically.
So it could be something else, let me take a deeper look

@pchaigno this should fix our issue I reckon https://github.com/cilium/cilium-cli/pull/1113

This should be fixed as part of recent PR.


Add inner hits support to hybrid query

Description
Adding support for inner hits to hybrid query. This is a feature of OpenSearch that is available for other queries but was not supported by hybrid query.
Inner hits will be tracked similarly to how they are tracked for all other queries. They will contain details of inner hits for cases of nested fields and parent/child relationships between documents. The only catch is the score of the inner hit—such scores will be before normalization. Having a normalized score is technically difficult because inner hits processing is done in the Fetch phase, which occurs after the normalization processor has finished its work.
Following are example of response that contains such inner hits section for nested and parent/child queries:
{
    "took": 79,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1,
            "relation": "eq"
        },
        "max_score": 1.540445,
        "hits": [
            {
                "_index": "index-test",
                "_id": "Sogqp48BjNYyAI8a4z9u",
                "_score": 1.540445,
                "_source": {
                    "doc_price": 100,
                    "doc_index": 4976,
                    "doc_location": {
                        "coordinates": [
                            [
                                -111.15,
                                45.12
                            ],
                            [
                                -109.83,
                                44.12
                            ]
                        ],
                        "type": "envelope"
                    },
                    "doc_location_2": "81.15, 44.12",
                    "doc_date": "02/03/2014",
                    "doc_point": {
                        "lon": 74.0,
                        "lat": 40.71
                    },
                    "id": "7ebe00c8-9858-11ee-b9d1-0242ac120002",
                    "doc_keyword": "workable",
                    "category": "permission",
                    "title": "Writing a list of random sentences is harder than I initially thought it would be.",
                    "user": {
                        "firstname": "john",
                        "age": 1,
                        "lastname": "black"
                    }
                },
                "inner_hits": {
                    "user": {
                        "hits": {
                            "total": {
                                "value": 1,
                                "relation": "eq"
                            },
                            "max_score": 1.540445,
                            "hits": [
                                {
                                    "_index": "index-test",
                                    "_id": "Sogqp48BjNYyAI8a4z9u",
                                    "_nested": {
                                        "field": "user",
                                        "offset": 0
                                    },
                                    "_score": 1.540445,
                                    "_source": {
                                        "firstname": "john",
                                        "age": 1,
                                        "lastname": "black"
                                    }
                                }
                            ]
                        }
                    }
                }
            }
        ]
    }
}

{
    "took": 134,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 2,
            "relation": "eq"
        },
        "max_score": 1.0,
        "hits": [
            {
                "_index": "index-test",
                "_id": "10",
                "_score": 1.0,
                "_routing": "1",
                "_source": {
                    "my_id": "10",
                    "text": "This is an answer",
                    "my_join_field": {
                        "name": "answer",
                        "parent": "5"
                    }
                },
                "inner_hits": {
                    "question": {
                        "hits": {
                            "total": {
                                "value": 1,
                                "relation": "eq"
                            },
                            "max_score": 1.2039728,
                            "hits": [
                                {
                                    "_index": "index-test",
                                    "_id": "5",
                                    "_score": 1.2039728,
                                    "_source": {
                                        "my_id": "5",
                                        "text": "This is a question",
                                        "my_join_field": "question"
                                    }
                                }
                            ]
                        }
                    }
                }
            },
            {
                "_index": "index-test",
                "_id": "11",
                "_score": 1.0,
                "_routing": "1",
                "_source": {
                    "my_id": "11",
                    "text": "This is second answer",
                    "my_join_field": {
                        "name": "answer",
                        "parent": "5"
                    }
                },
                "inner_hits": {
                    "question": {
                        "hits": {
                            "total": {
                                "value": 1,
                                "relation": "eq"
                            },
                            "max_score": 1.2039728,
                            "hits": [
                                {
                                    "_index": "index-test",
                                    "_id": "5",
                                    "_score": 1.2039728,
                                    "_source": {
                                        "my_id": "5",
                                        "text": "This is a question",
                                        "my_join_field": "question"
                                    }
                                }
                            ]
                        }
                    }
                }
            }
        ]
    }
}

Issues Resolved
https://github.com/opensearch-project/neural-search/issues/718
Check List

[ ] New functionality includes testing.

[ ] All tests pass

[ ] New functionality has been documented.

[ ] New functionality has javadoc added

[x] Commits are signed as per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

The only catch is the score of the inner hit—such scores will be before normalization. Having a normalized score is technically difficult because inner hits processing is done in the Fetch phase, which occurs after the normalization processor has finished its work.

@martin-gaievski do we have a path forward for resolving this technical challenge? Have we started the discussion around this with Core team.
Also, I would like to take a step back here and question what is the meaning of normalized score for inner hits?

@martin-gaievski is this feature scoped for 2.15?

@martin-gaievski is this feature scoped for 2.15?

There is no hard requirement, but ideally it would be great if it can be in 2.15 as it's odd version -> goes to AOS

The only catch is the score of the inner hit—such scores will be before normalization. Having a normalized score is technically difficult because inner hits processing is done in the Fetch phase, which occurs after the normalization processor has finished its work.

@martin-gaievski do we have a path forward for resolving this technical challenge? Have we started the discussion around this with Core team.
Also, I would like to take a step back here and question what is the meaning of normalized score for inner hits?

No path forward for now, there are many hurdles and such change requires

The only catch is the score of the inner hit—such scores will be before normalization. Having a normalized score is technically difficult because inner hits processing is done in the Fetch phase, which occurs after the normalization processor has finished its work.

@martin-gaievski do we have a path forward for resolving this technical challenge? Have we started the discussion around this with Core team.
Also, I would like to take a step back here and question what is the meaning of normalized score for inner hits?

For now no path clear forward, I'll be working on summarizing technical hurdles we do have. Short list is:

inner hits are collected in two steps, query phase and fetch phase
there is a separate query and query phase builder for inner hits. this inner query builder calls core TopDocsCollector to get scores.
hits are collected as part of the fetch phase, it's after or normalization processor run, so we cannot manipulate with inner scores and normalize them as part of existing processor
there is no way in core today to skip score calculation at fetch phase, so whatever we can came up with in normalization will be override in fetch phase

Closing for now as this needs some additional investigation


Changes in memory management, right usage of new option introduced in 3.3.20 / 3.4 series

Hello,
We experienced a big change in memory consumption of our databases since we upgraded to 3.3.20 our production environments and to 3.4 our staging environments.
We are wondering if we are properly using the new --rocksdb.total-write-buffer-size.
As shown on the graph, the great news is that the engine is way more stable now, but it feels like the database is not getting the benefit of all the available memory, this server has 600GB memory available and the options used at startup are:
--rocksdb.wal-file-timeout
15600
--server.authentication-system-only
false
--tcp.backlog-size
100
--rocksdb.block-cache-size
40000000
--rocksdb.total-write-buffer-size
10%

Can you confirm we are using the new option introduced in 3.3.20/3.4 series properly?
Many thanks.

Hi @soualid,
could you please share some details about your machine? Also, are you using edges in your database? Setting --rocksdb.block-cache-size to 40000000 seems a bit low, the default value is (system RAM size - 2GiB) * 0.3.
It will be easier to give you a suggestion once we know further details of your setup.

Thank you! There was obviously a mistake in the value chosen for our --rocksdb.block-cache-size configuration, sorry for bothering you for that. More information on the machine below:

768 Go DDR4 ECC
2x Intel® Xeon® Gold 6140
8x1 To SSD NVMe (RAID 1)

We just restarted this slave with a --rocksdb.block-cache-size of<PHONE_NUMBER>00 which seems more appropriate for the hardware used. I bet it will solve this issue, and hopefully give a great boost to read performances.
Thank you again for pointing this out!


Fix DrawerLayoutAndroid not able to set opacity

I'm trying to fix an issue where I was unable to set the opacity of DrawerLayoutAndroid. Refer here for the issue and screenshots.
This is a problem caused by setting backgroundColor: 'white' on the drawerSubview. This is the reason for the faded color when the opacity of 0.5 is set on the white background.
I checked that removing it fixes the problem.
Tested

The white background was needed to make elevation work. See #6100. Can you try backgroundColor: 'transparent' or backgroundColor 'white' + opacity 0 and test that elevation works properly? If that doesn't work maybe we could pass down opacity to the drawer background so if someone wants a transparent background it works.

+1 On defaulting to white unless another background color is passed in.

Tested that opacity can now be set with required background by either passing in
style={backgroundColor: 'green'} or style={backgroundColor: 'transparent'}, and then setting background color of the Component passed in through renderNavigationView props.


Update english version of Load management docs

Update english version of Load management docs

Thanks for the improvements 🙌


Using polymer component in vue component error

Vue.js template engine is DOM-based and uses native parser that comes with the browser instead of providing a custom one.

Because of that, template will parse error when I use a polymer component or a webComponent which created by document.registerElement.
See code in vue:
https://github.com/vuejs/vue/blob/7059c15d906f33fe993c505f9b8edfe860319b35/src/parsers/template.js#L119
    var node = document.createElement('div')

    node.innerHTML = prefix + templateString + suffix
    while (depth--) {
      node = node.lastChild
    }

In my case, after node.innerHTML = prefix + templateString + suffix  Node.innerHTML != prefix + templateString + suffix.
Any solution for this issue?

There is no way around this in 1.x
It should work in 2.0 if you use it in string templates
It seems the custom elements you are using is not using shadow DOM, and it's a bad idea to mix that with Vue.


Hide follow events

Hide or not?

Class is .follow

I’d vote to hide it. Would you do the same for ”watch repository” events too, or would you decide on what to do about it in a separate issue?

Separate issue, but if those are hidden what's left?

Oh, you meant to hide those events completely… What I meant was hiding the preview shown below.

I like them, but prefer to hide the previews as @lukaszklis mentioned.

👍 Let's do it

Hiding follow event is done → #909
Are we going to hide anything else?

#909 hides the preview only, which is already handled by #899.
This issue should completely hide follow events.


Azure free tier info update

Great resource, @karentamrazyan and @cloudcommunity!
I just noticed some info that updates the Azure info in the Cloud-Service-Providers-Free-Tier-Overview repo here
Azure now has a 12-month free tier
competitive with AWS 12 month free tier
check this out:

great news, @i5ik ! Thanks for the heads up. Will update the description soon

I just stoped my services on azure, my first month ended Sep - 15, it didn't cost anything, and now only 5 days later my cost is 3.03 dollars for 1 vm: 1vcpu, 1GB ram. I think it is not free after all

I just stoped my services on azure, my first month ended Sep - 15, it didn't cost anything, and now only 5 days later my cost is 3.03 dollars for 1 vm: 1vcpu, 1GB ram. I think it is not free after all

mustve not used the free services, and i was jever charged but this id why i also left azure i was afraid of using the wrong type of services.


Organization invite and updating readme.md

Name:
Info About the Pull Request You Made:

number of my pull request is at - #17

Our Website 🌐 | Repositories 📚 | LinkFree Profile 🔗
You have received an email to join, or press this link - https://github.com/App-Choreography - , and you'll see a banner, for you to join the organisation! 🎉
You can also show that you are part of this org, by making yourself public. This will show our profile org on your profile:
Follow This:

After you join the organisation, search your name in the list of people. 🔍
Follow this below: ⬇

When you join our org, you will be automatically be part of our community team, where you will get updates, and news about our org!
We are so happy you have joined! Welcome to App Choreography! 🥳


Implemented l/rstrip, strip function in Str.java , edited test_str accordingly , meets stylecheck norms

Fixed the buggy rstrip , lstrip function (had to completely rewrite it) , also implemented strip() for strings

@gautamworah96 Please note that all three methods fail when None is passed as the chars parameter.


uclibc add SOCK_RDM filed

Thanks for the pull request, and welcome! The Rust team is excited to review your changes, and you should hear from @JohnTitor (or someone else) soon.
If any changes to this PR are deemed necessary, please add them as extra commits. This ensures that the reviewer can see what has changed since they last reviewed the code. Due to the way GitHub handles out-of-date commits, this should also make it reasonably obvious what issues have or haven't been addressed. Large or tricky changes may require several passes of review and changes.
Please see the contribution instructions for more information.

28515e823bedd8e846e0df45e475adbaf14fde11 looks good but I assume you introduced unexpected commit. r=me once that commit is dropped.

I will close this pr then fork a new pr


Hang after stopping and restarting weave net

Use case: Stop and re-start weave net. Create a new cluster.
What happens: weave launch hangs during the setup of the docker plugin
Work around: In the simplest of scenarios, with no containers/systems actually using the weave network, you can seem to reset weave net with sudo weave reset --force. However, after running complex systems, this doesn't work. E.g. my mesos-cni setup, with the sock shop running, would not reset.
Recreate:
# Use these command to recreate.
# Setup:
# - AWS
# - Ubuntu 14.04
# - Docker

sudo curl -sL git.io/weave -o /usr/local/bin/weave
sudo chmod a+x /usr/local/bin/weave
sudo weave launch --no-dns
sudo weave expose # This appears to be the key. If you don't run this, it works.
sudo weave stop
sudo WEAVE_DEBUG=1 weave launch --no-dns
# It hangs here

Logs and further info:
# The output from the weave launch debug is:
+ return 0
+ util_op create-plugin-network weave weavemesh
+ command_exists weaveutil
+ command -v weaveutil
+ weaveutil create-plugin-network weave weavemesh
# Hangs here

$ docker version
Client:
 Version:      1.12.0
 API version:  1.24
 Go version:   go1.6.3
 Git commit:   8eab29e
 Built:        Thu Jul 28 22:00:36 2016
 OS/Arch:      linux/amd64

Server:
 Version:      1.12.0
 API version:  1.24
 Go version:   go1.6.3
 Git commit:   8eab29e
 Built:        Thu Jul 28 22:00:36 2016
 OS/Arch:      linux/amd64

$ weave version
weave script 1.6.0
weave router 1.6.0
weave proxy  1.6.0
weave plugin 1.6.0

$ uname -a
Linux ip-10-0-0-117 3.13.0-91-generic #138-Ubuntu SMP Fri Jun 24 17:00:34 UTC 2016 x86_64 x86_64 x86_64 GNU/Linux

$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=14.04
DISTRIB_CODENAME=trusty
DISTRIB_DESCRIPTION="Ubuntu 14.04.4 LTS"

ubuntu@ip-10-0-0-117:~$ sudo docker logs weave
INFO: 2016/08/04 09:44:20.795362 Command line options: map[ipalloc-range:<IP_ADDRESS>/12 name:96:4e:9d:45:1b:1f nickname:ip-10-0-0-117 no-dns:true dns-effective-listen-address:<IP_ADDRESS> http-addr:<IP_ADDRESS>:6784 port:6783 resolv-conf:/var/run/weave/etc/resolv.conf datapath:datapath]
INFO: 2016/08/04 09:44:20.796139 Communication between peers is unencrypted.
INFO: 2016/08/04 09:44:20.797636 Our name is 96:4e:9d:45:1b:1f(ip-10-0-0-117)
INFO: 2016/08/04 09:44:20.797774 Launch detected - using supplied peer list: []
INFO: 2016/08/04 09:44:20.815432 Docker API on unix:///var/run/docker.sock: &[BuildTime=2016-07-28T22:00:36.714318713+00:00 Version=1.12.0 ApiVersion=1.24 GitCommit=8eab29e GoVersion=go1.6.3 Os=linux Arch=amd64 KernelVersion=3.13.0-91-generic]
INFO: 2016/08/04 09:44:20.816017 [allocator 96:4e:9d:45:1b:1f] Initialising with persisted data
INFO: 2016/08/04 09:44:20.819929 Sniffing traffic on datapath (via ODP)
INFO: 2016/08/04 09:44:20.846252 Listening for HTTP control messages on <IP_ADDRESS>:6784

$ sudo docker logs weaveproxy
INFO: 2016/08/04 09:44:21.077832 weave proxy 1.6.0
INFO: 2016/08/04 09:44:21.077889 Command line arguments: -H unix:///var/run/weave/weave.sock
INFO: 2016/08/04 09:44:21.079187 Docker API on unix:///var/run/docker.sock: &[GoVersion=go1.6.3 Os=linux Arch=amd64 KernelVersion=3.13.0-91-generic BuildTime=2016-07-28T22:00:36.714318713+00:00 Version=1.12.0 ApiVersion=1.24 GitCommit=8eab29e]
INFO: 2016/08/04 09:44:21.083861 proxy listening on unix:///var/run/weave/weave.sock
INFO: 2016/08/04 09:44:21.086228 Attaching weave router container: 3196c647d27e0d249133323cdde3861cd51c93dcae6def06ffc244a97a798851

$ sudo docker logs weaveplugin
INFO: 2016/08/04 09:45:09.901597 [ipam] RequestPool weavelocal   map[]
INFO: 2016/08/04 09:45:09.902074 [ipam] RequestPool weavelocal   map[]
INFO: 2016/08/04 09:45:09.902528 [ipam] RequestPool weavelocal   map[]
INFO: 2016/08/04 09:45:09.903006 [ipam] RequestPool weavelocal   map[]
INFO: 2016/08/04 09:45:09.903515 [ipam] RequestPool weavelocal   map[]
INFO: 2016/08/04 09:45:09.903986 [ipam] RequestPool weavelocal   map[]
INFO: 2016/08/04 09:45:09.904492 [ipam] RequestPool weavelocal   map[]
INFO: 2016/08/04 09:45:09.904934 [ipam] RequestPool weavelocal   map[]
...
goes on for ever.

$ sudo cat /var/log/upstart/docker.log
Waiting for /var/run/docker.sock
time="2016-08-04T09:42:04.758171635Z" level=info msg="libcontainerd: new containerd process, pid: 3902"
/var/run/docker.sock is up
time="2016-08-04T09:42:05.844457719Z" level=info msg="Graph migration to content-addressability took 0.00 seconds"
time="2016-08-04T09:42:05.844730760Z" level=warning msg="Your kernel does not support swap memory limit."
time="2016-08-04T09:42:05.844840594Z" level=warning msg="mountpoint for pids not found"
time="2016-08-04T09:42:05.845021606Z" level=info msg="Loading containers: start."
time="2016-08-04T09:42:05.850932851Z" level=warning msg="Running modprobe bridge br_netfilter failed with message: modprobe: WARNING: Module br_netfilter not found.\ninsmod /lib/modules/3.13.0-91-generic/kernel/net/llc/llc.ko \ninsmod /lib/modules/3.13.0-91-generic/kernel/net/802/stp.ko \ninsmod /lib/modules/3.13.0-91-generic/kernel/net/bridge/bridge.ko \n, error: exit status 1"
time="2016-08-04T09:42:05.860731110Z" level=info msg="Firewalld running: false"
time="2016-08-04T09:42:05.918106874Z" level=info msg="Default bridge (docker0) is assigned with an IP address <IP_ADDRESS>/16. Daemon option --bip can be used to set a preferred IP address"

time="2016-08-04T09:42:05.953384249Z" level=info msg="Loading containers: done."
time="2016-08-04T09:42:05.953466831Z" level=info msg="Daemon has completed initialization"
time="2016-08-04T09:42:05.953494018Z" level=info msg="Docker daemon" commit=8eab29e graphdriver=aufs version=1.12.0
time="2016-08-04T09:42:05.958769091Z" level=info msg="API listen on /var/run/docker.sock"
time="2016-08-04T09:42:59.993332224Z" level=error msg="Handler for POST /v1.24/containers/create returned error: No such image: weaveworks/weaveexec:1.6.0"
time="2016-08-04T09:43:20.411278469Z" level=error msg="Handler for GET /v1.18/containers/weave/json returned error: No such container: weave"
time="2016-08-04T09:43:20.411695780Z" level=error msg="Handler for GET /v1.18/images/weave/json returned error: No such image: weave"
time="2016-08-04T09:43:20.428498059Z" level=error msg="Handler for GET /v1.18/containers/weaveproxy/json returned error: No such container: weaveproxy"
time="2016-08-04T09:43:20.428817861Z" level=error msg="Handler for GET /v1.18/images/weaveproxy/json returned error: No such image: weaveproxy"
time="2016-08-04T09:43:20.445824760Z" level=error msg="Handler for GET /v1.18/containers/weaveplugin/json returned error: No such container: weaveplugin"
time="2016-08-04T09:43:20.446163031Z" level=error msg="Handler for GET /v1.18/images/weaveplugin/json returned error: No such image: weaveplugin"
time="2016-08-04T09:43:20.543731019Z" level=error msg="Handler for GET /v1.18/containers/weavedb/json returned error: No such container: weavedb"
time="2016-08-04T09:43:20.544156626Z" level=error msg="Handler for GET /v1.18/images/weavedb/json returned error: No such image: weavedb"
time="2016-08-04T09:43:20.561451388Z" level=error msg="Handler for GET /v1.18/containers/weaveplugin/json returned error: No such container: weaveplugin"
time="2016-08-04T09:43:20.561789061Z" level=error msg="Handler for GET /v1.18/images/weaveplugin/json returned error: No such image: weaveplugin"
time="2016-08-04T09:43:21.083062810Z" level=error msg="Handler for GET /v1.18/containers/weavevolumes-1.6.0/json returned error: No such container: weavevolumes-1.6.0"
time="2016-08-04T09:43:21.083456020Z" level=error msg="Handler for GET /v1.18/images/weavevolumes-1.6.0/json returned error: No such image: weavevolumes-1.6.0"
time="2016-08-04T09:43:21.098700011Z" level=error msg="Handler for GET /v1.18/containers/weaveplugin/json returned error: No such container: weaveplugin"
time="2016-08-04T09:43:21.099023566Z" level=error msg="Handler for GET /v1.18/images/weaveplugin/json returned error: No such image: weaveplugin"
time="2016-08-04T09:43:21.600509130Z" level=error msg="Handler for GET /v1.18/containers/weaveplugin/json returned error: No such container: weaveplugin"
time="2016-08-04T09:43:21.600868409Z" level=error msg="Handler for GET /v1.18/images/weaveplugin/json returned error: No such image: weaveplugin"
time="2016-08-04T09:43:21.807131963Z" level=info msg="remote ipam driver weavemesh does not support capabilities"
time="2016-08-04T09:43:40.122224883Z" level=warning msg="libcontainerd: container e81b3d50c62b9c25ed079aa978741b014769b9d8a35a1c22773a2c81375c913e restart canceled"
time="2016-08-04T09:43:40.217907788Z" level=warning msg="libcontainerd: container b444ce55e5725b3c2653a3fae6eff90472c6654204fcdf6a145098ce3491a721 restart canceled"
time="2016-08-04T09:43:40.369828265Z" level=warning msg="libcontainerd: container 33775aea3f535865583a77caf18dba7294d400cce5aeb86f128cb0ba73472161 restart canceled"
time="2016-08-04T09:44:21.209766951Z" level=error msg="Handler for GET /v1.18/containers/weaveplugin/json returned error: No such container: weaveplugin"
time="2016-08-04T09:44:21.210133222Z" level=error msg="Handler for GET /v1.18/images/weaveplugin/json returned error: No such image: weaveplugin"

$ top
top - 09:48:57 up 9 min,  1 user,  load average: 1.13, 0.83, 0.42
Tasks: 138 total,   1 running, 137 sleeping,   0 stopped,   0 zombie
%Cpu(s): 25.9 us,  1.7 sy,  0.0 ni, 71.8 id,  0.0 wa,  0.0 hi,  0.2 si,  0.4 st
KiB Mem:  16433116 total,  2626328 used, 13806788 free,    78484 buffers
KiB Swap:        0 total,        0 used,        0 free.  1991340 cached Mem

  PID USER      PR  NI    VIRT    RES    SHR S  %CPU %MEM     TIME+ COMMAND
 3890 root      20   0<PHONE_NUMBER>04  16200 S 103.2  1.1   4:16.72 dockerd
 6011 root      20   0  288184   9948   3884 S  22.0  0.1   1:57.52 plugin
 5747 root      20   0  665188  81880   7120 S   8.0  0.5   0:45.61 weaver
 5996 root      20   0  209056   2184   1408 S   3.7  0.0   0:10.10 docker-containe
    7 root      20   0       0      0      0 S   0.3  0.0   0:00.78 rcu_sched
   11 root      20   0       0      0      0 S   0.3  0.0   0:00.38 rcuos/3
    1 root      20   0   33476   2844   1468 S   0.0  0.0   0:01.49 init

This is caused by (a) Docker implementing a check that the returned IP address 'pool' does not conflict with any existing addresses and (b) the IP address left over from the preceding weave expose.
Docker expect that when the 'pool' they are given has an overlap, they can ask for another one and it won't overlap.  So they carry on asking and asking and asking...
See https://github.com/docker/libnetwork/blob/master/network.go#L1197


Add date_format param to the CSV reader

Adds the date_format param introduced in Pandas 2.0 to the CSV reader and improves its type hints.

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.


Added support for creating cloudevent and custom schema topics

Added in the creation of cloud event and custom topics in test-resources.json, which creates our live test resources during the live test runs for Event Grid. Now we should be able to write tests that publish cloud events and custom events.

LGTM - once the CI finishes lets run the live test pipeline to verify that the test resource creation is working

/azp run net - eventgrid - tests


[TRI-1444] Only allow logins from whitelisted_emails (for self-hosting)

Currently there isn't a convenient way to lock down logins to a self-hosted Trigger.dev service.
We should add a new ENV var which allows a regex pattern to be used to only allow certain emails.
WHITELISTED_EMAILS="matt<EMAIL_ADDRESS>
Only emails that match this would be able to sign-up or login.
It must be added to env.server.ts file, and it needs to be optional. Probably the easiest way to implement this would be to modify the findOrCreateUser() function in user.server.ts to first check the whitelist if it exists and just throw an error.
TRI-1444

I think it could be from whitelisted, TLDs too not just emails, so that it can cover for an organization

TLDs too not just emails

regex will cover this

I would like to give it a try

No need to ask for permission, feel free to give it a go!

#692

#692

hey @nicktrn can you review my pr


Texture mesh quality is not good, how to adjust

I think this is probably related to https://github.com/bennyguo/instant-nsr-pl/issues/60. Could you try training without view-dependent color as demonstrated here and see how it comes out?


Broken link postman beta PlayFab

Broken link when trying to access the Postman Beta GitHub repository

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 94cbc73e-6e15-1f64-9454-ac7755422878
Version Independent ID: 9bbdcfc1-5832-2be3-31dd-466942a32263
Content: Quickstart PlayFab REST API collection for Postman - PlayFab
Content Source: playfab-docs/sdks/postman/postman-quickstart.md
Product: playfab
GitHub Login: @nathankong-microsoft
Microsoft Alias: timcra

Not sure who should own this but its in the SDK area of the docs

Link is fixed. Thanks for reporting!


non-ONNX version of new VAD model doesn't work with 8 kHz audio

New version of VAD non-ONNX model doesn't work with 8 kHz audio.
Code from example:
SAMPLING_RATE = 8000
USE_ONNX = False
model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
model='silero_vad',
force_reload=True,
onnx=USE_ONNX)
(get_speech_timestamps,
save_audio,
read_audio,
VADIterator,
collect_chunks) = utils
wav = read_audio('any_8k_audio_file.wav', sampling_rate=SAMPLING_RATE)
speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLING_RATE)
ValueError: only one element tensors can be converted to Python scalars
on line 252 of utils_vad: speech_prob = model(chunk, sampling_rate).item()

for my example model returns dim 3 tensor and it kills it on item()

Plus maybe it's not quite the same issue, but we found (subjectively) quality degradation of new version of VAD (ONNX version). We tested it on clear samples and it skips lots of voiced segments now. At the same time previous version works excellent.

'any_8k_audio_file.wav',

Please provide your audio file.


feat: create pages group1 login and signup page

Overview
I created login and signup pages
Changes

implement styles and form function in /, /login.
create /signup, /forgot-password, /reset-password page.
create Logo component.
create a password validation function.
fix the tab index of password input. /components/ui/input.tsx
remove capitalize class from /components/ui/button.tsx

Review points

[ ] Ensure the UI is properly implemented.
[ ] Form input validation is properly implemented.

Screenshots or videos
https://github.com/Tascurator/tascurator-frontend/assets/132441773/f33cf92b-2f2c-4102-acd8-5524ac4fb16e
Notes
/ page will be implemented in 📺Implement logic phase.
*redirect depending on login status. (After login: /sharehouses/, Before login: /login )
milestone
Assignee Checklist:

[x] The base branch is correct (no accidental merges)
[x] The branch name follows our branch naming rules
[x] The PR title follows our PR title rules
[x] My code follows our coding style
[x] I have commented on my code, particularly in hard-to-understand areas
[x] My changes generate no new warnings or errors in the console

Reviewer Checklist:

[ ] Code readability and simplicity
[ ] Follows best practices and coding standards
[ ] Understandable and maintainable code

Thank you Satomi for your hard work! I have some feedbacks so please check them and consider about changes :)

Logo disappears on the "Reset Password" and "Fogot Password" screens for smart phone size

Error message seems like doesn’t match to the actual error

the text below the input box looks not suitable

Less than or equal to 8 characters long. => Greater than or equal to 8 characters long.
Greater than or equal to 30 characters long.  => Less than or equal to 30 characters long.
Also, I feel like it is a bit odd to see checked even I don’t input any letters.

Sorry, I forgot to leave comments about this.
After discussing with Rina, we've decided to hide the logo on mobile views. (It will still appear on tablet and laptop sizes.)
Reason:
We believe the logo is not essential on this page.
Additionally, it causes the page to elongate, requiring users to scroll to see input areas and buttons

Logo disappears on the "Reset Password" and "Fogot Password" screens for smart phone size

@yuki-92c
Thank you for pointing that out. I've corrected the error in the Zod schema for signup and reset-password
    .min(PASSWORD_MIN_NUMBERS, minLength('Password', PASSWORD_MIN_NUMBERS))
↓
    .min(PASSWORD_MIN_LENGTH, minLength('Password', PASSWORD_MIN_LENGTH))

Error message seems like doesn’t match to the actual error

the text below the input box looks not suitable

Less than or equal to 8 characters long. => Greater than or equal to 8 characters long.
Greater than or equal to 30 characters long.  => Less than or equal to 30 characters long.
Also, I feel like it is a bit odd to see checked even I don’t input any letters.

I fixed this issue! I've merged both into a single constraint statement.
Between 8 and 30 characters long.


add new words in any model during recognition (dynamic) ; for example "zipformer"

Can i add a list of phrases in real time during decoding in a zipformer or any other models to recognize those new words.
I have watched this toturial on Youtube https://youtu.be/gtVWuiCzz2c #62 context braising, but I am not sure if there is an implemention available?
Thanks in advance

@kerolos  We only implement this feature in one of our old recipe, see https://github.com/k2-fsa/icefall/pull/1038 . It will be good if you can add it to zipformer recipe. You only need to add some code in zipformer/decode.py with this as a reference.


fix cursor size

Description
Close #1982 by fixing cursor size on init. I'll be honest its not 100% clear to me why this was actually working before, nor why #1955 broke it. This definitely seems like the needed behavior though, and I've added a test to confirm it.
I'm hoping there won't be any other surprise regressions from #1955.
Type of change

[x] Bug-fix (non-breaking change which fixes an issue)

Ok all critical tests are passing. This looks good! Will merge now


updated the testimonial part

@garvit027 Please see the conflicts . Resolve the conflicts . Ensure you pull all the latest changes before pushing your feature/bug fixes to the main branch. After resolving the conflicts, please open a new PR for review.
Thank you!


Streamline error-related code

See individual commits for details.
Part of work on on #165.

@bkutil

The failed Travis CI job looks like an unrelated issue. Filed #175, will rerun.

+1


[Bug] When importing a document with data validation set but not edited, and adding rows and columns, the selection within the data validation will change accordingly.

Before you submit this issue, have you checked the following

[X] Is this really a problem?
[X] I have searched the Github Issues for similar issues, but did not find anything.

Affected packages and versions
dev
Reproduction link
操作步骤：
1、框选B1单元格新增数据验证
2、不在单元格内输入内容直接导出
3、导入该表后在B1单元格插入列
4、观察数据验证内的应用范围
Expected behavior
预期是新增列到B1时应用范围不变嘛？
Actual behavior
新增B1列时，应用范围显示为C1
样张：https://staging.univer.plus/?unit=zE36he-kR6O41AUPpcJNQw&type=2&subunit=uni1

System information
No response

这个问题应该是ref的变更文本，与导入导出无关


Chat Improvements

Spam Cooldown
Better Swear Filter (Even though "F u c k" is in the list it does not get blocked)

Reference to the filter.
https://github.com/valkyrienyanko/io-game/blob/4eca92c6f855a4a39fb4c4b57dbfb1d34ace9a99/public/scripts/model/GUI/Chat.js#L79-L88


feat(curriculum): add CSS Backgrounds and Borders Quiz

Checklist:

[x] I have read and followed the contribution guidelines.
[x] I have read and followed the how to open a pull request guide.
[x] My pull request targets the main branch of freeCodeCamp.
[x] I have tested these changes either locally on my machine, or GitPod.

Closes #56180

@ilenia-magoni Hey I have added backticks for all code snippets including for css values. Please review it and let me know if any other changes u would like me to do.
Thank you!

Hi @rakshixh. Congrats on your first pull request (PR)! 🎉
Thank you for your contribution to the page! 👍
We are happy to accept these changes and look forward to future contributions. 📝

Hey @naomi-lgbt ,
Please find my discord username @rakshixh to assign me a role on discord channel.
Thank you!


Debugging - question

Hi,
I'd like to ask. I downloaded the source code and run ASC.Web.Studio project, it works just fine on my localhost. But I'd like to know how could I debug for example CRM module. My intention is to customize this module a bit, but I have found no information regarding debugging modules. Any help will be much appreciated. I know, there is a guide that describes how to add custom module, but no guide that would describe testing it. Thanks.
Lukas

Hello @lukasbelak adding custom module is our functional of community server. You can debug and customize code in Visual Studio, if have enought skills.

Closed for a reason - there has been no activity for a long time.


Freeswitch registration count

Does the exporter collects freeswitch registration count? Looks like currently it doesn't.

This is the command fs_cli -x "show registrations". Do we need to add another entry in the collector.go same as below.

Yes, but the output needs to be parsed properly.
Could you provide me some samples? With different results. One with no results, one with some results and another one with different results.
Thanks


Replace static_casts with vector operations

Description
Along the same lines as #3061 and #2692. The resulting code is shorter and in my opinion easier to read.

Pull Request Test Coverage Report for Build<PHONE_NUMBER>
Details

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.008%) to 56.205%

Totals

Change from base Build<PHONE_NUMBER>:
-0.008%

Covered Lines:
11663

Relevant Lines:
19692

💛  - Coveralls


Godot 4 Crash when editing a ScriptExtension on something that does not have a script.

Godot version
Godot_v4.0-beta3_linux.x86_64
System information
Ubuntu 20.04
Issue description
When you make a script extention on something that does not have a script it and try to edit that script extension it will crash godot 4.
Why it may seem like why would someone do this? a user seeing what things will do can get to this crash in only 11 clicks deep from a new project. curiosity on what's a Script Extension got me to this I was lucklly not working on anything, but I could have been working on something and had that same curiosity and clicked with out thinking.
if you have the output open you see this before crash
Godot Engine v4.0.beta3.official (c) 2007-2022 Juan Linietsky, Ariel Manzur & Godot Contributors.
  modules/gltf/register_types.cpp:76 - Blend file import is enabled in the project settings, but no Blender path is configured in the editor settings. Blend files will not be imported.
  modules/gltf/register_types.cpp:100 - FBX file import is enabled in the project settings, but no FBX2glTF path is configured in the editor settings. FBX files will not be imported.
--- Debug adapter server started ---
--- GDScript language server started ---
New Scene Root
Create Node
Set tile_set
  ./core/object/script_language_extension.h:54 - Required virtual method ScriptExtension::_can_instantiate must be overridden before calling.
  ./core/object/script_language_extension.h:65 - Required virtual method ScriptExtension::_placeholder_instance_create must be overridden before calling.
Set script
  ./core/object/script_language_extension.h:118 - Required virtual method ScriptExtension::_has_property_default_value must be overridden before calling.
  ./core/object/script_language_extension.h:57 - Required virtual method ScriptExtension::_get_instance_base_type must be overridden before calling.
  ./core/object/script_language_extension.h:55 - Required virtual method ScriptExtension::_get_base_script must be overridden before calling.

Godot is working up to this point but if you try to edit the ScriptExtension it will crash. with this
ERROR: Required virtual method ScriptExtension::_can_instantiate must be overridden before calling.
   at: _gdvirtual__can_instantiate_call (./core/object/script_language_extension.h:54)
ERROR: Required virtual method ScriptExtension::_placeholder_instance_create must be overridden before calling.
   at: _gdvirtual__placeholder_instance_create_call (./core/object/script_language_extension.h:65)
ERROR: Required virtual method ScriptExtension::_has_property_default_value must be overridden before calling.
   at: _gdvirtual__has_property_default_value_call (./core/object/script_language_extension.h:118)
ERROR: Required virtual method ScriptExtension::_get_instance_base_type must be overridden before calling.
   at: _gdvirtual__get_instance_base_type_call (./core/object/script_language_extension.h:57)
ERROR: Required virtual method ScriptExtension::_get_base_script must be overridden before calling.
   at: _gdvirtual__get_base_script_call (./core/object/script_language_extension.h:55)
ERROR: Required virtual method ScriptExtension::_get_source_code must be overridden before calling.
   at: _gdvirtual__get_source_code_call (./core/object/script_language_extension.h:74)
ERROR: Required virtual method ScriptExtension::_get_language must be overridden before calling.
   at: _gdvirtual__get_language_call (./core/object/script_language_extension.h:105)

================================================================
handle_crash: Program crashed with signal 11
Engine version: Godot Engine v4.0.beta3.official (01ae26d31befb6679ecd92cd3c73aa5a76162e95)
Dumping the backtrace. Please include this when reporting the bug to the project developer.
[1] /lib/x86_64-linux-gnu/libc.so.6(+0x46210) [0x7fc1227ae210] (??:0)
[2] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x2258f09] (??:0)
[3] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x225de66] (??:0)
[4] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x1b61f05] (??:0)
[5] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x1d18911] (??:0)
[6] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x1d2554d] (??:0)
[7] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x42befaa] (??:0)
[8] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x1b1cdc1] (??:0)
[9] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x1b0d251] (??:0)
[10] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x4803aa5] (??:0)
[11] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x42c0f77] (??:0)
[12] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0x294c8d5] (??:0)
[13] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0xe57eb5] (??:0)
[14] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0xd928ac] (??:0)
[15] /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf3) [0x7fc12278f0b3] (??:0)
[16] /home/alexander/Downloads/Compressed/Godot_v4.0-beta3_linux.x86_64() [0xdb3e3e] (??:0)
-- END OF BACKTRACE --
================================================================

Steps to reproduce
on a linux system
run the app image with ./Godot_v4.0-beta3_linux.x86_64
make a new project pick mobile
make a new 2D Scene
Add a TileMap (do not add a script)
Add a New TileSet
edit that TileSet
at the bottem of the inspector on the right under RefCounted
Script click "NewScriptExtenstion"
(after clicking for me it has the box icon of the script extenstion but no text like it does not exist)
edit that ScriptExtenstion
for me crash happens now
ctrl-C  to exit the BackTrace
run again with ./Godot_v4.0-beta3_linux.x86_64
Make a new project pick Forward+
make a new 3D Scene
Add a Camera3D (do NOT add a script)
Add a NewEnvironment
edit that Environment
at the bottem of the inspector on the right under RefCounted
Script click "NewScriptExtenstion"
edit that ScriptExtenstion
crash happens
ctrl-C  to exit the BackTrace
run again
open one of the projects that should be fresh because nothing get saved :(
make a new User Interface
add a Label (do NOT add a script)
add New LabelSettings
edit that LabelSettings
at the bottem of the inspector on the right under RefCounted
Script click "NewScriptExtenstion"
Script click on the blank space where text should be next to the ScriptExtenstion box icon that is the same as clicking the edit
crash happens
Minimal reproduction project
No response

@AlexanderLife  Please surround large blocks of text with triple backticks like this:
```
text here
```
This prevents them from taking too much space and also makes sure issue references aren't accidentally created. I edited your comment, but remember to do this next time :slightly_smiling_face:

Duplicate of #67267


Need a means of checking lib availability.

I'm facing an issue with RxJS where we've moved to support AsyncIterable, but if a user compiles a project that uses RxJS with TypeScript and a lib that is less than ES2018, then the type inference is all messed up because AsyncIterable doesn't exist.

Is there a way of enforcing a lib requirement on TS users of RxJS?
Is there a way to check to see if AsyncIterable exists and "fallback" to other types?
Is there a way to provide different types for different libs?

We have similar issues with trying to accurately type fromEvent, etc for both the DOM and Node.
Thanks for your time!

cc @DanielRosenwasser @ahejlsberg ... you might have a quick answer for this one.

Is there a way of enforcing a lib requirement on TS users of RxJS?

It's not ideal, but you can write /// <reference lib="esnext.asynciterable" /> in your .d.ts to force that type definition to be in scope. The caveat is that this will surprise your users who set target: es5 because they'll start seeing ESNext types

@RyanCavanaugh would we have to add that to the .d.ts manually?
It's definitely not ideal. Ideally we'd be able to change what we're doing based off of what lib they're using. This is a problem we have in other places too.
In RxJS, we can convert iterables, promises, async iterables, et al, to observables. If they don't support async iterables, ideally we wouldn't have that in the union type we're using. I guess I was looking for something like:
type ObservableInput<T> = Observable<T> | Promise<T> | Iterable<T> | (IsDefined<AsyncIterable> ? AsyncIterable<T> : never)

... or something. Haha. I have no idea how it would work. It seems like we can already do so much meta programming in the type system that it's almost possible.


[VQC-CHALLENGE] [qubytes] [2022-11-3]

Team members
Emily, Frroggiee, Kabir, Rick
Submission summary
contact<EMAIL_ADDRESS>HAQS-2022/qml-classifier-challenge/vqc_challenge.ipynb
Additional comments

Thanks for your submission! Your model achieved 100% accuracy on our judging data set with a quantum cost of 365.25, and currently ranks 12th on the overall leaderboard.
REMINDER: The final submission deadline is Friday, November 4th 10am EST, and any new PR's opened will not be reviewed until after the deadline.