Error: Size mismatch for resnet_transformer

E đang thử mô hình resnet_transformer thì cũng đang gặp lỗi size mismatch (version đang là 0.1.9):
Đây là code em chạy:
config = Cfg.load_config_from_name('resnet_transformer')
config['device'] = 'cuda:0'
config['predictor']['beamsearch'] = False
detector = Predictor(config)

Đây là thông báo lỗi:

File "/home/duycuong/PycharmProjects/research_py3/text_recognition/classifier/vietocr/vietocr/eval.py", line 24, in <module>
    detector = Predictor(config)
  File "/home/duycuong/PycharmProjects/research_py3/text_recognition/classifier/vietocr/vietocr/tool/predictor.py", line 19, in __init__
    model.load_state_dict(torch.load(weights, map_location=torch.device(device)))
  File "/home/duycuong/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 839, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for VietOCR:
	Missing key(s) in state_dict: "cnn.model.conv0_1.weight", "cnn.model.bn0_1.weight", "cnn.model.bn0_1.bias", "cnn.model.bn0_1.running_mean", "cnn.model.bn0_1.running_var", "cnn.model.conv0_2.weight", "cnn.model.bn0_2.weight", "cnn.model.bn0_2.bias", "cnn.model.bn0_2.running_mean", "cnn.model.bn0_2.running_var", "cnn.model.layer1.0.conv1.weight", "cnn.model.layer1.0.bn1.weight", "cnn.model.layer1.0.bn1.bias", "cnn.model.layer1.0.bn1.running_mean", "cnn.model.layer1.0.bn1.running_var", "cnn.model.layer1.0.conv2.weight", "cnn.model.layer1.0.bn2.weight", "cnn.model.layer1.0.bn2.bias", "cnn.model.layer1.0.bn2.running_mean", "cnn.model.layer1.0.bn2.running_var", "cnn.model.layer1.0.downsample.0.weight", "cnn.model.layer1.0.downsample.1.weight", "cnn.model.layer1.0.downsample.1.bias", "cnn.model.layer1.0.downsample.1.running_mean", "cnn.model.layer1.0.downsample.1.running_var", "cnn.model.conv1.weight", "cnn.model.bn1.weight", "cnn.model.bn1.bias", "cnn.model.bn1.running_mean", "cnn.model.bn1.running_var", "cnn.model.layer2.0.conv1.weight", "cnn.model.layer2.0.bn1.weight", "cnn.model.layer2.0.bn1.bias", "cnn.model.layer2.0.bn1.running_mean", "cnn.model.layer2.0.bn1.running_var", "cnn.model.layer2.0.conv2.weight", "cnn.model.layer2.0.bn2.weight", "cnn.model.layer2.0.bn2.bias", "cnn.model.layer2.0.bn2.running_mean", "cnn.model.layer2.0.bn2.running_var", "cnn.model.layer2.0.downsample.0.weight", "cnn.model.layer2.0.downsample.1.weight", "cnn.model.layer2.0.downsample.1.bias", "cnn.model.layer2.0.downsample.1.running_mean", "cnn.model.layer2.0.downsample.1.running_var", "cnn.model.layer2.1.conv1.weight", "cnn.model.layer2.1.bn1.weight", "cnn.model.layer2.1.bn1.bias", "cnn.model.layer2.1.bn1.running_mean", "cnn.model.layer2.1.bn1.running_var", "cnn.model.layer2.1.conv2.weight", "cnn.model.layer2.1.bn2.weight", "cnn.model.layer2.1.bn2.bias", "cnn.model.layer2.1.bn2.running_mean", "cnn.model.layer2.1.bn2.running_var", "cnn.model.conv2.weight", "cnn.model.bn2.weight", "cnn.model.bn2.bias", "cnn.model.bn2.running_mean", "cnn.model.bn2.running_var", "cnn.model.layer3.0.conv1.weight", "cnn.model.layer3.0.bn1.weight", "cnn.model.layer3.0.bn1.bias", "cnn.model.layer3.0.bn1.running_mean", "cnn.model.layer3.0.bn1.running_var", "cnn.model.layer3.0.conv2.weight", "cnn.model.layer3.0.bn2.weight", "cnn.model.layer3.0.bn2.bias", "cnn.model.layer3.0.bn2.running_mean", "cnn.model.layer3.0.bn2.running_var", "cnn.model.layer3.0.downsample.0.weight", "cnn.model.layer3.0.downsample.1.weight", "cnn.model.layer3.0.downsample.1.bias", "cnn.model.layer3.0.downsample.1.running_mean", "cnn.model.layer3.0.downsample.1.running_var", "cnn.model.layer3.1.conv1.weight", "cnn.model.layer3.1.bn1.weight", "cnn.model.layer3.1.bn1.bias", "cnn.model.layer3.1.bn1.running_mean", "cnn.model.layer3.1.bn1.running_var", "cnn.model.layer3.1.conv2.weight", "cnn.model.layer3.1.bn2.weight", "cnn.model.layer3.1.bn2.bias", "cnn.model.layer3.1.bn2.running_mean", "cnn.model.layer3.1.bn2.running_var", "cnn.model.layer3.2.conv1.weight", "cnn.model.layer3.2.bn1.weight", "cnn.model.layer3.2.bn1.bias", "cnn.model.layer3.2.bn1.running_mean", "cnn.model.layer3.2.bn1.running_var", "cnn.model.layer3.2.conv2.weight", "cnn.model.layer3.2.bn2.weight", "cnn.model.layer3.2.bn2.bias", "cnn.model.layer3.2.bn2.running_mean", "cnn.model.layer3.2.bn2.running_var", "cnn.model.layer3.3.conv1.weight", "cnn.model.layer3.3.bn1.weight", "cnn.model.layer3.3.bn1.bias", "cnn.model.layer3.3.bn1.running_mean", "cnn.model.layer3.3.bn1.running_var", "cnn.model.layer3.3.conv2.weight", "cnn.model.layer3.3.bn2.weight", "cnn.model.layer3.3.bn2.bias", "cnn.model.layer3.3.bn2.running_mean", "cnn.model.layer3.3.bn2.running_var", "cnn.model.layer3.4.conv1.weight", "cnn.model.layer3.4.bn1.weight", "cnn.model.layer3.4.bn1.bias", "cnn.model.layer3.4.bn1.running_mean", "cnn.model.layer3.4.bn1.running_var", "cnn.model.layer3.4.conv2.weight", "cnn.model.layer3.4.bn2.weight", "cnn.model.layer3.4.bn2.bias", "cnn.model.layer3.4.bn2.running_mean", "cnn.model.layer3.4.bn2.running_var", "cnn.model.conv3.weight", "cnn.model.bn3.weight", "cnn.model.bn3.bias", "cnn.model.bn3.running_mean", "cnn.model.bn3.running_var", "cnn.model.layer4.0.conv1.weight", "cnn.model.layer4.0.bn1.weight", "cnn.model.layer4.0.bn1.bias", "cnn.model.layer4.0.bn1.running_mean", "cnn.model.layer4.0.bn1.running_var", "cnn.model.layer4.0.conv2.weight", "cnn.model.layer4.0.bn2.weight", "cnn.model.layer4.0.bn2.bias", "cnn.model.layer4.0.bn2.running_mean", "cnn.model.layer4.0.bn2.running_var", "cnn.model.layer4.1.conv1.weight", "cnn.model.layer4.1.bn1.weight", "cnn.model.layer4.1.bn1.bias", "cnn.model.layer4.1.bn1.running_mean", "cnn.model.layer4.1.bn1.running_var", "cnn.model.layer4.1.conv2.weight", "cnn.model.layer4.1.bn2.weight", "cnn.model.layer4.1.bn2.bias", "cnn.model.layer4.1.bn2.running_mean", "cnn.model.layer4.1.bn2.running_var", "cnn.model.layer4.2.conv1.weight", "cnn.model.layer4.2.bn1.weight", "cnn.model.layer4.2.bn1.bias", "cnn.model.layer4.2.bn1.running_mean", "cnn.model.layer4.2.bn1.running_var", "cnn.model.layer4.2.conv2.weight", "cnn.model.layer4.2.bn2.weight", "cnn.model.layer4.2.bn2.bias", "cnn.model.layer4.2.bn2.running_mean", "cnn.model.layer4.2.bn2.running_var", "cnn.model.conv4_1.weight", "cnn.model.bn4_1.weight", "cnn.model.bn4_1.bias", "cnn.model.bn4_1.running_mean", "cnn.model.bn4_1.running_var", "cnn.model.conv4_2.weight", "cnn.model.bn4_2.weight", "cnn.model.bn4_2.bias", "cnn.model.bn4_2.running_mean", "cnn.model.bn4_2.running_var".
	Unexpected key(s) in state_dict: "cnn.cnn.features.0.weight", "cnn.cnn.features.0.bias", "cnn.cnn.features.1.weight", "cnn.cnn.features.1.bias", "cnn.cnn.features.1.running_mean", "cnn.cnn.features.1.running_var", "cnn.cnn.features.1.num_batches_tracked", "cnn.cnn.features.3.weight", "cnn.cnn.features.3.bias", "cnn.cnn.features.4.weight", "cnn.cnn.features.4.bias", "cnn.cnn.features.4.running_mean", "cnn.cnn.features.4.running_var", "cnn.cnn.features.4.num_batches_tracked", "cnn.cnn.features.7.weight", "cnn.cnn.features.7.bias", "cnn.cnn.features.8.weight", "cnn.cnn.features.8.bias", "cnn.cnn.features.8.running_mean", "cnn.cnn.features.8.running_var", "cnn.cnn.features.8.num_batches_tracked", "cnn.cnn.features.10.weight", "cnn.cnn.features.10.bias", "cnn.cnn.features.11.weight", "cnn.cnn.features.11.bias", "cnn.cnn.features.11.running_mean", "cnn.cnn.features.11.running_var", "cnn.cnn.features.11.num_batches_tracked", "cnn.cnn.features.14.weight", "cnn.cnn.features.14.bias", "cnn.cnn.features.15.weight", "cnn.cnn.features.15.bias", "cnn.cnn.features.15.running_mean", "cnn.cnn.features.15.running_var", "cnn.cnn.features.15.num_batches_tracked", "cnn.cnn.features.17.weight", "cnn.cnn.features.17.bias", "cnn.cnn.features.18.weight", "cnn.cnn.features.18.bias", "cnn.cnn.features.18.running_mean", "cnn.cnn.features.18.running_var", "cnn.cnn.features.18.num_batches_tracked", "cnn.cnn.features.20.weight", "cnn.cnn.features.20.bias", "cnn.cnn.features.21.weight", "cnn.cnn.features.21.bias", "cnn.cnn.features.21.running_mean", "cnn.cnn.features.21.running_var", "cnn.cnn.features.21.num_batches_tracked", "cnn.cnn.features.23.weight", "cnn.cnn.features.23.bias", "cnn.cnn.features.24.weight", "cnn.cnn.features.24.bias", "cnn.cnn.features.24.running_mean", "cnn.cnn.features.24.running_var", "cnn.cnn.features.24.num_batches_tracked", "cnn.cnn.features.27.weight", "cnn.cnn.features.27.bias", "cnn.cnn.features.28.weight", "cnn.cnn.features.28.bias", "cnn.cnn.features.28.running_mean", "cnn.cnn.features.28.running_var", "cnn.cnn.features.28.num_batches_tracked", "cnn.cnn.features.30.weight", "cnn.cnn.features.30.bias", "cnn.cnn.features.31.weight", "cnn.cnn.features.31.bias", "cnn.cnn.features.31.running_mean", "cnn.cnn.features.31.running_var", "cnn.cnn.features.31.num_batches_tracked", "cnn.cnn.features.33.weight", "cnn.cnn.features.33.bias", "cnn.cnn.features.34.weight", "cnn.cnn.features.34.bias", "cnn.cnn.features.34.running_mean", "cnn.cnn.features.34.running_var", "cnn.cnn.features.34.num_batches_tracked", "cnn.cnn.features.36.weight", "cnn.cnn.features.36.bias", "cnn.cnn.features.37.weight", "cnn.cnn.features.37.bias", "cnn.cnn.features.37.running_mean", "cnn.cnn.features.37.running_var", "cnn.cnn.features.37.num_batches_tracked", "cnn.cnn.features.40.weight", "cnn.cnn.features.40.bias", "cnn.cnn.features.41.weight", "cnn.cnn.features.41.bias", "cnn.cnn.features.41.running_mean", "cnn.cnn.features.41.running_var", "cnn.cnn.features.41.num_batches_tracked", "cnn.cnn.features.43.weight", "cnn.cnn.features.43.bias", "cnn.cnn.features.44.weight", "cnn.cnn.features.44.bias", "cnn.cnn.features.44.running_mean", "cnn.cnn.features.44.running_var", "cnn.cnn.features.44.num_batches_tracked", "cnn.cnn.features.46.weight", "cnn.cnn.features.46.bias", "cnn.cnn.features.47.weight", "cnn.cnn.features.47.bias", "cnn.cnn.features.47.running_mean", "cnn.cnn.features.47.running_var", "cnn.cnn.features.47.num_batches_tracked", "cnn.cnn.features.49.weight", "cnn.cnn.features.49.bias", "cnn.cnn.features.50.weight", "cnn.cnn.features.50.bias", "cnn.cnn.features.50.running_mean", "cnn.cnn.features.50.running_var", "cnn.cnn.features.50.num_batches_tracked", "cnn.cnn.classifier.0.weight", "cnn.cnn.classifier.0.bias", "cnn.cnn.classifier.3.weight", "cnn.cnn.classifier.3.bias", "cnn.cnn.classifier.6.weight", "cnn.cnn.classifier.6.bias".
	size mismatch for transformer.embed_tgt.weight: copying a param with shape torch.Size([232, 512]) from checkpoint, the shape in current model is torch.Size([233, 256]).
	size mismatch for transformer.pos_enc.pe: copying a param with shape torch.Size([10000, 1, 512]) from checkpoint, the shape in current model is torch.Size([1024, 1, 256]).
	size mismatch for transformer.transformer.encoder.layers.0.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.encoder.layers.0.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.encoder.layers.0.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.encoder.layers.0.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.0.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.encoder.layers.0.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.encoder.layers.0.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.0.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.0.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.0.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.0.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.1.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.encoder.layers.1.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.encoder.layers.1.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.encoder.layers.1.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.1.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.encoder.layers.1.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.encoder.layers.1.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.1.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.1.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.1.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.1.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.2.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.encoder.layers.2.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.encoder.layers.2.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.encoder.layers.2.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.2.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.encoder.layers.2.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.encoder.layers.2.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.2.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.2.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.2.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.2.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.3.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.encoder.layers.3.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.encoder.layers.3.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.encoder.layers.3.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.3.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.encoder.layers.3.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.encoder.layers.3.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.3.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.3.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.3.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.3.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.4.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.encoder.layers.4.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.encoder.layers.4.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.encoder.layers.4.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.4.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.encoder.layers.4.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.encoder.layers.4.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.4.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.4.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.4.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.4.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.5.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.encoder.layers.5.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.encoder.layers.5.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.encoder.layers.5.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.5.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.encoder.layers.5.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.encoder.layers.5.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.5.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.5.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.5.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.layers.5.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.norm.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.encoder.norm.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.0.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.0.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.0.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.multihead_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.0.multihead_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.0.multihead_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.0.multihead_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.decoder.layers.0.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.decoder.layers.0.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.norm3.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.0.norm3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.1.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.1.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.1.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.multihead_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.1.multihead_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.1.multihead_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.1.multihead_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.decoder.layers.1.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.decoder.layers.1.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.norm3.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.1.norm3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.2.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.2.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.2.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.multihead_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.2.multihead_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.2.multihead_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.2.multihead_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.decoder.layers.2.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.decoder.layers.2.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.norm3.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.2.norm3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.3.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.3.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.3.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.multihead_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.3.multihead_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.3.multihead_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.3.multihead_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.decoder.layers.3.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.decoder.layers.3.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.norm3.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.3.norm3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.4.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.4.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.4.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.multihead_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.4.multihead_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.4.multihead_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.4.multihead_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.decoder.layers.4.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.decoder.layers.4.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.norm3.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.4.norm3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.self_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.5.self_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.5.self_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.5.self_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.multihead_attn.in_proj_weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([768, 256]).
	size mismatch for transformer.transformer.decoder.layers.5.multihead_attn.in_proj_bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([768]).
	size mismatch for transformer.transformer.decoder.layers.5.multihead_attn.out_proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([256, 256]).
	size mismatch for transformer.transformer.decoder.layers.5.multihead_attn.out_proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.linear1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([2048, 256]).
	size mismatch for transformer.transformer.decoder.layers.5.linear2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([256, 2048]).
	size mismatch for transformer.transformer.decoder.layers.5.linear2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.norm3.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.layers.5.norm3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.norm.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.transformer.decoder.norm.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for transformer.fc.weight: copying a param with shape torch.Size([232, 512]) from checkpoint, the shape in current model is torch.Size([233, 256]).
	size mismatch for transformer.fc.bias: copying a param with shape torch.Size([232]) from checkpoint, the shape in current model is torch.Size([233]).

hiện tại chỉ có pretrain cho vgg thôi e nhé

hiện tại chỉ có pretrain cho vgg thôi e nhé
anh sẽ bổ sung dần sau.

ok anh,
Em đang test với 1 số test set thì kết quả rất tốt, CER đều dưới 0.15. Tuy nhiên khi em tăng kích thước ảnh từ 32-64 trong file base.yml thì CER vẫn vậy, ko biết em config trong đó đã đúng chuaw?


Update debugging information

@ZanSara Yes that makes sense! Thanks for pointing that out. I've made changes to adjust.


draft test fixes

For #

[ ] Pull request represents a single change (i.e. not fixing disparate/unrelated things in a single PR)
[ ] Title summarizes what is changing
[ ] Has a news entry file (remember to thank yourself!)
[ ] Appropriate comments and documentation strings in the code
[ ] Has sufficient logging.
[ ] Has telemetry for enhancements.
[ ] Unit tests & system/integration tests are added/updated
[ ] Test plan is updated as appropriate
[ ] package-lock.json has been regenerated by running npm install (if dependencies have changed)
[ ] The wiki is updated with any design decisions/details.

Codecov Report

Merging #8544 into master will increase coverage by 0.67%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master    #8544      +/-   ##
==========================================
+ Coverage    58.7%   59.37%   +0.67%
==========================================
  Files         511      511
  Lines       23306    23684     +378
  Branches     3824     3829       +5
==========================================
+ Hits        13682    14063     +381
+ Misses       8705     8700       -5
- Partials      919      921       +2

Impacted Files
Coverage Δ

...client/datascience/jupyter/jupyterCommandFinder.ts
74.21% <100%> (+0.61%)
:arrow_up:

...ractive-window/interactiveWindowCommandListener.ts
60% <100%> (+0.38%)
:arrow_up:

src/client/common/installer/productInstaller.ts
76.71% <0%> (-0.53%)
:arrow_down:

...t/datascience/jupyter/jupyterDataRateLimitError.ts
100% <0%> (ø)
:arrow_up:

src/client/formatters/serviceRegistry.ts
100% <0%> (ø)
:arrow_up:

...lient/datascience/jupyter/jupyterSelfCertsError.ts
100% <0%> (ø)
:arrow_up:

src/client/debugger/extension/serviceRegistry.ts
100% <0%> (ø)
:arrow_up:

...ience/jupyter/jupyterDebuggerRemoteNotSupported.ts
100% <0%> (ø)
:arrow_up:

src/client/common/variables/serviceRegistry.ts
100% <0%> (ø)
:arrow_up:

src/client/common/process/serviceRegistry.ts
100% <0%> (ø)
:arrow_up:

... and 32 more

Continue to review full report at Codecov.

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Add "Undo" operation to riemann-dash

As riemann-dash is not stable enough, Sometimes I move panels and they disappear.
Maybe it's a good idea to add an "Undo" operation to riemann-dash.
If you have a basic Idea on how to implement it, I'll be happy to implement it.

Riemann-dash has save/load for transactional changes.
On Oct 7, 2015 4:13 AM, Shani Elharrar<EMAIL_ADDRESS>wrote:As riemann-dash is not stable enough, Sometimes I move panels and they disappear.
Maybe it's a good idea to add an "Undo" operation to riemann-dash.
If you have a basic Idea on how to implement it, I'll be happy to use it. If not - I'll try to implement one.
—Reply to this email directly or view it on GitHub.

something like om would make this particularly easy


Digest canonicalisation breaks on SHA-256

MesageDigest only acepts "SHA-256" not "sha256" so our canonicalisation algorithm is broken. It turns out only sha-1 has aliases so we should just hardcode a mapping table.

Canon
Aliases

MD5
(none)

SHA-1
SHA1, SHA

SHA-256
(none)

SHA-512
(none)

Wasfixed in 66b495b8013c9b5e578841a3846ca688c8f255a4


Filter out enumerable from JS completion candidates

Before submitting a PR make sure the following things have been done:

[x] The commits are consistent with our contribution guidelines
[x] You've added tests to cover your change(s)
[x] All tests are passing
[x] The new code is not generating reflection warnings
[x] You've updated the changelog (if adding/changing user-visible functionality)

Thanks!
Summary
Candidates like .-0, .-1 etc were visible, because we didn't exclude enumerable properties from JS objects:

In JS such properties can be used to access array elements by index (also applicable for strings) - "abc"[0] => 'a'.
Close #39

Tested locally, works as expected:

Looking great - thanks!
Are there possible enumerable properties that would be suitable completions, or they are all uninteresting for any given type?

vemv @.***> writes:

Looking great - thanks!
Are there possible enumerable properties that would be suitable
completions, or they are all uninteresting for any given type?

I don't think so. See
https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/getOwnPropertyDescriptors#enumerable
Best regards, Roman

Perfect then - thanks!
Another nice improvement could be https://github.com/clojure-emacs/clj-suitable/issues/11 (I refined it now)
Else, of course, it's no issue.

Perfect then - thanks!
Another nice improvement could be #11 (I refined it now)
Else, of course, it's no issue.

I'll take a look tomorrow :)


Add BallotpediaApiCounter

Currently we keep track of all of the times we reach out to the Google Civic Api (search for "GoogleCivicApiCounterManager()"). Please add the same functionality for when we reach out to the Ballotpedia Api. You can find where reach out by searching for "BALLOTPEDIA_API_".

This is now ready for Dale to test when he works on importing from Ballotpedia next.

Hello @jangsoo, I've been able to test this code and it is working! Thank you. Issue resolved.


Enforce version check on wasm lib

when used used as dynamic lib.
Follow up from https://github.com/CosmWasm/wasmd/pull/988#discussion_r965976607
Would a make build-linux target with static compilation be a sufficient solution?

The only issue I see is that you can only build static binaries with muslc, which would probably require docker.

The solution shown in https://github.com/public-awesome/stargaze/blob/2b06ae1cb8518dd19b128b65317da125efb9702d/cmd/starsd/cmd/start.go#L11-L22 is pretty cool. It does not differentiate between static libraries (which can be wrong by not updating the .a file in the builer docker) and shared libraries (which can be wrong when juggling with custom build systems).
I only wonder about this duplication. The line var LibwasmVersion = "1.0.0" is something that you should be able to get from the Go dependency management using
$ go list -m github.com/CosmWasm/wasmvm | cut -d" " -f2 | cut -d"v" -f2
1.1.1

Any idea how to get the build system information into the .go file?

You can use build info to get the version
package main

import (
	"fmt"
	"runtime/debug"

	_ "github.com/CosmWasm/wasmd/app"
)

func main() {
	buildInfo, ok := debug.ReadBuildInfo()
	if !ok {
		panic("can't read build info")
	}
	for _, d := range buildInfo.Deps {
		if d.Path != "github.com/CosmWasm/wasmvm" {
			continue
		}
		if d.Replace != nil {
			fmt.Printf("%s@%s => %s@%s\n", d.Path, d.Version, d.Replace.Path, d.Replace.Version)

		}
		fmt.Printf("%s@%s\n", d.Path, d.Version)
	}
}


Fix 'setsockopt failed while setting timeout...'

A bug introduced some time ago. Results in "setsockopt failed while setting timeout value, Socket operation on non-socket" on every closed client connection.

I'm happy I could help :)


Dpl 197

Closes #
Changes proposed in this pull request:

...

Could you resolve the conflicts so that the checks can run?  Thanks


Add my/your voices to the dataset

I would like to enrich the dataset by adding my voice to the dataset, I would appreciate it if you provide information on how to participate in that.

very interesting idea to have opt-in allowed voice cloning. would you be willing to give your voice to the community? we can try to look into how that's most easily doable if you want

As a Korean speaker, I can confirm that the Korean dataset is relatively smaller compared to other datasets. Therefore, I am willing to contribute my voice to help improve it.


Procedure Call Failed Error

"error_message: ""{code: Neo.ClientError.Procedure.ProcedureCallFailed} {message: Failed to invoke procedure apoc.merge.node: Caused by: java.lang.IllegalArgumentException: The list of label names may not contain any NULL or empty STRING values. If you wish to merge a NODE without a label, pass an empty list instead.}""
failed_count: 1
file_name: ""edges.csv"""

I've also encountered this issue. I tried to reprocess it, and the error did not occur again.


[$500] mWeb - Scan - Camera on Scan tab on mweb uses ultrawide camera lens instead of regular lens

If you haven’t already, check out our contributing guidelines for onboarding and email<EMAIL_ADDRESS>to request to join our Slack channel!

Version Number: v1.3.83-5
Reproducible in staging?: Y
Reproducible in production?: N
If this was caught during regression testing, add the test name, ID and link from TestRail:
Email or phone of affected tester (no customers):
Logs: https://stackoverflow.com/c/expensify/questions/4856
Expensify/Expensify Issue URL:
Issue reported by: Applause-Internal Team
Slack conversation: @
Action Performed:
Precondition: The device used should be a Samsung with ultrawide lens (Samsung to ensure reproducibility). Able to reproduce with SG Note 10+, SG Z Fold 4, SG M52.

Go to staging.new.expensify.com
Go to + > Request money > Scan.
3, Allow camera permission if not yet.
Compare the difference of the camera preview on Android app.
Compare the difference on camera native app by switching between ultrawide lens and regular lens.

Expected Result:
The camera lens used on Scan tab on mweb should be the regular lens, as it does on Android native app.
Actual Result:
The camera lens used on Scan tab on mweb is ultrawide lens.
Workaround:
Unknown
Platforms:

Which of our officially supported platforms is this issue occurring on?

[ ] Android: Native
[x] Android: mWeb Chrome
[ ] iOS: Native
[x] iOS: mWeb Safari
[ ] MacOS: Chrome / Safari
[ ] MacOS: Desktop

Screenshots/Videos

Android: Native

Android: mWeb Chrome
https://github.com/Expensify/App/assets/115492554/ed6f1c3f-85aa-43c2-9cc4-f2e9136b3430

iOS: Native

iOS: mWeb Safari
https://github.com/Expensify/App/assets/115492554/00a457d2-ebf3-4a76-9cfa-c3df72627bbf

MacOS: Chrome / Safari

MacOS: Desktop

View all open jobs on GitHub
Upwork Automation - Do Not Edit

Upwork Job URL: https://www.upwork.com/jobs/~01cfe35a5937765358
Upwork Job ID:<PHONE_NUMBER>832860672
Last Price Increase: 2023-10-12

Camera capture is not available on Prod

:wave: Friendly reminder that deploy blockers are time-sensitive ⏱ issues! Check out the open StagingDeployCash deploy checklist to see the list of PRs included in this release, then work quickly to do one of the following:

Identify the pull request that introduced this issue and revert it.
Find someone who can quickly fix the issue.
Fix the issue yourself.

Hey @AndrewGable, do you think you could take a look at this when you get a chance. I believe this originates from this PR, not sure if this should remain a blocker or if it can be addressed in a follow up PR.

Seems OK for a follow up cc @MitchExpensify  @JmillsExpensify @kevinksullivan

Seems like this is possible: https://github.com/mozmorris/react-webcam/issues/47#issuecomment-314884057
We are open for proposals on this one!

@tienifr Can you provide any examples/screenshots/data that the last device returned by the enumerateDevices() would be the medium focal length lens. Preferably from couple of devices?

How does @tienifr's propsal look @aimane-chnaif?

@tienifr can you please make sure to provide the examples/screenshots/data that the last device returned by the enumerateDevices() would be the medium focal length lens. Preferably from couple of devices?
This is a high priority issue that we need to make sure is updated quickly. Thanks!

@aimane-chnaif any luck with those approaches @tienifr shared?

Issue not reproducible during KI retests. (First week)

Cool sounds good whats the eta looking like now @tienifr?

Bumping the priority on this one

How's this one tracking @tienifr?

Looks like we're in the final testing stages here

Still testing the PR on all platforms

How's this one going @tienifr?

@aimane-chnaif looks like this one is back to you for testing

It does look like this PR is causing the regression. @rlinoz asked if @aimane-chnaif or @tienifr can take a look but if they're not available in the next couple of hours I'll go ahead with a revert PR for now to resolve the blocker.

Switching to daily so I action payment tomorrow.

Payments are complete. @aimane-chnaif please complete the BZ checklist - thank you!

Bump here @aimane-chnaif

OK, just running through this one myself since Aimane is still OOO.
BugZero Checklist: The PR fixing this issue has been merged! The following checklist (instructions) will need to be completed before the issue can be closed:

[ ] The PR that introduced the bug has been identified. Link to the PR: https://github.com/Expensify/App/pull/28411
[ ] The offending PR has been commented on, pointing out the bug it caused and why, so the author and reviewers can learn from the mistake. Link to comment: https://github.com/Expensify/App/issues/29484#issuecomment-1760297970
[ ] A discussion in #expensify-bugs has been started about whether any other steps should be taken (e.g. updating the PR review checklist) in order to catch this type of bug sooner. Link to discussion: N/A
[ ] Determine if we should create a regression test for this bug. N/A - this was caught during testing for the other PR, and the flow is a common one (Scan on mweb). No additional test needed.
[ ] If we decide to create a regression test for the bug, please propose the regression test steps to ensure the same bug will not reach production again. N/A
[ ] Link the GH issue for creating/updating the regression test once above steps have been agreed upon: N/A


ACM-13458: Add imageclusterinstall to MCE gather

Related Issue: https://issues.redhat.com/browse/ACM-13458
Description of Changes: Adds imageclusterinstall to MCE must-gather
What resource(s) are being added: imageclusterinstall.extensions.hive.openshift.io
Is this a Hub or Managed cluster change?:

[x] Hub Cluster
[ ] Managed Cluster
[ ] N/A

Notes: Required for image-based-install-operator GA

No problem and thanks for the pointer.

Created the backplane gather PR here: https://github.com/stolostron/backplane-must-gather/pull/127


cl12 cl20 cl21 cl22 - User defined size while querying clGetDeviceInfo and CL_DEVICE_EXTENSIONS

This can generate test errors when extension string is bigger than test expects (then CL_INVALID_VALUE will be returned).
The following tests will be affected:

all spir tests
char extensions[1024] = {0};   size_t size;   // Querying the device for its supported extensions   cl_int errcode = clGetDeviceInfo(devId,                                    CL_DEVICE_EXTENSIONS,                                    sizeof(extensions),                                    extensions,                                    &size);
computeinfo
For example computeinfo tests defines config_data union where one of the filed is :
char                            string[1024];

There are PRs open for OpenCL 2.0/2.1/2.2.  Is the patch expected to apply to OpenCL 1.2 as well?

All PRs merged, closing.

Note: this hasn't been fixed on cl12_trunk.


chore(SpecialFunctions): re-instate many simp lemmas

Remove some now-addressed porting notes; re-instate a few more lemmas which were formerly marked as simp.

@mattrobball what does your keying tool say about these simp lemmas? If you point me to it, I can check myself.

#discr_tree_simp_key my_lemma

!bench

If we wanted to, we could still scope them to intervalIntegral, although in that case, it would be nice if there were a comment in the docstring for each lemma.

Since intervalIntegral is the head symbol, the discrimination tree already effectively does the scoping. The main issue is that it doesn't see anything beyond that head symbol for the most part. I think these are ok for now because the domain is pretty small. It will probably be fine indefinitely.

I do not have a strong opinion about the newly marked simp lemmas: I am happy to also just remove the porting note.

I think just merging is fine.

bors merge

bors r-
bors d+

One of the runners ran out of space (and I've temporarily removed it from the pool), so re-running this should work. I'm not sure if there's another reason this was given bors r- though.

bors r+


purpose field should be bigger

Make the purpose field on the account request page bigger so you can put in 3 sentences or so, and add text encouraging people to provide enough input that we can make an intelligent decision.
Imported from trac ticket #97,  created by ahelsing on 08-12-2014 at 16:55, last modified: 08-12-2014 at 16:55

See issue #98


xref in 'pool' metabolite

Hello HumanGEM team,
There are xref of defined metabolites into 'pool metabolite' see this example:
M_MAM00535 1-lysolecithin pool M_MAM00535c C9H19NO7PR 0 chebi:71980;kegg:C04233;lipidmaps:LMGP0105AA00;mnx:MNXM447;vmhmetabolite:M00535
Maybe better to have xref of other pool metabolites from other models? Because with these xref into 'pool' during the reconciliation process we get a group of metabolites that at the end do not belong to the same group of metabolites:
chebi:71980   N-retinylidene-N-retinylethanolamine
kegg:C04233  1-Lysolecithin
LMGP0105AA00     2-acyl-sn-glycero-3-phosphocholine
I checked other issues, and maybe this one is related to issue #197 ? sorry in advance.
best regards,
Anne
I hereby confirm that I have:

[ X] Checked that a similar issue does not exist already

@ANiknejad sorry for the late response. What do you suggest here specially?

Hi @Hao-Chalmers, no worries of course!
M_MAM00535 1-lysolecithin pool M_MAM00535c C9H19NO7PR 0 chebi:71980;kegg:C04233;lipidmaps:LMGP0105AA00;mnx:MNXM447;vmhmetabolite:M00535
Actually, I guess, the issue is that chebi:71980 is a wrong xref for 1-lysolecithin, right? then the other xrefs sound ok to report '1-lysolecithin pool'. My bad about a wrong description of the problem in my first message, sorry about that!

@ANiknejad it does appear so. Please move on to fix this by removing the chebi id.

@Hao-Chalmers  but this is in your side, right?
https://metabolicatlas.org/explore/Human-GEM/gem-browser/metabolite/MAM00535c
"
External databases
ChEBI | CHEBI:71980
"
how can I remove this xref?

the annotation revisions can be directly made to metabolites.tsv or reactions.tsv files first in the develop branch of this repo. And then these changes will be incorporated into Met Atlas web portal later once new version is released.

fixed in #302


Pods unable to connect to server API

Environmental Info:
K3s Version:
k3s version v1.18.9+k3s1 (630bebf9)

Node(s) CPU architecture, OS, and Version:
Linux vsdr-kube 5.4.68-amd64-vyos #1 SMP Tue Oct 13 19:30:42 UTC 2020 x86_64 GNU/Linux

Cluster Configuration:
single node cluster

Describe the bug:
The pods are unable to connect to the API on the server.

Steps To Reproduce:

Installed K3s: problem starts right after initial install on a fresh system

Additional context / logs:

NAMESPACE     NAME                                     READY   STATUS             RESTARTS   AGE
kube-system   coredns-7944c66d8d-6p472                 0/1     Running            0          17m
kube-system   metrics-server-7566d596c8-wtkhp          0/1     CrashLoopBackOff   7          17m
kube-system   local-path-provisioner-6d59f47c7-qwsxh   0/1     CrashLoopBackOff   7          17m
kube-system   helm-install-traefik-r6sg7               0/1     CrashLoopBackOff   6          17m

describeNode.txt
describePods.txt
iptables.txt
corednsLogs.txt

How about k3s logs?

The internal firewall is currently not enabled and there are no NAT/FW rules set.  K3s logs attached.
Kernel Log:
[ 9566.855077] IPv6: ADDRCONF(NETDEV_CHANGE): veth97f7d471: link becomes ready
[ 9566.855155] IPv6: ADDRCONF(NETDEV_CHANGE): eth0: link becomes ready
[ 9566.857780] cni0: port 1(veth97f7d471) entered blocking state
[ 9566.857781] cni0: port 1(veth97f7d471) entered disabled state
[ 9566.857940] device veth97f7d471 entered promiscuous mode
[ 9566.858017] cni0: port 1(veth97f7d471) entered blocking state
[ 9566.858017] cni0: port 1(veth97f7d471) entered forwarding state
[ 9566.911164] IPv6: ADDRCONF(NETDEV_CHANGE): eth0: link becomes ready
[ 9566.913222] IPv6: ADDRCONF(NETDEV_CHANGE): eth0: link becomes ready
[ 9566.914632] cni0: port 2(veth568b0fa2) entered blocking state
[ 9566.914633] cni0: port 2(veth568b0fa2) entered disabled state
[ 9566.914854] device veth568b0fa2 entered promiscuous mode
[ 9566.914914] cni0: port 2(veth568b0fa2) entered blocking state
[ 9566.914915] cni0: port 2(veth568b0fa2) entered forwarding state
[ 9566.918748] cni0: port 3(veth5479e9f2) entered blocking state
[ 9566.918750] cni0: port 3(veth5479e9f2) entered disabled state
[ 9566.918918] device veth5479e9f2 entered promiscuous mode
[ 9566.918978] cni0: port 3(veth5479e9f2) entered blocking state
[ 9566.918978] cni0: port 3(veth5479e9f2) entered forwarding state
[ 9566.921823] IPv6: ADDRCONF(NETDEV_CHANGE): veth4eec8bad: link becomes ready
[ 9566.921909] IPv6: ADDRCONF(NETDEV_CHANGE): eth0: link becomes ready
[ 9566.924577] cni0: port 4(veth4eec8bad) entered blocking state
[ 9566.924579] cni0: port 4(veth4eec8bad) entered disabled state
[ 9566.924771] device veth4eec8bad entered promiscuous mode
[ 9566.924894] cni0: port 4(veth4eec8bad) entered blocking state
[ 9566.924896] cni0: port 4(veth4eec8bad) entered forwarding state
[ 9566.930299] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9566.930302] ll header: 00000000: ff ff ff ff ff ff 22 d6 b4 9e 34 36 08 06
[ 9566.961053] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9566.961058] ll header: 00000000: ff ff ff ff ff ff 8e b2 53 de f7 fe 08 06
[ 9566.969875] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9566.969878] ll header: 00000000: ff ff ff ff ff ff fe 05 66 5a 27 20 08 06
[ 9566.973473] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9566.973475] ll header: 00000000: ff ff ff ff ff ff e6 bd 46 09 c9 59 08 06
[ 9567.460714] __mptcp_init4_subsockets: MPTCP subsocket connect() failed, error -101
[ 9568.481429] __mptcp_init4_subsockets: MPTCP subsocket connect() failed, error -101
[ 9577.225085] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9577.225088] ll header: 00000000: ff ff ff ff ff ff 8e b2 53 de f7 fe 08 06
[ 9578.395623] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9578.395625] ll header: 00000000: ff ff ff ff ff ff fe 05 66 5a 27 20 08 06
[ 9578.896997] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9578.897000] ll header: 00000000: ff ff ff ff ff ff 22 d6 b4 9e 34 36 08 06
[ 9589.023257] IPv4: martian source <IP_ADDRESS> from <IP_ADDRESS>, on dev eth0
[ 9589.023259] ll header: 00000000: ff ff ff ff ff ff e6 bd 46 09 c9 59 08 06

k3s.txt

I also flushed iptables and then restarted k3s and still had the same issue.

The k3s service itself writes logs, without those I can't tell much about what's going on.

Can you grab all k3s logs from service start onwards, as opposed to just the tail of the log? By the time it's been up for a bit all you get is the same retrying failure messages over and over again.

I don't see anything particularly incriminating in there. Is there anything interesting in /var/lib/rancher/k3s/agent/containerd/containerd.log?
There's no way to get rid of all these VYATTA iptables rules?
 Completed on Mon Oct 19 19:31:33 2020
# Generated by iptables-save v1.8.2 on Mon Oct 19 19:31:33 2020
*filter
:INPUT ACCEPT [0:0]
:FORWARD ACCEPT [0:0]
:OUTPUT ACCEPT [0:0]
:KUBE-EXTERNAL-SERVICES - [0:0]
:KUBE-FIREWALL - [0:0]
:KUBE-FORWARD - [0:0]
:KUBE-KUBELET-CANARY - [0:0]
:KUBE-PROXY-CANARY - [0:0]
:KUBE-SERVICES - [0:0]
:VYATTA_POST_FW_FWD_HOOK - [0:0]
:VYATTA_POST_FW_IN_HOOK - [0:0]
:VYATTA_POST_FW_OUT_HOOK - [0:0]
:VYATTA_PRE_FW_FWD_HOOK - [0:0]
:VYATTA_PRE_FW_IN_HOOK - [0:0]
:VYATTA_PRE_FW_OUT_HOOK - [0:0]
-A INPUT -m conntrack --ctstate NEW -m comment --comment "kubernetes service portals" -j KUBE-SERVICES
-A INPUT -m conntrack --ctstate NEW -m comment --comment "kubernetes externally-visible service portals" -j KUBE-EXTERNAL-SERVICES
-A INPUT -j KUBE-FIREWALL
-A INPUT -j VYATTA_PRE_FW_IN_HOOK
-A INPUT -j VYATTA_POST_FW_IN_HOOK
-A FORWARD -m comment --comment "kubernetes forwarding rules" -j KUBE-FORWARD
-A FORWARD -m conntrack --ctstate NEW -m comment --comment "kubernetes service portals" -j KUBE-SERVICES
-A FORWARD -j VYATTA_PRE_FW_FWD_HOOK
-A FORWARD -j VYATTA_POST_FW_FWD_HOOK
-A FORWARD -s <IP_ADDRESS>/16 -j ACCEPT
-A FORWARD -d <IP_ADDRESS>/16 -j ACCEPT
-A OUTPUT -m conntrack --ctstate NEW -m comment --comment "kubernetes service portals" -j KUBE-SERVICES
-A OUTPUT -j KUBE-FIREWALL
-A OUTPUT -j VYATTA_PRE_FW_OUT_HOOK
-A OUTPUT -j VYATTA_POST_FW_OUT_HOOK

I cleared them out by flushing and then re-ran k3s.  Didn't seem to make a difference.  Those chains are all empty and have no rules since i don't have the vyos firewall enabled.  I also checked packet counts and none are hitting drop/reject rules and KUBE rules have all the packet counts in the FORWARD chain, so the packets are being forwarded.
Chain FORWARD (policy ACCEPT 0 packets, 0 bytes)
 pkts bytes target     prot opt in     out     source               destination
  149  8550 KUBE-FORWARD  all  --  any    any     anywhere             anywhere             /* kubernetes forwarding rules */
    0     0 KUBE-SERVICES  all  --  any    any     anywhere             anywhere             ctstate NEW /* kubernetes service portals */
  148  8505 ACCEPT     all  --  any    any     <IP_ADDRESS>/16         anywhere
    0     0 ACCEPT     all  --  any    any     anywhere             <IP_ADDRESS>/16

containerd.log

What version of vyos are you using, and how have you configured it for use with k3s? Out of the box on a fresh install from vyos-1.3-rolling-202010012001-amd64.iso k3s won't run because the rootfs is itself overlayfs, which won't work. It looks like it's also using iptables-nft, which doesn't work well with k3s 1.18. After making a couple changes, I was able to get it running though:
update-alternatives --set iptables /usr/sbin/iptables-legacy.  # NEEDS REBOOT AFTERWARDS
mkdir -p /usr/lib/live/mount/persistence/rancher/k3s/agent/kubelet
ln -sf /usr/lib/live/mount/persistence/rancher/k3s/agent/kubelet /var/lib/kubelet
curl -sfL https://get.k3s.io | INSTALL_K3S_EXEC="server --data-dir=/usr/lib/live/mount/persistence/rancher/k3s" bash -

After doing that I have:
root@vyos:~# show version

Version:          VyOS 1.3-rolling-202010012001
Release Train:    equuleus

Built by<EMAIL_ADDRESS>Built on:         Thu 01 Oct 2020 20:01 UTC
Build UUID:       d2c36cfb-a5e0-4d4c-8b4d-99657b8432f6
Build Commit ID:<PHONE_NUMBER>f9a5

Architecture:     x86_64
Boot via:         installed image
System type:      KVM guest

Hardware vendor:  QEMU
Hardware model:   Standard PC (Q35 + ICH9, 2009)
Hardware S/N:
Hardware UUID:    2ceda07e-4421-4a25-b4e4-26aaad6cd36f

Copyright:        VyOS maintainers and contributors
root@vyos:~# kubectl get pods -A -o wide
NAMESPACE     NAME                                     READY   STATUS    RESTARTS   AGE     IP          NODE   NOMINATED NODE   READINESS GATES
kube-system   coredns-7944c66d8d-9n56m                 0/1     Running   0          7m30s   <IP_ADDRESS>   vyos   <none>           <none>
kube-system   helm-install-traefik-v24rk               1/1     Running   2          7m30s   <IP_ADDRESS>   vyos   <none>           <none>
kube-system   local-path-provisioner-6d59f47c7-nkzf5   1/1     Running   4          7m30s   <IP_ADDRESS>   vyos   <none>           <none>
kube-system   metrics-server-7566d596c8-p54k7          1/1     Running   4          7m30s   <IP_ADDRESS>   vyos   <none>           <none>

I have a standard ext4 filesystem mounted to /var/lib/rancher after installing vyos to disk.  The only error I get is the pods are not able to talk to the server API.  I even tried switching to calico, but it had the exact same issue.

Yeah, it seems like all pod to service communication is broken. This is pretty much entirely done with iptables, and it's still broken even after manually clearing all firewall rules before starting k3s, so I'm at a bit of a loss as to exactly how they broke this.

The packets are hitting the forward chain.  When I run tcpdump on the cni interface, I see the requests, but no responses.

I reached out the the vyos maintainers.  They think it may have something to do with how k3s does NATing.  https://forum.vyos.io/t/kubernetes-server-and-agent-support/6071

The iptables stuff is managed by the flannel CNI and kubelet/kube-proxy, so it's not really k3s specific. You might try a different CNI?

I tried calico and had it behaved the same.

I switched to calico and I've made a little progress:
I switched the pod network to <IP_ADDRESS>/16 since the 192.168/16 was taken.  The issue i'm running into is that the kube-system pods are trying to call in on <IP_ADDRESS>
tigera-operator   tigera-operator-6bb69b5569-tdvg7           1/1     Running            0          15m   <IP_ADDRESS>   vsdr   <none>           <none>
calico-system     calico-typha-d84954c59-r5969               1/1     Running            0          14m   <IP_ADDRESS>   vsdr   <none>           <none>
calico-system     calico-node-j584s                          1/1     Running            0          14m   <IP_ADDRESS>   vsdr   <none>           <none>
kube-system       local-path-provisioner-6d59f47c7-sl2jg     0/1     CrashLoopBackOff   6          15m   <IP_ADDRESS>     vsdr   <none>           <none>
kube-system       metrics-server-7566d596c8-6zt8x            0/1     CrashLoopBackOff   6          15m   <IP_ADDRESS>     vsdr   <none>           <none>
calico-system     calico-kube-controllers-749bc89dc9-xhqwp   0/1     CrashLoopBackOff   7          14m   <IP_ADDRESS>     vsdr   <none>           <none>
kube-system       coredns-7944c66d8d-jgbmb                   0/1     CrashLoopBackOff   6          15m   <IP_ADDRESS>     vsdr   <none>           <none>

coredns logs:
I1028 16:12:44.381284       1 trace.go:116] Trace[2019727887]: "Reflector ListAndWatch" name:pkg/mod/k8s.io/client-go@v0.17.4/tools/cache/reflector.go:105 (started: 2020-10-28 16:12:14.380908715 +0000 UTC m=+0.025812336) (total time: 30.000298173s):
Trace[2019727887]: [30.000298173s] [30.000298173s] END
E1028 16:12:44.381394       1 reflector.go:153] pkg/mod/k8s.io/client-go@v0.17.4/tools/cache/reflector.go:105: Failed to list *v1.Namespace: Get "https://<IP_ADDRESS>:443/api/v1/namespaces?limit=500&resourceVersion=0": dial tcp <IP_ADDRESS>:443: i/o timeout
I1028 16:12:44.381508       1 trace.go:116] Trace[1427131847]: "Reflector ListAndWatch" name:pkg/mod/k8s.io/client-go@v0.17.4/tools/cache/reflector.go:105 (started: 2020-10-28 16:12:14.381005989 +0000 UTC m=+0.025909610) (total time: 30.000206212s):
Trace[1427131847]: [30.000206212s] [30.000206212s] END
E1028 16:12:44.381615       1 reflector.go:153] pkg/mod/k8s.io/client-go@v0.17.4/tools/cache/reflector.go:105: Failed to list *v1.Endpoints: Get "https://<IP_ADDRESS>:443/api/v1/endpoints?limit=500&resourceVersion=0": dial tcp <IP_ADDRESS>:443: i/o timeout
I1028 16:12:44.381795       1 trace.go:116] Trace[939984059]: "Reflector ListAndWatch" name:pkg/mod/k8s.io/client-go@v0.17.4/tools/cache/reflector.go:105 (started: 2020-10-28 16:12:14.381188332 +0000 UTC m=+0.026091942) (total time: 30.00058906s):
Trace[939984059]: [30.00058906s] [30.00058906s] END
E1028 16:12:44.381839       1 reflector.go:153] pkg/mod/k8s.io/client-go@v0.17.4/tools/cache/reflector.go:105: Failed to list *v1.Service: Get "https://<IP_ADDRESS>:443/api/v1/services?limit=500&resourceVersion=0": dial tcp <IP_ADDRESS>:443: i/o timeout

The pod CIDR and service CIDR are separate settings. If you change the service CIDR you need to also change the cluster DNS endpoint to something within your new service range. All of these things must be set on first cluster startup; you effectively cannot change them after the fact since the IPAM carves out smaller blocks and assigns them to nodes when they join the cluster.


Transactions date range filter not working

Hi,
According to the example in the README it should be possible to fetch transactions for a specific date range.
However, when I try this I get the following error: get_transactions() got an unexpected keyword argument 'date_from'.
Could it be that this functionality is not yet available in the last released version (v 1.0.1)?

Hello.
We've just released a new version, could you please take a look and let us know if it works.
Thank You!

Thanks, it works perfectly now!


Doesn't work with VSCode

Cloned the repo then in VSCode I ran start with debugging and it never ran.
I think you are missing the .vscode/* files.

I see from your .gitignore you don't want to add these so please disregard.

We could add them in there if you had some working vscode files you wanted to make a PR and add to the repo? VSCode is the best 👍


Bounding box moves around map when zooming on phone

When the widget was used to select a city which has a bounding box on the map
then when zooming, the bounding box leaves the place and flies around the map.

can you please add screen record for your issue
and what is the version you are using

try version 0.0.9

yep, that fixed it. Many thanks :D


[EventRequest] JobTransferLine.Codeunit.al (codeunit 1004 "Job Transfer Line")

Hi MS,
Please help us in migrating our verticalized solution by adding below events in JobTransferLine.Codeunit.al Codeunit.
Standard Code

Modified Code

Event Signature

Thanks
SUD

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. We will update this issue with information about availability.


DRT not failing routing on (wrong) input parameters

Describe the bug
By passing -top_routing_layer met2 and -bottom_routing_layer met1 to, DRT used to fail because given the guides it is impossible to route the design given the input parameters - I assume. However, DRT now partially routes the design and exits cleanly.
detailed_route\
    -bottom_routing_layer met1\
    -top_routing_layer met2\
    -or_seed 42\
    -verbose 1

Expected Behavior
I think it is OK for the router to partially route the design, however this might be a bit misleading in case of user error. Possible suggestions are adding warnings or event explicit flag to allow such behavior.
Environment
Git commit: 82450fd6fd1a8d241016dd33973399dd3d63919c
kernel: Linux 6.11.7-2-liquorix-amd64
os: Ubuntu 22.04.5 LTS (Jammy Jellyfish)
cmake version 3.25.1
-- The CXX compiler identification is GNU 11.4.0
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- OpenROAD version: v2.0-17048-g82450fd6f
-- System name: Linux
-- Compiler: GNU 11.4.0
-- Build type: RELEASE
-- Install prefix: /usr/local
-- C++ Standard: 17
-- C++ Standard Required: ON
-- C++ Extensions: OFF
-- The C compiler identification is GNU 11.4.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Performing Test C_COMPILER_SUPPORTS__-Wall
-- Performing Test C_COMPILER_SUPPORTS__-Wall - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wall
-- Performing Test CXX_COMPILER_SUPPORTS__-Wall - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-array-bounds
-- Performing Test C_COMPILER_SUPPORTS__-Wno-array-bounds - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-array-bounds
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-array-bounds - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-nonnull
-- Performing Test C_COMPILER_SUPPORTS__-Wno-nonnull - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-nonnull
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-nonnull - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-maybe-uninitialized
-- Performing Test C_COMPILER_SUPPORTS__-Wno-maybe-uninitialized - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-maybe-uninitialized
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-maybe-uninitialized - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-format-overflow
-- Performing Test C_COMPILER_SUPPORTS__-Wno-format-overflow - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-format-overflow
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-format-overflow - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-unused-variable
-- Performing Test C_COMPILER_SUPPORTS__-Wno-unused-variable - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-unused-variable
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-unused-variable - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-unused-function
-- Performing Test C_COMPILER_SUPPORTS__-Wno-unused-function - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-unused-function
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-unused-function - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-write-strings
-- Performing Test C_COMPILER_SUPPORTS__-Wno-write-strings - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-write-strings
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-write-strings - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-sign-compare
-- Performing Test C_COMPILER_SUPPORTS__-Wno-sign-compare - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-sign-compare
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-sign-compare - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-deprecated
-- Performing Test C_COMPILER_SUPPORTS__-Wno-deprecated - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-deprecated
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-deprecated - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-c++11-narrowing
-- Performing Test C_COMPILER_SUPPORTS__-Wno-c++11-narrowing - Failed
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-c++11-narrowing
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-c++11-narrowing - Failed
-- Performing Test C_COMPILER_SUPPORTS__-Wno-register
-- Performing Test C_COMPILER_SUPPORTS__-Wno-register - Failed
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-register
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-register - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-format
-- Performing Test C_COMPILER_SUPPORTS__-Wno-format - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-format
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-format - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-reserved-user-defined-literal
-- PerformingCMake Warning at src/CMakeLists.txt:267 (message):
  spdlog: SPDLOG_FMT_EXTERNAL=ON

 Test C_COMPILER_SUPPORTS__-Wno-reserved-user-defined-literal - Failed
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-reserved-user-defined-literal
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-reserved-user-defined-literal - Failed
-- Performing Test C_COMPILER_SUPPORTS__-fpermissive
-- Performing Test C_COMPILER_SUPPORTS__-fpermissive - Failed
-- Performing Test CXX_COMPILER_SUPPORTS__-fpermissive
-- Performing Test CXX_COMPILER_SUPPORTS__-fpermissive - Success
-- Performing Test C_COMPILER_SUPPORTS__-x
-- Performing Test C_COMPILER_SUPPORTS__-x - Failed
-- Performing Test CXX_COMPILER_SUPPORTS__-x
-- Performing Test CXX_COMPILER_SUPPORTS__-x - Failed
-- Performing Test C_COMPILER_SUPPORTS__c++
-- Performing Test C_COMPILER_SUPPORTS__c++ - Failed
-- Performing Test CXX_COMPILER_SUPPORTS__c++
-- Performing Test CXX_COMPILER_SUPPORTS__c++ - Failed
-- Performing Test C_COMPILER_SUPPORTS__-std=c++17
-- Performing Test C_COMPILER_SUPPORTS__-std=c++17 - Failed
-- Performing Test CXX_COMPILER_SUPPORTS__-std=c++17
-- Performing Test CXX_COMPILER_SUPPORTS__-std=c++17 - Success
-- Performing Test C_COMPILER_SUPPORTS__-Wno-unused-but-set-variable
-- Performing Test C_COMPILER_SUPPORTS__-Wno-unused-but-set-variable - Success
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-unused-but-set-variable
-- Performing Test CXX_COMPILER_SUPPORTS__-Wno-unused-but-set-variable - Success
-- TCL library: /usr/lib/x86_64-linux-gnu/libtcl.so
-- TCL header: /usr/include/tcl/tcl.h
-- TCL readline library: /usr/lib/x86_64-linux-gnu/libtclreadline.so
-- TCL readline header: /usr/include/x86_64-linux-gnu
-- Found SWIG: /usr/bin/swig4.0 (found suitable version "4.0.2", minimum required is "4.0")
-- Found Boost: /usr/local/lib/cmake/Boost-1.81.0/BoostConfig.cmake (found version "1.81.0")
-- boost: 1.81.0
-- Found GTest: /home/karim/.local/lib/cmake/GTest/GTestConfig.cmake (found version "1.13.0")
-- GTest: 1.13.0
-- Found Python3: /usr/include/python3.10 (found version "3.10.12") found components: Development Development.Module Development.Embed
-- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version "1.2.11")
-- Found Threads: TRUE
-- spdlog: 1.9.2
-- Found BISON: /usr/bin/bison (found version "3.8.2")
-- Could NOT find Doxygen (missing: DOXYGEN_EXECUTABLE)
-- STA version: 2.6.0
-- STA git sha: aa598a2f14c5c142e90391a69988523505e7db3d
-- System name: Linux
-- Compiler: GNU 11.4.0
-- Build type: RELEASE
-- Build CXX_FLAGS: -O3 -DNDEBUG
-- Install prefix: /usr/local
-- Found FLEX: /usr/bin/flex (found version "2.6.4")
-- TCL library: /usr/lib/x86_64-linux-gnu/libtcl.so
-- TCL header: /usr/include/tcl/tcl.h
-- TCL readline library: /usr/lib/x86_64-linux-gnu/libtclreadline.so
-- TCL readline header: /usr/include/x86_64-linux-gnu/tclreadline.h
-- CUDD library: /usr/local/lib/libcudd.a
-- CUDD header: /usr/local/include/cudd.h
-- SSTA: 0
-- Found SWIG: /usr/bin/swig4.0 (found suitable version "4.0.2", minimum required is "3.0")
-- STA executable: /home/karim/work/OpenROAD/src/sta/app/sta
-- Found re2: /home/karim/Downloads/or-tools_x86_64_Ubuntu-22.10_cpp_v9.5.2237/lib/cmake/re2/re2Config.cmake (found version "9.0.0")
-- Found Clp: /home/karim/Downloads/or-tools_x86_64_Ubuntu-22.10_cpp_v9.5.2237/lib/cmake/Clp/ClpConfig.cmake (found version "1.17.7")
-- Found Cbc: /home/karim/Downloads/or-tools_x86_64_Ubuntu-22.10_cpp_v9.5.2237/lib/cmake/Cbc/CbcConfig.cmake (found version "2.10.7")
-- Found SCIP: /home/karim/Downloads/or-tools_x86_64_Ubuntu-22.10_cpp_v9.5.2237/lib/cmake/scip/scip-config.cmake (found version "8.0.1")
-- Found OpenMP_CXX: -fopenmp (found version "4.5")
-- Found OpenMP: TRUE (found version "4.5")
-- Found OR-Tools: /home/karim/Downloads/or-tools_x86_64_Ubuntu-22.10_cpp_v9.5.2237/lib/cmake/ortools (version: 9.5.2237)
-- TCL library: /usr/lib/x86_64-linux-gnu/libtcl.so
-- TCL header: /usr/include/tcl/tcl.h
-- Found OpenMP_C: -fopenmp (found version "4.5")
-- Found OpenMP: TRUE (found version "4.5")
-- Found OpenMP: TRUE (found version "4.5")
-- GUI is enabled
-- Charts widget is not enabled
-- Found Boost: /usr/lNumber of processor cores: 20
ocal/lib/cmake/Boost-1.81.0/BoostConfig.cmake (found version "1.81.0") found components: serialization
-- Could NOT find VTune (missing: VTune_LIBRARIES VTune_INCLUDE_DIRS)
-- Found Boost: /usr/local/lib/cmake/Boost-1.81.0/BoostConfig.cmake (found suitable version "1.81.0", minimum required is "1.78")
-- TCL library: /usr/lib/x86_64-linux-gnu/libtcl.so
-- TCL header: /usr/include/tcl/tcl.h
-- Found Boost: /usr/local/lib/cmake/Boost-1.81.0/BoostConfig.cmake (found version "1.81.0") found components: serialization system thread
-- Found Boost: /usr/local/lib/cmake/Boost-1.81.0/BoostConfig.cmake (found version "1.81.0")
-- Found Eigen3: /usr/share/eigen3/cmake/Eigen3Config.cmake (found version "3.4.0")
-- TCL readline enabled
-- Tcl Extended disabled
-- Python3 enabled
-- Configuring done
-- Generating done
-- Build files have been written to: /tmp/tmp.y2RNaCO2JB

To Reproduce
test-case.tar.gz
Relevant log output
No response
Screenshots
No response
Additional Context
Possibly related: https://github.com/The-OpenROAD-Project/OpenROAD/discussions/6179

Could you collaborate more on "Partially routes the design"? Is it not routing nets with guides or is it ignoring nets without guides?

It seems that I have attached a wrong test case. Let me double check and get back to you. This is possibly not an issue at all..

Not an issue. Got confused by the new DRT ability to route only the nets that have guides and ignore the others (see discussion)


Integration with  GitHub Copilot HELP!!!

Ah, I see what you're missing. Looks like you're didn't forget this step:

Awesome, this works!! :tada:

@fabidick22
The issue should be closed since it has been resolved...


Recognition process deepstack does not run automatically[BUG]

Describe the bug
All is working fine except when a person is detected it remains identified as "unknown". When I click reprocess image it does recognize the person.
Version of Double Take

Expected behavior
Automatically start recognition and identification
Screenshots
If applicable, add screenshots to help explain your problem.
Hardware
Docker running on Proxmox. All lates builds
HASSOS running on Proxmox latest builds
Additional context
Configuration :

try to use my fork: https://github.com/skrashevich/double-take
I fixed a similar problem


Another way to display tags in dendron-published pages

Please select if your request is either something new or an enhancement

[x] Enhancement of an existing Feature.
[ ] Request of a new feature.

Please select the area your request applies to. (Multiple selections are Possible. You can leave blank if you're not sure.)

[ ] Workspace - VSCode workspace, vaults, Intellisense/autocomplete, Dendron settings
[ ] Lookup - Dendron's Lookup Command
[ ] Views - Dendron Preview, Tree View, Side Panels in the UI
[ ] Schema - Dendron Schemas
[ ] Pod - Data import from / export to Dendron
[x] Publish - External Site Publish
[ ] Markdown - Markdown features, such as syntax support and features

Is your feature request related to a problem? Please describe
Currently, in a published page, Dendron display the nested / hierarchical tags as a single link to "tag" page

Describe the solution you'd like
I suggest to display the hierarchical tags in a similar way to Logseq, which explicitly show the hierarchy of tags (parent / child tag). And user can also click to navigate to the parent / child tag respectively.

Describe alternatives you've considered
N/A
Additional context
N/A

Hey @andrey-jef , Thanks for your suggestion.
So the example from above should look something like, right?
Tags

example/from-frontmatter

Hey @andrey-jef , Thanks for your suggestion.
So the example from above should look something like, right?
Tags

example/from-frontmatter

Yes. Absolutely it was my imagination 😄


feat: argo sso group field change to configurable param.

Code Change For: feat: argo sso group field change to configurable param.

Replaced by #10957


Unknown Command for Java-based Airbyte Sources

When using AireByte sources like source-mssql or source-postgres an exception is raised like:

tap_airbyte.tap.AirbyteException: Discover failed with return code 1: Unknown option: discover

Doing a bit of digging, it seems like the issue is related to the command being sent to docker.
I added a print statement to the output of to_command: https://github.com/MeltanoLabs/tap-airbyte-wrapper/blob/e1349aab8c3b338babd0b1de51374733ce68ecb4/tap_airbyte/tap.py#L360
And I found that the command is the following:

['docker', 'run', '--rm', '-i', '-v', '/tmp/tmpio_5ikd9:/tmp', 'airbyte/source-mssql:latest', '--', 'discover', '--config', '/tmp/config.json']

i.e.

docker run --rm -i -v /tmp/tmpio_5ikd9:/tmp airbyte/source-mssql:latest --discover --config /tmp/config.json

Which causes the container to fail executing. Changing --discover to discover resolves the problem:

docker run --rm -i -v /tmp/tmpio_5ikd9:/tmp airbyte/source-mssql:latest discover --config /tmp/config.json

However, I do not know if this applies to all Java containers, or non-python containers.

Actually, it looks like this is a bug introduced in the latest commit and noted here:
https://github.com/MeltanoLabs/tap-airbyte-wrapper/commit/e1349aab8c3b338babd0b1de51374733ce68ecb4#commitcomment-143275245
This is a super easy fix, just need to remove the --. I created a PR for it:
https://github.com/MeltanoLabs/tap-airbyte-wrapper/pull/20

Closed with PR https://github.com/MeltanoLabs/tap-airbyte-wrapper/pull/23#issue-2484950070


WIP: feat(validation): improve error handling on invalid karma configuration.

This PR contains a:

[ ] bugfix
[x] new feature
[ ] code refactor
[ ] test update
[ ] typo fix
[ ] metadata update

Motivation / Use-Case

So this is currently a work in progress for the next branch and I'd like to get some feedback before moving forward. Using the "next" branch, I ran into a few problem with weird stack trace errors that had me jumping through hoops to figure out what was wrong, the first being that my karma configuration needed "webpack" inside of its frameworks array.
This introduces a simple validation service that can be used to ensure that these prerequisites are met and return a more helpful error message if that is not the case.
Some questions I have before going forward

Does this seem like a worthwhile endeavor?
What other pieces of validation should I consider in this module
is throwing an error the appropriate way to handle these kinds of failures? I don't really like the idea of throwing errors, but as we are running in the context of karma, I don't see any better way to stop execution and report in any kind of fail fast manner.
(more of a comment) I also have some minor refactors in here that I don't plan on including in this merge request, such as moving the hash service out, I plan to put this into another mr eventually.

Breaking Changes
I'm currently under the impression that with V5, these requirements will apply to everyone across the board, but if that is not the case, perhaps warnings might be more appropriate.

Additional Info

Another thought is that we could verify compatibility with the currently used version of karma

Another thought is that we could verify compatibility with the currently used version of karma


db2_instance_homedir not used

The values vor db2_instance_homedir and db2_instance_fenced_homedir seem to be ignored.
The db2inst1 user's homedir will be set to /home/db2inst1, regardless of these parameters.. later it will fail when it tries to register the licence...

Thanks for the feedback, internal issue ticket created to investigate.


Create dashing.io widget

i.e. https://gist.github.com/petehamilton/5494978

I think this is definitely interesting, but outside the scope of the main drone repository. If there is a community contributed widget I'm happy to link to it from the official docs.


Manage SELinux booleans

SELinux Booleans are switches that contains a set of rules to make a selective adjustment in behavior to an SELinux policy. SELinux Booleans provide an easy interface to change the behavior of an SELinux policy rule.

It would be interesting if the operator could toggle SELinux booleans combined with policies. I'm not exactly sure if we would require a new CRD for this, but the API would require an extension in any case.

I don't think we'd explicitly need a CRD for this. I think it would be appropriate to add such a configuration to the SPOD CRD instead.

I don't have a list ready from the top of my head. The tricky thing is that a given system might have many. It would surely be easier just to take a plain map and issue an event if it's not supported. But that doesn't provide for great usability, as an administrator wouldn't have a good notion of what's available. On the other hand, adding the available booleans as a status would also be quite overwhelming. I don't have a good answer for this... The best thing I can think of is having the SPOD output the booleans to a configmap, and store the config map reference in the SPOD's status.
If we merely want to have support for folks easily toggling booleans, the plain map is the best I can think of.

@JAORMX sounds good to me! I'm not sure if the list of available booleans would vary across systems, then we could not plainly sync them from a config map or status field.
Having a plain map which the SPO tries to enforce seems the best way forward, though.

@saschagrunert the booleans will vary accross systems depending on what policy they're based on. Basically, there are two main SELinux base policy groups out there:

Upstream SELinux-based
Fedora-based

They have variations in how policies are implemented and some booleans may also vary.
The ConfigMap outputting the booleans could be merely a status implementation... if nothing else.