Can't download 3,2gb model

It's currently not possible to download the 3.20GB model.
The download fails at ~95%. This reproduceable on colab and local
Downloading:  92% 2.95G/3.20G [01:54<00:05, 46.1MB/s]

Downloading:  93% 2.97G/3.20G [01:54<00:05, 48.3MB/s]

Downloading:  93% 2.98G/3.20G [01:55<00:06, 34.6MB/s]

Downloading:  94% 3.00G/3.20G [01:55<00:06, 32.4MB/s]

Downloading:  94% 3.01G/3.20G [01:57<00:10, 19.7MB/s]

Downloading:  95% 3.04G/3.20G [01:58<00:07, 21.9MB/s]

Downloading:  95% 3.05G/3.20G [01:59<00:08, 20.7MB/s]Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/urllib3/response.py", line 710, in _error_catcher
    yield
  File "/usr/local/lib/python3.10/dist-packages/urllib3/response.py", line 814, in _raw_read
    data = self._fp_read(amt) if not fp_closed else b""
  File "/usr/local/lib/python3.10/dist-packages/urllib3/response.py", line 799, in _fp_read
    return self._fp.read(amt) if amt is not None else self._fp.read()
  File "/usr/lib/python3.10/http/client.py", line 466, in read
    s = self.fp.read(amt)
  File "/usr/lib/python3.10/socket.py", line 705, in readinto
    return self._sock.recv_into(b)
ConnectionResetError: [Errno 104] Connection reset by peer

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/requests/models.py", line 816, in generate
    yield from self.raw.stream(chunk_size, decode_content=True)
  File "/usr/local/lib/python3.10/dist-packages/urllib3/response.py", line 940, in stream
    data = self.read(amt=amt, decode_content=decode_content)
  File "/usr/local/lib/python3.10/dist-packages/urllib3/response.py", line 879, in read
    data = self._raw_read(amt)
  File "/usr/local/lib/python3.10/dist-packages/urllib3/response.py", line 813, in _raw_read
    with self._error_catcher():
  File "/usr/lib/python3.10/contextlib.py", line 153, in __exit__
    self.gen.throw(typ, value, traceback)
  File "/usr/local/lib/python3.10/dist-packages/urllib3/response.py", line 727, in _error_catcher
    raise ProtocolError(f"Connection broken: {e!r}", e) from e
urllib3.exceptions.ProtocolError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer'))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/gradio/routes.py", line 488, in run_predict
    output = await app.get_blocks().process_api(
  File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 1431, in process_api
    result = await self.call_function(
  File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 1109, in call_function
    prediction = await anyio.to_thread.run_sync(
  File "/usr/local/lib/python3.10/dist-packages/anyio/to_thread.py", line 33, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
    return await future
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 807, in run
    result = context.run(func, *args)
  File "/usr/local/lib/python3.10/dist-packages/gradio/utils.py", line 706, in wrapper
    response = f(*args, **kwargs)
  File "/content/facechain/app.py", line 184, in run
    data_process_fn(instance_data_dir, True)
  File "/content/facechain/facechain/inference.py", line 23, in data_process_fn
    data_process_fn = Blipv2()
  File "/content/facechain/facechain/data_process/preprocessing.py", line 202, in __init__
    self.model = DeepDanbooru()
  File "/content/facechain/facechain/data_process/deepbooru.py", line 721, in __init__
    snapshot_path = snapshot_download(foundation_model_id, revision='v4.0')
  File "/usr/local/lib/python3.10/dist-packages/modelscope/hub/snapshot_download.py", line 140, in snapshot_download
    parallel_download(
  File "/usr/local/lib/python3.10/dist-packages/modelscope/hub/file_download.py", line 243, in parallel_download
    list(executor.map(download_part, tasks))
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 621, in result_iterator
    yield _result_or_cancel(fs.pop())
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 319, in _result_or_cancel
    return fut.result(timeout)
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 458, in result
    return self.__get_result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
  File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/modelscope/hub/file_download.py", line 203, in download_part
    for chunk in r.iter_content(chunk_size=API_FILE_DOWNLOAD_CHUNK_SIZE):
  File "/usr/local/lib/python3.10/dist-packages/requests/models.py", line 818, in generate
    raise ChunkedEncodingError(e)
requests.exceptions.ChunkedEncodingError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer'))

What is your environment please? Local machine or Colab? I met the same error on Colab occasionally.

I tried it on colab.

yeah I can reproduce that... but i cannot figure out the root cause...
can you try this:
from modelscope import snapshot_download
snapshot_download('ly261666/cv_portrait_model', revision='v4.0')

before you run python app.py?

I'm gettting another error, but I think your snippet solved the original error, thanks. :)
2023-08-18 16:38:28,438 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
load checkpoint from local path: /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
2023-08-18 16:38:28,481 - modelscope - INFO - load model done
2023-08-18 16:38:28,489 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_resnet34_face-attribute-recognition_fairface/pytorch_model.pt
2023-08-18 16:38:29,178 - modelscope - INFO - load model done
2023-08-18 16:38:33,000 - modelscope - INFO - Model revision not specified, use the latest revision: v2.5
2023-08-18 16:38:33,360 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm
2023-08-18 16:38:33,360 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm.
2023-08-18 16:38:33,362 - modelscope - WARNING - No preprocessor field found in cfg.
2023-08-18 16:38:33,362 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-08-18 16:38:33,362 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm'}. trying to build by task and model information.
2023-08-18 16:38:33,362 - modelscope - WARNING - Find task: face-2d-keypoints, model type: None. Insufficient information to build preprocessor, skip building preprocessor
2023-08-18 16:38:34,929 - modelscope - INFO - Model revision not specified, use the latest revision: v1.1
2023-08-18 16:38:35,256 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-18 16:38:35,260 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd.
2023-08-18 16:38:35,262 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-18 16:38:35,337 - mmcv - INFO - initialize PAFPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2023-08-18 16:38:35,339 - mmcv - INFO -
lateral_convs.0.conv.weight - torch.Size([16, 64, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,339 - mmcv - INFO -
lateral_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
lateral_convs.1.conv.weight - torch.Size([16, 120, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
lateral_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
lateral_convs.2.conv.weight - torch.Size([16, 160, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
lateral_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
fpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
fpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
fpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
fpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
fpn_convs.2.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
fpn_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
downsample_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
downsample_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
downsample_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
downsample_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,340 - mmcv - INFO -
pafpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,340 - mmcv - INFO -
pafpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,341 - mmcv - INFO -
pafpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

2023-08-18 16:38:35,341 - mmcv - INFO -
pafpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling `init_weights` of PAFPN

2023-08-18 16:38:35,341 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
load checkpoint from local path: /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
2023-08-18 16:38:35,405 - modelscope - INFO - load model done
2023-08-18 16:38:35,411 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm/pytorch_model.pt
2023-08-18 16:38:35,473 - modelscope - INFO - load model done
cathed for image process of 001.jpg
Error: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

cathed for image process of 002.jpg
Error: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

cathed for image process of 000.jpg
Error: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

[]
Error: result is empty.
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/gradio/routes.py", line 488, in run_predict
    output = await app.get_blocks().process_api(
  File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 1431, in process_api
    result = await self.call_function(
  File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 1109, in call_function
    prediction = await anyio.to_thread.run_sync(
  File "/usr/local/lib/python3.10/dist-packages/anyio/to_thread.py", line 33, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
    return await future
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 807, in run
    result = context.run(func, *args)
  File "/usr/local/lib/python3.10/dist-packages/gradio/utils.py", line 706, in wrapper
    response = f(*args, **kwargs)
  File "/content/facechain/app.py", line 174, in run
    data_process_fn(instance_data_dir, True)
  File "/content/facechain/facechain/inference.py", line 24, in data_process_fn
    out_json_name = data_process_fn(input_img_dir)
  File "/content/facechain/facechain/data_process/preprocessing.py", line 335, in __call__
    exit()
  File "/usr/lib/python3.10/_sitebuiltins.py", line 26, in __call__
    raise SystemExit(code)
SystemExit: None```

Error: CUDA error: no kernel image is available for execution on the device
looks like you are using an incompatible gpu device (most likely compability issue with mmcv), can you list your colab config (what device you are using)?

Same issue, here is the output from colab env information:
Sun Aug 20 10:20:41 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   35C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
Name: torch
Version: 2.0.1+cu118
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Home-page: https://pytorch.org/
Author: PyTorch Team
Author-email<EMAIL_ADDRESS>License: BSD-3
Location: /usr/local/lib/python3.10/dist-packages
Requires: filelock, jinja2, networkx, sympy, triton, typing-extensions
Required-by: accelerate, fastai, timm, torchaudio, torchdata, torchtext, torchvision, triton
Double-click (or enter) to edit
Installing mmcv-full
Several of the underlying models depend on mmcv-full, which could be tricky to install since it is environment-dependent, you may refer to mmcv's official documentation for more details..
A prebuilt package is provided here for convenience, and was verified on (cuda 12.0, torch2.0.1+cu118, Nvidia-A100 40G)
If the prebuilt package does not work on your env setup, please use the alternative installation as suggested by mmcv's official documentation
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use prebuilt mmcv-full package provided by ModelScope (verified on cuda 12.0, torch2.0.1+cu118, Nvidia-A100 40G)
!pip3 install https://modelscope.oss-cn-beijing.aliyuncs.com/packages/mmcv/mmcv_full-1.7.0-cp310-cp310-linux_x86_64.whl
alternative manual installtation. note that it may take 30+ mins for building dependencies.
!pip3 install -U openmim
!mim install mmcv-full==1.7.0
Collecting mmcv-full==1.7.0
Downloading https://modelscope.oss-cn-beijing.aliyuncs.com/packages/mmcv/mmcv_full-1.7.0-cp310-cp310-linux_x86_64.whl (30.4 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 30.4/30.4 MB 8.0 MB/s eta 0:00:00
Requirement already satisfied: addict in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.7.0) (2.4.0)
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.7.0) (1.22.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.7.0) (23.1)
Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.7.0) (9.4.0)
Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.7.0) (6.0.1)
Requirement already satisfied: yapf in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.7.0) (0.40.1)
Requirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full==1.7.0) (6.8.0)
Requirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full==1.7.0) (3.10.0)
Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full==1.7.0) (2.0.1)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->mmcv-full==1.7.0) (3.16.2)
Installing collected packages: mmcv-full
Successfully installed mmcv-full-1.7.0
Start facechain WebUI service
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from modelscope import snapshot_download
snapshot_download('ly261666/cv_portrait_model', revision='v4.0')
[10]
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pwd
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cd ..
/content/facechain
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import os
os.chdir('facechain') # Note: replace with your facechain root
print(os.getcwd())
/content/facechain
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import os
# os.chdir('facechain') # Note: replace with your facechain root
print(os.getcwd())
Click on the gradio URL to start building your FaceChain digital-twin!
!python3 app.py
2023-08-20 10:38:00,196 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found.
2023-08-20 10:38:00,199 - modelscope - INFO - TensorFlow version 2.8.0 Found.
2023-08-20 10:38:00,200 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer
2023-08-20 10:38:00,252 - modelscope - INFO - Loading done! Current index file version is 1.8.3, with md5 057eeb0e4d159929a7309966b4dfa0a6 and a total number of 895 components indexed
/content/facechain/app.py:302: GradioDeprecationWarning: The style method is deprecated. Please set these arguments in the constructor instead.
output_images = gr.Gallery(label='Output', show_label=False).style(columns=3, rows=2, height=600,
Running on local URL:  http://<IP_ADDRESS>:7860/
Running on public URL: https://2e928b95e2a67c9f30.gradio.live/
This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run gradio deploy from Terminal to deploy to Spaces (https://huggingface.co/spaces)
--------uuid:  qw
----------work_dir:  /tmp/qw/personalizaition_lora
2023-08-20 10:39:01,788 - modelscope - INFO - Use user-specified model revision: v1.0.0
2023-08-20 10:39:03,400 - modelscope - INFO - Use user-specified model revision: v1.0.0
2023-08-20 10:39:04,908 - modelscope - INFO - Use user-specified model revision: v1.0.0
2023-08-20 10:39:06,414 - modelscope - INFO - Use user-specified model revision: v1.0.0
2023-08-20 10:39:08,866 - modelscope - INFO - Use user-specified model revision: v4.0
2023-08-20 10:39:11,267 - modelscope - INFO - Model revision not specified, use the latest revision: v1.0.0
2023-08-20 10:39:12,112 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_unet_skin-retouching
2023-08-20 10:39:12,112 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_unet_skin-retouching.
2023-08-20 10:39:12,114 - modelscope - WARNING - No preprocessor field found in cfg.
2023-08-20 10:39:12,114 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-08-20 10:39:12,114 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_unet_skin-retouching'}. trying to build by task and model information.
2023-08-20 10:39:12,115 - modelscope - WARNING - Find task: skin-retouching, model type: None. Insufficient information to build preprocessor, skip building preprocessor
2023-08-20 10:39:16,992 - modelscope - INFO - Model revision not specified, use the latest revision: v2.0.2
2023-08-20 10:39:18,735 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_resnet50_face-detection_retinaface
2023-08-20 10:39:18,735 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_resnet50_face-detection_retinaface.
2023-08-20 10:39:18,737 - modelscope - WARNING - No preprocessor field found in cfg.
2023-08-20 10:39:18,737 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-08-20 10:39:18,737 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_resnet50_face-detection_retinaface'}. trying to build by task and model information.
2023-08-20 10:39:18,737 - modelscope - WARNING - Find task: face-detection, model type: None. Insufficient information to build preprocessor, skip building preprocessor
2023-08-20 10:39:18,738 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_resnet50_face-detection_retinaface/pytorch_model.pt
2023-08-20 10:39:19,538 - modelscope - INFO - load model done
WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/modelscope/pipelines/cv/skin_retouching_pipeline.py:84: FastGFile.init (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.gfile.GFile.
2023-08-20 10:39:22,925 - modelscope - INFO - Model revision not specified, use the latest revision: v1.1
2023-08-20 10:39:23,261 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-20 10:39:23,261 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd.
2023-08-20 10:39:23,262 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-20 10:39:26,387 - mmcv - INFO - initialize PAFPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2023-08-20 10:39:26,389 - mmcv - INFO -
lateral_convs.0.conv.weight - torch.Size([16, 64, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,389 - mmcv - INFO -
lateral_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,389 - mmcv - INFO -
lateral_convs.1.conv.weight - torch.Size([16, 120, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,389 - mmcv - INFO -
lateral_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,389 - mmcv - INFO -
lateral_convs.2.conv.weight - torch.Size([16, 160, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,389 - mmcv - INFO -
lateral_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,389 - mmcv - INFO -
fpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,389 - mmcv - INFO -
fpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,389 - mmcv - INFO -
fpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,389 - mmcv - INFO -
fpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,389 - mmcv - INFO -
fpn_convs.2.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,389 - mmcv - INFO -
fpn_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,389 - mmcv - INFO -
downsample_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,389 - mmcv - INFO -
downsample_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,389 - mmcv - INFO -
downsample_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,390 - mmcv - INFO -
downsample_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,390 - mmcv - INFO -
pafpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,390 - mmcv - INFO -
pafpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,390 - mmcv - INFO -
pafpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:26,390 - mmcv - INFO -
pafpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:26,390 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
load checkpoint from local path: /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
2023-08-20 10:39:26,428 - modelscope - INFO - load model done
2023-08-20 10:39:27,465 - modelscope - INFO - Model revision not specified, use the latest revision: v1.0.1
2023-08-20 10:39:28,013 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_resnet101_image-multiple-human-parsing
2023-08-20 10:39:28,013 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_resnet101_image-multiple-human-parsing.
2023-08-20 10:39:28,014 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_resnet101_image-multiple-human-parsing
2023-08-20 10:39:28,588 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_resnet101_image-multiple-human-parsing/pytorch_model.pt
2023-08-20 10:39:30,658 - modelscope - INFO - criterion.empty_weight doesn't exist in current model, skip loading.
2023-08-20 10:39:30,721 - modelscope - INFO - load model done
2023-08-20 10:39:30,725 - modelscope - WARNING - No preprocessor field found in cfg.
2023-08-20 10:39:30,725 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-08-20 10:39:30,725 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_resnet101_image-multiple-human-parsing'}. trying to build by task and model information.
2023-08-20 10:39:30,725 - modelscope - WARNING - No preprocessor key ('m2fp', 'image-segmentation') found in PREPROCESSOR_MAP, skip building preprocessor.
2023-08-20 10:39:32,270 - modelscope - INFO - Model revision not specified, use the latest revision: v2.0.2
2023-08-20 10:39:33,779 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_resnet34_face-attribute-recognition_fairface
2023-08-20 10:39:33,779 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_resnet34_face-attribute-recognition_fairface.
2023-08-20 10:39:33,781 - modelscope - WARNING - No preprocessor field found in cfg.
2023-08-20 10:39:33,782 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-08-20 10:39:33,782 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_resnet34_face-attribute-recognition_fairface'}. trying to build by task and model information.
2023-08-20 10:39:33,782 - modelscope - WARNING - Find task: face-attribute-recognition, model type: None. Insufficient information to build preprocessor, skip building preprocessor
2023-08-20 10:39:34,824 - modelscope - INFO - Model revision not specified, use the latest revision: v1.1
2023-08-20 10:39:35,166 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-20 10:39:35,166 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd.
2023-08-20 10:39:35,167 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-20 10:39:35,223 - mmcv - INFO - initialize PAFPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2023-08-20 10:39:35,225 - mmcv - INFO -
lateral_convs.0.conv.weight - torch.Size([16, 64, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,226 - mmcv - INFO -
lateral_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,226 - mmcv - INFO -
lateral_convs.1.conv.weight - torch.Size([16, 120, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,226 - mmcv - INFO -
lateral_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,226 - mmcv - INFO -
lateral_convs.2.conv.weight - torch.Size([16, 160, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,226 - mmcv - INFO -
lateral_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,226 - mmcv - INFO -
fpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,226 - mmcv - INFO -
fpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,226 - mmcv - INFO -
fpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,226 - mmcv - INFO -
fpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,226 - mmcv - INFO -
fpn_convs.2.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,226 - mmcv - INFO -
fpn_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,226 - mmcv - INFO -
downsample_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,226 - mmcv - INFO -
downsample_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,227 - mmcv - INFO -
downsample_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,227 - mmcv - INFO -
downsample_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,227 - mmcv - INFO -
pafpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,227 - mmcv - INFO -
pafpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,227 - mmcv - INFO -
pafpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:35,227 - mmcv - INFO -
pafpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:35,228 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
load checkpoint from local path: /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
2023-08-20 10:39:35,291 - modelscope - INFO - load model done
2023-08-20 10:39:35,297 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_resnet34_face-attribute-recognition_fairface/pytorch_model.pt
2023-08-20 10:39:35,862 - modelscope - INFO - load model done
2023-08-20 10:39:36,995 - modelscope - INFO - Model revision not specified, use the latest revision: v2.5
2023-08-20 10:39:37,360 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm
2023-08-20 10:39:37,360 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm.
2023-08-20 10:39:37,363 - modelscope - WARNING - No preprocessor field found in cfg.
2023-08-20 10:39:37,363 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-08-20 10:39:37,363 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm'}. trying to build by task and model information.
2023-08-20 10:39:37,363 - modelscope - WARNING - Find task: face-2d-keypoints, model type: None. Insufficient information to build preprocessor, skip building preprocessor
2023-08-20 10:39:38,838 - modelscope - INFO - Model revision not specified, use the latest revision: v1.1
2023-08-20 10:39:39,175 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-20 10:39:39,175 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd.
2023-08-20 10:39:39,177 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd
2023-08-20 10:39:39,233 - mmcv - INFO - initialize PAFPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2023-08-20 10:39:39,234 - mmcv - INFO -
lateral_convs.0.conv.weight - torch.Size([16, 64, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
lateral_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,235 - mmcv - INFO -
lateral_convs.1.conv.weight - torch.Size([16, 120, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
lateral_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,235 - mmcv - INFO -
lateral_convs.2.conv.weight - torch.Size([16, 160, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
lateral_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,235 - mmcv - INFO -
fpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
fpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,235 - mmcv - INFO -
fpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
fpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,235 - mmcv - INFO -
fpn_convs.2.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
fpn_convs.2.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,235 - mmcv - INFO -
downsample_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
downsample_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,235 - mmcv - INFO -
downsample_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,235 - mmcv - INFO -
downsample_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,236 - mmcv - INFO -
pafpn_convs.0.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,236 - mmcv - INFO -
pafpn_convs.0.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,236 - mmcv - INFO -
pafpn_convs.1.conv.weight - torch.Size([16, 16, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
2023-08-20 10:39:39,236 - mmcv - INFO -
pafpn_convs.1.conv.bias - torch.Size([16]):
The value is the same before and after calling init_weights of PAFPN
2023-08-20 10:39:39,237 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
load checkpoint from local path: /root/.cache/modelscope/hub/damo/cv_ddsar_face-detection_iclr23-damofd/pytorch_model.pt
2023-08-20 10:39:39,309 - modelscope - INFO - load model done
2023-08-20 10:39:39,318 - modelscope - INFO - loading model from /root/.cache/modelscope/hub/damo/cv_manual_facial-landmark-confidence_flcm/pytorch_model.pt
2023-08-20 10:39:39,799 - modelscope - INFO - load model done
cathed for image process of 003.jpg
Error: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
cathed for image process of 001.jpg
Error: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
cathed for image process of 002.jpg
Error: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
cathed for image process of 000.jpg
Error: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
[]
Error: result is empty.
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/gradio/routes.py", line 488, in run_predict
output = await app.get_blocks().process_api(
File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 1431, in process_api
result = await self.call_function(
File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 1109, in call_function
prediction = await anyio.to_thread.run_sync(
File "/usr/local/lib/python3.10/dist-packages/anyio/to_thread.py", line 33, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
return await future
File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 807, in run
result = context.run(func, *args)
File "/usr/local/lib/python3.10/dist-packages/gradio/utils.py", line 706, in wrapper
response = f(*args, **kwargs)
File "/content/facechain/app.py", line 174, in run
data_process_fn(instance_data_dir, True)
File "/content/facechain/facechain/inference.py", line 24, in data_process_fn
out_json_name = data_process_fn(input_img_dir)
File "/content/facechain/facechain/data_process/preprocessing.py", line 335, in call
exit()
File "/usr/lib/python3.10/_sitebuiltins.py", line 26, in call
raise SystemExit(code)
SystemExit: None
Keyboard interruption in main thread... closing server.
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 2198, in block_thread
time.sleep(0.1)
KeyboardInterrupt
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/content/facechain/app.py", line 318, in
demo.queue(status_update_rate=1).launch(share=True)
File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 2114, in launch
self.block_thread()
File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 2202, in block_thread
self.server.close()
File "/usr/local/lib/python3.10/dist-packages/gradio/networking.py", line 49, in close
self.thread.join()
File "/usr/lib/python3.10/threading.py", line 1096, in join
self._wait_for_tstate_lock()
File "/usr/lib/python3.10/threading.py", line 1116, in _wait_for_tstate_lock
if lock.acquire(block, timeout):
KeyboardInterrupt
Killing tunnel <IP_ADDRESS>:7860 <> https://2e928b95e2a67c9f30.gradio.live/
^C
[17]
0s
1
2
import torch
[25]
1
torch.cuda.cudart(.cudaMemGetInfo(torch.device(type='cuda',index=0))
[14]
0s
1
!nvidia-smi
Sun Aug 20 10:42:03 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   49C    P0    28W /  70W |      0MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
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System RAM
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Disk
41.0 / 78.2 GB

yeah I can reproduce that... but i cannot figure out the root cause... can you try this:
from modelscope import snapshot_download
snapshot_download('ly261666/cv_portrait_model', revision='v4.0')

before you run python app.py?

I'm afraid this also fails sometimes. I seems like the server the models are hosted on are not too reliable.

currently ModelScope models are hosted on servers in Asia and North America. downloading models from other regions may be less stable especially when the model files are large.

Reliability aside, it seems that modelscope may have auth for anonymous connections that appears (from my experience) to expire around the 2minute mark.
https://github.com/modelscope/modelscope/issues/51#issuecomment-1365844229
I was able to make iterative progress by 'reruning' the workaround defined in https://github.com/modelscope/facechain/issues/52#issuecomment-1684122345
until all data was downloaded.
Could a more reasonble solution be to obtain an API key and assert it, as suggested in the first issue?

@yingdachen I've same issue on Colab. Did you solve that?

And when I try to start training, got this:


Add Liveness Probe to Strider

Issue: When a pod is moved or restarted, it doesn’t always come back with the data it needs.
Solution: Add a liveness probe to Strider.

We have the helm chart configured to do liveliness probe on the deployment specs. The probe sends out post request using the example query provided on strider's query endpoint. the query is configured to timeout in 10 seconds and if it fails more than 5 times strider pod is restarted.
For KP registry we have probe setup to send GET requests to '/kps' endpoint similarly failures above 5 will trigger a restart .
Aside from this we have a cron job pod that runs every minute to refresh kp, this will ensure that we don't have to manually make post request to refresh the kps.
All of these options are configurable if we need to change them.

@YaphetKG is this done?

@patrickkwang , yes


ImportError for mxnet: cannot import name libinfo

Environment info
Operating System: windows 7, 64bit
MXNet version:7.0 or 5.0 ( tried several different versions for cpu from https://github.com/dmlc/mxnet/releases
Python version and distribution: WinPython-64bit-<IP_ADDRESS>
At first when I installed the mxnet, it worked and the second time I tried it gave the following error and I have tried to delete and install the mxnet package several times and it keeps giving the following error :
Error Message:
import mxnet
WindowsError                              Traceback (most recent call last)
 in ()
----> 1 import mxnet
C:\Localdata\winpython\WinPython-64bit-<IP_ADDRESS>\python-2.7.10.amd64\lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet_init_.py in ()
5
6 from .context import Context, current_context, cpu, gpu
----> 7 from .base import MXNetError
8 from . import base
9 from . import ndarray
C:\Localdata\winpython\WinPython-64bit-<IP_ADDRESS>\python-2.7.10.amd64\lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\base.py in ()
41 version = libinfo.version
42 # library instance of mxnet
---> 43 _LIB = _load_lib()
44
45 # type definitions
C:\Localdata\winpython\WinPython-64bit-<IP_ADDRESS>\python-2.7.10.amd64\lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\base.py in _load_lib()
33     """Load libary by searching possible path."""
34     lib_path = libinfo.find_lib_path()
---> 35     lib = ctypes.cdll.LoadLibrary(lib_path[0])
36     # DMatrix functions
37     lib.MXGetLastError.restype = ctypes.c_char_p
C:\Localdata\winpython\WinPython-64bit-<IP_ADDRESS>\python-2.7.10.amd64\lib\ctypes_init_.pyc in LoadLibrary(self, name)
441
442     def LoadLibrary(self, name):
--> 443         return self._dlltype(name)
444
445 cdll = LibraryLoader(CDLL)
C:\Localdata\winpython\WinPython-64bit-<IP_ADDRESS>\python-2.7.10.amd64\lib\ctypes_init_.pyc in init(self, name, mode, handle, use_errno, use_last_error)
363
364         if handle is None:
--> 365             self._handle = _dlopen(self._name, mode)
366         else:
367             self._handle = handle
WindowsError: [Error 126] The specified module could not be found
import mxnet
ImportError                               Traceback (most recent call last)
 in ()
----> 1 import mxnet
C:\Localdata\winpython\WinPython-64bit-<IP_ADDRESS>\python-2.7.10.amd64\lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet_init_.py in ()
5
6 from .context import Context, current_context, cpu, gpu
----> 7 from .base import MXNetError
8 from . import base
9 from . import ndarray
C:\Localdata\winpython\WinPython-64bit-<IP_ADDRESS>\python-2.7.10.amd64\lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\base.py in ()
8 import numpy as np
9 import atexit
---> 10 from . import libinfo
11
12 all = ['MXNetError']
ImportError: cannot import name libinfo

Hey, thanks for bringing this up. Could you elaborate on the installation process, so that I can try to reproduce the problem? To start, I'm interested in:

the exact version of mxnet you tried, and the instructions you followed for installation. just one version is sufficient. a link would be perfect.
the link/instructions on getting WinPython-64bit-<IP_ADDRESS>

The first mxnet versions I tried was: windows binary build 20160531_win10_x64_cpu.7z
for installation after I extracted all the files in one folder, I tried to install it using the "WinPython Command Prompt": ...setup.py install. this worked but then I got that error when running in Jupyter notebook.
https://sourceforge.net/projects/winpython/files/WinPython_2.7/<IP_ADDRESS>/

how can I disable cython?

@gisoo2017 Have you solved this problem?I got same problem. #4794

yes actually you need to check for the mxnet path in Environment variables of your PC and give the correct path there. In my case it was going wrong there

@gisoo2017 how to set a correct path?

just copy and paste the location where you stored the mxnet folder from the window explorer in the Environment variables

i put "C:\Anaconda2\lib\site-packages\mxnet-0.9.3-py2.7.egg\mxnet"in Environment variables Path,but still not work

maybe try to run "setupenv" directly! and then check the path?

Did you solve this problem and how to solve it ? @gisoo2017 @QuantumLiu

One of the reason for this that you run the python command in the exact directory you install MXNet.
Say that you clone the MXNet project into /home/user1/incubator-mxnet, you build the mxnet library then go to the python directory to run install command python setup.py install --user.
After that , please exit the python directory before you run python to test mxnet.
As there is a directory named mxnet in the incubator-mxnet/python directory, if you run python in directory incubator-mxnet/python and then import mxnet as mx, python will try to import the directory instead of the mxnet lib.
In conclusion, do not start python shell in the incubator-mxnet/python directory, or else you will get the cannot import name libinfo  error message.
Please create a new issue to report it if you didn't run python shell in incubator-mxnet/python  while you get the same error message cannot import name libinfo

Shouldn't be a problem any more in the newer versions. Feel free to reopen if issue persists

Still having this problem.
(C:\Program Files\Anaconda2) C:\Users\Fred>activate mxnetenv
(mxnetenv) C:\Users\Fred>python
Python 2.7.13 |Continuum Analytics, Inc.| (default, May 11 2017, 13:17:26) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org

import mxnet as mx
Traceback (most recent call last):
File "", line 1, in
File "C:\Program Files\Anaconda2\envs\mxnetenv\lib\site-packages\mxnet-0.11.1-py2.7.egg\mxnet_init_.py", line 25, in
from .base import MXNetError
File "C:\Program Files\Anaconda2\envs\mxnetenv\lib\site-packages\mxnet-0.11.1-py2.7.egg\mxnet\base.py", line 110, in
_LIB = _load_lib()
File "C:\Program Files\Anaconda2\envs\mxnetenv\lib\site-packages\mxnet-0.11.1-py2.7.egg\mxnet\base.py", line 102, in load_lib
lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
File "C:\Program Files\Anaconda2\envs\mxnetenv\lib\ctypes_init.py", line 362, in init
self._handle = _dlopen(self._name, mode)
WindowsError: [Error 126] The specified module could not be found

Used yajidesign release build 'prebuildbase_win10_x64_vc14_v2' and '20170926_mxnet_x64_vc14_gpu' in combination with Anaconda2 / Python 2.7 on Windows 10, 64 bit Professional. As the prebuild comes with cudnn64_7.dll I did not download it as suggested in the readme.txt.
Tried solutions suggested above and in other thread, but getting still stuck with this error message. Package seems to be installed fine in the environment. Checked the PATH variable, as well as tried to set it explicitly in Python before import mxnet.
Any suggestion is welcome.

@yajiedesign

Did we fixed this  ?

@Bumblebee1964 do you install cuda 8.0?

yes I did install cuda 8.0.
I found a workaround based on a post on issue https://github.com/apache/incubator-mxnet/issues/7362 by using: pip install -U --pre mxnet-cu80

@Bumblebee1964 ,yes it is simple way,this two way (pip and my rep) use same binary.if you don't want download 200mb+ every time,you can use my rep update it.


Warn about duplicate identifiers

[x] Images
[x] Storyboards
[x] Storyboard segues
[x] Storyboard viewControllers
[x] Nibs
[x] Reusable cells

For images this will be fixed with PR https://github.com/mac-cain13/R.swift/pull/44

I think duplicate reusable cells warning may be valuable - as there is no real requirement for cells to be unique across project. I have couple of default "Cell" identifiers which result in uncompilable code.
I fixed it on private branch by de-duplication of reusable cells variables, which should(?) do no harm.

@tomekc Thanks for replying!
Yes, true, it's valid and can be used. I would consider it a bad practice, it's better to explicitly and uniquely name your cells to make them easily reusable and prevent confusion. My opinion is that R.swift should warn you about it and just skip those cells in the struct. Of course it should never generate uncompilable code.

Segues should be changed so they are an extension on the viewcontroller and not global things. This makes duplicates less likely and if in that situation there are duplicates they really are a problem so a warning is appropriate then.
This also would enable us to make typed segues in the end.

Duplicate viewController identifiers give compile errors from Apple's storyboard compiler. So this is a lower priority thing. Won't fix it now, maybe sometime in the future.
It's not a problem that R.swift generated uncompilable code from such a storyboard since the storyboard itself also won't compile.

True, but it is a good practice to have multiple storyboards in larger projects. R.swift bundles all identifiers from all storyboards. You do not really have to use globally unique cell or segue identifiers across all storyboards, do you?

Issue #71 proposes to scope segues to their source ViewController, so that would fix that problem.
Cells in different storyboards don't really have to be unique that's correct, outside storyboards (in nib files) it is good practice to have unique identifiers. I think it's not very user friendly/handy to namespace the reuseIdentifiers.
What would be your ideal solution?

I think if the reuseIdentifiers are in different storyboards you shouldn't flag a warning. That would be ideal.
I use the slightly juvenile reuseIdentifier lol for my UITableViews that only have one reuseIdentifier. Would it be possible to not flag a warning if the reuseIdentifiers are across separate storyboards?

I think the reuseIdentifiers should be unique only for a particular UIViewController. A storyboard can have multiple view controllers and different view controllers can have same reuseIdentifiers. That should work without giving any warnings.


not running

This is related to your system configurations. Please google how to enable running scripts on your windows machine to be able to run it.


omitnull IsSet != !IsUnset

Omitnull has an issue where IsSet != !IsUnset because when the null state enters the equation they can both be false which is unexpected behavior from several developers and even Chat GPT.
The solution is:

⚠ Breaking change: IsSet now returns true for StateSet && StateNull
Add a new function to omitnull: IsSetNonNull that retains the current functionality of IsSet (returns true if StateSet, false otherwise)

I was wondering if there are any plans to merge the related pull request soon?
After the last issue I wrote #7 I am in the middle of removing all the pointers, but currently I have something like this:
var on omitnull.Val... // HTTP request uses omitnull for optional nullable fields
var n null.Val... // DB uses only null

if on.Msg.IsNull() || on.Msg.IsSet() {
	n.Msg.SetPtr(on.Msg.MustPtr())
}

It is working, but I would really like to just use IsSet()

I can clearly see, why you hesitate to merge the pull request. After using the pull request in my fork for a day, I noticed that I regularly have to think about the difference between omit, null and omitnull when using IsSet(). So I thought a little bit about the problem and tried to write it down:
current implemenation:

package
func
returns
default state

null
IsSet()
!null
null

omit
IsSet()
!omitted
omitted

omitnull
IsSet()
!omitted && !null
omitted

pull request changes:

package
func
returns
default state

omitnull
IsSet()
!omitted
omitted

omitnull
IsSetNotNull()
!omitted && !null
omitted

I think the problem is null.IsSet() which hat nothing to do with an omitted state. So if the idea is to have a IsSetNotNull() function then null.IsSet() should be renamed to null.IsSetNotNull() to make clear that it checks the null-state, because IMHO a null.IsSet() is always true, as it can not be omitted.
IsSet()

should check if the value is !omitted

IsSetNotNull()

should check if the value is !omitted && !null

Alternative ideas

rename IsSetNotNull() to HasValue()

possible changes

todo
package
func
returns
default state
comment

remove
null
IsSet()
!null
null
is always set / can not be omitted

omit
IsSet()
!omitted
omitted

change
omitnull
IsSet()
!omitted
omitted
should just check the omitted state

add
null
IsSetNotNull()
!omitted && !null
null
consistent with omitnull

add
omitnull
IsSetNotNull()
!omitted && !null
omitted


Read input from stdin

Read input from stdin - I think this is the absolute primary usecase for tail in general.

You're right, planning to support all the tail functionalities


Label Attribute Class

Describe the bug
Custom class in field Label.
To Reproduce
Steps to reproduce the behavior:

add field Label
Set method class

Expected behavior
A clear and concise description of what you expected to happen.
Additional context
Label::make('')
    ->class('text-black')
    ->title('My Title'),

I don't understand what's the issue here. Could you provide more information, please?

As far as I understand, this is a request for the function of specifying the css class for this field


Remove confusing else statement in cache.go

There is no need for the else block here because the if err is nil, it always returns in the if block.

/assign @efiacor @kushnaidu @Catalin-Stratulat-Ericsson

/retest

Looks good to me

/approve
/lgtm


generic proxy: change the encoding interface to sync mode

Commit Message: generic proxy: change the encode interface to sync mode
Additional Description:
In the initial design of generic codec, full async interface is used for both encoding and decoding. it make that offloading encoding and decoding to other hardware is possible. But, after the practice in the production env, we found the offloading just a illusion.
The async decoding is just well. But the async encoding bring nothing except complexity. This PR removed the async encoding and make it to use sync interface.
Risk Level: low.
Testing: unit, integration
Docs Changes: n/a.
Release Notes: n/a.
Platform Specific Features: n/a.

friendly ping for a blind approve for this contrib only change @jmarantz 🌷

@jmarantz can you PTAL?


Does it supports UWP?

I found answer

Hopefully soon! https://github.com/chrfalch/NControl/pull/71


another TiKV bug for the trophy case :)

Cc @brson thanks for the ping!

@bors r+

:pushpin: Commit 0835ac6ec3d071440cc4fa7490a884615dad862d has been approved by RalfJung

:hourglass: Testing commit 0835ac6ec3d071440cc4fa7490a884615dad862d with merge 158639c98566c82e86317e992e0eda68620885e8...

:sunny: Test successful - checks-actions
Approved by: RalfJung
Pushing 158639c98566c82e86317e992e0eda68620885e8 to master...


Add a variable for default.replication.factor within server.properties

Would there be any interest in adding default.replication.factor variable for Kafka Broker (server.properties file)?
I'm willing to submit a PR (change seems to be fairly simple) if you find it useful. Thanks!

Alright, seems I've missed that somehow. That covers the case, thank you!


Rewriting the fors to find the files

Codecov Report

Merging #247 into master will increase coverage by 0.06%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #247      +/-   ##
==========================================
+ Coverage   97.42%   97.49%   +0.06%
==========================================
  Files          79       79
  Lines        3070     3307     +237
  Branches       18       36      +18
==========================================
+ Hits         2991     3224     +233
- Misses         76       78       +2
- Partials        3        5       +2

Impacted Files
Coverage Δ

...mands/compiler/etherlime-contract-sources/index.js
100% <100%> (ø)
:arrow_up:

...ckages/etherlime/cli-commands/compiler/compiler.js
98.41% <100%> (-0.08%)
:arrow_down:

packages/etherlime/cli-commands/commands.js
98.58% <0%> (-0.34%)
:arrow_down:

...src/deployed-contract/etherlime-ganache-wrapper.ts
100% <0%> (ø)
:arrow_up:

...etherlime-lib/src/deployer/private-key-deployer.ts
100% <0%> (ø)
:arrow_up:

...src/deployed-contract/deployed-contract-wrapper.ts
100% <0%> (ø)
:arrow_up:

...yer/infura-deployer/infura-private-key-deployer.ts
100% <0%> (ø)
:arrow_up:

...etherlime-lib/src/deployed-contract/contract-at.ts
100% <0%> (ø)
:arrow_up:

...r/jsonrpc-deployer/jsonrpc-private-key-deployer.ts
100% <0%> (ø)
:arrow_up:

packages/etherlime/cli-commands/ganache/ganache.js
100% <0%> (ø)
:arrow_up:

... and 1 more

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[10.x] Artisan Test command missing highlighting

Laravel Version: 10.0
PHP Version: 8.2
Database Driver & Version: MySQL 8.0.30
OS: Windows 11

Description:
After upgrading to Laravel v10.0, the artisan test command doesn't have the highlighting around the "Pass" / "Failed" / "Warn" message next to each test's path as it did as recent as v9.51
Steps To Reproduce:

Create a Laravel 10 project (or upgrade or existing project to Laravel 10)
Run php artisan test

Realized this was actually an issue with the recommended update of nunomaduro/collision to^7.0 from the docs and in the skeleton. As a result, I opened an issue on that repo instead for this: https://github.com/nunomaduro/collision/issues/249


Prepare for 0.12.0-rc2

Which issue does this PR close?

Closes https://github.com/apache/arrow-ballista/issues/926
Rationale for this change

0.12.0-rc1 failed because we are pinned to a non-release version of DataFusion. We will need to wait for the imminent 35.0.0 release.
What changes are included in this PR?

Pin to newer version. I will update this PR once DF 35.0.0 is released.
Are there any user-facing changes?

@avantgardnerio @Dandandan I plan on merging this when CI passes and will then cut a new RC


OCPBUGS-33759: FRR: squash advertisements with the same prefix

Passing multiple advertisements with the same prefix to the template causes duplicate entries. This happens for example when the shared ip annotation is used with the services.
Since a given advertisement has the related peers as fields, the right place to squash is when we manage the advertisements related to a given neighbor.

/jira cherry-pick OCPBUGS-33365

/jira refresh

/label backport-risk-assessed

/lgtm

/retitle [release-4.13] OCPBUGS-33759: FRR: squash advertisements with the same prefix

/label cherry-pick-approved

/cherry-pick release-4.12


Improvement - Smart Automation

Hi there,

could there be a smart URL detection mode to be enabled that grabs supported URLs from clipboard.
this would save the paste as long as the smart mode is on, there won’t be a dialog to enter multiple URLs,

options within smart mode
a. if option b and c are not checked. the user gets prompted, so just auto grab the video
b. option for video, or just music. each with a default profile. i guess those would be the most used.
c. a specific path for music and a separate for videos.
changes in UI

Add an Icon for smart mode.
Add an icon for auto music
Add an icon for auto video

Thankyou

Hi @JPluess,
just trying to reiterate to make sure I understood your suggestion. So when enabled, this "smart mode" should do the following:

in the most basic form, it should automatically grab video urls from the user's clipboard and fetch them into the UI (a)
when a "download profile" is configured, it should not only fetch the video into the UI, but also directly download it with the configured options (b) & (c)

Please let me know if this is what you thought of. Anyway, I'll look into implementing something in this direction :)

Perfect 😀
09.01.2022 00:07:51 alxnull @.***>:

Hi @JPluess[https://github.com/JPluess],
just trying to reiterate to make sure I understood your suggestion. So when enabled, this "smart mode" should do the following:

in the most basic form, it should automatically grab video urls from the user's clipboard and fetch them into the UI (a)

when a "download profile" is configured, it should not only fetch the video into the UI, but also directly download it with the configured options (b) & (c)

Please let me know if this is what you thought of. Anyway, I'll look into implementing something in this direction :)
—
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Hey, I released part of this in version 0.6.0. When the automation mode is activated, e.g. via the button with the lightning in the toolbar, Vividl will automatically grab urls from the clipboard and add them to the video list. Optionally, the videos can be directly downloaded (configure under Settings -> Advanced.
I'll look into adding support for download profiles in the near future. These then also should be usable with this automation mode.


More informative name for "Aggregate All Rows" option

It may be helpful for "Aggregate All Rows" to describe better that recycled/archived items will be included in the rollup. I made the assumption this option just meant a LIMIT won't be applied to the query. After doing a mass delete of records and re-calculating a rolloup field, I got pretty stuck on the rollup producing a result based on deleted records. It could just be me, but I think it could be more obvious in the description, maybe something like "Aggregate All Rows (including recycled/archived records)".

This would be an awesome improvement for our documentation. We'd love to have your contributions on our newer documentation repository.
https://github.com/SFDO-Community-Sprints/DLRS-Documentation

This should be improved in the new LWC wizard released as part of v2.21.


View Create Title: Get Reviewer

What are Reviewers? What is the name of the entity? Users?
(p. 52)

Reviewers are Users linked in the field allocatedTo of a ReviewRequest. Those in turn will be connected to the title itself via a one-to-many association. This function will generate a new ReviewRequest allocated to a User with an optional note.

So a reviewer is a user.

like #126


NTLM V2 Not Working

Hi,
I am trying to use node-ntlm-client  for consuming onpremise SharePoint instance which has NTLM V2 authentication.
I have configured SharePoint system to use NTLM V2 by following setup-ntlm-v2.
I have also checked registry value as mentioned below: HKLM\SYSTEM\CurrentControlSet\Control\Lsa\LmCompatibilityLevel to 5.
However, when I am trying to consume REST APIs it throws me below error:
The server didnt respond properly: Error: Invalid message signature: NTLM
at Object.decodeType2Message (....\node-ntlm-client\lib\ntlm.js:85:9)

Can you share some samples code how can I use this node module as those are missing in readme.

Possibly fixed in https://github.com/gautamsi/node-ntlm-client/pull/1


ApplyTheme Updates – convert older changeCSS JS to CSS variables part 1

Summary
This PR consolidates 10 related branches that each deal with converting older changeCSS theming applied using JS to newer CSS Properties (variables).
This PR also changes from using opacity baked into CSS Variables to using RGBA. For example:
background: var(--center-channel-color-08);

changes to
background: rgba(var(--center-channel-color-rgb), 0.08);

It is challenging to deal with each branch separately as there is crossover, and as such, each successive PR is based on the previous so I've elected to consolidate work to date into a single PR.
To help with reviewing/testing, I'm going to list each commit grouped by theme variable name so related changes can be more easily seen in context given this consolidated PR shows all compounded changes together. Additionally, the git description for each commit indicates what is affected within the webapp.
Commits:

Sidebar BG: https://github.com/mattermost/mattermost-webapp/commit/eb797a6107eb39a36df468deb09ee37efcdcf2b2
Sidebar Text: https://github.com/mattermost/mattermost-webapp/commit/826c5954c3703e3ef994c7a428337b2924630920
Sidebar Text Hover GB: https://github.com/mattermost/mattermost-webapp/commit/a7ab7ddbf869ec92b7d9e9339cd7191139a75015
Sidebar Text Active Color: https://github.com/mattermost/mattermost-webapp/commit/a4b61cdf94326ae5d5055d52225ff3821c5dc919
Remove Change Opacity:

https://github.com/mattermost/mattermost-webapp/commit/589c10618eda90bada63ca45a34ab2aeff5c32a9
https://github.com/mattermost/mattermost-webapp/commit/2801e3f3728218a2cc8609de31bd8fdcaff7b6ea
https://github.com/mattermost/mattermost-webapp/commit/50ba7dfa6c209d9d2341a0a679a9e1dda7262f24

Sidebar Header BG: https://github.com/mattermost/mattermost-webapp/commit/0e512fc3784606aae63f842da6e49ef7a2d65138
Sidebar Header Text Color: https://github.com/mattermost/mattermost-webapp/commit/8011f940edb7985745c048b3d56698a55cfc6412
DND Indicator: https://github.com/mattermost/mattermost-webapp/commit/f8406061084e27f2c4f5eb1de745b8f28b14a753
Mention BG: https://github.com/mattermost/mattermost-webapp/commit/f30e1cb830b982dbe6450db2e2c89315b5425701
Mention Color: https://github.com/mattermost/mattermost-webapp/commit/1bd3463b66a74626174e2178a85cbf4c80a3f8d5

Please reach out to me if further help is needed to review/test as I know the old CSS is not the easiest to navigate!
Edits:

added baked-in opacity variables back to ensure backwards compatibility for plugins etc.
removed what looked to be unused legacy compatibility code (thanks @hmhealey!)

/release-note-none

@michaelgamble, @jgilliam17, test servers are back up and running! 🎉

Thanks @michaelgamble, good catch! Looks like a change from another PR that was merged to master recently messed up the css for that one. Should be fixed now.

Try now? Same reason, different location ... 🤷♂
FWIW, I'm not updating that to use the newer rgba(var(...), ...) approach, just leaving it as is as that is beyond the scope of this PR.

Thanks @deanwhillier
Overall looks good, seeing few smaller issues

Legacy sidebar dm icons fill color in light themes is off

Dark team system console hamburger menu dropdown incorrectly themed

DND background color, shows black or white on the main channel

Theme on Add member modal dropdown (this was recently fixed on master)

Note sure if this is related to your changes, but About modal header border is missing when accessing system console from dark team and opening about modal there

Thanks @jgilliam17!

status icon colours should be fixed now
I merged master to inherit the other fix
system console bugs are likely in master (I verified that menu one is at least), can you please double check on community and file separate tickets for the messaging platform if so?


[feature request] create nesting relationship when pasting

Archi prompts to create a nesting relationship when one element is dragged on top of another but not when it is pasted there which allows one to create a diagram which visually implies a relationship but actually doesn't have one. I'd like it to prompt for nesting when pasting too.

This is a duplicate of #745


Start Container with VNC

[marthoc/deconz] Starting deCONZ...

[marthoc/deconz] Current deCONZ version: 2.05.58

[marthoc/deconz] Web UI port: 80

[marthoc/deconz] Websockets port: 443

[marthoc/deconz] VNC Display: :0 on port 5900

hostname: No address associated with hostname

hostname: No address associated with hostname

/usr/bin/xauth:  file /root/.Xauthority does not exist

New ':0 (root)' desktop at :0 on machine

Starting applications specified in /etc/X11/Xvnc-session
Log file is /root/.vnc/:0.log
Use xtigervncviewer -SecurityTypes VncAuth,TLSVnc -passwd /root/.vnc/passwd :0 to connect to the VNC server.

libEGL warning: DRI2: failed to open swrast (search paths /usr/lib/arm-linux-gnueabihf/dri:${ORIGIN}/dri:/usr/lib/dri)

libEGL warning: DRI2: failed to open swrast (search paths /usr/lib/arm-linux-gnueabihf/dri:${ORIGIN}/dri:/usr/lib/dri)

libpng warning: iCCP: known incorrect sRGB profile

@Iskenof Could you please comment on how you solved this so that others (me) can benefit?

@Iskenof Could you please comment on how you solved this so that others (me) can benefit?

That would be nice ...


Can't mount a tape

Describe the bug
Index parser returns failure when symlink target tag is longer than 255 characters.
To Reproduce

Create a symlink that the target name is longer than 255 bytes
unmount the tape
mount the tape again

Expected behavior
Do not check the length of symlink that the target. Check only for name.

Fixed by

2fa7f0f5a349331b824c3752441340d19f819886 for v2.4.1
1f0f950e4025d3bcc35239b9413048c275ba6cd7 for master


Can't run in /tmp (but can in /tmp/some-dir)

Describe the bug
Running in /tmp gives an error:
[2023-09-24T18:13:29Z ERROR typst_preview::actor::typst] TypstActor: failed to watch filesystem events
thread '<unnamed>' panicked at 'explicit panic', src/actor/typst.rs:119:13
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

Which also means nothing will be re-rendered.
Full log
[2023-09-24T18:40:16Z INFO  typst_preview] Arguments: CliArguments {
        font_paths: [],
        root: None,
        data_plane_host: "<IP_ADDRESS>:23625",
        control_plane_host: "<IP_ADDRESS>:23626",
        static_file_host: "<IP_ADDRESS>:23627",
        dont_open_in_browser: true,
        enable_partial_rendering: false,
        input: "a.typ",
    }
[2023-09-24T18:40:17Z INFO  typst_preview] Data plane server listening on: <IP_ADDRESS>:23625
[2023-09-24T18:40:17Z INFO  typst_preview] Control plane server listening on: <IP_ADDRESS>:23626
[2023-09-24T18:40:17Z INFO  typst_ts_compiler::service::diag] /a.typ: compiling ...
[2023-09-24T18:40:17Z INFO  typst_preview] Static file server listening on: <IP_ADDRESS>:23627
[2023-09-24T18:40:17Z INFO  typst_ts_compiler::service::diag] /a.typ: Compilation succeeded in 330.052µs
[2023-09-24T18:40:17Z ERROR typst_preview::actor::typst] TypstActor: failed to watch filesystem events
thread '<unnamed>' panicked at 'explicit panic', src/actor/typst.rs:119:13
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
[2023-09-24T18:40:18Z INFO  typst_preview] Peer address: <IP_ADDRESS>:46826
[2023-09-24T18:40:18Z INFO  typst_preview] New WebSocket connection: <IP_ADDRESS>:46826
[2023-09-24T18:40:18Z DEBUG typst_preview::actor::render] RenderActor: waiting for message
[2023-09-24T18:40:18Z DEBUG typst_preview::actor::webview] WebviewActor: received message from websocket: Ok(Text("current"))
[2023-09-24T18:40:18Z DEBUG typst_preview::actor::render] RenderActor: has_full_render: true
[2023-09-24T18:40:18Z DEBUG typst_preview::actor::render] RenderActor: waiting for message
[2023-09-24T18:40:18Z DEBUG typst_preview::actor::webview] WebviewActor: received svg from renderer

To Reproduce
Steps to reproduce the behavior:

cd /tmp
typst-preview file.typ
See error

Expected behavior
No errors.
Software version:
typst-preview version: v0.8.1 (https://github.com/Enter-tainer/typst-preview/commit/bf83af63e62515881b192387b6f0e552f9346c02)

The /tmp directory is owned by root. You may have to run sudo typst-preview /tmp/main.typ If you want to do that.

Owning a directory means nothing if others can do whatever inside it (rwx):
$ ls -ldh /tmp
drwxrwxrwt 49 root root 112K Sep 24 22:20 /tmp

I had a similar problem with https://github.com/neoclide/coc.nvim/issues/4538, but now it is resolved.

It is not quite similar, typst-preview regards the /tmp as the root directory of your document file. And so that typst-preview cannot watch anything if it meets a access denied when it watches the root directory.
For some reason, we don't watch files separately but watch a single root directory. https://github.com/Enter-tainer/typst-preview/issues/114

Is the Node.js used as a JS runtime? Or is it some Rust server? Is there a way to watch a dir that can be read or written to? I mean, inotifywait can read /tmp just fine.

We are using it, https://docs.rs/notify/latest/notify/. And I have printed the error just now, looks like it is complaining that it does not have permission to /tmp/systemd-private-..., some subdirectory of /tmp, instead of permission to /tmp itself.

Yes, it's exactly the same as in https://github.com/neoclide/coc.nvim/issues/4538.
Is the "reading the whole root dir" comes from typst-lsp or from the typst itself? Either way, it should be allowed to work in a single-file-only mode (with the support of local/public packages).

We are using it, https://docs.rs/notify/latest/notify/.

If it's possible, we can just throw a warning about the fact that some dirs/files can't be accessed, but if all necessary files can be accessed, then it shouldn't matter anyway. You know, support more aggressive/unideal environment (root dir).

Is the "reading the whole root dir" comes from typst-lsp or from the typst itself?

This is from typst itself.

it should be allowed to work in a single-file-only mode.

I think this is reasonable.

You know, support more aggressive/unideal environment (root dir).

It won't be supported to watch entire system before we changing idea on #114. @Enter-tainer perhaps have some comment on it.

support more aggressive/unideal environment (root dir).

https://github.com/typst/typst/issues/2227

This is from typst itself.

No, typst itself (and typst-lsp) work fine in the /tmp: https://github.com/typst/typst/issues/2227#issuecomment-1733111711.

We rejected it since watch files separately watching files will cause performance degrades, more resources overhead, and relative big code base to handle them.
Whatever, I have added a (optional) large patch to the typst.ts to make watch files separately work. There is 1.5k LoC code to come. Hopefully that it will work well.
I don't know which one is correct, but I do that.
Related typst.ts: compiler/src/service/watch.rs in typst.ts
Based on rust analyzer: crates/vfs-notify/src/lib.rs in rust-analyzer


Perfetto sql related question

I can not use some sql commnad like "insert into" on perfetto sql query interface.
Could you please provide me some information about supported sql instruction?

You can't use insert into on Perfetto tables, only on standard tables which
are no longer preferred, as their performance is worse than performance of
perfetto tables.
Create table is still enabled, so as long as you don't want to submit your
code to Perfetto codebase, using it should be fine.
Feel free to let me know if you have any other questions.
Anna
On Mon, Aug 21, 2023, 8:19 AM c-jchen @.***> wrote:

I can not use some sql commnad like "insert into" on perfetto sql query
interface.
Could you please provide me some information about supported sql
instruction?
—
Reply to this email directly, view it on GitHub
https://github.com/google/perfetto/issues/564, or unsubscribe
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You are receiving this because you are subscribed to this thread.Message
ID: @.***>

Many thanks for your kind work.
I also can not use "IF or drop" command
Do you have opinions for this?
-----------------   Error  msg -------------
SQL error: Traceback (most recent call last):
File "stdin" line 1 col 14
create table table_cj1 AS
^
table table_cj1 already exists

I use perfetto to open android trace file.
Below are my source and produced error msg
--------------------  source ---------------------------
DROP IF EXISTS table_cj1
DROP IF EXISTS table_cj2
create table table_cj1 AS
select ts, value FROM counter where track_id=226;
create table table_cj2 AS
select ts, value FROM counter where track_id=133;
select ts, value+vaule_1 from table_cj1 t1
join table_cj2 t2 on  t1.ts = t2.ts
-------------------------error ------------------------------
SQL error: Traceback (most recent call last):
File "stdin" line 1 col 6
DROP IF EXISTS table_cj1
^
near "IF": syntax error

You need to add semicolon at the end of drop statement :)
On Mon, Aug 21, 2023, 8:55 AM c-jchen @.***> wrote:

I use perfetto to open android trace file.
Below are my source and produced error msg
-------------------- source ---------------------------
DROP IF EXISTS table_cj1
DROP IF EXISTS table_cj2
create table table_cj1 AS
select ts, value FROM counter where track_id=226;
create table table_cj2 AS
select ts, value FROM counter where track_id=133;
select ts, value+vaule_1 from table_cj1 t1
join table_cj2 t2 on t1.ts = t2.ts
-------------------------error ------------------------------
SQL error: Traceback (most recent call last):
File "stdin" line 1 col 6
DROP IF EXISTS table_cj1
^
near "IF": syntax error
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You are receiving this because you commented.Message ID:
@.***>

I tried add semicolon too..
It is not work... XD
-----------------------  source -------------------------
DROP IF EXISTS table_cj1**;**
DROP IF EXISTS table_cj2**;**
create table table_cj1 AS
select ts, value FROM counter where track_id=226;
create table table_cj2 AS
select ts, value FROM counter where track_id=133;
select ts, value+vaule_1 from table_cj1 t1
join table_cj2 t2 on  t1.ts = t2.ts

On Mon, Aug 21, 2023, 9:02 AM c-jchen @.***> wrote:

I tried add semicolon too..
It is not work... XD
----------------------- source -------------------------
DROP IF EXISTS table_cj1**;**

DROP TABLE IF EXISTS table_cj1:

DROP IF EXISTS table_cj2**;**

DROP TABLE IF EXISTS table_cj2:

create table table_cj1 AS
select ts, value FROM counter where track_id=226;
create table table_cj2 AS
select ts, value FROM counter where track_id=133;
select ts, value+vaule_1 from table_cj1 t1
join table_cj2 t2 on t1.ts = t2.ts
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@.***>

Many Thanks
It is work.
However, I only know SQLite used by perfetto.
I try to read perfetto doc, but it seemd not mention about some instruction of SQL can not be use.
Does perfetto have some suggestion user guide for this SQL command?

We are in the middle of writing documentation for Perfetto SQL, so it's not
available yet. Fortunately changes are quite straightforward - use Perfetto
table as you would use standard table, just add a Perfetto keyword to
create table. Perfetto tables are immutable, so you can't insert anything
inside later.
On Mon, Aug 21, 2023, 9:19 AM c-jchen @.***> wrote:

Many Thanks
It is work.
However, I only know SQLite used by perfetto.
I try to read perfetto doc, but it seemd not mention about some
instruction of SQL can not be use.
Does perfetto have some suggestion user guide for this SQL command?
—
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https://github.com/google/perfetto/issues/564#issuecomment-1685870892,
or unsubscribe
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You are receiving this because you commented.Message ID:
@.***>

We are in the middle of writing documentation for Perfetto SQL, so it's not available yet. Fortunately changes are quite straightforward - use Perfetto table as you would use standard table, just add a Perfetto keyword to create table. Perfetto tables are immutable, so you can't insert anything inside later.
…
On Mon, Aug 21, 2023, 9:19 AM c-jchen @.> wrote: Many Thanks It is work. However, I only know SQLite used by perfetto. I try to read perfetto doc, but it seemd not mention about some instruction of SQL can not be use. Does perfetto have some suggestion user guide for this SQL command? — Reply to this email directly, view it on GitHub <#564 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AZ3CHTW6KVIHV7QDT6U5PODXWMKXXANCNFSM6AAAAAA3X55OFQ . You are receiving this because you commented.Message ID: @.>

Looking forward to read documentation for Perfetto SQL


Native Teams Templates support - provisioning

Summary
With this release the app template will be able to provision templates created through the admin center.
Intended Outcome
To be able to integrate with the new native templates functionality.
How will it work
You will be able to define templates in the Teams Admin Centre.
You will then copy the ID of the template and paste it into the 'Teams Templates' list.
The templates list will be simplified to remove 'First Party Template', 'Team ID' and any other columns that are no longer required.
We will also remove the functionality that 'clones' teams from the Logic App to simplify this.
Finally, the documentation will be updated.

We are currently testing this and it appears to work 👍

When will it be released?

@adm-mp76 technically today, it's just been approved and is live in the master branch.
I am going to write up documentation next week on how to use it 👍

This is awesome, works great!
We are using the old version and you suggest upgrading to newer to support upcoming Naming Conventions functionality
How do we upgrade?.....what parts/files needs update? (maybe a guide for this would be great)
Thanks!

Summary
With this release the app template will be able to provision templates created through the admin center.
Intended Outcome
To be able to integrate with the new native templates functionality.
How will it work
You will be able to define templates in the Teams Admin Centre.
You will then copy the ID of the template and paste it into the 'Teams Templates' list.
The templates list will be simplified to remove 'First Party Template', 'Team ID' and any other columns that are no longer required.
We will also remove the functionality that 'clones' teams from the Logic App to simplify this.
Finally, the documentation will be updated.

Do you have an ETA or is the a current way to do it with the current version?

Summary
With this release the app template will be able to provision templates created through the admin center.
Intended Outcome
To be able to integrate with the new native templates functionality.
How will it work
You will be able to define templates in the Teams Admin Centre.
You will then copy the ID of the template and paste it into the 'Teams Templates' list.
The templates list will be simplified to remove 'First Party Template', 'Team ID' and any other columns that are no longer required.
We will also remove the functionality that 'clones' teams from the Logic App to simplify this.
Finally, the documentation will be updated.

Do you have an ETA or is the a current way to do it with the current version?

Previous versions of Request-a-team
If you are using a previous version of Request-a-team and wish to use the Admin Center templates, please follow the steps below:
Step 1: Define the Template in the Admin Center
Navigate to the Teams Admin Center.
Select 'Team templates' from the left hand menu (under 'Teams').
Click the '+Add' button to create a new template.
Choose whether to create a brand new template, use an existing team or start with an existing template.
Fill out the details and click 'Next' (Note - only English (United States) is supported with Request-a-team at the moment)
Create channels and configure apps for the template and click 'Submit'.
Once created, click on the template and copy the 'Template ID' value.
Step 2: Add the template to the Request-a-team templates list
_Note - After the above steps have been completed please WAIT 24 hours before adding the template to the SharePoint list. Failure to wait 24 hours means provisioning may fail and if the template is added to the list then users of Request-a-team can use it. _
Navigate to the SharePoint site used by Request-a-team.
Open the 'Teams Templates' list.
Create a new list item with the following values:
Title - Title of the template you created in the Admin Center (does not have to match)
Description - A description for the template e.g. the long description specified when creating the template
Base Template Id - The Id for the template which you copied earlier
Base Template Type - Leave blank
Team Id - Leave blank
First Party Template - Yes
The template should now be ready for use.
We do recommend upgrading to the latest version if possible as this will be a prerequisite for the upcoming Naming Conventions functionality - Naming Conventions - Single.

@adm-mp76 technically today, it's just been approved and is live in the master branch.
I am going to write up documentation next week on how to use it 👍

Please Alex......How do we upgrade if we have already installed the old solution?

@adm-mp76 The upgrade process is a bit tricky, we are trying to work on a way to simplify this but due to the fact the solution uses so many components and we can't tell if customers have customised these, we can't just assume they haven't and deploy updates over the top.
The SharePoint site has been updated and so has the ProcessTeamRequest logic app.
To update the SPO site, you can simply make these changes manually by deploying the updated app to a separate environment, comparing the changes and adding the new fields.
For the Logic App, you can as above deploy to a new tenant so you can see the changes. You can then either make these manually or edit the production Logic App in code view and paste the code from the other tenant, you will however need to change some values.
I can perhaps work with you to find a way to get this updated for you on an unofficial capacity outside of my work hours. Please ping me an email -<EMAIL_ADDRESS>

@jcmmarq Yes you can do this in the current version. If you follow the Teams Templates documentation in the Wiki, it explains how to do it.

@adm-mp76 The upgrade process is a bit tricky, we are trying to work on a way to simplify this but due to the fact the solution uses so many components and we can't tell if customers have customised these, we can't just assume they haven't and deploy updates over the top.
The SharePoint site has been updated and so has the ProcessTeamRequest logic app.
To update the SPO site, you can simply make these changes manually by deploying the updated app to a separate environment, comparing the changes and adding the new fields.
For the Logic App, you can as above deploy to a new tenant so you can see the changes. You can then either make these manually or edit the production Logic App in code view and paste the code from the other tenant, you will however need to change some values.
I can perhaps work with you to find a way to get this updated for you on an unofficial capacity outside of my work hours. Please ping me an email -<EMAIL_ADDRESS>
Thanks Alex!
I believe we will wait for the next update (we need to be able to add suffix in form of projectnumber for our team)
Do you have an ETA of next update.....will it be jan or mars?


(Android) Swahili Translation

Added SWAHILI values for android

Thank you for this PR, we'll look into it ASAP.

@dpad85 sure thing. just volunteering my time.

great. i'll borrow a macbook and do the ios version in due time. thanks for the merge.


rs.AddArcBlend

We already have the rs.AddBlendCurve, but it would be great if rs.AddArcBlend could be added too (resembling of the "ArcBlend" Rhino's command).
Thank you.

http://mcneel.myjetbrains.com/youtrack/issue/RH-30064


Support expiring log level task configurations

With the log configuration task. My typical use case is to temporarily change the log level, get the relevant information, then revert the change. I would like the option to set a duration at which the log change reverts to the previous state. The example below takes in an ISO8601 duration as optional parameter.
curl -X POST -d "logger=com.example.helloworld&level=INFO&duration=PT10M" http://localhost:8081/tasks/log-level

https://www.dropwizard.io/1.3.2/docs/manual/core.html#logging-configuration-via-http
https://github.com/dropwizard/dropwizard/blob/v1.3.2/dropwizard-servlets/src/main/java/io/dropwizard/servlets/tasks/LogConfigurationTask.java

pr > #2375


Remove executable bit from plain text files

Removes the executable bit (chmod a-x) from the license and readme, as it doesn't make sense for plain text files to be marked as executable.
Right now, this causes lint warnings when building a Debian package for a project that pulls in object-path as a dependency, as it thinks the file is an executable:
W: yarn: non-standard-executable-perm usr/share/yarn/node_modules/object-path/LICENSE 0744 != 0755

Coverage remained the same at 99.424% when pulling 60e866170bc7fa5b51116c0b2a9914978b0ce90a on Daniel15:noexec into f01ab7ce2c9ca09cf3e7ad39ca9f38a7f9cf9dc2 on mariocasciaro:master.

Makes sense, thanks!


Add optional reportBatchItemFailures setting

Start from the Use-case
When the event source mapping is created using the Lift plugin, the functionResponseType attribute is set to "ReportBatchItemFailures". For whatever reason, this causes every message sent from this trigger to fail in my Lambda function and not be processed. Manually re-creating the event source without this feature allows the message to be consumed normally by my Lambda function.
Example Config
constructs:
  my-queue:
    type: queue
...
    reportBatchItemFailures: false
    worker:
      handler: handler.handler
...

Implementation Idea
My suggestion is to allow this attribute to be added by default as it currently is, but allow a setting reportBatchItemFailures to be passed to the plugin which allows it to be turned off. My work-around is to fork this repo and remove that setting.

One reason I see for this behavior is that your function returns an invalid response that lambda considers a failure.
See what is considered invalid here https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html#sqs-batchfailurereporting-conditions, maybe you fall into one of those cases?


ignore object properties different only take extensionNumber as key

Coverage increased (+0.01%) to 39.343% when pulling 6231c3a8753ede5e4da9b97ea8640ed8b95974ba on alvita:regression-1.0 into 5b59468dd1b5c65a6195cd8d7ad781770911096c on ringcentral:dynamics-regression.


Use aws-sdk@v3 (broken down by service)

I took out the ignore clause in the Dependabot config because these clients seem to get updated much less often.
Closes #213.

That command style is their suggested usage, but they also support a v2 compatible style.

Shall we switch this PR to the command syntax?
Pros:

More tree-shakable, but we're not using import or a build process here right now
Sounds like it's the direction AWS wants to take, if/when they release v4
Performance?

Cons:

Bigger diff in this PR
Different, more verbose syntax than people are used to

(Feel free to add more)

I would say yes because AWS doesn't guarantee support for v2 style in the next major version anyway. I'll make those changes.

LGTM now.


TODO

[ ] Timer
[ ] Change socket to pipe in windows.

Note:
wepoll only supports Socket.
So pipe and waitable timers are not supported by wepoll.