Wicked_Chicken

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Posts posted by Wicked_Chicken

    [Support] binhex - qBittorrentVPN

    in Docker Containers

    Posted

    I'm suddenly having an issue with this, getting the following from my logs:

     

    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 109, in emit
        self.flush()
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 68, in flush
        self.stream.flush()
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    Traceback (most recent call last):
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 102, in emit
        self.stream.write(msg)
    OSError: [Errno 107] Transport endpoint is not connected
    OSError: [Errno 107] Transport endpoint is not connected

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last):
      File "/usr/bin/supervisord", line 33, in <module>
        sys.exit(load_entry_point('supervisor==4.2.5', 'console_scripts', 'supervisord')())
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/usr/lib/python3.11/site-packages/supervisor/supervisord.py", line 361, in main
        options.close_logger()
      File "/usr/lib/python3.11/site-packages/supervisor/options.py", line 1250, in close_logger
        self.logger.close()
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 311, in close
        handler.close()
      File "/usr/lib/python3.11/site-packages/supervisor/loggers.py", line 86, in close
        self.stream.close()
    OSError: [Errno 107] Transport endpoint is not connected
    2023-08-09 15:29:07,428 DEBG 'watchdog-script' stderr output:
    sed: can't read /config/qBittorrent/config/qBittorrent.conf: Transport endpoint is not connected

    2023-08-09 15:29:31,452 WARN received SIGTERM indicating exit request
    2023-08-09 15:29:31,452 DEBG killing watchdog-script (pid 301) with signal SIGTERM
    2023-08-09 15:29:31,452 INFO waiting for start-script, watchdog-script to die
    2023-08-09 15:29:31,453 DEBG fd 11 closed, stopped monitoring <POutputDispatcher at 23441593907920 for <Subprocess at 23441593916432 with name watchdog-script in state STOPPING> (stdout)>
    2023-08-09 15:29:31,453 DEBG fd 15 closed, stopped monitoring <POutputDispatcher at 23441592009040 for <Subprocess at 23441593916432 with name watchdog-script in state STOPPING> (stderr)>
    2023-08-09 15:29:31,453 WARN stopped: watchdog-script (exit status 143)
    2023-08-09 15:29:31,453 DEBG received SIGCHLD indicating a child quit
    2023-08-09 15:29:31,454 DEBG killing start-script (pid 300) with signal SIGTERM
    2023-08-09 15:29:32,457 DEBG fd 8 closed, stopped monitoring <POutputDispatcher at 23441593901904 for <Subprocess at 23441593998288 with name start-script in state STOPPING> (stdout)>
    2023-08-09 15:29:32,457 DEBG fd 10 closed, stopped monitoring <POutputDispatcher at 23441608411088 for <Subprocess at 23441593998288 with name start-script in state STOPPING> (stderr)>
    2023-08-09 15:29:32,457 WARN stopped: start-script (terminated by SIGTERM)
    2023-08-09 15:29:32,457 DEBG received SIGCHLD indicating a child quit

     

    Any thoughts? Thank you!

    Unassigned Devices - Managing Disk Drives and Remote Shares Outside of The Unraid Array

    in Plugin Support

    Posted

    Hello,

     

    I am having difficulty with with a drive that was previously working with UD. Here's the log I'm getting. 

     

    Mar 24 18:25:21 UNRAID emhttpd: read SMART /dev/sdb
    Mar 24 21:11:08 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 24 21:11:08 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 12:31:23 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 12:31:23 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 12:32:38 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 12:32:38 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 12:33:31 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 12:33:31 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 12:35:00 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 12:35:00 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 14:10:50 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 14:10:50 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 14:11:45 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 14:11:45 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 14:12:06 UNRAID unassigned.devices: Warning: Cannot change the disk label on device 'sdb1'.
    Mar 26 14:12:08 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 14:12:08 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.
    Mar 26 14:14:48 UNRAID sudo: root : TTY=pts/1 ; PWD=/root ; USER=root ; COMMAND=/sbin/fsck -N /dev/sdb
    Mar 26 14:15:33 UNRAID emhttpd: spinning down /dev/sdb
    Mar 26 14:16:06 UNRAID unassigned.devices: Partition 'sdb1' does not have a file system and cannot be mounted.
    Mar 26 14:16:06 UNRAID unassigned.devices: Error: Device '/dev/sdb2' mount point 'Media' - name is reserved, used in the array or by an unassigned device.

     

    Any help is greatly appreciated!

     

    Deepstack Docker GPU Version

    in Docker Engine

    Posted

    Edit:


    Issue was confirmed as insufficient CUDA memory. 

     

    https://imgur.com/a/tvN804n

     

    So it appears each custom model essentially runs as an independent process. I did not realize this, and am going to have to do some testing with with Yolov5s models to see if I can get decent models to lower GPU headroom, consider changing my GPU in the server, or offloading deepstack to my main PC with a far better GPU. 

    @ndetar, you are a rockstar for helping me figure this out. 

    • Like 1

    Deepstack Docker GPU Version

    in Docker Engine

    Posted · Edited by Wicked_Chicken

    So that's interesting.

    https://imgur.com/a/1uCqaWG

     

    I stripped the image and reinstalled, and it appears the GPU is now being taxed per nvidia-smi. What's funny, however, is that as soon as I try to load any custom models, it fails entirely. I expect I have headroom based on the ram utilization of 625mb/2000mb for the base models on high, but cannot actually recall how I pulled that more detailed log which suggested a CUDA memory issue. 

     

    Correction, I found the command: Here it is:

     

    Quote

    sudo docker exec -it container-name /bin/bash
    once in the container, run
    cat ../logs/stderr.txt

    And the log:

    Traceback (most recent call last):
      File "/usr/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
        self.run()
      File "/usr/lib/python3.7/multiprocessing/process.py", line 99, in run
        self._target(*self._args, **self._kwargs)
      File "/app/intelligencelayer/shared/detection.py", line 69, in objectdetection
        detector = YOLODetector(model_path, reso, cuda=CUDA_MODE)
      File "/app/intelligencelayer/shared/./process.py", line 36, in __init__
        self.model = attempt_load(model_path, map_location=self.device)
      File "/app/intelligencelayer/shared/./models/experimental.py", line 159, in attempt_load
        torch.load(w, map_location=map_location)["model"].float().fuse().eval()
      File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 584, in load
        return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
      File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 842, in _load
        result = unpickler.load()
      File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 834, in persistent_load
        load_tensor(data_type, size, key, _maybe_decode_ascii(location))
      File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 823, in load_tensor
        loaded_storages[key] = restore_location(storage, location)
      File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 803, in restore_location
        return default_restore_location(storage, str(map_location))
      File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 174, in default_restore_location
        result = fn(storage, location)
      File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 156, in _cuda_deserialize
        return obj.cuda(device)
      File "/usr/local/lib/python3.7/dist-packages/torch/_utils.py", line 77, in _cuda
        return new_type(self.size()).copy_(self, non_blocking)
      File "/usr/local/lib/python3.7/dist-packages/torch/cuda/__init__.py", line 480, in _lazy_new
        return super(_CudaBase, cls).__new__(cls, *args, **kwargs)
    RuntimeError: CUDA error: out of memory

     

    Same CUDA error. I'll fiddle with this to see if I can get any custom models to run. It'll be really disappointing for 2gb isn't enough for any. 

     

    Deepstack Docker GPU Version

    in Docker Engine

    Posted

    1 hour ago, ndetar said:

    I have been using it with a GPU for a while now and its been working great. Could you provide some additional information such as the log output from the container, maybe a screenshot of your config, etc. It's hard to troubleshoot without more context.

     

    Hey @ndetar!

     

    Thanks for responding! I have loved your container and hope we can figure this out. I really appreciate your time and assistance. 

     

    Screenshots:

    http://imgur.com/a/nkxBZcz

     

    Logs:

     

    Blockquoteroot@UNRAID:~# sudo docker exec -it DeepstackGPUOfficial /bin/bash
    root@ed10552468a7:/app/server# cat …/logs/stderr.txt
    Process Process-1:
    Traceback (most recent call last):
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 297, in _bootstrap
    self.run()
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 99, in run
    self._target(*self._args, **self._kwargs)
    File “/app/intelligencelayer/shared/detection.py”, line 69, in objectdetection
    detector = YOLODetector(model_path, reso, cuda=CUDA_MODE)
    File “/app/intelligencelayer/shared/./process.py”, line 36, in init
    self.model = attempt_load(model_path, map_location=self.device)
    File “/app/intelligencelayer/shared/./models/experimental.py”, line 159, in attempt_load
    torch.load(w, map_location=map_location)[“model”].float().fuse().eval()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 584, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 842, in _load
    result = unpickler.load()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 834, in persistent_load
    load_tensor(data_type, size, key, _maybe_decode_ascii(location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 823, in load_tensor
    loaded_storages[key] = restore_location(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 803, in restore_location
    return default_restore_location(storage, str(map_location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 174, in default_restore_location
    result = fn(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 156, in _cuda_deserialize
    return obj.cuda(device)
    File “/usr/local/lib/python3.7/dist-packages/torch/_utils.py”, line 77, in cuda
    return new_type(self.size()).copy(self, non_blocking)
    File “/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py”, line 480, in _lazy_new
    return super(_CudaBase, cls).new(cls, *args, **kwargs)
    RuntimeError: CUDA error: out of memory
    Process Process-1:
    Traceback (most recent call last):
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 297, in _bootstrap
    self.run()
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 99, in run
    self._target(*self._args, **self._kwargs)
    File “/app/intelligencelayer/shared/detection.py”, line 69, in objectdetection
    detector = YOLODetector(model_path, reso, cuda=CUDA_MODE)
    File “/app/intelligencelayer/shared/./process.py”, line 36, in init
    self.model = attempt_load(model_path, map_location=self.device)
    File “/app/intelligencelayer/shared/./models/experimental.py”, line 159, in attempt_load
    torch.load(w, map_location=map_location)[“model”].float().fuse().eval()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 584, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 842, in _load
    result = unpickler.load()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 834, in persistent_load
    load_tensor(data_type, size, key, _maybe_decode_ascii(location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 823, in load_tensor
    loaded_storages[key] = restore_location(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 803, in restore_location
    return default_restore_location(storage, str(map_location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 174, in default_restore_location
    result = fn(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 156, in _cuda_deserialize
    return obj.cuda(device)
    File “/usr/local/lib/python3.7/dist-packages/torch/_utils.py”, line 77, in cuda
    return new_type(self.size()).copy(self, non_blocking)
    File “/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py”, line 480, in _lazy_new
    return super(_CudaBase, cls).new(cls, *args, **kwargs)
    RuntimeError: CUDA error: out of memory
    Process Process-1:
    Traceback (most recent call last):
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 297, in _bootstrap
    self.run()
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 99, in run
    self._target(*self._args, **self._kwargs)
    File “/app/intelligencelayer/shared/detection.py”, line 69, in objectdetection
    detector = YOLODetector(model_path, reso, cuda=CUDA_MODE)
    File “/app/intelligencelayer/shared/./process.py”, line 36, in init
    self.model = attempt_load(model_path, map_location=self.device)
    File “/app/intelligencelayer/shared/./models/experimental.py”, line 159, in attempt_load
    torch.load(w, map_location=map_location)[“model”].float().fuse().eval()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 584, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 842, in _load
    result = unpickler.load()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 834, in persistent_load
    load_tensor(data_type, size, key, _maybe_decode_ascii(location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 823, in load_tensor
    loaded_storages[key] = restore_location(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 803, in restore_location
    return default_restore_location(storage, str(map_location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 174, in default_restore_location
    result = fn(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 156, in _cuda_deserialize
    return obj.cuda(device)
    File “/usr/local/lib/python3.7/dist-packages/torch/_utils.py”, line 77, in cuda
    return new_type(self.size()).copy(self, non_blocking)
    File “/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py”, line 480, in _lazy_new
    return super(_CudaBase, cls).new(cls, *args, **kwargs)
    RuntimeError: CUDA error: out of memory
    Process Process-2:
    Traceback (most recent call last):
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 297, in _bootstrap
    self.run()
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 99, in run
    self._target(*self._args, **self._kwargs)
    File “/app/intelligencelayer/shared/face.py”, line 73, in face
    cuda=SharedOptions.CUDA_MODE,
    File “/app/intelligencelayer/shared/./recognition/process.py”, line 31, in init
    self.model = self.model.cuda()
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 458, in cuda
    return self._apply(lambda t: t.cuda(device))
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 354, in _apply
    module._apply(fn)
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 354, in _apply
    module._apply(fn)
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 376, in _apply
    param_applied = fn(param)
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 458, in
    return self._apply(lambda t: t.cuda(device))
    RuntimeError: CUDA error: out of memory
    Process Process-1:
    Traceback (most recent call last):
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 297, in _bootstrap
    self.run()
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 99, in run
    self._target(*self._args, **self._kwargs)
    File “/app/intelligencelayer/shared/scene.py”, line 65, in scenerecognition
    SharedOptions.CUDA_MODE,
    File “/app/intelligencelayer/shared/scene.py”, line 38, in init
    self.model = self.model.cuda()
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 458, in cuda
    return self._apply(lambda t: t.cuda(device))
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 354, in _apply
    module._apply(fn)
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 376, in _apply
    param_applied = fn(param)
    File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 458, in
    return self._apply(lambda t: t.cuda(device))
    RuntimeError: CUDA error: out of memory
    Process Process-1:
    Traceback (most recent call last):
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 297, in _bootstrap
    self.run()
    File “/usr/lib/python3.7/multiprocessing/process.py”, line 99, in run
    self._target(*self._args, **self._kwargs)
    File “/app/intelligencelayer/shared/detection.py”, line 69, in objectdetection
    detector = YOLODetector(model_path, reso, cuda=CUDA_MODE)
    File “/app/intelligencelayer/shared/./process.py”, line 36, in init
    self.model = attempt_load(model_path, map_location=self.device)
    File “/app/intelligencelayer/shared/./models/experimental.py”, line 159, in attempt_load
    torch.load(w, map_location=map_location)[“model”].float().fuse().eval()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 584, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 842, in _load
    result = unpickler.load()
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 834, in persistent_load
    load_tensor(data_type, size, key, _maybe_decode_ascii(location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 823, in load_tensor
    loaded_storages[key] = restore_location(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 803, in restore_location
    return default_restore_location(storage, str(map_location))
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 174, in default_restore_location
    result = fn(storage, location)
    File “/usr/local/lib/python3.7/dist-packages/torch/serialization.py”, line 156, in _cuda_deserialize
    return obj.cuda(device)
    File “/usr/local/lib/python3.7/dist-packages/torch/_utils.py”, line 77, in cuda
    return new_type(self.size()).copy(self, non_blocking)
    File “/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py”, line 480, in _lazy_new
    return super(_CudaBase, cls).new(cls, *args, **kwargs)
    RuntimeError: CUDA error: out of memory

    DeepStack: Version 2021.09.01

    v1/vision/custom/dark

    v1/vision/custom/poolcam

    v1/vision/custom/unagi
    /v1/vision/face

    /v1/vision/face/recognize

    /v1/vision/face/register

    /v1/vision/face/match

    /v1/vision/face/list

    /v1/vision/face/delete

    /v1/vision/detection

    /v1/vision/scene

    p

    v1/restore

    Timeout Log:

    [GIN] 2021/10/01 - 19:46:30 | 500 | 1m0s | 54.86.50.139 | POST “/v1/vision/detection”
    [GIN] 2021/10/01 - 19:46:30 | 500 | 1m0s | 54.86.50.139 | POST “/v1/vision/detection”

    Deepstack Docker GPU Version

    in Docker Engine

    Posted

    Has anyone recently had any luck using Deepstack with a GPU within Unraid? I've been using the CPU version of @ndetar's which has been working wonderfully, but I have been unable to get either his (which has great instructions for converting it to GPU) or the officially documented Deepstack Docker GPU image here working correctly.

     

    It does appear that Deepstack released a new version of GPU three days ago, but I have still not had luck either with the latest version or the second-most recent revision. I have nvidia-drivers up and running with a recommended device, but am still getting timeouts for some reason despite being able to confirm deepstacks activation. 

     

    Any help is much appreciated!

    Extremely slow Parity rebuild (new parity drive)

    in General Support

    Posted

    I'm getting extremely slow speeds on a parity rebuild as I put in a bigger drive. It seems to be getting progressively slower. 

     

    Total size:2 TB

    Elapsed time:22 hours, 18 minutes

    Current position:1.88 TB (94.2 %)

    Estimated speed:2.5 MB/sec

    Estimated finish:12 hours, 57 minutes

    Sync errors corrected:

     

    I've attached my diagnostics file. Any help is much appreciated.

     

     

    unraid-diagnostics-20210928-0912.zip

    Unraid Becoming Unresponsive Suddenly

    in General Support

    Posted

    Hello, Unraid Community. 

     

    Over the past 2-3 days, Unraid has suddenly become unstable and stops responding after the array is mounted. No significant changes to hardware or software have been made. The array did become close to full yesterday, which I resolved by deleted unneeded files, which appeared to resolve the issue at the time. This morning, unfortunately, Unraid was again unresponsive. 

     

    I've attached the diagnostics file. Any assistance is greatly appreciated!

     

    -WC

    unraid-diagnostics-20210927-0757.zip

    [Plugin] Nvidia-Driver

    in Plugin Support

    Posted

    5 minutes ago, ich777 said:

    The GeForce GT 520 has 48 Cuda cores I think this would be a waste of electricity even if it draws around 30 Watts...

    A Quadro P400 has 256 Cuda cores and also draws around 30Watts...

     

    Anyways, no the drivers or better speaking the container toolkit, runtime,... only supports drivers >= 418.81.07 so you couldn't get it to work in a container anyways, besides that I only compile the latest Nvidia driver that is available when a new unRAID version is released and also the following Nvidia drivers for this release cycle and start over again when a new unRAID version is released.

    That makes sense, Lol. I appreciate the response and your work as a dev!!! I'll see if I can't snag something more recent for this project.

    • Like 1

    [Plugin] Nvidia-Driver

    in Plugin Support

    Posted

    On 9/21/2021 at 5:58 AM, ich777 said:

    That's because I'm listing only the 8 last drivers since this can be a really big mess...

     

    Edit the file "/boot/config/plugins/nvidia-driver/settings.cfg" and change the line where it says "driver_version=..." to: 

    driver_version=460.73.01

     

    After that open up the plugin page and you should see that nothing is selected, just ignore that and click the Download button, then you should see that it downloads the driver 460.73.01, after it finished reboot your server to install it.

     Will this work with 390.144?

    Unassigned Devices - Managing Disk Drives and Remote Shares Outside of The Unraid Array

    in Plugin Support

    Posted

    I have successfully mounted a drive using "Unassigned Devices", which I can see and read from, but when trying to copy files to that drive I now get errors from windows telling me the drive is "write protected". I have ensured "read only" is not checked and have tried both authenticated SMB mode as well as public settings, both of which still result in the same error. Any assistance is greatly appreciated!

    [Support] omada-controller

    in Docker Containers

    Posted

    Having an issue with the docker. Log as as such: 

     

    at com.tplink.omada.start.task.MetaDataInitTask.a(SourceFile:51)
    at com.tplink.omada.start.task.f.a(SourceFile:13)
    at com.tplink.omada.start.OmadaBootstrap.f(SourceFile:321)
    at com.tplink.omada.start.OmadaLinuxMain.b(SourceFile:87)
    at com.tplink.omada.start.OmadaLinuxMain.main(SourceFile:25)
    Caused by: java.io.IOException: No space left on device
    at java.io.FileOutputStream.writeBytes(Native Method)
    at java.io.FileOutputStream.write(FileOutputStream.java:326)
    at org.apache.logging.log4j.core.appender.OutputStreamManager.writeToDestination(OutputStreamManager.java:256)
    ... 27 more

    2021-05-08 11:07:18,740 main ERROR Unable to write to stream ../logs/server.log for appender RollingFile: org.apache.logging.log4j.core.appender.AppenderLoggingException: Error writing to stream ../logs/server.log
    2021-05-08 11:07:18,740 main ERROR An exception occurred processing Appender RollingFile org.apache.logging.log4j.core.appender.AppenderLoggingException: Error writing to stream ../logs/server.log
    at org.apache.logging.log4j.core.appender.OutputStreamManager.writeToDestination(OutputStreamManager.java:258)
    at org.apache.logging.log4j.core.appender.FileManager.writeToDestination(FileManager.java:177)
    at org.apache.logging.log4j.core.appender.rolling.RollingFileManager.writeToDestination(RollingFileManager.java:185)
    at org.apache.logging.log4j.core.appender.OutputStreamManager.flushBuffer(OutputStreamManager.java:288)
    at org.apache.logging.log4j.core.appender.OutputStreamManager.flush(OutputStreamManager.java:297)
    at org.apache.logging.log4j.core.appender.AbstractOutputStreamAppender.directEncodeEvent(AbstractOutputStreamAppender.java:179)
    at org.apache.logging.log4j.core.appender.AbstractOutputStreamAppender.tryAppend(AbstractOutputStreamAppender.java:170)
    at org.apache.logging.log4j.core.appender.AbstractOutputStreamAppender.append(AbstractOutputStreamAppender.java:161)
    at org.apache.logging.log4j.core.appender.RollingFileAppender.append(RollingFileAppender.java:268)
    at org.apache.logging.log4j.core.config.AppenderControl.tryCallAppender(AppenderControl.java:156)
    at org.apache.logging.log4j.core.config.AppenderControl.callAppender0(AppenderControl.java:129)
    at org.apache.logging.log4j.core.config.AppenderControl.callAppenderPreventRecursion(AppenderControl.java:120)
    at org.apache.logging.log4j.core.config.AppenderControl.callAppender(AppenderControl.java:84)
    at org.apache.logging.log4j.core.config.LoggerConfig.callAppenders(LoggerConfig.java:448)
    at org.apache.logging.log4j.core.config.LoggerConfig.processLogEvent(LoggerConfig.java:433)
    at org.apache.logging.log4j.core.config.LoggerConfig.log(LoggerConfig.java:417)
    at org.apache.logging.log4j.core.config.LoggerConfig.log(LoggerConfig.java:403)
    at org.apache.logging.log4j.core.config.AwaitCompletionReliabilityStrategy.log(AwaitCompletionReliabilityStrategy.java:63)
    at org.apache.logging.log4j.core.Logger.logMessage(Logger.java:146)
    at org.apache.logging.log4j.spi.AbstractLogger.logMessageSafely(AbstractLogger.java:2091)
    at org.apache.logging.log4j.spi.AbstractLogger.logMessage(AbstractLogger.java:1993)
    at org.apache.logging.log4j.spi.AbstractLogger.logIfEnabled(AbstractLogger.java:1852)
    at org.apache.logging.slf4j.Log4jLogger.error(Log4jLogger.java:299)
    at com.tplink.omada.start.task.FailExitTask.a(SourceFile:18)
    at com.tplink.omada.start.task.f.a(SourceFile:13)
    at com.tplink.omada.start.OmadaBootstrap.f(SourceFile:321)
    at com.tplink.omada.start.OmadaLinuxMain.b(SourceFile:87)
    at com.tplink.omada.start.OmadaLinuxMain.main(SourceFile:25)
    Caused by: java.io.IOException: No space left on device
    at java.io.FileOutputStream.writeBytes(Native Method)
    at java.io.FileOutputStream.write(FileOutputStream.java:326)
    at org.apache.logging.log4j.core.appender.OutputStreamManager.writeToDestination(OutputStreamManager.java:256)
    ... 27 more

    Failed to start omada controller, going to exit
    2021-05-08 11:07:18 [main] [ERROR]-[SourceFile:51] - Failed to get WebApplicationContext, Met2021-05-08 11:07:19 [nioEventLoopGroup-5-1] [INFO]-[SourceFile:50] - Omada Controller isn't prepared to handle event
    2021-05-08 11:07:27 [nioEventLoopGroup-5-1] [INFO]-[SourceFile:50] - Omada Controller isn't prepared to handle event
    ShutdownHook: service stopped.