Semi-automatic and Automatic Annotation
⚠ WARNING: Do not use
docker compose up
If you did, make sure all containers are stopped bydocker compose down
.
-
To bring up cvat with auto annotation tool, from cvat root directory, you need to run:
If you did any changes to the Docker Compose files, make sure to add
--build
at the end.To stop the containers, simply run:
-
You have to install
nuctl
command line tool to build and deploy serverless functions. Download version 1.8.14. It is important that the version you download matches the version in docker-compose.serverless.yml. For example, using wget.After downloading the nuclio, give it a proper permission and do a softlink.
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Deploy a couple of functions. This will automatically create a
cvat
Nuclio project to contain the functions. Commands below should be run only after CVAT has been installed usingdocker compose
because it runs nuclio dashboard which manages all serverless functions.GPU Support
You will need to install Nvidia Container Toolkit. Also you will need to add
--resource-limit nvidia.com/gpu=1 --triggers '{"myHttpTrigger": {"maxWorkers": 1}}'
to the nuclio deployment command. You can increase the maxWorker if you have enough GPU memory. As an example, below will run on the GPU:Note:
- The number of GPU deployed functions will be limited to your GPU memory.
- See deploy_gpu.sh script for more examples.
- For some models (namely SiamMask) you need an Nvidia driver version greater than or equal to 450.80.02.
Note for Windows users:
If you want to use nuclio under Windows CVAT installation you should install Nvidia drivers for WSL according to this instruction and follow the steps up to “2.3 Installing Nvidia drivers”. Important requirement: you should have the latest versions of Docker Desktop, Nvidia drivers for WSL, and the latest updates from the Windows Insider Preview Dev channel.
Troubleshooting Nuclio Functions:
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You can open nuclio dashboard at localhost:8070. Make sure status of your functions are up and running without any error.
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Test your deployed DL model as a serverless function. The command below should work on Linux and Mac OS.
-
To check for internal server errors, run
docker ps -a
to see the list of containers. Find the container that you are interested, e.g.,nuclio-nuclio-tf-faster-rcnn-inception-v2-coco-gpu
. Then check its logs bydocker logs <name of your container>
e.g., -
To debug a code inside a container, you can use vscode to attach to a container instructions. To apply your changes, make sure to restart the container.