Semi-automatic and Automatic Annotation

Information about the installation of components needed for semi-automatic and automatic annotation.

⚠ WARNING: Do not use docker compose up If you did, make sure all containers are stopped by docker compose down.

To bring up cvat with auto annotation tool, from cvat root directory, you need to run:
```
docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml up -d
```
If you did any changes to the Docker Compose files, make sure to add --build at the end.

To stop the containers, simply run:
```
docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml down
```
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.
```
wget https://github.com/nuclio/nuclio/releases/download/<version>/nuctl-<version>-linux-amd64
```
After downloading the nuclio, give it a proper permission and do a softlink.
```
sudo chmod +x nuctl-<version>-linux-amd64
sudo ln -sf $(pwd)/nuctl-<version>-linux-amd64 /usr/local/bin/nuctl
```
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 using docker compose because it runs nuclio dashboard which manages all serverless functions.
```
./serverless/deploy_cpu.sh serverless/openvino/dextr
./serverless/deploy_cpu.sh serverless/openvino/omz/public/yolo-v3-tf
```
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:
```
nuctl deploy --project-name cvat \
  --path serverless/tensorflow/matterport/mask_rcnn/nuclio \
  --platform local --base-image tensorflow/tensorflow:1.15.5-gpu-py3 \
  --desc "GPU based implementation of Mask RCNN on Python 3, Keras, and TensorFlow." \
  --image cvat/tf.matterport.mask_rcnn_gpu \
  --triggers '{"myHttpTrigger": {"maxWorkers": 1}}' \
  --resource-limit nvidia.com/gpu=1
```
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:

You can open nuclio dashboard at localhost:8070. Make sure status of your functions are up and running without any error.

Test your deployed DL model as a serverless function. The command below should work on Linux and Mac OS.

image=$(curl https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png --output - | base64 | tr -d '\n')
cat << EOF > /tmp/input.json
{"image": "$image"}
EOF
cat /tmp/input.json | nuctl invoke openvino-omz-public-yolo-v3-tf -c 'application/json'

20.07.17 12:07:44.519    nuctl.platform.invoker (I) Executing function {"method": "POST", "url": "http://:57308", "headers": {"Content-Type":["application/json"],"X-Nuclio-Log-Level":["info"],"X-Nuclio-Target":["openvino-omz-public-yolo-v3-tf"]}}
20.07.17 12:07:45.275    nuctl.platform.invoker (I) Got response {"status": "200 OK"}
20.07.17 12:07:45.275                     nuctl (I) >>> Start of function logs
20.07.17 12:07:45.275 ino-omz-public-yolo-v3-tf (I) Run yolo-v3-tf model {"worker_id": "0", "time": 1594976864570.9353}
20.07.17 12:07:45.275                     nuctl (I) <<< End of function logs

> Response headers:
Date = Fri, 17 Jul 2020 09:07:45 GMT
Content-Type = application/json
Content-Length = 100
Server = nuclio

> Response body:
[
    {
        "confidence": "0.9992254",
        "label": "person",
        "points": [
            39,
            124,
            408,
            512
        ],
        "type": "rectangle"
    }
]

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 by docker logs <name of your container> e.g.,
```
docker logs nuclio-nuclio-tf-faster-rcnn-inception-v2-coco-gpu
```
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.
```
docker restart <name_of_the_container>
```

Semi-automatic and Automatic Annotation

GPU Support