Semi-automatic and Automatic Annotation

This page provides 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 1.5.16. It is important that the version you download matches the version in docker-compose.serverless.yml 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
```

Create cvat project inside nuclio dashboard where you will deploy new serverless functions and deploy a couple of DL models. Commands below should be run only after CVAT has been installed using docker-compose because it runs nuclio dashboard which manages all serverless functions.

nuctl create project cvat

nuctl deploy --project-name cvat \
  --path serverless/openvino/dextr/nuclio \
  --volume `pwd`/serverless/common:/opt/nuclio/common \
  --platform local

nuctl deploy --project-name cvat \
  --path serverless/openvino/omz/public/yolo-v3-tf/nuclio \
  --volume `pwd`/serverless/common:/opt/nuclio/common \
  --platform local

Note:

See deploy_cpu.sh for more examples.

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.

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>
```