cvat-ai · nmanovic · Dec 15, 2020 · Dec 9, 2020 · Dec 9, 2020 · Dec 9, 2020
@@ -61,16 +61,16 @@ via its command line tool and Python library.
 
 ## Deep learning models for automatic labeling
 
-| Name                                                                                                    | Type       | Framework  |
-| ------------------------------------------------------------------------------------------------------- | ---------- | ---------- |
-| [Deep Extreme Cut](/serverless/openvino/dextr/nuclio)                                                   | interactor | OpenVINO   |
-| [Faster RCNN](/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio)                              | detector   | TensorFlow |
-| [Mask RCNN](/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio)           | detector   | OpenVINO   |
-| [YOLO v3](/serverless/openvino/omz/public/yolo-v3-tf/nuclio)                                            | detector   | OpenVINO   |
-| [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio)                          | detector   | OpenVINO   |
-| [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector   | OpenVINO   |
-| [Mask RCNN](/serverless/tensorflow/matterport/mask_rcnn/nuclio)                                         | detector   | TensorFlow |
-| [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio)     | reid       | OpenVINO   |
+| Name                                                                                                    | Type       | Framework  | CPU | GPU |
+| ------------------------------------------------------------------------------------------------------- | ---------- | ---------- | --- | --- |
+| [Deep Extreme Cut](/serverless/openvino/dextr/nuclio)                                                   | interactor | OpenVINO   | X   |
+| [Faster RCNN](/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio)                              | detector   | TensorFlow | X   | X   |
+| [Mask RCNN](/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio)           | detector   | OpenVINO   | X   |
+| [YOLO v3](/serverless/openvino/omz/public/yolo-v3-tf/nuclio)                                            | detector   | OpenVINO   | X   |
+| [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio)                          | detector   | OpenVINO   | X   |
+| [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector   | OpenVINO   | X   |
+| [Mask RCNN](/serverless/tensorflow/matterport/mask_rcnn/nuclio)                                         | detector   | TensorFlow | X   |
+| [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio)     | reid       | OpenVINO   | X   |
 
 ## Online demo: [cvat.org](https://cvat.org)
 

@@ -2,7 +2,7 @@ version: '3.3'
 services:
   serverless:
     container_name: nuclio
-    image: quay.io/nuclio/dashboard:1.4.8-amd64
+    image: quay.io/nuclio/dashboard:1.5.8-amd64
     restart: always
     networks:
       default:

@@ -290,32 +290,7 @@ docker-compose -f docker-compose.yml -f components/analytics/docker-compose.anal
 
 ### Semi-automatic and automatic annotation
 
-- You have to install `nuctl` command line tool to build and deploy serverless
-  functions. Download [the latest release](https://github.com/nuclio/nuclio/releases).
-- 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.
-
-```bash
-nuctl create project cvat
-```
-
-```bash
-nuctl deploy --project-name cvat \
-    --path serverless/openvino/dextr/nuclio \
-    --volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
-    --platform local
-```
-
-```bash
-nuctl deploy --project-name cvat \
-    --path serverless/openvino/omz/public/yolo-v3-tf/nuclio \
-    --volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
-    --platform local
-```
-
-Note: see [deploy.sh](/serverless/deploy.sh) script for more examples.
+Please follow [instructions](/cvat/apps/documentation/installation_automatic_annotation.md)
 
 ### Stop all containers
 

@@ -0,0 +1,91 @@
+
+### 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:
+  ```bash
+  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:
+
+  ```bash
+  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 1.5.8](https://github.com/nuclio/nuclio/releases).
+  It is important that the version you download matches the version in
+  [docker-compose.serverless.yml](/components/serverless/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.
+
+  ```bash
+  nuctl create project cvat
+  ```
+
+  ```bash
+  nuctl deploy --project-name cvat \
+    --path serverless/openvino/dextr/nuclio \
+    --volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
+    --platform local
+  ```
+
+  ```bash
+  nuctl deploy --project-name cvat \
+    --path serverless/openvino/omz/public/yolo-v3-tf/nuclio \
+    --volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
+    --platform local
+  ```
+  **Note:**
+  - See [deploy_cpu.sh](/serverless/deploy_cpu.sh) for more examples.
+
+  #### GPU Support
+  You will need to install Nvidia Container Toolkit and make sure your docker supports GPU. Follow [Nvidia docker instructions](https://www.tensorflow.org/install/docker#gpu_support).
+  Also you will need to add `--resource-limit nvidia.com/gpu=1` to the nuclio deployment command.
+  As an example, below will run on the GPU:
+
+  ```bash
+  nuctl deploy tf-faster-rcnn-inception-v2-coco-gpu \
+    --project-name cvat --path "serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio" --platform local \
+    --base-image tensorflow/tensorflow:2.1.1-gpu \
+    --desc "Faster RCNN from Tensorflow Object Detection GPU API" \
+    --image cvat/tf.faster_rcnn_inception_v2_coco_gpu \
+    --resource-limit nvidia.com/gpu=1
+  ```
+
+  **Note:**
+    - Since the model is loaded during deployment, the number of GPU functions you can deploy will be limited to your GPU memory.
+
+  -  See [deploy_gpu.sh](/serverless/deploy_gpu.sh) script for more examples.
+
+####Debugging Nuclio Functions:
+
+- You can open nuclio dashboard at [localhost:8070](http://localhost:8070). Make sure status of your functions are up and running without any error.
+
+- 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
+
+  ```bash
+  docker logs <name of your container>
+  ```
+  e.g.,
+
+  ```bash
+  docker logs nuclio-nuclio-tf-faster-rcnn-inception-v2-coco-gpu
+  ```
+
+- If you would like to debug a code inside a container, you can use vscode to directly attach to a container [instructions](https://code.visualstudio.com/docs/remote/attach-container). To apply your changes, make sure to restart the container.
+  ```bash
+  docker restart <name_of_the_container>
+  ```
+
+  > **⚠ WARNING:**
+  >  Do not use nuclio dashboard to stop the container because with any modifications, it rebuilds the container and you will lose your changes.
@@ -1,4 +1,5 @@
 #!/bin/bash
+# Sample commands to deploy nuclio functions on CPU
 
 SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 

@@ -0,0 +1,15 @@
+#!/bin/bash
+# Sample commands to deploy nuclio functions on GPU
+
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+
+nuctl create project cvat
+
+nuctl deploy --project-name cvat \
+    --path "$SCRIPT_DIR/tensorflow/faster_rcnn_inception_v2_coco/nuclio" \
+    --platform local --base-image tensorflow/tensorflow:2.1.1-gpu \
+    --desc "Faster RCNN from Tensorflow Object Detection GPU API" \
+    --image cvat/tf.faster_rcnn_inception_v2_coco_gpu \
+    --resource-limit nvidia.com/gpu=1
+
+nuctl get function
@@ -35,7 +35,8 @@ def infer(self, image):
         width, height = image.size
         if width > 1920 or height > 1080:
             image = image.resize((width // 2, height // 2), Image.ANTIALIAS)
-        image_np = np.array(image.getdata()).reshape((image.height, image.width, 3)).astype(np.uint8)
+        image_np = np.array(image.getdata())[:, :3].reshape(
+            (image.height, image.width, -1)).astype(np.uint8)
         image_np = np.expand_dims(image_np, axis=0)
 
         return self.session.run(