Update serverless-tutorial.md

site/content/en/docs/manual/advanced/serverless-tutorial.md

@@ -24,8 +24,7 @@ that can _perfectly_ annotate 50% of your data equates to reducing manual annota
 Since we know DL models can help us to annotate faster, how then do we use them?
 In CVAT all such DL models are implemented as serverless functions using the [Nuclio][nuclio-homepage]
 serverless platform. There are multiple implemented functions that can be
-found in the [serverless][cvat-builtin-serverless] directory such as _Mask RCNN,
-Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc.
+found in the [serverless][cvat-builtin-serverless] directory such as _Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc.
 Follow [the installation guide][cvat-auto-annotation-guide] to build and deploy
 these serverless functions. See [the user guide][cvat-ai-tools-user-guide] to
 understand how to use these functions in the UI to automatically annotate data.
@@ -161,7 +160,6 @@ Finally you will get bounding boxes.
 ![SiamMask results](/images/siammask_results.gif)
 
 `SiamMask` model is more optimized to work on Nvidia GPUs.
-For more information about deploying the model for the GPU, [read on](#objects-segmentation-using-mask-rcnn).
 
 ### Object detection using YOLO-v3
 
@@ -215,60 +213,6 @@ CVAT will run the serverless function on every frame of the task and submit
 results directly into database. For more details please read
 [the guide][cvat-auto-annotation-user-guide].
 
-### Objects segmentation using Mask-RCNN
-
-If you have a detector, which returns polygons, you can segment objects. One
-of such detectors is `Mask-RCNN`. There are several implementations of the
-detector available out of the box:
-
-- `serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco` is
-  optimized using [Intel OpenVINO framework][intel-openvino-url] and works well
-  if it is run on an Intel CPU.
-- `serverless/tensorflow/matterport/mask_rcnn/` is optimized for GPU.
-
-The deployment process for a serverless function optimized for GPU is similar.
-Just need to run `serverless/deploy_gpu.sh` script. It runs mostly the same
-commands but utilize `function-gpu.yaml` configuration file instead of
-`function.yaml` internally. See next sections if you want to understand the
-difference.
-
-_Note: Please do not run several GPU functions at the same time. In many cases it
-will not work out of the box. For now you should manually schedule different
-functions on different GPUs and it requires source code modification. Nuclio
-autoscaler does not support the local platform (docker)._
-
-<details>
-<summary>
-
-```bash
-serverless/deploy_gpu.sh serverless/tensorflow/matterport/mask_rcnn
-```
-
-</summary>
-
-```
-Deploying serverless/tensorflow/matterport/mask_rcnn function...
-21.07.12 16:48:48.995                     nuctl (I) Deploying function {"name": ""}
-21.07.12 16:48:48.995                     nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
-21.07.12 16:48:49.356                     nuctl (I) Cleaning up before deployment {"functionName": "tf-matterport-mask-rcnn"}
-21.07.12 16:48:49.470                     nuctl (I) Function already exists, deleting function containers {"functionName": "tf-matterport-mask-rcnn"}
-21.07.12 16:48:50.247                     nuctl (I) Staging files and preparing base images
-21.07.12 16:48:50.248                     nuctl (I) Building processor image {"imageName": "cvat/tf.matterport.mask_rcnn:latest"}
-21.07.12 16:48:50.249     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"}
-21.07.12 16:48:53.674     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
-21.07.12 16:48:57.424            nuctl.platform (I) Building docker image {"image": "cvat/tf.matterport.mask_rcnn:latest"}
-21.07.12 16:48:57.763            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/tf.matterport.mask_rcnn:latest", "registry": ""}
-21.07.12 16:48:57.764            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/tf.matterport.mask_rcnn:latest"}
-21.07.12 16:48:57.764                     nuctl (I) Build complete {"result": {"Image":"cvat/tf.matterport.mask_rcnn:latest","UpdatedFunctionConfig":{"metadata":{"name":"tf-matterport-mask-rcnn","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"tensorflow","name":"Mask RCNN via Tensorflow","spec":"[\n  { \"id\": 0, \"name\": \"BG\" },\n  { \"id\": 1, \"name\": \"person\" },\n  { \"id\": 2, \"name\": \"bicycle\" },\n  { \"id\": 3, \"name\": \"car\" },\n  { \"id\": 4, \"name\": \"motorcycle\" },\n  { \"id\": 5, \"name\": \"airplane\" },\n  { \"id\": 6, \"name\": \"bus\" },\n  { \"id\": 7, \"name\": \"train\" },\n  { \"id\": 8, \"name\": \"truck\" },\n  { \"id\": 9, \"name\": \"boat\" },\n  { \"id\": 10, \"name\": \"traffic_light\" },\n  { \"id\": 11, \"name\": \"fire_hydrant\" },\n  { \"id\": 12, \"name\": \"stop_sign\" },\n  { \"id\": 13, \"name\": \"parking_meter\" },\n  { \"id\": 14, \"name\": \"bench\" },\n  { \"id\": 15, \"name\": \"bird\" },\n  { \"id\": 16, \"name\": \"cat\" },\n  { \"id\": 17, \"name\": \"dog\" },\n  { \"id\": 18, \"name\": \"horse\" },\n  { \"id\": 19, \"name\": \"sheep\" },\n  { \"id\": 20, \"name\": \"cow\" },\n  { \"id\": 21, \"name\": \"elephant\" },\n  { \"id\": 22, \"name\": \"bear\" },\n  { \"id\": 23, \"name\": \"zebra\" },\n  { \"id\": 24, \"name\": \"giraffe\" },\n  { \"id\": 25, \"name\": \"backpack\" },\n  { \"id\": 26, \"name\": \"umbrella\" },\n  { \"id\": 27, \"name\": \"handbag\" },\n  { \"id\": 28, \"name\": \"tie\" },\n  { \"id\": 29, \"name\": \"suitcase\" },\n  { \"id\": 30, \"name\": \"frisbee\" },\n  { \"id\": 31, \"name\": \"skis\" },\n  { \"id\": 32, \"name\": \"snowboard\" },\n  { \"id\": 33, \"name\": \"sports_ball\" },\n  { \"id\": 34, \"name\": \"kite\" },\n  { \"id\": 35, \"name\": \"baseball_bat\" },\n  { \"id\": 36, \"name\": \"baseball_glove\" },\n  { \"id\": 37, \"name\": \"skateboard\" },\n  { \"id\": 38, \"name\": \"surfboard\" },\n  { \"id\": 39, \"name\": \"tennis_racket\" },\n  { \"id\": 40, \"name\": \"bottle\" },\n  { \"id\": 41, \"name\": \"wine_glass\" },\n  { \"id\": 42, \"name\": \"cup\" },\n  { \"id\": 43, \"name\": \"fork\" },\n  { \"id\": 44, \"name\": \"knife\" },\n  { \"id\": 45, \"name\": \"spoon\" },\n  { \"id\": 46, \"name\": \"bowl\" },\n  { \"id\": 47, \"name\": \"banana\" },\n  { \"id\": 48, \"name\": \"apple\" },\n  { \"id\": 49, \"name\": \"sandwich\" },\n  { \"id\": 50, \"name\": \"orange\" },\n  { \"id\": 51, \"name\": \"broccoli\" },\n  { \"id\": 52, \"name\": \"carrot\" },\n  { \"id\": 53, \"name\": \"hot_dog\" },\n  { \"id\": 54, \"name\": \"pizza\" },\n  { \"id\": 55, \"name\": \"donut\" },\n  { \"id\": 56, \"name\": \"cake\" },\n  { \"id\": 57, \"name\": \"chair\" },\n  { \"id\": 58, \"name\": \"couch\" },\n  { \"id\": 59, \"name\": \"potted_plant\" },\n  { \"id\": 60, \"name\": \"bed\" },\n  { \"id\": 61, \"name\": \"dining_table\" },\n  { \"id\": 62, \"name\": \"toilet\" },\n  { \"id\": 63, \"name\": \"tv\" },\n  { \"id\": 64, \"name\": \"laptop\" },\n  { \"id\": 65, \"name\": \"mouse\" },\n  { \"id\": 66, \"name\": \"remote\" },\n  { \"id\": 67, \"name\": \"keyboard\" },\n  { \"id\": 68, \"name\": \"cell_phone\" },\n  { \"id\": 69, \"name\": \"microwave\" },\n  { \"id\": 70, \"name\": \"oven\" },\n  { \"id\": 71, \"name\": \"toaster\" },\n  { \"id\": 72, \"name\": \"sink\" },\n  { \"id\": 73, \"name\": \"refrigerator\" },\n  { \"id\": 74, \"name\": \"book\" },\n  { \"id\": 75, \"name\": \"clock\" },\n  { \"id\": 76, \"name\": \"vase\" },\n  { \"id\": 77, \"name\": \"scissors\" },\n  { \"id\": 78, \"name\": \"teddy_bear\" },\n  { \"id\": 79, \"name\": \"hair_drier\" },\n  { \"id\": 80, \"name\": \"toothbrush\" }\n]\n","type":"detector"}},"spec":{"description":"Mask RCNN optimized for GPU","handler":"main:handler","runtime":"python:3.6","env":[{"name":"MASK_RCNN_DIR","value":"/opt/nuclio/Mask_RCNN"}],"resources":{"limits":{"nvidia.com/gpu":"1"}},"image":"cvat/tf.matterport.mask_rcnn:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":1,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/home/nmanovic/Workspace/cvat/serverless/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"functionConfigPath":"serverless/tensorflow/matterport/mask_rcnn/nuclio/function-gpu.yaml","image":"cvat/tf.matterport.mask_rcnn","baseImage":"tensorflow/tensorflow:1.15.5-gpu-py3","directives":{"postCopy":[{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"apt update \u0026\u0026 apt install --no-install-recommends -y git curl"},{"kind":"RUN","value":"git clone --depth 1 https://github.com/matterport/Mask_RCNN.git"},{"kind":"RUN","value":"curl -L https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 -o Mask_RCNN/mask_rcnn_coco.h5"},{"kind":"RUN","value":"pip3 install numpy cython pyyaml keras==2.1.0 scikit-image Pillow"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}}
-21.07.12 16:48:59.071            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
-21.07.12 16:49:00.437                     nuctl (I) Function deploy complete {"functionName": "tf-matterport-mask-rcnn", "httpPort": 49155}
-```
-
-</details>
-
-Now you should be able to annotate objects using segmentation masks.
-
-![Mask RCNN results](/images/mask_rcnn_results.jpg)
 
 ## Adding your own DL models