diff --git a/site/content/en/docs/manual/advanced/serverless-tutorial.md b/site/content/en/docs/manual/advanced/serverless-tutorial.md
index 3a795073..c2c35c10 100644
--- a/site/content/en/docs/manual/advanced/serverless-tutorial.md
+++ b/site/content/en/docs/manual/advanced/serverless-tutorial.md
@@ -28,8 +28,8 @@ When we know that DL models can help us to annotate data faster, the next
 question is how to use them? In CVAT all such DL models are implemented
 as serverless functions for the [Nuclio][nuclio-homepage] serverless platform.
 And there are multiple implemented functions which 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 _Mask RCNN,
+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.
@@ -57,9 +57,6 @@ the tutorial.
 
 ## Using builtin DL models in practice
 
-Let's see on examples how to use DL models for annotation in different
-computer vision tasks.
-
 In the tutorial it is assumed that you already have the cloned
 [CVAT GitHub repo][cvat-github].
 To build CVAT with serverless support you need to run `docker-compose` command
@@ -95,8 +92,8 @@ $ nuctl get functions
 No functions found
 ```
 
-Let's look at specific use cases which can help you to annotate data
-for different tasks.
+Let's see on examples how to use DL models for annotation in different
+computer vision tasks.
 
 ### Tracking using SiamMask
 
@@ -553,7 +550,35 @@ Full code can be found here: [detectron2/retinanet/nuclio/main.py][retinanet-mai
 
 To use the new serverless function you have to deploy it using `nuctl` command.
 The actual deployment process is described in
-[automatic annotation guide][cvat-auto-annotation-guide]
+[automatic annotation guide][cvat-auto-annotation-guide].
+
+<details>
+<summary>
+
+```console
+$ ./serverless/deploy_cpu.sh ./serverless/pytorch/facebookresearch/detectron2/retinanet/
+```
+
+</summary>
+
+```console
+21.07.21 15:20:31.011                     nuctl (I) Deploying function {"name": ""}
+21.07.21 15:20:31.011                     nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
+21.07.21 15:20:31.407                     nuctl (I) Cleaning up before deployment {"functionName": "pth.facebookresearch.detectron2.retinanet_r101"}
+21.07.21 15:20:31.497                     nuctl (I) Function already exists, deleting function containers {"functionName": "pth.facebookresearch.detectron2.retinanet_r101"}
+21.07.21 15:20:31.914                     nuctl (I) Staging files and preparing base images
+21.07.21 15:20:31.915                     nuctl (I) Building processor image {"imageName": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest"}
+21.07.21 15:20:31.916     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"}
+21.07.21 15:20:34.495     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
+21.07.21 15:20:37.524            nuctl.platform (I) Building docker image {"image": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest"}
+21.07.21 15:20:37.852            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest", "registry": ""}
+21.07.21 15:20:37.853            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest"}
+21.07.21 15:20:37.853                     nuctl (I) Build complete {"result": {"Image":"cvat/pth.facebookresearch.detectron2.retinanet_r101:latest","UpdatedFunctionConfig":{"metadata":{"name":"pth.facebookresearch.detectron2.retinanet_r101","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"pytorch","name":"RetinaNet R101","spec":"[\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\":13, \"name\": \"stop_sign\" },\n  { \"id\":14, \"name\": \"parking_meter\" },\n  { \"id\":15, \"name\": \"bench\" },\n  { \"id\":16, \"name\": \"bird\" },\n  { \"id\":17, \"name\": \"cat\" },\n  { \"id\":18, \"name\": \"dog\" },\n  { \"id\":19, \"name\": \"horse\" },\n  { \"id\":20, \"name\": \"sheep\" },\n  { \"id\":21, \"name\": \"cow\" },\n  { \"id\":22, \"name\": \"elephant\" },\n  { \"id\":23, \"name\": \"bear\" },\n  { \"id\":24, \"name\": \"zebra\" },\n  { \"id\":25, \"name\": \"giraffe\" },\n  { \"id\":27, \"name\": \"backpack\" },\n  { \"id\":28, \"name\": \"umbrella\" },\n  { \"id\":31, \"name\": \"handbag\" },\n  { \"id\":32, \"name\": \"tie\" },\n  { \"id\":33, \"name\": \"suitcase\" },\n  { \"id\":34, \"name\": \"frisbee\" },\n  { \"id\":35, \"name\": \"skis\" },\n  { \"id\":36, \"name\": \"snowboard\" },\n  { \"id\":37, \"name\": \"sports_ball\" },\n  { \"id\":38, \"name\": \"kite\" },\n  { \"id\":39, \"name\": \"baseball_bat\" },\n  { \"id\":40, \"name\": \"baseball_glove\" },\n  { \"id\":41, \"name\": \"skateboard\" },\n  { \"id\":42, \"name\": \"surfboard\" },\n  { \"id\":43, \"name\": \"tennis_racket\" },\n  { \"id\":44, \"name\": \"bottle\" },\n  { \"id\":46, \"name\": \"wine_glass\" },\n  { \"id\":47, \"name\": \"cup\" },\n  { \"id\":48, \"name\": \"fork\" },\n  { \"id\":49, \"name\": \"knife\" },\n  { \"id\":50, \"name\": \"spoon\" },\n  { \"id\":51, \"name\": \"bowl\" },\n  { \"id\":52, \"name\": \"banana\" },\n  { \"id\":53, \"name\": \"apple\" },\n  { \"id\":54, \"name\": \"sandwich\" },\n  { \"id\":55, \"name\": \"orange\" },\n  { \"id\":56, \"name\": \"broccoli\" },\n  { \"id\":57, \"name\": \"carrot\" },\n  { \"id\":58, \"name\": \"hot_dog\" },\n  { \"id\":59, \"name\": \"pizza\" },\n  { \"id\":60, \"name\": \"donut\" },\n  { \"id\":61, \"name\": \"cake\" },\n  { \"id\":62, \"name\": \"chair\" },\n  { \"id\":63, \"name\": \"couch\" },\n  { \"id\":64, \"name\": \"potted_plant\" },\n  { \"id\":65, \"name\": \"bed\" },\n  { \"id\":67, \"name\": \"dining_table\" },\n  { \"id\":70, \"name\": \"toilet\" },\n  { \"id\":72, \"name\": \"tv\" },\n  { \"id\":73, \"name\": \"laptop\" },\n  { \"id\":74, \"name\": \"mouse\" },\n  { \"id\":75, \"name\": \"remote\" },\n  { \"id\":76, \"name\": \"keyboard\" },\n  { \"id\":77, \"name\": \"cell_phone\" },\n  { \"id\":78, \"name\": \"microwave\" },\n  { \"id\":79, \"name\": \"oven\" },\n  { \"id\":80, \"name\": \"toaster\" },\n  { \"id\":81, \"name\": \"sink\" },\n  { \"id\":83, \"name\": \"refrigerator\" },\n  { \"id\":84, \"name\": \"book\" },\n  { \"id\":85, \"name\": \"clock\" },\n  { \"id\":86, \"name\": \"vase\" },\n  { \"id\":87, \"name\": \"scissors\" },\n  { \"id\":88, \"name\": \"teddy_bear\" },\n  { \"id\":89, \"name\": \"hair_drier\" },\n  { \"id\":90, \"name\": \"toothbrush\" }\n]\n","type":"detector"}},"spec":{"description":"RetinaNet R101 from Detectron2","handler":"main:handler","runtime":"python:3.8","resources":{},"image":"cvat/pth.facebookresearch.detectron2.retinanet_r101:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":2,"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":{"image":"cvat/pth.facebookresearch.detectron2.retinanet_r101","baseImage":"ubuntu:20.04","directives":{"preCopy":[{"kind":"ENV","value":"DEBIAN_FRONTEND=noninteractive"},{"kind":"RUN","value":"apt-get update \u0026\u0026 apt-get -y install curl git python3 python3-pip"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html"},{"kind":"RUN","value":"pip3 install 'git+https://github.com/facebookresearch/detectron2@v0.4'"},{"kind":"RUN","value":"curl -O https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/model_final_971ab9.pkl"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/local/bin/pip"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}}
+21.07.21 15:20:39.042            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
+21.07.21 15:20:40.480                     nuctl (I) Function deploy complete {"functionName": "pth.facebookresearch.detectron2.retinanet_r101", "httpPort": 49153}
+```
+
+</details>
 
 ## Advanced capabilities
 
@@ -606,12 +631,12 @@ For `RetinaNet R101` which was added above modifications will look like:
        restartPolicy:
 ```
 
-_Note: But need to know that GPU has very limited amount of memory and it doesn't
-allow to run multiple serverless functions in parallel for now using free
-open-source Nuclio version on the local platform because scaling to zero
-feature is absent. Theoretically it is possible to run different functions
-on different GPUs, but it requires to change source code on corresponding
-serverless functions to choose a free GPU._
+_Note: GPU has very limited amount of memory and it doesn't allow to run
+multiple serverless functions in parallel for now using free open-source
+Nuclio version on the local platform because scaling to zero feature is
+absent. Theoretically it is possible to run different functions on different
+GPUs, but it requires to change source code on corresponding serverless
+functions to choose a free GPU._
 
 ### Debugging a serverless function