Automatic Annotation

I deployed my custom model for automatic annotation and it seems perfect.
It shows the inference progress bar and the docker logs show normal.
However, the Annotation did not show on my image dataset in CVAT.
What can I do?

More info
The following is what I send to CVAT. In other words, it is the context.Response part.
<class 'list'>---[{'confidence': '0.4071217', 'label': '0.0', 'points': [360.0, 50.0, 1263.0, 720.0], 'type': 'rectangle'}]

@glenn-jocher Can you help me with that? What can I do?

@QuarTerll I don't maintain this repository, I maintain Ultralytics YOLOv3 and YOLOv5.

@QuarTerll I don't maintain this repository, I maintain Ultralytics YOLOv3 and YOLOv5.

@glenn-jocher ops， srrrrry... I was working on yolov5 recently. So I just remind your name when I get in trouble... Sorry to bother :<

I found that the logs show "Run SiamMask Model" every time when my inference runs. So I guess the SiamMask model is to draw something on the image such rectangle.
Then I just found that this is a model called SiamMask in the serverless directory. So I am trying to deploy this model first then try to do the Automatic Annotation again.  Hope it can work :)

@nmanovic Could you please help me with that? What can I do?
For now, my local "SiamMask Model" is still building. It already passed three hours and I cannot see any logs :<

I have finished deployed the SiamMask model and It is still not working. On the local website of cvat, it shows the following.

I have finished deployed the "saic_vul" model in the directory named serverless/pytorch and It is still not working. On the local website of cvat, it shows that "Automatic annotation finished for task 17". However, there is still no annotations showed on my image dataset.

So, @nmanovic, I see that you add a milestone hmmmmm... Does it mean that this function is not finished for the moment?

@QuarTerll There are a couple of issues in the semi-automatic nuctl pipeline I will address them in my soon-to-be PR for the Automatic annotation probably by tomorrow. The major one is that make sure to install nuctl 1.4.8 for now, until I update the documentation and boost the version to new versions. If you are still having problems I suggest wait a couple of days, I will ping you here when I am done.

I had a problem with nuctl 1.4.8 while deploying functions, but was able to do with 1.5.7
However, models didn't appear @ http://localhost:8080/models

I have also submitted the full issue at Issue#2541
Can you please help me on this as well?
Is it something related with version issues?

@beep-love yes, most likely.
Change line
Change the version to 1.5.7
Then rebuild the container.
docker-compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml up —build
To debug the functions, you can use nuclio dashboard at localhost:8070 make sure the function is up and running there.
Wait by tonight I will do a PR

Changed the line.
Also, I have a running instance of the function in my nuclio dashboard
Still the same error!
I have renamed my nuclio release file from nuctl-1.5.7-linux-amd64 to nuctl-1.5.7
Does that make any difference to this relating problem?

How do you deploy your function?
nuctl-1.5.7 deploy ....
Put a screenshot from your docker ps -a and also the nuclio dashboard -> in cvat that shows the function is up and running

@QuarTerll it might be due to the label. It might be an int instead of 0.0 .double check that

Command to deploy function
sudo ./nuctl-1.5.7 create project cvat
sudo ./nuctl-1.5.7 deploy --project-name cvat \
--path serverless/openvino/dextr/nuclio \
--volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
--platform local

And the sc for docker ps -a with nuclio dashboard's sc :

I have finished deployed the "saic_vul" model in the directory named serverless/pytorch and It is still not working. On the local website of cvat, it shows that "Automatic annotation finished for task 17". However, there is still no annotations showed on my image dataset.
So, @nmanovic, I see that you add a milestone hmmmmm... Does it mean that this function is not finished for the moment?

Same here. Able to finish the automatic annotation, but no actual annotation is displayed after complete and click into the job. While the result of using AI tools is fine.  It seems there is a problem in saving the results of automatic annotation.

@QuarTerll it might be due to the label. It might be an int instead of 0.0 .double check that

@jahaniam Thanks for your help.
For the moment, I checked that in my YAML file, there is something like int such as id: 0, but my response is 0.0.
However, it is still not working after I just tried to use labels of an int instead of float, such as 0 instead of 0.0.
And another question:
Is my response type correct?
I didn't find the interface docs. I just followed one of the sample code in dirs named serverless.
I would like to know how to check the interface type? Lol...

Command to deploy function
sudo ./nuctl-1.5.7 create project cvat
sudo ./nuctl-1.5.7 deploy --project-name cvat \
--path serverless/openvino/dextr/nuclio \
--volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
--platform local

And the sc for docker ps -a with nuclio dashboard's sc :

looks ok to me. I don't know why it's not working for you. Try without sudo and give it chmod +x nuctl-1.5.7
I've seen in the logs some errors of permissions for nuctl. Before deploying remove the current function as well using dashboard.

the same to me, look the docker contrainer logs.

I tested the base64 images in nuclio dashboard and it returned the result normally.

I tested the base64 images in nuclio dashboard and it returned the result normally.

I guess it may be some bugs there about the function of drawing a rectangle.
I am trying to do this semi-anna myself. I will do the inference and save the results by some type of dataset and load them to the CVAT platform and then do the annotations.

@leemengxing @jahaniam @gen-ko @beep-love
I have finished the semi-auto annotation myself. Here is the way that may have a little help.

Create your task
Download dataset. (bug here, not just annotation, see issues 2473)
Unzip the dataset. And change the dir_path and dir_txt to the path of the dataset where save images and annotations.
Do the inference and save results of needed format to txt.
Upload the dataset to your task.
Finished.

Here is my python code for example.
dir_path = './path/to/obj_train_data/your_task'
dir_txt = './path/to/obj_train_data/your_task'

for file_name in os.listdir(dir_path):
    if '.txt' in file_name:
        continue
    name, ext = os.path.splitext(file_name)

    file_path = os.path.join(dir_path, file_name)
    txt_path = os.path.join(dir_txt, f"{name}.txt")

    im = cv2.imread(file_path)

    if im is None:
        print('Image is None')
        continue

    inference(model, im, txt_path)

I am also seeing that automatic annotation does not get displayed in the images if you try to do it for a whole task, but it seems to work if you run the annotation on the images individually.

I have finished the semi-auto annotation myself. Here is the way that may have a little help.

Create your task
Download dataset. (bug here, not just annotation, see issues 2473)
Unzip the dataset. And change the dir_path and dir_txt to the path of the dataset where save images and annotations.
Do the inference and save results of needed format to txt.
Upload the dataset to your task.
Finished.

Here is my python code for example.
dir_path = './path/to/obj_train_data/your_task'
dir_txt = './path/to/obj_train_data/your_task'

for file_name in os.listdir(dir_path):
    if '.txt' in file_name:
        continue
    name, ext = os.path.splitext(file_name)

    file_path = os.path.join(dir_path, file_name)
    txt_path = os.path.join(dir_txt, f"{name}.txt")

    im = cv2.imread(file_path)

    if im is None:
        print('Image is None')
        continue

    inference(model, im, txt_path)

@Inquisitive-ME
YES. What I did is to do the automatic annotations for the whole task. For now, you can do the above to do this.
How do you do to run the auto anno for single images? Maybe my version of CVAT is not the latest?

Yea doing the annotations outside of CVAT is not desirable for me.
But if you click on the AI Tools icon (picture below) in the toolbar you can go to detectors and should be able to select your model to annotate that image.

Yea doing the annotations outside of CVAT is not desirable for me.
But if you click on the AI Tools icon (picture below) in the toolbar you can go to detectors and should be able to select your model to annotate that image.

Ty

I can confirm automatic annotation is broken for tasks. It only works for single images I have tried two models. Faster rcnn and mask rcnn. It works fine for a single image but when I use it on a task containing multiple images although it shows it has completed but it doesn't show any annotation results on the images.
@nmanovic  can we have a look on this problem please? I am working on the gpu version of the maskrcnn rn and a fix for cpu version. I will do a PR soon for that.

I can confirm automatic annotation is broken for tasks. It only works for single images I have tried two models. Faster rcnn and mask rcnn. It works fine for a single image but when I use it on a task containing multiple images although it shows it has completed but it doesn't show any annotation results on the images.
@nmanovic  can we have a look on this problem please? I am working on the gpu version of the maskrcnn rn and a fix for cpu version. I will do a PR soon for that.

Same problem documented here, possible duplication https://github.com/openvinotoolkit/cvat/issues/2644
F-RCNN on CPU

Same problem documented here, possible duplication https://github.com/openvinotoolkit/cvat/issues/2644
F-RCNN on CPU

Do we know which commit broke the automatic annotation for Tasks?

Do we know which commit broke the automatic annotation for Tasks?

Do we know which commit broke the automatic annotation for Tasks?

I do not know which one broke it but it is for sure broken. I think it was working maybe a month ago. It would be great if you can pinpoint it somehow.

Do we know which commit broke the automatic annotation for Tasks?

I do not know which one broke it but it is for sure broken. I think it was working maybe a month ago. It would be great if you can pinpoint it somehow.

@jahaniam I am building different versions moving back and testing. Will let you know.
I am on Kubernetes though, so won't be able to go as far as 1.11.

@jahaniam I am building different versions moving back and testing. Will let you know.
I am on Kubernetes though, so won't be able to go as far as 1.11.

By branch/tag:

develop - broken
rekease-1.2.0 - broken
v1.2.0-beta - broken
v1.2.0-alpha - unable to launch task annotation, button is grayed out

By branch/tag:

develop - broken
rekease-1.2.0 - broken
v1.2.0-beta - broken
v1.2.0-alpha - unable to launch task annotation, button is grayed out

Interestingly, it does work on cvat.org

Interestingly, it does work on cvat.org

Over here it says 1.5.8 https://github.com/openvinotoolkit/cvat/blob/develop/components/serverless/docker-compose.serverless.yml
I'm running nuclio at 1.5.12, so perhaps this is why its not working for me.

Over here it says 1.5.8 https://github.com/openvinotoolkit/cvat/blob/develop/components/serverless/docker-compose.serverless.yml
I'm running nuclio at 1.5.12, so perhaps this is why its not working for me.

I've installed nuclio helm from branch 1.5.8 but it does not create any annotations either.

I've installed nuclio helm from branch 1.5.8 but it does not create any annotations either.

I have also tested with corresponding versions of nuctl, as it deploys a specific base image. No luck.

I have also tested with corresponding versions of nuctl, as it deploys a specific base image. No luck.

We need to find out how are they running it correctly on cvat.org

We need to find out how are they running it correctly on cvat.org

I've installed nuclio helm from branch 1.5.8 but it does not create any annotations either. Neither does 1.5.9.

It is not related to nuclio. Nuclio is doing ok and the version mismatch should be ok as well.
Nuclio was boosted in my PR in https://github.com/openvinotoolkit/cvat/pull/2546 from 1.4.8 to 1.5.8 to allow gpu support. You can test 1.4.8 but I don't think it is the nuclio version as functions are working fine.

I've installed nuclio helm from branch 1.5.8 but it does not create any annotations either. Neither does 1.5.9.

It is not related to nuclio. Nuclio is doing ok and the version mismatch should be ok as well.
Nuclio was boosted in my PR in https://github.com/openvinotoolkit/cvat/pull/2546 from 1.4.8 to 1.5.8 to allow gpu support. You can test 1.4.8 but I don't think it is the nuclio version as functions are working fine.

@jahaniam do you know what commit is running on cvat.org? It does work in there.

@jahaniam do you know what commit is running on cvat.org? It does work in there.

I've installed nuclio helm from branch 1.5.8 but it does not create any annotations either. Neither does 1.5.9.

It is not related to nuclio. Nuclio is doing ok and the version mismatch should be ok as well between the versions you mentioned.
Nuclio was boosted in my PR in #2546 from 1.4.8 to 1.5.8 to allow gpu support. You can test 1.4.8 but I don't think it is the nuclio version as functions are working fine.

I get that but I have reverted CVAT many versions back and it does not help at all. We need that secret sauce from cvat.org.

I've installed nuclio helm from branch 1.5.8 but it does not create any annotations either. Neither does 1.5.9.

It is not related to nuclio. Nuclio is doing ok and the version mismatch should be ok as well between the versions you mentioned.
Nuclio was boosted in my PR in #2546 from 1.4.8 to 1.5.8 to allow gpu support. You can test 1.4.8 but I don't think it is the nuclio version as functions are working fine.

I get that but I have reverted CVAT many versions back and it does not help at all. We need that secret sauce from cvat.org.

It says:
Computer Vision Annotation Tool
CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team.

Server version: 1.2rc0

Core version: 3.10.0

Canvas version: 2.2.1

UI version: 1.13.1

It says:
Computer Vision Annotation Tool
CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team.

Server version: 1.2rc0

Core version: 3.10.0

Canvas version: 2.2.1

UI version: 1.13.1

@jahaniam I know that, but we need to know specific commits and nuclio version.

@jahaniam I know that, but we need to know specific commits and nuclio version.

for me cvat.org doesn't work either for fast-rcnn for task auto annotation. what model are you using?

for me cvat.org doesn't work either for fast-rcnn for task auto annotation. what model are you using?

The Faster RCNN on cvat.org works fine detecting persons for me. But now I realize what difference we may have. On my k8s cluster, I am trying to use TF F-RCNN, which doesn't use the volume handler, and perhaps the TensorFlow models are the ones that are broken.

The Faster RCNN on cvat.org works fine detecting persons for me. But now I realize what difference we may have. On my k8s cluster, I am trying to use TF F-RCNN, which doesn't use the volume handler, and perhaps the TensorFlow models are the ones that are broken.

The Mask RCNN also works on cvat.org for me. Through vino.

The Mask RCNN also works on cvat.org for me. Through vino.

I'm unable to deploy any openvino to my k8s cluster though...
First, I had to fix the f-rcnn's name as it contained _.
But now it hangs on the following command when running the nuctl deploy:
nuctl deploy --project-name cvat --path "serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio" --volume "serverless/openvino/common:/opt/nuclio/common" -n nuclio --registry something.azurecr.io --verbose
21.01.06 09:05:54.286            nuctl.platform (D) Using kubeconfig {"kubeconfigPath": "/root/.kube/config"}
21.01.06 09:05:54.290 tl.platform.docker.runner (D) Executing {"command": "docker version"}
21.01.06 09:05:54.367 tl.platform.docker.runner (D) Command executed successfully {"output": "Client: Docker Engine - Community\n Version:           20.10.0\n API version:       1.41\n Go version:        go1.13.15\n Git commit:        7287ab3\n Built:             Tue Dec  8 18:59:40 2020\n OS/Arch:           linux/amd64\n Context:           default\n Experimental:      true\n\nServer: Docker Engine - Community\n Engine:\n  Version:          20.10.0\n  API version:      1.41 (minimum version 1.12)\n  Go version:       go1.13.15\n  Git commit:       eeddea2\n  Built:            Tue Dec  8 18:58:04 2020\n  OS/Arch:          linux/amd64\n  Experimental:     false\n containerd:\n  Version:          v1.4.3\n  GitCommit:        269548fa27e0089a8b8278fc4fc781d7f65a939b\n runc:\n  Version:          1.0.0-rc92\n  GitCommit:        ff819c7e9184c13b7c2607fe6c30ae19403a7aff\n docker-init:\n  Version:          0.19.0\n  GitCommit:        de40ad0\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:54.647                     nuctl (D) Created platform {"name": "kube"}
21.01.06 09:05:54.647                     nuctl (D) Deploying function {"functionConfig": {"metadata":{"namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"}},"spec":{"resources":{},"targetCPU":75,"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"path":"serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio","registry":"something.azurecr.io"},"platform":{},"readinessTimeoutSeconds":60,"eventTimeout":""}}}
21.01.06 09:05:54.650            nuctl.platform (D) Enriching function HTTP trigger with service type {"functionName": "", "triggerName": "default-http", "serviceType": "ClusterIP"}
21.01.06 09:05:54.738                     nuctl (I) Deploying function {"name": ""}
21.01.06 09:05:54.738                     nuctl (I) Building {"versionInfo": "Label: 1.5.12, Git commit: 215f85cc4130b869df4b332cdbc8fe7a8768335f, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
21.01.06 09:05:54.738                     nuctl (D) Function configuration found in directory {"configFilePath": "serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio/function.yaml"}
21.01.06 09:05:54.738                     nuctl (D) Read function configuration file {"contents": "metadata:\n  name: openvino-omz-public-faster-rcnn-inception-v2-coco\n  namespace: cvat\n  annotations:\n    name: Faster RCNN\n    type: detector\n    framework: openvino\n    spec: |\n      [\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\nspec:\n  description: Faster RCNN inception v2 COCO via Intel OpenVINO toolkit\n  runtime: 'python:3.6'\n  handler: main:handler\n  eventTimeout: 30s\n  env:\n    - name: NUCLIO_PYTHON_EXE_PATH\n      value: /opt/nuclio/common/python3\n\n  build:\n    image: cvat/openvino.omz.public.faster_rcnn_inception_v2_coco\n    baseImage: openvino/ubuntu18_dev:2020.2\n\n    directives:\n      preCopy:\n        - kind: USER\n          value: root\n        - kind: WORKDIR\n          value: /opt/nuclio\n        - kind: RUN\n          value: ln -s /usr/bin/pip3 /usr/bin/pip\n        - kind: RUN\n          value: /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo\n        - kind: RUN\n          value: /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo\n\n  triggers:\n    myHttpTrigger:\n      maxWorkers: 2\n      kind: 'http'\n      workerAvailabilityTimeoutMilliseconds: 10000\n      attributes:\n        maxRequestBodySize: 33554432 # 32MB\n\n  platform:\n    attributes:\n      restartPolicy:\n        name: always\n        maximumRetryCount: 3\n"}
21.01.06 09:05:54.741                     nuctl (D) Created base temporary dir {"dir": "/tmp/nuclio-build-071852609"}
21.01.06 09:05:54.741                     nuctl (D) Created temporary dir {"dir": "/tmp/nuclio-build-071852609/staging"}
21.01.06 09:05:54.741                     nuctl (D) Created staging dir {"dir": "/tmp/nuclio-build-071852609/staging"}
21.01.06 09:05:54.741                     nuctl (D) Executing {"command": "docker version"}
21.01.06 09:05:54.819                     nuctl (D) Command executed successfully {"output": "Client: Docker Engine - Community\n Version:           20.10.0\n API version:       1.41\n Go version:        go1.13.15\n Git commit:        7287ab3\n Built:             Tue Dec  8 18:59:40 2020\n OS/Arch:           linux/amd64\n Context:           default\n Experimental:      true\n\nServer: Docker Engine - Community\n Engine:\n  Version:          20.10.0\n  API version:      1.41 (minimum version 1.12)\n  Go version:       go1.13.15\n  Git commit:       eeddea2\n  Built:            Tue Dec  8 18:58:04 2020\n  OS/Arch:          linux/amd64\n  Experimental:     false\n containerd:\n  Version:          v1.4.3\n  GitCommit:        269548fa27e0089a8b8278fc4fc781d7f65a939b\n runc:\n  Version:          1.0.0-rc92\n  GitCommit:        ff819c7e9184c13b7c2607fe6c30ae19403a7aff\n docker-init:\n  Version:          0.19.0\n  GitCommit:        de40ad0\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:54.819                     nuctl (D) Enriched configuration {"functionConfig": {"metadata":{"name":"openvino-omz-public-faster-rcnn-inception-v2-coco","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"openvino","name":"Faster RCNN","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":"Faster RCNN inception v2 COCO via Intel OpenVINO toolkit","handler":"main:handler","runtime":"python:3.6","env":[{"name":"NUCLIO_PYTHON_EXE_PATH","value":"/opt/nuclio/common/python3"}],"resources":{},"targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"path":"/workspaces/cvat/serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio","registry":"something.azurecr.io","image":"cvat/openvino.omz.public.faster_rcnn_inception_v2_coco","baseImage":"openvino/ubuntu18_dev:2020.2","directives":{"preCopy":[{"kind":"USER","value":"root"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/bin/pip"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo"}]},"codeEntryType":"image"},"platform":{"attributes":{"restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}, "platform": "kube", "dependantImagesRegistryURL": "", "outputImageFile": "", "processorImage": {}}
21.01.06 09:05:54.819            nuctl.platform (D) Enriching function HTTP trigger with service type {"functionName": "openvino-omz-public-faster-rcnn-inception-v2-coco", "triggerName": "myHttpTrigger", "serviceType": "ClusterIP"}
21.01.06 09:05:54.905            nuctl.platform (D) Getting function {"namespace": "nuclio", "name": "openvino-omz-public-faster-rcnn-inception-v2-coco"}
21.01.06 09:05:54.947                     nuctl (D) Creating/updating function {"functionExists": false, "functionInstance": null}
21.01.06 09:05:54.948                     nuctl (D) Populated function with configuration and status {"function": {"metadata":{"name":"openvino-omz-public-faster-rcnn-inception-v2-coco","namespace":"nuclio","creationTimestamp":null,"labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"openvino","name":"Faster RCNN","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":"Faster RCNN inception v2 COCO via Intel OpenVINO toolkit","handler":"main:handler","runtime":"python:3.6","env":[{"name":"NUCLIO_PYTHON_EXE_PATH","value":"/opt/nuclio/common/python3"}],"resources":{},"targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432,"serviceType":"ClusterIP"}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"alias":"latest","build":{"registry":"something.azurecr.io","image":"cvat/openvino.omz.public.faster_rcnn_inception_v2_coco","baseImage":"openvino/ubuntu18_dev:2020.2","directives":{"preCopy":[{"kind":"USER","value":"root"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/bin/pip"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo"}]},"codeEntryType":"image"},"platform":{"attributes":{"restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"},"status":{"state":"building"}}}
21.01.06 09:05:55.007                     nuctl (I) Staging files and preparing base images
21.01.06 09:05:55.008                     nuctl (D) Runtime provided handler objects to staging dir {"handlerDirObjectPaths": ["/workspaces/cvat/serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio"], "handlerDirInStaging": "/tmp/nuclio-build-071852609/staging/handler", "handlerDirIncludingSubpath": "/tmp/nuclio-build-071852609/staging/handler"}
21.01.06 09:05:55.009                     nuctl (D) Created processor Dockerfile {"dockerfileInfo": "# Multistage builds\n\n\n\n# From the base image\nFROM openvino/ubuntu18_dev:2020.2\n\n# Old(er) Docker support - must use all build args\nARG NUCLIO_LABEL\nARG NUCLIO_ARCH\nARG NUCLIO_BUILD_LOCAL_HANDLER_DIR\n\n\n# Run the pre-copy directives\n\nUSER root\n\nWORKDIR /opt/nuclio\n\nRUN ln -s /usr/bin/pip3 /usr/bin/pip\n\nRUN /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo\n\nRUN /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo\n\n\n\n# Copy required objects from the suppliers\n\nCOPY artifacts/processor /usr/local/bin/processor\n\nCOPY artifacts/py /opt/nuclio/\n\n\n\nCOPY handler /opt/nuclio\n\n\n\n\n# Run the post-copy directives\n\nRUN pip install nuclio-sdk msgpack --no-index --find-links /opt/nuclio/whl\n\n\n# Run processor with configuration and platform configuration\nCMD [ \"processor\" ]\n", "baseImageRegistry": "", "onbuildImageRegistry": "quay.io"}
21.01.06 09:05:55.009                     nuctl (I) Building processor image {"imageName": "cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest"}
21.01.06 09:05:55.009     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64"}
21.01.06 09:05:55.009 tl.platform.docker.runner (D) Executing {"command": "docker pull quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64"}
21.01.06 09:05:57.581 tl.platform.docker.runner (D) Command executed successfully {"output": "1.5.12-amd64: Pulling from nuclio/handler-builder-python-onbuild\nDigest: sha256:8e1623b938e34d049d5a6a8dc2c55c61525aa5f023a0d2c40933880d92e3c25e\nStatus: Image is up to date for quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\nquay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:57.582            nuctl.platform (D) Generated onbuild Dockerfile {"contents": "FROM quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\nARG NUCLIO_LABEL\nARG NUCLIO_ARCH\n"}
21.01.06 09:05:57.582     nuctl.platform.docker (D) Building image {"buildOptions": {"Image":"nuclio-onbuild-bvqnqt8ibcio71lmipeg","ContextDir":"/tmp/nuclio-build-071852609/staging","DockerfilePath":"/tmp/nuclio-build-071852609/staging/Dockerfile.onbuild","NoCache":false,"BuildArgs":{"NUCLIO_ARCH":"amd64","NUCLIO_BUILD_LOCAL_HANDLER_DIR":"handler","NUCLIO_LABEL":"1.5.12"}}}
21.01.06 09:05:57.582 tl.platform.docker.runner (D) Executing {"command": "docker build --network host --force-rm -t nuclio-onbuild-bvqnqt8ibcio71lmipeg -f /tmp/nuclio-build-071852609/staging/Dockerfile.onbuild  --build-arg NUCLIO_LABEL=1.5.12 --build-arg NUCLIO_ARCH=amd64 --build-arg NUCLIO_BUILD_LOCAL_HANDLER_DIR=handler  ."}
21.01.06 09:05:57.905 tl.platform.docker.runner (D) Command executed successfully {"output": "", "stderr": "#1 [internal] load build definition from Dockerfile.onbuild\n#1 sha256:1de3cccbd085fb96ab2b75f038aaddeaf77e4fe002114415124bedbd5ad97ee5\n#1 transferring dockerfile: 142B done\n#1 DONE 0.0s\n\n#2 [internal] load .dockerignore\n#2 sha256:9bd2604440c32207551e0d4cb705a5e48c857b7affd3dfffb53806777ac48fad\n#2 transferring context: 2B done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#3 sha256:6fb3008c058fc79bf7207583135d460bfe6c809256a6cede99a73ee72e1aa18e\n#3 DONE 0.0s\n\n#4 [1/1] FROM quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#4 sha256:4cb1d92adda11efc35f148d087c99d59fe9b59e9ec3fd51c863cfb471a6373a1\n#4 CACHED\n\n#5 exporting to image\n#5 sha256:e8c613e07b0b7ff33893b694f7759a10d42e180f2b4dc349fb57dc6b71dcab00\n#5 exporting layers done\n#5 writing image sha256:d2976478040a95f6ccba78dfee5271f1446ef628f987e0de0faebac254540c1f done\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg done\n#5 DONE 0.0s\n", "exitCode": 0}
21.01.06 09:05:57.906     nuctl.platform.docker (W) Docker command outputted to stderr - this may result in errors {"workingDir": "/tmp/nuclio-build-071852609/staging", "cmd": "docker build --network host --force-rm -t nuclio-onbuild-bvqnqt8ibcio71lmipeg -f /tmp/nuclio-build-071852609/staging/Dockerfile.onbuild  --build-arg NUCLIO_LABEL=1.5.12 --build-arg NUCLIO_ARCH=amd64 --build-arg NUCLIO_BUILD_LOCAL_HANDLER_DIR=handler  .", "stderr": "#1 [internal] load build definition from Dockerfile.onbuild\n#1 sha256:1de3cccbd085fb96ab2b75f038aaddeaf77e4fe002114415124bedbd5ad97ee5\n#1 transferring dockerfile: 142B done\n#1 DONE 0.0s\n\n#2 [internal] load .dockerignore\n#2 sha256:9bd2604440c32207551e0d4cb705a5e48c857b7affd3dfffb53806777ac48fad\n#2 transferring context: 2B done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#3 sha256:6fb3008c058fc79bf7207583135d460bfe6c809256a6cede99a73ee72e1aa18e\n#3 DONE 0.0s\n\n#4 [1/1] FROM quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#4 sha256:4cb1d92adda11efc35f148d087c99d59fe9b59e9ec3fd51c863cfb471a6373a1\n#4 CACHED\n\n#5 exporting to image\n#5 sha256:e8c613e07b0b7ff33893b694f7759a10d42e180f2b4dc349fb57dc6b71dcab00\n#5 exporting layers done\n#5 writing image sha256:d2976478040a95f6ccba78dfee5271f1446ef628f987e0de0faebac254540c1f done\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg done\n#5 DONE 0.0s\n"}
21.01.06 09:05:57.906     nuctl.platform.docker (D) Successfully built image {"image": "nuclio-onbuild-bvqnqt8ibcio71lmipeg"}
21.01.06 09:05:57.906 tl.platform.docker.runner (D) Executing {"command": "docker create nuclio-onbuild-bvqnqt8ibcio71lmipeg /bin/sh"}
21.01.06 09:05:57.997 tl.platform.docker.runner (D) Command executed successfully {"output": "8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:57.997 tl.platform.docker.runner (D) Executing {"command": "docker cp 8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62:/home/nuclio/bin/processor /tmp/nuclio-build-071852609/staging/artifacts/processor"}
21.01.06 09:05:58.830 tl.platform.docker.runner (D) Command executed successfully {"output": "", "stderr": "", "exitCode": 0}
21.01.06 09:05:58.830 tl.platform.docker.runner (D) Executing {"command": "docker cp 8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62:/home/nuclio/bin/py /tmp/nuclio-build-071852609/staging/artifacts/py"}
21.01.06 09:05:58.942 tl.platform.docker.runner (D) Command executed successfully {"output": "", "stderr": "", "exitCode": 0}
21.01.06 09:05:58.942 tl.platform.docker.runner (D) Executing {"command": "docker rm -f 8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62"}
21.01.06 09:05:59.012 tl.platform.docker.runner (D) Command executed successfully {"output": "8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:59.012 tl.platform.docker.runner (D) Executing {"command": "docker rmi -f nuclio-onbuild-bvqnqt8ibcio71lmipeg"}
21.01.06 09:05:59.076 tl.platform.docker.runner (D) Command executed successfully {"output": "Untagged: nuclio-onbuild-bvqnqt8ibcio71lmipeg:latest\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:59.076            nuctl.platform (I) Building docker image {"image": "cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest"}
21.01.06 09:05:59.076     nuctl.platform.docker (D) Building image {"buildOptions": {"Image":"cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest","ContextDir":"/tmp/nuclio-build-071852609/staging","DockerfilePath":"/tmp/nuclio-build-071852609/staging/Dockerfile.processor","NoCache":false,"BuildArgs":{"NUCLIO_ARCH":"amd64","NUCLIO_BUILD_LOCAL_HANDLER_DIR":"handler","NUCLIO_LABEL":"1.5.12"}}}
21.01.06 09:05:59.076 tl.platform.docker.runner (D) Executing {"command": "docker build --network host --force-rm -t cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest -f /tmp/nuclio-build-071852609/staging/Dockerfile.processor  --build-arg NUCLIO_LABEL=1.5.12 --build-arg NUCLIO_ARCH=amd64 --build-arg NUCLIO_BUILD_LOCAL_HANDLER_DIR=handler  ."}

I'm unable to deploy any openvino to my k8s cluster though...
First, I had to fix the f-rcnn's name as it contained _.
But now it hangs on the following command when running the nuctl deploy:
nuctl deploy --project-name cvat --path "serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio" --volume "serverless/openvino/common:/opt/nuclio/common" -n nuclio --registry something.azurecr.io --verbose
21.01.06 09:05:54.286            nuctl.platform (D) Using kubeconfig {"kubeconfigPath": "/root/.kube/config"}
21.01.06 09:05:54.290 tl.platform.docker.runner (D) Executing {"command": "docker version"}
21.01.06 09:05:54.367 tl.platform.docker.runner (D) Command executed successfully {"output": "Client: Docker Engine - Community\n Version:           20.10.0\n API version:       1.41\n Go version:        go1.13.15\n Git commit:        7287ab3\n Built:             Tue Dec  8 18:59:40 2020\n OS/Arch:           linux/amd64\n Context:           default\n Experimental:      true\n\nServer: Docker Engine - Community\n Engine:\n  Version:          20.10.0\n  API version:      1.41 (minimum version 1.12)\n  Go version:       go1.13.15\n  Git commit:       eeddea2\n  Built:            Tue Dec  8 18:58:04 2020\n  OS/Arch:          linux/amd64\n  Experimental:     false\n containerd:\n  Version:          v1.4.3\n  GitCommit:        269548fa27e0089a8b8278fc4fc781d7f65a939b\n runc:\n  Version:          1.0.0-rc92\n  GitCommit:        ff819c7e9184c13b7c2607fe6c30ae19403a7aff\n docker-init:\n  Version:          0.19.0\n  GitCommit:        de40ad0\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:54.647                     nuctl (D) Created platform {"name": "kube"}
21.01.06 09:05:54.647                     nuctl (D) Deploying function {"functionConfig": {"metadata":{"namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"}},"spec":{"resources":{},"targetCPU":75,"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"path":"serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio","registry":"something.azurecr.io"},"platform":{},"readinessTimeoutSeconds":60,"eventTimeout":""}}}
21.01.06 09:05:54.650            nuctl.platform (D) Enriching function HTTP trigger with service type {"functionName": "", "triggerName": "default-http", "serviceType": "ClusterIP"}
21.01.06 09:05:54.738                     nuctl (I) Deploying function {"name": ""}
21.01.06 09:05:54.738                     nuctl (I) Building {"versionInfo": "Label: 1.5.12, Git commit: 215f85cc4130b869df4b332cdbc8fe7a8768335f, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
21.01.06 09:05:54.738                     nuctl (D) Function configuration found in directory {"configFilePath": "serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio/function.yaml"}
21.01.06 09:05:54.738                     nuctl (D) Read function configuration file {"contents": "metadata:\n  name: openvino-omz-public-faster-rcnn-inception-v2-coco\n  namespace: cvat\n  annotations:\n    name: Faster RCNN\n    type: detector\n    framework: openvino\n    spec: |\n      [\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\nspec:\n  description: Faster RCNN inception v2 COCO via Intel OpenVINO toolkit\n  runtime: 'python:3.6'\n  handler: main:handler\n  eventTimeout: 30s\n  env:\n    - name: NUCLIO_PYTHON_EXE_PATH\n      value: /opt/nuclio/common/python3\n\n  build:\n    image: cvat/openvino.omz.public.faster_rcnn_inception_v2_coco\n    baseImage: openvino/ubuntu18_dev:2020.2\n\n    directives:\n      preCopy:\n        - kind: USER\n          value: root\n        - kind: WORKDIR\n          value: /opt/nuclio\n        - kind: RUN\n          value: ln -s /usr/bin/pip3 /usr/bin/pip\n        - kind: RUN\n          value: /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo\n        - kind: RUN\n          value: /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo\n\n  triggers:\n    myHttpTrigger:\n      maxWorkers: 2\n      kind: 'http'\n      workerAvailabilityTimeoutMilliseconds: 10000\n      attributes:\n        maxRequestBodySize: 33554432 # 32MB\n\n  platform:\n    attributes:\n      restartPolicy:\n        name: always\n        maximumRetryCount: 3\n"}
21.01.06 09:05:54.741                     nuctl (D) Created base temporary dir {"dir": "/tmp/nuclio-build-071852609"}
21.01.06 09:05:54.741                     nuctl (D) Created temporary dir {"dir": "/tmp/nuclio-build-071852609/staging"}
21.01.06 09:05:54.741                     nuctl (D) Created staging dir {"dir": "/tmp/nuclio-build-071852609/staging"}
21.01.06 09:05:54.741                     nuctl (D) Executing {"command": "docker version"}
21.01.06 09:05:54.819                     nuctl (D) Command executed successfully {"output": "Client: Docker Engine - Community\n Version:           20.10.0\n API version:       1.41\n Go version:        go1.13.15\n Git commit:        7287ab3\n Built:             Tue Dec  8 18:59:40 2020\n OS/Arch:           linux/amd64\n Context:           default\n Experimental:      true\n\nServer: Docker Engine - Community\n Engine:\n  Version:          20.10.0\n  API version:      1.41 (minimum version 1.12)\n  Go version:       go1.13.15\n  Git commit:       eeddea2\n  Built:            Tue Dec  8 18:58:04 2020\n  OS/Arch:          linux/amd64\n  Experimental:     false\n containerd:\n  Version:          v1.4.3\n  GitCommit:        269548fa27e0089a8b8278fc4fc781d7f65a939b\n runc:\n  Version:          1.0.0-rc92\n  GitCommit:        ff819c7e9184c13b7c2607fe6c30ae19403a7aff\n docker-init:\n  Version:          0.19.0\n  GitCommit:        de40ad0\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:54.819                     nuctl (D) Enriched configuration {"functionConfig": {"metadata":{"name":"openvino-omz-public-faster-rcnn-inception-v2-coco","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"openvino","name":"Faster RCNN","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":"Faster RCNN inception v2 COCO via Intel OpenVINO toolkit","handler":"main:handler","runtime":"python:3.6","env":[{"name":"NUCLIO_PYTHON_EXE_PATH","value":"/opt/nuclio/common/python3"}],"resources":{},"targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"path":"/workspaces/cvat/serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio","registry":"something.azurecr.io","image":"cvat/openvino.omz.public.faster_rcnn_inception_v2_coco","baseImage":"openvino/ubuntu18_dev:2020.2","directives":{"preCopy":[{"kind":"USER","value":"root"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/bin/pip"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo"}]},"codeEntryType":"image"},"platform":{"attributes":{"restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}, "platform": "kube", "dependantImagesRegistryURL": "", "outputImageFile": "", "processorImage": {}}
21.01.06 09:05:54.819            nuctl.platform (D) Enriching function HTTP trigger with service type {"functionName": "openvino-omz-public-faster-rcnn-inception-v2-coco", "triggerName": "myHttpTrigger", "serviceType": "ClusterIP"}
21.01.06 09:05:54.905            nuctl.platform (D) Getting function {"namespace": "nuclio", "name": "openvino-omz-public-faster-rcnn-inception-v2-coco"}
21.01.06 09:05:54.947                     nuctl (D) Creating/updating function {"functionExists": false, "functionInstance": null}
21.01.06 09:05:54.948                     nuctl (D) Populated function with configuration and status {"function": {"metadata":{"name":"openvino-omz-public-faster-rcnn-inception-v2-coco","namespace":"nuclio","creationTimestamp":null,"labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"openvino","name":"Faster RCNN","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":"Faster RCNN inception v2 COCO via Intel OpenVINO toolkit","handler":"main:handler","runtime":"python:3.6","env":[{"name":"NUCLIO_PYTHON_EXE_PATH","value":"/opt/nuclio/common/python3"}],"resources":{},"targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432,"serviceType":"ClusterIP"}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"alias":"latest","build":{"registry":"something.azurecr.io","image":"cvat/openvino.omz.public.faster_rcnn_inception_v2_coco","baseImage":"openvino/ubuntu18_dev:2020.2","directives":{"preCopy":[{"kind":"USER","value":"root"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/bin/pip"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo"}]},"codeEntryType":"image"},"platform":{"attributes":{"restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"},"status":{"state":"building"}}}
21.01.06 09:05:55.007                     nuctl (I) Staging files and preparing base images
21.01.06 09:05:55.008                     nuctl (D) Runtime provided handler objects to staging dir {"handlerDirObjectPaths": ["/workspaces/cvat/serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio"], "handlerDirInStaging": "/tmp/nuclio-build-071852609/staging/handler", "handlerDirIncludingSubpath": "/tmp/nuclio-build-071852609/staging/handler"}
21.01.06 09:05:55.009                     nuctl (D) Created processor Dockerfile {"dockerfileInfo": "# Multistage builds\n\n\n\n# From the base image\nFROM openvino/ubuntu18_dev:2020.2\n\n# Old(er) Docker support - must use all build args\nARG NUCLIO_LABEL\nARG NUCLIO_ARCH\nARG NUCLIO_BUILD_LOCAL_HANDLER_DIR\n\n\n# Run the pre-copy directives\n\nUSER root\n\nWORKDIR /opt/nuclio\n\nRUN ln -s /usr/bin/pip3 /usr/bin/pip\n\nRUN /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name faster_rcnn_inception_v2_coco -o /opt/nuclio/open_model_zoo\n\nRUN /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name faster_rcnn_inception_v2_coco --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo\n\n\n\n# Copy required objects from the suppliers\n\nCOPY artifacts/processor /usr/local/bin/processor\n\nCOPY artifacts/py /opt/nuclio/\n\n\n\nCOPY handler /opt/nuclio\n\n\n\n\n# Run the post-copy directives\n\nRUN pip install nuclio-sdk msgpack --no-index --find-links /opt/nuclio/whl\n\n\n# Run processor with configuration and platform configuration\nCMD [ \"processor\" ]\n", "baseImageRegistry": "", "onbuildImageRegistry": "quay.io"}
21.01.06 09:05:55.009                     nuctl (I) Building processor image {"imageName": "cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest"}
21.01.06 09:05:55.009     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64"}
21.01.06 09:05:55.009 tl.platform.docker.runner (D) Executing {"command": "docker pull quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64"}
21.01.06 09:05:57.581 tl.platform.docker.runner (D) Command executed successfully {"output": "1.5.12-amd64: Pulling from nuclio/handler-builder-python-onbuild\nDigest: sha256:8e1623b938e34d049d5a6a8dc2c55c61525aa5f023a0d2c40933880d92e3c25e\nStatus: Image is up to date for quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\nquay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:57.582            nuctl.platform (D) Generated onbuild Dockerfile {"contents": "FROM quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\nARG NUCLIO_LABEL\nARG NUCLIO_ARCH\n"}
21.01.06 09:05:57.582     nuctl.platform.docker (D) Building image {"buildOptions": {"Image":"nuclio-onbuild-bvqnqt8ibcio71lmipeg","ContextDir":"/tmp/nuclio-build-071852609/staging","DockerfilePath":"/tmp/nuclio-build-071852609/staging/Dockerfile.onbuild","NoCache":false,"BuildArgs":{"NUCLIO_ARCH":"amd64","NUCLIO_BUILD_LOCAL_HANDLER_DIR":"handler","NUCLIO_LABEL":"1.5.12"}}}
21.01.06 09:05:57.582 tl.platform.docker.runner (D) Executing {"command": "docker build --network host --force-rm -t nuclio-onbuild-bvqnqt8ibcio71lmipeg -f /tmp/nuclio-build-071852609/staging/Dockerfile.onbuild  --build-arg NUCLIO_LABEL=1.5.12 --build-arg NUCLIO_ARCH=amd64 --build-arg NUCLIO_BUILD_LOCAL_HANDLER_DIR=handler  ."}
21.01.06 09:05:57.905 tl.platform.docker.runner (D) Command executed successfully {"output": "", "stderr": "#1 [internal] load build definition from Dockerfile.onbuild\n#1 sha256:1de3cccbd085fb96ab2b75f038aaddeaf77e4fe002114415124bedbd5ad97ee5\n#1 transferring dockerfile: 142B done\n#1 DONE 0.0s\n\n#2 [internal] load .dockerignore\n#2 sha256:9bd2604440c32207551e0d4cb705a5e48c857b7affd3dfffb53806777ac48fad\n#2 transferring context: 2B done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#3 sha256:6fb3008c058fc79bf7207583135d460bfe6c809256a6cede99a73ee72e1aa18e\n#3 DONE 0.0s\n\n#4 [1/1] FROM quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#4 sha256:4cb1d92adda11efc35f148d087c99d59fe9b59e9ec3fd51c863cfb471a6373a1\n#4 CACHED\n\n#5 exporting to image\n#5 sha256:e8c613e07b0b7ff33893b694f7759a10d42e180f2b4dc349fb57dc6b71dcab00\n#5 exporting layers done\n#5 writing image sha256:d2976478040a95f6ccba78dfee5271f1446ef628f987e0de0faebac254540c1f done\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg done\n#5 DONE 0.0s\n", "exitCode": 0}
21.01.06 09:05:57.906     nuctl.platform.docker (W) Docker command outputted to stderr - this may result in errors {"workingDir": "/tmp/nuclio-build-071852609/staging", "cmd": "docker build --network host --force-rm -t nuclio-onbuild-bvqnqt8ibcio71lmipeg -f /tmp/nuclio-build-071852609/staging/Dockerfile.onbuild  --build-arg NUCLIO_LABEL=1.5.12 --build-arg NUCLIO_ARCH=amd64 --build-arg NUCLIO_BUILD_LOCAL_HANDLER_DIR=handler  .", "stderr": "#1 [internal] load build definition from Dockerfile.onbuild\n#1 sha256:1de3cccbd085fb96ab2b75f038aaddeaf77e4fe002114415124bedbd5ad97ee5\n#1 transferring dockerfile: 142B done\n#1 DONE 0.0s\n\n#2 [internal] load .dockerignore\n#2 sha256:9bd2604440c32207551e0d4cb705a5e48c857b7affd3dfffb53806777ac48fad\n#2 transferring context: 2B done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#3 sha256:6fb3008c058fc79bf7207583135d460bfe6c809256a6cede99a73ee72e1aa18e\n#3 DONE 0.0s\n\n#4 [1/1] FROM quay.io/nuclio/handler-builder-python-onbuild:1.5.12-amd64\n#4 sha256:4cb1d92adda11efc35f148d087c99d59fe9b59e9ec3fd51c863cfb471a6373a1\n#4 CACHED\n\n#5 exporting to image\n#5 sha256:e8c613e07b0b7ff33893b694f7759a10d42e180f2b4dc349fb57dc6b71dcab00\n#5 exporting layers done\n#5 writing image sha256:d2976478040a95f6ccba78dfee5271f1446ef628f987e0de0faebac254540c1f done\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg\n#5 naming to docker.io/library/nuclio-onbuild-bvqnqt8ibcio71lmipeg done\n#5 DONE 0.0s\n"}
21.01.06 09:05:57.906     nuctl.platform.docker (D) Successfully built image {"image": "nuclio-onbuild-bvqnqt8ibcio71lmipeg"}
21.01.06 09:05:57.906 tl.platform.docker.runner (D) Executing {"command": "docker create nuclio-onbuild-bvqnqt8ibcio71lmipeg /bin/sh"}
21.01.06 09:05:57.997 tl.platform.docker.runner (D) Command executed successfully {"output": "8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:57.997 tl.platform.docker.runner (D) Executing {"command": "docker cp 8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62:/home/nuclio/bin/processor /tmp/nuclio-build-071852609/staging/artifacts/processor"}
21.01.06 09:05:58.830 tl.platform.docker.runner (D) Command executed successfully {"output": "", "stderr": "", "exitCode": 0}
21.01.06 09:05:58.830 tl.platform.docker.runner (D) Executing {"command": "docker cp 8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62:/home/nuclio/bin/py /tmp/nuclio-build-071852609/staging/artifacts/py"}
21.01.06 09:05:58.942 tl.platform.docker.runner (D) Command executed successfully {"output": "", "stderr": "", "exitCode": 0}
21.01.06 09:05:58.942 tl.platform.docker.runner (D) Executing {"command": "docker rm -f 8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62"}
21.01.06 09:05:59.012 tl.platform.docker.runner (D) Command executed successfully {"output": "8611f49b4d8563f7db34b6de86487b7427ae38157a68e1a4876dcb4058fddf62\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:59.012 tl.platform.docker.runner (D) Executing {"command": "docker rmi -f nuclio-onbuild-bvqnqt8ibcio71lmipeg"}
21.01.06 09:05:59.076 tl.platform.docker.runner (D) Command executed successfully {"output": "Untagged: nuclio-onbuild-bvqnqt8ibcio71lmipeg:latest\n", "stderr": "", "exitCode": 0}
21.01.06 09:05:59.076            nuctl.platform (I) Building docker image {"image": "cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest"}
21.01.06 09:05:59.076     nuctl.platform.docker (D) Building image {"buildOptions": {"Image":"cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest","ContextDir":"/tmp/nuclio-build-071852609/staging","DockerfilePath":"/tmp/nuclio-build-071852609/staging/Dockerfile.processor","NoCache":false,"BuildArgs":{"NUCLIO_ARCH":"amd64","NUCLIO_BUILD_LOCAL_HANDLER_DIR":"handler","NUCLIO_LABEL":"1.5.12"}}}
21.01.06 09:05:59.076 tl.platform.docker.runner (D) Executing {"command": "docker build --network host --force-rm -t cvat/openvino.omz.public.faster_rcnn_inception_v2_coco:latest -f /tmp/nuclio-build-071852609/staging/Dockerfile.processor  --build-arg NUCLIO_LABEL=1.5.12 --build-arg NUCLIO_ARCH=amd64 --build-arg NUCLIO_BUILD_LOCAL_HANDLER_DIR=handler  ."}

The last command from the log above hangs indefinitely

The last command from the log above hangs indefinitely

I have deployed the Open VINO versions of F-RCNN and Mask RCNN - NO LUCK.

I have deployed the Open VINO versions of F-RCNN and Mask RCNN - NO LUCK.

I've also noticed that Open VINO F-RCNN requires TF F-RCNN to exist. What is the point of this?

I've also noticed that Open VINO F-RCNN requires TF F-RCNN to exist. What is the point of this?

Regardless, both TF F-RCNN and OpenVINO F-RCNN do not work as a bulk annotation on a Task.

Regardless, both TF F-RCNN and OpenVINO F-RCNN do not work as a bulk annotation on a Task.

@turowicz Your information was really helpful. I investigated why cvat.org automatic annotation doesn't work for me. I realized if a task is assigned to a project this feature fails, otherwise works fine.
Can you also try it on the develop branch and create a task without assigning it to a project and see if bulk annotation works?
I believe it might work that way.

@turowicz Your information was really helpful. I investigated why cvat.org automatic annotation doesn't work for me. I realized if a task is assigned to a project this feature fails, otherwise works fine.
Can you also try it on the develop branch and create a task without assigning it to a project and see if bulk annotation works?
I believe it might work that way.

@nmanovic
The project PR was introduced in https://github.com/openvinotoolkit/cvat/pull/2255
If a task is assigned to a project, the automatic annotation for that task is failing (showing success but there is no result ).

@nmanovic
The project PR was introduced in https://github.com/openvinotoolkit/cvat/pull/2255
If a task is assigned to a project, the automatic annotation for that task is failing (showing success but there is no result ).

@jahaniam woop woop!
After removing all the projects on my k8s, the automatic annotation works on Tasks!

@jahaniam woop woop!
After removing all the projects on my k8s, the automatic annotation works on Tasks!

@jahaniam also cancelling automated tasks is broken

@jahaniam also cancelling automated tasks is broken

@turowicz Your information was really helpful. I investigated why cvat.org automatic annotation doesn't work for me. I realized if a task is assigned to a project this feature fails, otherwise works fine.
Can you also try it on the develop branch and create a task without assigning it to a project and see if bulk annotation works?
I believe it might work that way.

Yes this is how it works, thanks!

@turowicz Your information was really helpful. I investigated why cvat.org automatic annotation doesn't work for me. I realized if a task is assigned to a project this feature fails, otherwise works fine.
Can you also try it on the develop branch and create a task without assigning it to a project and see if bulk annotation works?
I believe it might work that way.

Yes this is how it works, thanks!

@turowicz , we will look at the problem after public holidays in Russia. Sorry for the experience. "I realized if a task is assigned to a project this feature fails, otherwise works fine" - it looks like a regression. @ActiveChooN , could you please look?

@turowicz , we will look at the problem after public holidays in Russia. Sorry for the experience. "I realized if a task is assigned to a project this feature fails, otherwise works fine" - it looks like a regression. @ActiveChooN , could you please look?

Hi, I was also using tf-faster rcnn for automatic annotation.
I was able to deploy the function and run auto annotation using nuclio 1.5.8 and clearing all other error functions at nuclio dashboard. Also, i had to make a change in yaml file in the line no. of worker for 2 to 1 .
I ran auto annotation. It ran well in the first three videos of length less than 3 mins for traffic annotation. The later 3 was also able to run the annotation till the end with a success message and few error message.
Attaching the screenshot here:
[(https://drive.google.com/file/d/1B8yvmU4KR6K_-QMKv_B0wmI1vjYT6K0M/view?usp=sharing)]

Hi, I was also using tf-faster rcnn for automatic annotation.
I was able to deploy the function and run auto annotation using nuclio 1.5.8 and clearing all other error functions at nuclio dashboard. Also, i had to make a change in yaml file in the line no. of worker for 2 to 1 .
I ran auto annotation. It ran well in the first three videos of length less than 3 mins for traffic annotation. The later 3 was also able to run the annotation till the end with a success message and few error message.
Attaching the screenshot here:
[(https://drive.google.com/file/d/1B8yvmU4KR6K_-QMKv_B0wmI1vjYT6K0M/view?usp=sharing)]

Also,
The later videos were of length more than 5 mins. So I will again check with the shorter videos if it throws an error or not.
In my opinion, this issue might be related to any runtime parameters in the YAML file.

Also,
The later videos were of length more than 5 mins. So I will again check with the shorter videos if it throws an error or not.
In my opinion, this issue might be related to any runtime parameters in the YAML file.

@turowicz , we will look at the problem after public holidays in Russia. Sorry for the experience. "I realized if a task is assigned to a project this feature fails, otherwise works fine" - it looks like a regression. @ActiveChooN , could you please look?

@ActiveChooN Would also be nice to support changing project ID of a task through the API or UI.
We cannot change Project ID of a task and set it to null or any other project IDs to be able to move a task between projects or assign a task to a project that is not assigned to any initially.

@turowicz , we will look at the problem after public holidays in Russia. Sorry for the experience. "I realized if a task is assigned to a project this feature fails, otherwise works fine" - it looks like a regression. @ActiveChooN , could you please look?

@ActiveChooN Would also be nice to support changing project ID of a task through the API or UI.
We cannot change Project ID of a task and set it to null or any other project IDs to be able to move a task between projects or assign a task to a project that is not assigned to any initially.

Hi, I was also using tf-faster rcnn for automatic annotation.
I was able to deploy the function and run auto annotation using nuclio 1.5.8 and clearing all other error functions at nuclio dashboard. Also, i had to make a change in yaml file in the line no. of worker for 2 to 1 .
I ran auto annotation. It ran well in the first three videos of length less than 3 mins for traffic annotation. The later 3 was also able to run the annotation till the end with a success message and few error message.
Attaching the screenshot here:
https://drive.google.com/file/d/1B8yvmU4KR6K_-QMKv_B0wmI1vjYT6K0M/view?usp=sharing

I think the best way to optimize for now is to do it outside and create a task and upload annotation using API. see https://github.com/openvinotoolkit/cvat/tree/develop/utils/cli or localhost:8080/api/swagger

Hi, I was also using tf-faster rcnn for automatic annotation.
I was able to deploy the function and run auto annotation using nuclio 1.5.8 and clearing all other error functions at nuclio dashboard. Also, i had to make a change in yaml file in the line no. of worker for 2 to 1 .
I ran auto annotation. It ran well in the first three videos of length less than 3 mins for traffic annotation. The later 3 was also able to run the annotation till the end with a success message and few error message.
Attaching the screenshot here:
https://drive.google.com/file/d/1B8yvmU4KR6K_-QMKv_B0wmI1vjYT6K0M/view?usp=sharing

I think the best way to optimize for now is to do it outside and create a task and upload annotation using API. see https://github.com/openvinotoolkit/cvat/tree/develop/utils/cli or localhost:8080/api/swagger

OK so issues identified:

Tasks assigned to Projects cannot be annotated automagically.
Cancellation of automated Task annotation throws errors when setting timeout.
In the current state of automated annotations some people are forced to run it through the API.

Anything else? How about the fact that OpenVINO F-RCNN requires TF-RCNN to co-exist?

OK so issues identified:

Tasks assigned to Projects cannot be annotated automagically.
Cancellation of automated Task annotation throws errors when setting timeout.
In the current state of automated annotations some people are forced to run it through the API.

Anything else? How about the fact that OpenVINO F-RCNN requires TF-RCNN to co-exist?

OK so issues identified:

Tasks assigned to Projects cannot be annotated automagically.
Cancellation of automated Task annotation throws errors when setting timeout.
In the current state of automated annotations some people are forced to run it through the API.

Anything else? How about the fact that OpenVINO F-RCNN requires TF-RCNN to co-exist?

Nuclio functions are independent of each other hence OpenVINO F-RCNN requires TF-RCNN to co-exist is wrong. these are two independent functions.

Tasks assigned to Projects cannot be annotated automagically.
Cancellation of automated Task annotation throws errors when setting timeout.
Moving/assigning a task to a project through UI/API fails (considering two projects have the same annotations)

OK so issues identified:

Tasks assigned to Projects cannot be annotated automagically.
Cancellation of automated Task annotation throws errors when setting timeout.
In the current state of automated annotations some people are forced to run it through the API.

Anything else? How about the fact that OpenVINO F-RCNN requires TF-RCNN to co-exist?

Nuclio functions are independent of each other hence OpenVINO F-RCNN requires TF-RCNN to co-exist is wrong. these are two independent functions.

Tasks assigned to Projects cannot be annotated automagically.
Cancellation of automated Task annotation throws errors when setting timeout.
Moving/assigning a task to a project through UI/API fails (considering two projects have the same annotations)

if task is assigned to a project, dumping the annotation/dataset as PASCAL VOC and many other formats fails (succeeds but annotation is empty although it shouldn't)

if task is assigned to a project, dumping the annotation/dataset as PASCAL VOC and many other formats fails (succeeds but annotation is empty although it shouldn't)

I can confirm automatic annotation is broken for tasks. It only works for single images I have tried two models. Faster rcnn and mask rcnn. It works fine for a single image but when I use it on a task containing multiple images although it shows it has completed but it doesn't show any annotation results on the images.
@nmanovic can we have a look on this problem please? I am working on the gpu version of the maskrcnn rn and a fix for cpu version. I will do a PR soon for that.

Thanks @jahaniam. Do you have a ball-park ETA on this?

I can confirm automatic annotation is broken for tasks. It only works for single images I have tried two models. Faster rcnn and mask rcnn. It works fine for a single image but when I use it on a task containing multiple images although it shows it has completed but it doesn't show any annotation results on the images.
@nmanovic can we have a look on this problem please? I am working on the gpu version of the maskrcnn rn and a fix for cpu version. I will do a PR soon for that.

Thanks @jahaniam. Do you have a ball-park ETA on this?

Guess problem with auto-annotation of the task in project should be fixed with @2725.

@ActiveChooN Would also be nice to support changing project ID of a task through the API or UI.

@jahaniam, do you mean moving task between projects? It's quite complicated task and will be implemented in future releases with developing project feature.

Guess problem with auto-annotation of the task in project should be fixed with @2725.

@ActiveChooN Would also be nice to support changing project ID of a task through the API or UI.

@jahaniam, do you mean moving task between projects? It's quite complicated task and will be implemented in future releases with developing project feature.

@ActiveChooN There is a project field for tasks. Wouldn’t changing it change the task project?

@ActiveChooN There is a project field for tasks. Wouldn’t changing it change the task project?

@jahaniam, it would, but there is annotation in the task that depend on project labels. So we need merge annotation with new label somehow before moving the task.

@jahaniam, it would, but there is annotation in the task that depend on project labels. So we need merge annotation with new label somehow before moving the task.

This is issue of semi annotation by model is fixed ? And where can I find instructions how to configure for semi annotation? Thanks


Add schdule cache feature.

Add schedule cache

It's better to split it into two small patches. One is to fix in test_hisi_sec.c, and the other is for schedule cache.
If you can add more comments in commit for schedule cache, it would be better. It could help other developers/users to understand how it works.

I delete the change of test_hisi_sec.c, and add some commit info.

better to write subject line:
test: sched: xxx

better to write subject line:
test: sched: xxx

Fixed


[5.2] Add a map method to the AbstractPaginator

This will allow a user to modify collection data before rendering paginator, or returning its value.
Say, you're creating an api resource for User model, and you want every returned entity to contain URL, created by route() function.
Right now it might be a pain, but with adding map() method to Paginator, things become much more pretty.
Thanks.

You should probably squash that into a single commit.

What if you want to do something else with the items? Maybe you want to filter it or something?
I think it makes more sense to add a setItems method. Then you can do your map like so:
$paginator->setItems($paginator->getCollection()->map($callback));

@JosephSilber is correct. We don't want to start re-creating every collection call in the paginator. Please send a PR with that implementation.


Support for Regression Problems

Where the output is real values (R^n) and the loss function is something like MSE.

You can use the sum_squared cost (see neon/transforms/sum_squared.py)
We'll try to provide an example at some point, but it should be as simple
as swapping the CrossEntropy item in the CostLayer to be SumSquaredDiffs
instead
On Mon, May 18, 2015 at 3:41 PM, Alex Rothberg<EMAIL_ADDRESS>wrote:

Where the output is real values (R^n) and the loss function is something
like MSE.
—
Reply to this email directly or view it on GitHub
https://github.com/NervanaSystems/neon/issues/33.


DOMException: play() failed because the user didn't interact with the document first

Device Name: Linux OS
Device Version: 20 LTS
Browser Name: chrome browser
Browser Version: Version 100.0.4896.75 (Official Build) (64-bit)
NoSleep.js Version: 0.12

Expected Behavior
It should run the video muted so it does not throw a js error.

Actual Behavior
It throws a Js error "DOMException: play() failed because the user didn't interact with the document first"

Code
    var noSleep = new NoSleep();
    noSleep.enable();

How Do We Reproduce?
Use it on a blank page and check the console, please.

The error also has this helper link, hope that will be helpful.
https://developer.chrome.com/blog/autoplay/

and I found this from SO where it says, the fix is to make the video Muted
https://stackoverflow.com/questions/49930680/how-to-handle-uncaught-in-promise-domexception-play-failed-because-the-use
Appreciate your help.

I believe I had the same issue (Firefox 108.1.0, Android 6).

Error: Uncaught (in promise) DOMException: The play method is not allowed by the user agent or the platform in the current context, possibly because the user denied permission.

Solved it by adding this.noSleepVideo.muted = true; to have something like:
var NoSleep = function () {
  function NoSleep() {
    ...
    if (nativeWakeLock()) {
      ...
    } else if (oldIOS()) {
      this.noSleepTimer = null;
    } else {
      // Set up no sleep video element
      this.noSleepVideo = document.createElement("video");

      this.noSleepVideo.setAttribute("title", "No Sleep");
      this.noSleepVideo.setAttribute("playsinline", "");
      this.noSleepVideo.muted = true;  // <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

      this._addSourceToVideo(this.noSleepVideo, "webm", webm);
      ...
}

See:
    https://github.com/richtr/NoSleep.js/issues/53#issuecomment-443980576
    https://github.com/richtr/NoSleep.js/issues/10


Misleading message when the specified resource group does not exist.

Describe the bug
Misleading message when the specified resource group does not exist.
To Reproduce
19:35 $ az aks get-credentials --resource-group resource-group-name --name cluster-name --overwrite-existing
The client 'XXX@YYY' with object id '**********' does not have authorization to perform action 'Microsoft.ContainerService/managedClusters/listClusterUserCredential/action' over scope '/subscriptions/**********/resourceGroups/resource-group-name/providers/Microsoft.ContainerService/managedClusters/dev02-cluster'.

Expected behavior
I would expect the message to report that the resource group does not exist.
Environment summary
Installed using brew on mac OSX, version 2.0.54 .

it is hard to polish the error message just based on logged in as a service principal because a service principal can be as powerful as a admin while a user can have less permission than a service principal.
Let us live with it since we only have one user with such confusion, and the error itself is not that bad.

@yugangw-msft I am quite surprise by your message. Proper error messaging in key for a good CLI and basically ignoring user feedback is quite annoying. Maybe you do not care that much at MS.

@yugangw-msft I've just stumbled on this error today as well.
Unlike @andreacassioli I was deploying management-groups in the CLI but the issue was the same. I can create and show/list management-groups AFTER they have been created, but if I try and show/list for a management-group that doesn't exist I get the error below:
Code: AuthorizationFailed
Message: The client '' with object id '' does not have authorization to perform action Microsoft.Management/managementGroups/read over scope '/providers/Microsoft.Management/managementGroups/testManagementGroupName'

PAS team should take a look and emit out a better error such as the resource doesn't exist

//CC @skwan

We have worked with ARM team to improve the error message. Now it shows

The client 'xxx' with object id 'xxx' does not have authorization to perform action 'Microsoft.Resources/subscriptions/resourcegroups/read' over scope '/subscriptions/xxx/resourcegroups/xxx' or the scope is invalid. If access was recently granted, please refresh your credentials.

Please let us know if this suits the need. Thanks.

Sorry to insist, but the message will not help that much. It might be a matter of wording, but to me invalid scope does not necessary mean that a resource does not exist!

If the signed-in user doesn't have permission to list resources, it is against security to report whether a resource exists or not. So user needs to check whether the scope is valid (matches the assigned scope).

Is it against security to have clear error messages?
For example:

"You do not have enough permissions for that action."
"You /do/ have enough permission for that action and that resource does not exist; check your spelling."
"You /do/ have enough permission for that action and the scope is invalid."
etc

You're encouraging me to escalate my security permissions until I notice I misspelled a resource name, currently. Like, this just happened and I'm not dropping my permissions back down because this has happened before for other reasons and maybe the new permissions will fix it? :shrug:

@cole-brown, the error message is sent by ARM service, instead of Azure CLI. You may submit your feedback at https://feedback.azure.com/


wasm: use npm install instead of clean-install.

Signed-off-by: Piotr Sikora<EMAIL_ADDRESS>

/retest

Is this a fix for a recent unstable wasm build?

@PiotrSikora you need to merge main

Superseded by #21487.


Feature kong 0.8.x postgres

Hi @juniorsysadmin
Thanks for making this kong module available!
This PR adds support for Kong 0.8.x and PostgreSQL.
It also contains a handful of other minor fixes.
Developed by Catalyst Net Ltd, sponsored by Museum of New Zealand Te Papa Tongarewa.

This seems to fail on Ruby < 2.2 due to the Syck gem change. I'm happy to pull out that change again if you want to retain compatibility with old Ruby versions.

Thanks for this @fkleon . This Pull Request contains most of the fixes that I have had in a yet-to-be-pushed-branch, and some that I did not. (I guess great minds think alike). I'll push some changes through shortly. With regard to supported Ruby versions, I'm intending to mirror what is done with the Voxpupuli modules, which is to support only 1.9.3 and above.

See #2


[Release] DDS: Trend Micro Vision One Endpoint Security: Crawler Integration v1.0.0

What does this PR do?
This is a initial release PR of Trend Micro Vision One Endpoint Security integration including all the required assets.
Additional Notes

Crawler code for this integration has been committed in its respective repo
Pipeline and Facet group created for this integration are available in our sandbox and would be shared separately with the required teams.
Samples for the pipeline review would also be shared separately with the required teams.
OOTB detection rules JSON would be shared separately with the required teams as a part of separate repository.
Since during the standard attribute remapping we are not preserving the source attributes as per suggested best practices, it would result in filters using these standard attributes populating the values of other integrations as well as per current Datadog behaviour.

Review checklist (to be filled by reviewers)

[ ] Feature or bugfix MUST have appropriate tests (unit, integration, e2e)
[ ] Changelog entries must be created for modifications to shipped code
[ ] Add the qa/skip-qa label if the PR doesn't need to be tested during QA.
[ ] If you need to backport this PR to another branch, you can add the backport/<branch-name> label to the PR and it will automatically open a backport PR once this one is merged

Thanks! I have created an editorial card for our team to review further.

Good morning, is it intentional that samples need to be double quoted ('""' or """") to actually be parsed ?
I tested in staging. I am asking as for most integrations, a single quote is expected for the sample.

Good morning, is it intentional that samples need to be double quoted ('"content"' or ""content"") to actually be parsed ? I tested in staging. I am asking as for most integrations, a single quote is expected for the sample.

Hi @mathieu-charbonnel,
Yes, this behavior is intentional. We ingest strings into Datadog, and these strings are automatically processed with double quotes.


Add PIZZA and BEER test currencies

This was requested by testers.
I also improved the error handling of the ticker data fetching.

Also

I also improved the error handling of the ticker data fetching.

I think this should be in a separate PR.

I think this should be in a separate PR.

Yeah, you're right. I was just lazy. It's needed for this PR as I noticed it was breaking when I added PIZZA.

ETOMIC is only needed for ETH/ERC20 swaps, it's kind of like a proxy token to enable the atomic swap as ETH doesn't have UTXOs. We don't support ETH/ERC20 yet and it might be a while till we do. Artem also talked about making the requirement of ETOMIC completely transparent, so this might end up being a special case where we hide this from the UI.

Ok. I didn't know that. I thought it was just another test coin. Removed.

PIZZA and BEER are test asset chains for stress testing the BarterDEX network and are given away for free. It's a good idea to enable these but I think they should only be enabled in dev/nightly builds. It might be confusing for normal users. What do you think?

I agree. Done: https://github.com/lukechilds/hyperdex/pull/225/commits/d7f22c1093c8372bc67a196a0830bdb4adb8002b
You can still add them manually in the preferences file for testing the stable build in production.