Advanced

• 1: CVAT Architecture
• 2: CVAT deployment on Kubernetes with Helm
• 3: Semi-automatic and Automatic Annotation
• 4: CVAT Analytics and monitoring
• 5: Mounting cloud storage
• 6: LDAP Backed Authentication
• 7: Backup guide
• 8: Upgrade guide
• 9: Webhooks

1 - CVAT Architecture

This guide is designed to provide a comprehensive overview of the architecture and components of the CVAT and to illustrate how each component interacts within the system.

2 - CVAT deployment on Kubernetes with Helm

• Prerequisites
  • Installing dependencies
  • Optional steps
• Configuration
  • Postgresql password?
  • (Optional) Enable Auto annotation feature
  • Analytics
• Deployment
  • With overrides:
  • Without overrides:
• Post-deployment configuration
  • How to create superuser?
• FAQ
  • What is kubernetes and how it is working?
  • What is helm and how it is working?
  • How to setup Minikube
  • How to understand what diff will be inflicted by ‘helm upgrade’?
  • I want to use my own postgresql/redis with your chart.
  • I want to override some settings in values.yaml.
  • Why you used external charts to provide redis and postgres?

Prerequisites

1. Installed and configured kubernetes cluster. If you do not already have a cluster, you can create one by using Minikube. How to setup Minikube.
2. Installed kubectl
3. Installed Helm.
4. Installed dependencies

Installing dependencies

To install and/or update run:

helm dependency update

Optional steps

1. Ingress configuration for the Traefik ingress controller is enabled by default.

   Note for Minikube use:

   • because the Traefik creates its main service with Loadbalanser type, which involve the assignment of externalIP by Cloud, what never happens on Minikube, you need to explicitly set the externalIP address for the traefic service. Add the following to values.override.yaml file:

     traefik:
       service:
         externalIPs:
           - "your minikube IP (can be obtained with `minikube ip` command)"
     

   • Also ensure that your CVAT ingress appears on your hosts file (/etc/hosts). You can do this by running this command: cvat.local is default domainname, you can override it via values.override.yaml.

     echo "$(minikube ip) cvat.local" | sudo tee -a /etc/hosts
     

Configuration

1. Create values.override.yaml file inside helm-chart directory.
2. Fill values.override.yaml with new parameters for chart.
3. Override postgresql password

Postgresql password?

Put below into your values.override.yaml

postgresql:
  secret:
    password: <insert_password>
    postgres_password: <insert_postgres_password>
    replication_password: <insert_replication_password>

Or create your own secret and use it with:

postgresql:
   global:
     postgresql:
       existingSecret: <secret>

(Optional) Enable Auto annotation feature

Before starting, ensure that the following prerequisites are met:

• The Nuclio CLI (nuctl) is installed. To install the CLI, simply download the appropriate CLI version to your installation machine.

1. Set nuclio.enabled: true in your values.override.yaml

2. Run helm dependency update in helm-chart directory

3. Because Nuclio functions are images that need to be pushed and pulled to/from the registry, you need to configure credentials to pull from your preferable registry with the following settings: Options:

   • values.override.yaml file:

     registry:
       loginUrl: someurl
       credentials:
         username: someuser
         password: somepass
     

   • Or you can create a secret with credentials as described in the guide and set registry.secretName=your-registry-credentials-secret-name in the values.override.yaml file.

   • In the case of using Minikube, you can run a local unsecured registry with minikube add-ons:

     minikube addons enable registry
     minikube addons enable registry-aliases
     

     Before Docker container images can be pushed to your newly created insecure registry, you need to add its address ($(minikube ip):5000) to the list of insecure registries to instruct Docker to accept working against it: follow the instructions in the Docker documentation

   You might also need to log into your registry account (docker login) on the installation machine before running the deployment command.

4. Create cvat project:

   nuctl --namespace <your cvat namespace> create project cvat
   

5. Finally deploy the function, i.e.:

   • using minikube registry:

     nuctl deploy --project-name cvat --path serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio --registry $(minikube ip):5000 --run-registry registry.minikube
     

   • using Docker hub:

     nuctl deploy --project-name cvat --path serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio --registry docker.io/your_username
     

Analytics

Analytics is enabled by default, to disable set analytics.enabled: false in your values.override.yaml

Deployment

Make sure you are using correct kubernetes context. You can check it with kubectl config current-context.

Warning: The k8s service name of Open Policy Agent is fixed to opa by default. This is done to be compatible with CVAT 2.0 but limits this helm chart to a single release per namespace. The OPA url currently can´t be set as an environment variable. As soon as this is possible you can set cvat.opa.composeCompatibleServiceName to false in your value.override.yaml and configure the opa url as additional env.

Execute following command from repo root directory

With overrides:

helm upgrade -n <desired_namespace> <release_name> -i --create-namespace ./helm-chart -f ./helm-chart/values.yaml -f ./helm-chart/values.override.yaml

Without overrides:

helm upgrade -n <desired_namespace> <release_name> -i --create-namespace ./helm-chart -f ./helm-chart/values.yaml

Post-deployment configuration

1. Create super user

How to create superuser?

HELM_RELEASE_NAMESPACE="<desired_namespace>" &&\
HELM_RELEASE_NAME="<release_name>" &&\
BACKEND_POD_NAME=$(kubectl get pod --namespace $HELM_RELEASE_NAMESPACE -l tier=backend,app.kubernetes.io/instance=$HELM_RELEASE_NAME,component=server -o jsonpath='{.items[0].metadata.name}') &&\
kubectl exec -it --namespace $HELM_RELEASE_NAMESPACE $BACKEND_POD_NAME -c cvat-backend -- python manage.py createsuperuser

FAQ

What is kubernetes and how it is working?

See https://kubernetes.io/

What is helm and how it is working?

See https://helm.sh/

How to setup Minikube

1. Please follow the official Minikube installation guide

2. minikube start --addons registry,registry-aliases
   

How to understand what diff will be inflicted by ‘helm upgrade’?

You can use https://github.com/databus23/helm-diff#install for that

I want to use my own postgresql with your chart.

Just set postgresql.enabled to false in the override file, then put the parameters of your database instance in the external field. You may also need to configure username, database and password fields to connect to your own database:

postgresql:
  enabled: false
  external:
    host: postgresql.default.svc.cluster.local
    port: 5432
  auth:
    username: cvat
    database: cvat
  secret:
    password: cvat_postgresql

In example above corresponding secret will be created automatically, but if you want to use existing secret change secret.create to false and set name of existing secret:

postgresql:
  enabled: false
  external:
    host: postgresql.default.svc.cluster.local
    port: 5432
  secret:
    create: false
    name: "my-postgresql-secret"

The secret must contain the database, username and password keys to access to the database like:

apiVersion: v1
kind: Secret
metadata:
  name: "my-postgresql-secret"
  namespace: default
type: generic
stringData:
  database: cvat
  username: cvat
  password: secretpassword

I want to use my own redis with your chart.

Just set redis.enabled to false in the override file, then put the parameters of your Redis instance in the external field. You may also need to configure password field to connect to your own Redis:

redis:
  enabled: false
  external:
    host: redis.hostname.local
  secret:
    password: cvat_redis

In the above example the corresponding secret will be created automatically, but if you want to use an existing secret change secret.create to false and set name of the existing secret:

redis:
  enabled: false
  external:
    host: redis.hostname.local
  secret:
    create: false
    name: "my-redis-secret"

The secret must contain the redis-password key like:

apiVersion: v1
kind: Secret
metadata:
  name: "my-redis-secret"
  namespace: default
type: generic
stringData:
  redis-password: secretpassword

I want to override some settings in values.yaml.

Just create file values.override.yaml and place your changes here, using same structure as in values.yaml. Then reference it in helm update/install command using -f flag

Why you used external charts to provide redis and postgres?

Because they definitely know what they do better then we are, so we are getting more quality and less support

How to use custom domain name with k8s deployment:

The default value cvat.local may be overridden with --set ingress.hosts[0].host option like this:

helm upgrade -n default cvat -i --create-namespace helm-chart -f helm-chart/values.yaml -f helm-chart/values.override.yaml --set ingress.hosts[0].host=YOUR_FQDN

How to fix fail of helm upgrade due label field is immutable reason?

If an error message like this:

Error: UPGRADE FAILED:cannot patch "cvat-backend-server" with kind Deployment: Deployment.apps "cvat-backend-server" is invalid: spec.selector: Invalid value: v1.LabelSelector{MatchLabels:map[string]string{"app":"cvat-app", "app.kubernetes.io/instance":"cvat", "app.kubernetes.io/managed-by":"Helm", "app.kubernetes.io/name":"cvat", "app.kubernetes.io/version":"latest", "component":"server", "helm.sh/chart":"cvat", "tier":"backend"}, MatchExpressions:[]v1.LabelSelectorRequirement(nil)}: field is immutable

To fix that, delete CVAT Deployments before upgrading

kubectl delete deployments --namespace=foo -l app=cvat-app

How to use existing PersistentVolume to store CVAT data instead of default storage

It is assumed that you have created a PersistentVolumeClaim named my-claim-name and a PersistentVolume that backing the claim. Claims must exist in the same namespace as the Pod using the claim. For details see. Add these values in the values.override.yaml:

cvat:
  backend:
    permissionFix:
      enabled: false
    defaultStorage:
      enabled: false
    server:
      additionalVolumes:
        - name: cvat-backend-data
          persistentVolumeClaim:
            claimName: my-claim-name
    worker:
      export:
        additionalVolumes:
          - name: cvat-backend-data
            persistentVolumeClaim:
              claimName: my-claim-name
      import:
        additionalVolumes:
          - name: cvat-backend-data
            persistentVolumeClaim:
              claimName: my-claim-name
      annotation:
        additionalVolumes:
          - name: cvat-backend-data
            persistentVolumeClaim:
              claimName: my-claim-name
    utils:
      additionalVolumes:
        - name: cvat-backend-data
          persistentVolumeClaim:
            claimName: my-claim-name

3 - Semi-automatic and Automatic Annotation

⚠ WARNING: Do not use docker compose up If you did, make sure all containers are stopped by docker compose down.

• To bring up cvat with auto annotation tool, from cvat root directory, you need to run:

  docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml up -d
  

  If you did any changes to the Docker Compose files, make sure to add --build at the end.

  To stop the containers, simply run:

  docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml down
  

• You have to install nuctl command line tool to build and deploy serverless functions. Download version 1.11.24. It is important that the version you download matches the version in docker-compose.serverless.yml. For example, using wget.

  wget https://github.com/nuclio/nuclio/releases/download/<version>/nuctl-<version>-linux-amd64
  

  After downloading the nuclio, give it a proper permission and do a softlink.

  sudo chmod +x nuctl-<version>-linux-amd64
  sudo ln -sf $(pwd)/nuctl-<version>-linux-amd64 /usr/local/bin/nuctl
  

• Deploy a couple of functions. This will automatically create a cvat Nuclio project to contain the functions. Commands below should be run only after CVAT has been installed using docker compose because it runs nuclio dashboard which manages all serverless functions.

  ./serverless/deploy_cpu.sh serverless/openvino/dextr
  ./serverless/deploy_cpu.sh serverless/openvino/omz/public/yolo-v3-tf
  

  GPU Support

  You will need to install Nvidia Container Toolkit. Also you will need to add --resource-limit nvidia.com/gpu=1 --triggers '{"myHttpTrigger": {"maxWorkers": 1}}' to the nuclio deployment command. You can increase the maxWorker if you have enough GPU memory. As an example, below will run on the GPU:

  nuctl deploy --project-name cvat \
    --path serverless/tensorflow/matterport/mask_rcnn/nuclio \
    --platform local --base-image tensorflow/tensorflow:1.15.5-gpu-py3 \
    --desc "GPU based implementation of Mask RCNN on Python 3, Keras, and TensorFlow." \
    --image cvat/tf.matterport.mask_rcnn_gpu \
    --triggers '{"myHttpTrigger": {"maxWorkers": 1}}' \
    --resource-limit nvidia.com/gpu=1
  

  Note:

  • The number of GPU deployed functions will be limited to your GPU memory.
  • See deploy_gpu.sh script for more examples.
  • For some models (namely SiamMask) you need an Nvidia driver version greater than or equal to 450.80.02.

  Note for Windows users:

  If you want to use nuclio under Windows CVAT installation you should install Nvidia drivers for WSL according to this instruction and follow the steps up to “2.3 Installing Nvidia drivers”. Important requirement: you should have the latest versions of Docker Desktop, Nvidia drivers for WSL, and the latest updates from the Windows Insider Preview Dev channel.

Troubleshooting Nuclio Functions:

• You can open nuclio dashboard at localhost:8070. Make sure status of your functions are up and running without any error.

• Test your deployed DL model as a serverless function. The command below should work on Linux and Mac OS.

  image=$(curl https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png --output - | base64 | tr -d '\n')
  cat << EOF > /tmp/input.json
  {"image": "$image"}
  EOF
  cat /tmp/input.json | nuctl invoke openvino-omz-public-yolo-v3-tf -c 'application/json'
  

  20.07.17 12:07:44.519    nuctl.platform.invoker (I) Executing function {"method": "POST", "url": "http://:57308", "headers": {"Content-Type":["application/json"],"X-Nuclio-Log-Level":["info"],"X-Nuclio-Target":["openvino-omz-public-yolo-v3-tf"]}}
  20.07.17 12:07:45.275    nuctl.platform.invoker (I) Got response {"status": "200 OK"}
  20.07.17 12:07:45.275                     nuctl (I) >>> Start of function logs
  20.07.17 12:07:45.275 ino-omz-public-yolo-v3-tf (I) Run yolo-v3-tf model {"worker_id": "0", "time": 1594976864570.9353}
  20.07.17 12:07:45.275                     nuctl (I) <<< End of function logs
  
  > Response headers:
  Date = Fri, 17 Jul 2020 09:07:45 GMT
  Content-Type = application/json
  Content-Length = 100
  Server = nuclio
  
  > Response body:
  [
      {
          "confidence": "0.9992254",
          "label": "person",
          "points": [
              39,
              124,
              408,
              512
          ],
          "type": "rectangle"
      }
  ]
  

• To check for internal server errors, run docker ps -a to see the list of containers. Find the container that you are interested, e.g., nuclio-nuclio-tf-faster-rcnn-inception-v2-coco-gpu. Then check its logs by docker logs <name of your container> e.g.,

  docker logs nuclio-nuclio-tf-faster-rcnn-inception-v2-coco-gpu
  

• To debug a code inside a container, you can use vscode to attach to a container instructions. To apply your changes, make sure to restart the container.

  docker restart <name_of_the_container>
  

4 - CVAT Analytics and monitoring

CVAT Analytics suite of tools is designed to track and understand users’ behavior, system performance, and for identifying potential issues in your application.

You can also visualize user activity through Grafana, and aggregate user working time by the jobs.

Gathered logs can be additionally filtered for efficient debugging.

By using analytics, you’ll gain valuable insights to optimize your system and enhance user satisfaction.

CVAT analytics are available from the top menu.

Note: CVAT analytics and monitoring are available only for on-prem solution.

See:

• High-level architecture
• CVAT Analytics
  • Ports settings
  • Events log structure
  • Types of supported events
  • Working time calculation
  • Request id for tracking
  • Fetching event data as CSV from the /api/events endpoint
• Dashboards
  • Dashboard: All Events
  • Dashboard: Management
  • Dashboard: Monitoring
  • Dashboards setup
• Example of use

High-level architecture

The CVAT analytics is based on Vector, ClickHouse, and Grafana.

CVAT Analytics

CVAT and its analytics module can be set up locally, for self-hosted solution analytics are enabled by default.

For detailed CVAT installation instructions, see Installation Guide or refer to the CVAT Course for installation videos.

All analytics-related features will be launched when you start CVAT containers with the following command:

docker compose up -d

Ports settings

If you cannot access analytics on development environnement, see Analytics Ports

Events log structure

Relational database schema with the following fields:

Types of supported events

Supported events change the scope of information displayed in Grafana.

Server events:

• create:project, update:project, delete:project

• create:task, update:task, delete:task

• create:job, update:job, delete:job

• create:organization, update:organization, delete:organization

• create:user, update:user, delete:user

• create:cloudstorage, update:cloudstorage, delete:cloudstorage

• create:issue, update:issue, delete:issue

• create:comment, update:comment, delete:comment

• create:annotations, update:annotations, delete:annotations

• create:label, update:label, delete:label

• export:dataset, import:dataset

Client events:

• load:cvat

• load:job, save:job, restore:job

• upload:annotations

• send:exception

• send:task_info

• draw:object, paste:object, copy:object, propagate:object, drag:object, resize:object, delete:object, lock:object, merge:objects, split:objects, group:objects, slice:object, join:objects

• change:attribute

• change:label

• change:frame

• zoom:image, fit:image, rotate:image

• action:undo, action:redo

• run:annotations_action

• click:element

• debug:info

Working time calculation

Here is a short overview of how CVAT deals with the user’s working time:

• The user interface collects events when a user interacts with the interface (resizing canvas, drawing objects, clicking buttons, etc) The structure of one single event is described here.

• The user interface sends these events in bulk to the server. Currently, it uses the following triggers to send events:

  • Periodical timer (~90 seconds)
  • A user clicks the “Save” button on the annotation view
  • A user opens the annotation view
  • A user closes the annotation view (but not the tab/browser)
  • A user clicks Logout button

• When events reach the server, it calculates working time based on timestamps of the events.

• The working time for an event is computed as the sum of the following:

  • The difference between the start time of the event and the end time of the previous event, if it is not more than 100 seconds.
  • The duration of the event, for events of type change:frame.

• After calculation, the server generates send:working_time events with time value in payload. These events may or may not be bound to a certain job/task/project, depending on the client-side events that were used to generate them.

• CVAT saves the event in the database and later these events are used to compute metrics for analytics.

Request id for tracking

Note, that every response to an API request made to the the server includes a header named X-Request-Id, for example: X-Request-Id: 6a2b7102-c4b9-4d57-8754-5658132ba37d.

This identifier is also recorded in all server events that occur as a result of the respective request.

For example, when an operation to create a task is performed, other related entities such as labels and attributes are generated on the server in addition to the Task object.

All events associated with this operation will have the same request_id in the payload field.

Fetching event data as CSV from the /api/events endpoint

The /api/events endpoint allows the fetching of event data with filtering parameters such as org_id, project_id, task_id, job_id, and user_id.

For more details, see Swagger API Documentation.

For example, to fetch all events associated with a specific job, the following curl command can be used:

curl --user 'user:pass' https://app.cvat.ai/api/events?job_id=123

In the response, you will receive a query ID:

{ "query_id": "150cac1f-09f1-4d73-b6a5-5f47aa5d0031" }

As this process may take some time to complete, the status of the request can be checked by adding the query parameter query_id to the request:

curl -I --user 'user:pass' https://app.cvat.ai/api/events?job_id=123&query_id=150cac1f-09f1-4d73-b6a5-5f47aa5d0031

Upon successful creation, the server will return a 201 Created status:

HTTP/2 201
allow: GET, POST, HEAD, OPTIONS
date: Tue, 16 May 2023 13:38:42 GMT
referrer-policy: same-origin
server: Apache
vary: Accept,Origin,Cookie
x-content-type-options: nosniff
x-frame-options: DENY
x-request-id: 4631f5fa-a4f0-42a8-b77b-7426fc298a85

The CSV file can be downloaded by adding the action=download query parameter to the request:

curl --user 'user:pass' https://app.cvat.ai/api/events?job_id=123&query_id=150cac1f-09f1-4d73-b6a5-5f47aa5d0031&action=download > /tmp/events.csv

This will download and save the file to /tmp/events.csv on your local machine.

Dashboards

By default, three dashboards are available in CVAT.

To access them, click General, you will be forwarded to the page with available dashboards.

Dashboard: All Events

The dashboard shows all events, their timestamps, and their source.

There are two fields with statistics at the bottom of the dashboard, about browser and OS users use.

Click on the column name to enable a filter.

If you want to inspect the value, hover over it and click on the eye icon.

Dashboard: Management

The dashboard shows user activity.

Click on the column name to enable a filter.

If you want to inspect the value, hover over it and click on the eye icon.

Dashboard: Monitoring

The dashboard shows server logs, helps handle errors, and shows user activity.

Click on the column name to enable a filter.

If you want to inspect the value, hover over it and click on the eye icon.

Dashboards setup

You can adjust the dashboards. To do this, click on the graph or table name and from the drop-down menu select Edit.

Adjust the query in the editor.

Example of query:

SELECT
    time,
    uniqExact(user_id) Users
FROM
(
    SELECT
      user_id,
      toStartOfInterval(timestamp, INTERVAL 15 minute) as time
    FROM cvat.events
    WHERE
      user_id IS NOT NULL
    GROUP BY
      user_id,
      time
    ORDER BY time ASC WITH FILL STEP toIntervalMinute(15)
)
GROUP BY time
ORDER BY time

Note, that by default the updated configuration will not be saved and will be reset to the default parameters after you restart the container.

To save the updated configuration, do the following:

1. Update Configuration: Start by making your desired changes in the query.

2. Apply Changes: Once you’ve made your changes, click the Apply button to ensure the changes are implemented.

   

3. Save Configuration: To save your applied changes, on the top of the dashboard, click the Save button.

   

4. Replace Configuration File: After saving, replace the existing Grafana dashboard configuration file is located at components/analytics/grafana/dashboards with the new JSON configuration file.

   

5. Restart Grafana Service: To ensure, that all changes take effect, restart the Grafana service. If you’re using Docker Compose, execute the following command: docker compose restart cvat_grafana.

For more information, see Grafana Dashboards.

Example of use

This video demonstrates available by default CVAT analytics features.

5 - Mounting cloud storage

AWS S3 bucket as filesystem

Ubuntu 20.04

Mount

1. Install s3fs:

   sudo apt install s3fs
   

2. Enter your credentials in a file ${HOME}/.passwd-s3fs and set owner-only permissions:

   echo ACCESS_KEY_ID:SECRET_ACCESS_KEY > ${HOME}/.passwd-s3fs
   chmod 600 ${HOME}/.passwd-s3fs
   

3. Uncomment user_allow_other in the /etc/fuse.conf file: sudo nano /etc/fuse.conf

4. Run s3fs, replace bucket_name, mount_point:

   s3fs <bucket_name> <mount_point> -o allow_other -o passwd_file=${HOME}/.passwd-s3fs
   

For more details see here.

Automatically mount

Follow the first 3 mounting steps above.

Using fstab

1. Create a bash script named aws_s3_fuse(e.g in /usr/bin, as root) with this content (replace user_name on whose behalf the disk will be mounted, backet_name, mount_point, /path/to/.passwd-s3fs):

   #!/bin/bash
   sudo -u <user_name> s3fs <backet_name> <mount_point> -o passwd_file=/path/to/.passwd-s3fs -o allow_other
   exit 0
   

2. Give it the execution permission:

   sudo chmod +x /usr/bin/aws_s3_fuse
   

3. Edit /etc/fstab adding a line like this, replace mount_point):

   /absolute/path/to/aws_s3_fuse  <mount_point>     fuse    allow_other,user,_netdev     0       0
   

Using systemd

1. Create unit file sudo nano /etc/systemd/system/s3fs.service (replace user_name, bucket_name, mount_point, /path/to/.passwd-s3fs):

   [Unit]
   Description=FUSE filesystem over AWS S3 bucket
   After=network.target
   
   [Service]
   Environment="MOUNT_POINT=<mount_point>"
   User=<user_name>
   Group=<user_name>
   ExecStart=s3fs <bucket_name> ${MOUNT_POINT} -o passwd_file=/path/to/.passwd-s3fs -o allow_other
   ExecStop=fusermount -u ${MOUNT_POINT}
   Restart=always
   Type=forking
   
   [Install]
   WantedBy=multi-user.target
   

2. Update the system configurations, enable unit autorun when the system boots, mount the bucket:

   sudo systemctl daemon-reload
   sudo systemctl enable s3fs.service
   sudo systemctl start s3fs.service
   

Check

A file /etc/mtab contains records of currently mounted filesystems.

cat /etc/mtab | grep 's3fs'

Unmount filesystem

fusermount -u <mount_point>

If you used systemd to mount a bucket:

sudo systemctl stop s3fs.service
sudo systemctl disable s3fs.service

Microsoft Azure container as filesystem

Ubuntu 20.04

Mount

1. Set up the Microsoft package repository.(More here)

   wget https://packages.microsoft.com/config/ubuntu/20.04/packages-microsoft-prod.deb
   sudo dpkg -i packages-microsoft-prod.deb
   sudo apt-get update
   

2. Install blobfuse and fuse:

   sudo apt-get install blobfuse fuse
   

   For more details see here

3. Create environments (replace account_name, account_key, mount_point):

   export AZURE_STORAGE_ACCOUNT=<account_name>
   export AZURE_STORAGE_ACCESS_KEY=<account_key>
   MOUNT_POINT=<mount_point>
   

4. Create a folder for cache:

   sudo mkdir -p /mnt/blobfusetmp
   

5. Make sure the file must be owned by the user who mounts the container:

   sudo chown <user> /mnt/blobfusetmp
   

6. Create the mount point, if it doesn’t exists:

   mkdir -p ${MOUNT_POINT}
   

7. Uncomment user_allow_other in the /etc/fuse.conf file: sudo nano /etc/fuse.conf

8. Mount container(replace your_container):

   blobfuse ${MOUNT_POINT} --container-name=<your_container> --tmp-path=/mnt/blobfusetmp -o allow_other
   

Automatically mount

Follow the first 7 mounting steps above.

Using fstab

1. Create configuration file connection.cfg with same content, change accountName, select one from accountKey or sasToken and replace with your value:

   accountName <account-name-here>
   # Please provide either an account key or a SAS token, and delete the other line.
   accountKey <account-key-here-delete-next-line>
   #change authType to specify only 1
   sasToken <shared-access-token-here-delete-previous-line>
   authType <MSI/SAS/SPN/Key/empty>
   containerName <insert-container-name-here>
   

2. Create a bash script named azure_fuse(e.g in /usr/bin, as root) with content below (replace user_name on whose behalf the disk will be mounted, mount_point, /path/to/blobfusetmp,/path/to/connection.cfg):

   #!/bin/bash
   sudo -u <user_name> blobfuse <mount_point> --tmp-path=/path/to/blobfusetmp  --config-file=/path/to/connection.cfg -o allow_other
   exit 0
   

3. Give it the execution permission:

   sudo chmod +x /usr/bin/azure_fuse
   

4. Edit /etc/fstab with the blobfuse script. Add the following line(replace paths):

   /absolute/path/to/azure_fuse </path/to/desired/mountpoint> fuse allow_other,user,_netdev
   

Using systemd

1. Create unit file sudo nano /etc/systemd/system/blobfuse.service. (replace user_name, mount_point, container_name,/path/to/connection.cfg):

   [Unit]
   Description=FUSE filesystem over Azure container
   After=network.target
   
   [Service]
   Environment="MOUNT_POINT=<mount_point>"
   User=<user_name>
   Group=<user_name>
   ExecStart=blobfuse ${MOUNT_POINT} --container-name=<container_name> --tmp-path=/mnt/blobfusetmp --config-file=/path/to/connection.cfg -o allow_other
   ExecStop=fusermount -u ${MOUNT_POINT}
   Restart=always
   Type=forking
   
   [Install]
   WantedBy=multi-user.target
   

2. Update the system configurations, enable unit autorun when the system boots, mount the container:

   sudo systemctl daemon-reload
   sudo systemctl enable blobfuse.service
   sudo systemctl start blobfuse.service
   

   Or for more detail see here

Check

A file /etc/mtab contains records of currently mounted filesystems.

cat /etc/mtab | grep 'blobfuse'

Unmount filesystem

fusermount -u <mount_point>

If you used systemd to mount a container:

sudo systemctl stop blobfuse.service
sudo systemctl disable blobfuse.service

If you have any mounting problems, check out the answers to common problems

Google Drive as filesystem

Ubuntu 20.04

Mount

To mount a google drive as a filesystem in user space(FUSE) you can use google-drive-ocamlfuse To do this follow the instructions below:

1. Install google-drive-ocamlfuse:

   sudo add-apt-repository ppa:alessandro-strada/ppa
   sudo apt-get update
   sudo apt-get install google-drive-ocamlfuse
   

2. Run google-drive-ocamlfuse without parameters:

   google-drive-ocamlfuse
   

   This command will create the default application directory (~/.gdfuse/default), containing the configuration file config (see the wiki page for more details about configuration). And it will start a web browser to obtain authorization to access your Google Drive. This will let you modify default configuration before mounting the filesystem.

   Then you can choose a local directory to mount your Google Drive (e.g.: ~/GoogleDrive).

3. Create the mount point, if it doesn’t exist(replace mount_point):

   mountpoint="<mount_point>"
   mkdir -p $mountpoint
   

4. Uncomment user_allow_other in the /etc/fuse.conf file: sudo nano /etc/fuse.conf

5. Mount the filesystem:

   google-drive-ocamlfuse -o allow_other $mountpoint
   

Automatically mount

Follow the first 4 mounting steps above.

Using fstab

1. Create a bash script named gdfuse(e.g in /usr/bin, as root) with this content (replace user_name on whose behalf the disk will be mounted, label, mount_point):

   #!/bin/bash
   sudo -u <user_name> google-drive-ocamlfuse -o allow_other -label <label> <mount_point>
   exit 0
   

2. Give it the execution permission:

   sudo chmod +x /usr/bin/gdfuse
   

3. Edit /etc/fstab adding a line like this, replace mount_point):

   /absolute/path/to/gdfuse  <mount_point>     fuse    allow_other,user,_netdev     0       0
   

   For more details see here

Using systemd

1. Create unit file sudo nano /etc/systemd/system/google-drive-ocamlfuse.service. (replace user_name, label(default label=default), mount_point):

   [Unit]
   Description=FUSE filesystem over Google Drive
   After=network.target
   
   [Service]
   Environment="MOUNT_POINT=<mount_point>"
   User=<user_name>
   Group=<user_name>
   ExecStart=google-drive-ocamlfuse -label <label> ${MOUNT_POINT}
   ExecStop=fusermount -u ${MOUNT_POINT}
   Restart=always
   Type=forking
   
   [Install]
   WantedBy=multi-user.target
   

2. Update the system configurations, enable unit autorun when the system boots, mount the drive:

   sudo systemctl daemon-reload
   sudo systemctl enable google-drive-ocamlfuse.service
   sudo systemctl start google-drive-ocamlfuse.service
   

   For more details see here

Check

A file /etc/mtab contains records of currently mounted filesystems.

cat /etc/mtab | grep 'google-drive-ocamlfuse'

Unmount filesystem

fusermount -u <mount_point>

If you used systemd to mount a drive:

sudo systemctl stop google-drive-ocamlfuse.service
sudo systemctl disable google-drive-ocamlfuse.service

6 - LDAP Backed Authentication

The creation of settings.py

When integrating LDAP login, we need to create an overlay to the default CVAT settings located in cvat/settings/production.py. This overlay is where we will configure Django to connect to the LDAP server.

The main issue with using LDAP is that different LDAP implementations have different parameters. So the options used for Active Directory backed authentication will differ if you were to be using FreeIPA.

Update docker-compose.override.yml

In your override config you need to passthrough your settings and tell CVAT to use them by setting the DJANGO_SETTINGS_MODULE variable.

services:
  cvat_server:
    environment:
      DJANGO_SETTINGS_MODULE: settings
    volumes:
      - ./settings.py:/home/django/settings.py:ro

Active Directory Example

The following example should allow for users to authenticate themselves against Active Directory. This example requires a dummy user named cvat_bind. The configuration for the bind account does not need any special permissions.

When updating AUTH_LDAP_BIND_DN, you can write out the account info in two ways. Both are documented in the config below.

This config is known to work with Windows Server 2022, but should work for older versions and Samba’s implementation of Active Directory.

# We are overlaying production
from cvat.settings.production import *

# Custom code below
import ldap
from django_auth_ldap.config import LDAPSearch
from django_auth_ldap.config import NestedActiveDirectoryGroupType

# Notify CVAT that we are using LDAP authentication
IAM_TYPE = 'LDAP'

# Talking to the LDAP server
AUTH_LDAP_SERVER_URI = "ldap://ad.example.com" # IP Addresses also work
ldap.set_option(ldap.OPT_REFERRALS, 0)

_BASE_DN = "CN=Users,DC=ad,DC=example,DC=com"

# Authenticating with the LDAP server
AUTH_LDAP_BIND_DN = "CN=cvat_bind,%s" % _BASE_DN
# AUTH_LDAP_BIND_DN = "cvat_bind@ad.example.com"
AUTH_LDAP_BIND_PASSWORD = "SuperSecurePassword^21"

AUTH_LDAP_USER_SEARCH = LDAPSearch(
    _BASE_DN,
    ldap.SCOPE_SUBTREE,
    "(sAMAccountName=%(user)s)"
)

AUTH_LDAP_GROUP_SEARCH = LDAPSearch(
    _BASE_DN,
    ldap.SCOPE_SUBTREE,
    "(objectClass=group)"
)

# Mapping Django field names to Active Directory attributes
AUTH_LDAP_USER_ATTR_MAP = {
    "user_name": "sAMAccountName",
    "first_name": "givenName",
    "last_name": "sn",
    "email": "mail",
}

# Group Management
AUTH_LDAP_GROUP_TYPE = NestedActiveDirectoryGroupType()

# Register Django LDAP backend
AUTHENTICATION_BACKENDS += ['django_auth_ldap.backend.LDAPBackend']

# Map Active Directory groups to Django/CVAT groups.
AUTH_LDAP_ADMIN_GROUPS = [
    'CN=CVAT Admins,%s' % _BASE_DN,
]
AUTH_LDAP_BUSINESS_GROUPS = [
    'CN=CVAT Managers,%s' % _BASE_DN,
]
AUTH_LDAP_WORKER_GROUPS = [
    'CN=CVAT Workers,%s' % _BASE_DN,
]
AUTH_LDAP_USER_GROUPS = [
    'CN=CVAT Users,%s' % _BASE_DN,
]

DJANGO_AUTH_LDAP_GROUPS = {
    "admin": AUTH_LDAP_ADMIN_GROUPS,
    "business": AUTH_LDAP_BUSINESS_GROUPS,
    "user": AUTH_LDAP_USER_GROUPS,
    "worker": AUTH_LDAP_WORKER_GROUPS,
}

FreeIPA Example

The following example should allow for users to authenticate themselves against FreeIPA. This example requires a dummy user named cvat_bind. The configuration for the bind account does not need any special permissions.

When updating AUTH_LDAP_BIND_DN, you can only write the user info in one way, unlike with Active Directory

This config is known to work with AlmaLinux 8, but may work for other versions and flavors of Enterprise Linux.

# We are overlaying production
from cvat.settings.production import *

# Custom code below
import ldap
from django_auth_ldap.config import LDAPSearch
from django_auth_ldap.config import GroupOfNamesType

# Notify CVAT that we are using LDAP authentication
IAM_TYPE = 'LDAP'

_BASE_DN = "CN=Accounts,DC=ipa,DC=example,DC=com"

# Talking to the LDAP server
AUTH_LDAP_SERVER_URI = "ldap://ipa.example.com" # IP Addresses also work
ldap.set_option(ldap.OPT_REFERRALS, 0)

# Authenticating with the LDAP server
AUTH_LDAP_BIND_DN = "UID=cvat_bind,CN=Users,%s" % _BASE_DN
AUTH_LDAP_BIND_PASSWORD = "SuperSecurePassword^21"

AUTH_LDAP_USER_SEARCH = LDAPSearch(
    "CN=Users,%s" % _BASE_DN,
    ldap.SCOPE_SUBTREE,
    "(uid=%(user)s)"
)

AUTH_LDAP_GROUP_SEARCH = LDAPSearch(
    "CN=Groups,%s" % _BASE_DN,
    ldap.SCOPE_SUBTREE,
    "(objectClass=groupOfNames)"
)

# Mapping Django field names to FreeIPA attributes
AUTH_LDAP_USER_ATTR_MAP = {
    "user_name": "uid",
    "first_name": "givenName",
    "last_name": "sn",
    "email": "mail",
}

# Group Management
AUTH_LDAP_GROUP_TYPE = GroupOfNamesType()

# Register Django LDAP backend
AUTHENTICATION_BACKENDS += ['django_auth_ldap.backend.LDAPBackend']

# Map FreeIPA groups to Django/CVAT groups.
AUTH_LDAP_ADMIN_GROUPS = [
    'CN=cvat_admins,CN=Groups,%s' % _BASE_DN,
]
AUTH_LDAP_BUSINESS_GROUPS = [
    'CN=cvat_managers,CN=Groups,%s' % _BASE_DN,
]
AUTH_LDAP_WORKER_GROUPS = [
    'CN=cvat_workers,CN=Groups,%s' % _BASE_DN,
]
AUTH_LDAP_USER_GROUPS = [
    'CN=cvat_users,CN=Groups,%s' % _BASE_DN,
]

DJANGO_AUTH_LDAP_GROUPS = {
    "admin": AUTH_LDAP_ADMIN_GROUPS,
    "business": AUTH_LDAP_BUSINESS_GROUPS,
    "user": AUTH_LDAP_USER_GROUPS,
    "worker": AUTH_LDAP_WORKER_GROUPS,
}

Resources

• Microsoft - LDAP Distinguished Names
  • Elements that make up a distinguished name. Used with user/group searches.
• Django LDAP Reference Manual
  • Other options that can be used for LDAP authentication in Django.
• Django LDAP guide using Active Directory (Unofficial)
  • This is not specific to CVAT but can provide insight about firewall rules.

7 - Backup guide

About CVAT data volumes

Docker volumes are used to store all CVAT data:

• cvat_db: PostgreSQL database files, used to store information about users, tasks, projects, annotations, etc. Mounted into cvat_db container by /var/lib/postgresql/data path.

• cvat_data: used to store uploaded and prepared media data. Mounted into cvat container by /home/django/data path.

• cvat_keys: used to store the Django secret key. Mounted into cvat container by /home/django/keys path.

• cvat_logs: used to store logs of CVAT backend processes managed by supevisord. Mounted into cvat container by /home/django/logs path.

• cvat_events: this is an optional volume that is used only when Analytics component is enabled and is used to store Elasticsearch database files. Mounted into cvat_elasticsearch container by /usr/share/elasticsearch/data path.

How to backup all CVAT data

All CVAT containers should be stopped before backup:

docker compose stop

Please don’t forget to include all the compose config files that were used in the docker compose command using the -f parameter.

Backup data:

mkdir backup
docker run --rm --name temp_backup --volumes-from cvat_db -v $(pwd)/backup:/backup ubuntu tar -czvf /backup/cvat_db.tar.gz /var/lib/postgresql/data
docker run --rm --name temp_backup --volumes-from cvat_server -v $(pwd)/backup:/backup ubuntu tar -czvf /backup/cvat_data.tar.gz /home/django/data
# [optional]
docker run --rm --name temp_backup --volumes-from cvat_elasticsearch -v $(pwd)/backup:/backup ubuntu tar -czvf /backup/cvat_events.tar.gz /usr/share/elasticsearch/data

Make sure the backup archives have been created, the output of ls backup command should look like this:

ls backup
cvat_data.tar.gz  cvat_db.tar.gz  cvat_events.tar.gz

How to restore CVAT from backup

Warning: use exactly the same CVAT version to restore DB. Otherwise it will not work because between CVAT releases the layout of DB can be changed. You always can upgrade CVAT later. It will take care to migrate your data properly internally.

Note: CVAT containers must exist (if no, please follow the installation guide). Stop all CVAT containers:

docker compose stop

Restore data:

cd <path_to_backup_folder>
docker run --rm --name temp_backup --volumes-from cvat_db -v $(pwd):/backup ubuntu bash -c "cd /var/lib/postgresql/data && tar -xvf /backup/cvat_db.tar.gz --strip 4"
docker run --rm --name temp_backup --volumes-from cvat_server -v $(pwd):/backup ubuntu bash -c "cd /home/django/data && tar -xvf /backup/cvat_data.tar.gz --strip 3"
# [optional]
docker run --rm --name temp_backup --volumes-from cvat_elasticsearch -v $(pwd):/backup ubuntu bash -c "cd /usr/share/elasticsearch/data && tar -xvf /backup/cvat_events.tar.gz --strip 4"

After that run CVAT as usual:

docker compose up -d

Additional resources

Docker guide about volume backups

8 - Upgrade guide

Upgrade guide

Note: updating CVAT from version 2.2.0 to version 2.3.0 requires additional manual actions with database data due to upgrading PostgreSQL base image major version. See details here

To upgrade CVAT, follow these steps:

• It is highly recommended backup all CVAT data before updating, follow the backup guide and backup all CVAT volumes.

• Go to the previously cloned CVAT directory and stop all CVAT containers with:

  docker compose down
  

  If you have included additional components, include all compose configuration files that are used, e.g.:

  docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml down
  

• Update CVAT source code by any preferable way: clone with git or download zip file from GitHub. Note that you need to download the entire source code, not just the Docker Compose configuration file. Check the installation guide for details.

• Verify settings: The installation process is changed/modified from version to version and you may need to export some environment variables, for example CVAT_HOST.

• Update local CVAT images. Pull or build new CVAT images, see How to pull/build/update CVAT images section for details.

• Start CVAT with:

  docker compose up -d
  

  When CVAT starts, it will upgrade its DB in accordance with the latest schema. It can take time especially if you have a lot of data. Please do not terminate the migration and wait till the process is complete. You can monitor the startup process with the following command:

  docker logs cvat_server -f
  

How to upgrade CVAT from v2.2.0 to v2.3.0.

Step by step commands how to upgrade CVAT from v2.2.0 to v2.3.0. Let’s assume that you have CVAT v2.2.0 working.

docker exec -it cvat_db pg_dumpall > cvat.db.dump
cd cvat
docker compose down
docker volume rm cvat_cvat_db
export CVAT_VERSION="v2.3.0"
cd ..
mv cvat cvat_220
wget https://github.com/cvat-ai/cvat/archive/refs/tags/${CVAT_VERSION}.zip
unzip ${CVAT_VERSION}.zip && mv cvat-${CVAT_VERSION:1} cvat
unset CVAT_VERSION
cd cvat
export CVAT_HOST=cvat.example.com
export ACME_EMAIL=example@example.com
docker compose pull
docker compose up -d cvat_db
docker exec -i cvat_db psql -q -d postgres < ../cvat.db.dump
docker compose -f docker-compose.yml -f docker-compose.dev.yml -f docker-compose.https.yml up -d

How to upgrade CVAT from v1.7.0 to v2.2.0.

Step by step commands how to upgrade CVAT from v1.7.0 to v2.2.0. Let’s assume that you have CVAT v1.7.0 working.

export CVAT_VERSION="v2.2.0"
cd cvat
docker compose down
cd ..
mv cvat cvat_170
wget https://github.com/cvat-ai/cvat/archive/refs/tags/${CVAT_VERSION}.zip
unzip ${CVAT_VERSION}.zip && mv cvat-${CVAT_VERSION:1} cvat
cd cvat
docker pull cvat/server:${CVAT_VERSION}
docker tag cvat/server:${CVAT_VERSION} openvino/cvat_server:latest
docker pull cvat/ui:${CVAT_VERSION}
docker tag cvat/ui:${CVAT_VERSION} openvino/cvat_ui:latest
docker compose up -d

How to upgrade PostgreSQL database base image

1. It is highly recommended backup all CVAT data before updating, follow the backup guide and backup CVAT database volume.

2. Run previously used CVAT version as usual

3. Backup current database with pg_dumpall tool:

   docker exec -it cvat_db pg_dumpall > cvat.db.dump
   

4. Stop CVAT:

   docker compose down
   

5. Delete current PostgreSQL’s volume, that’s why it’s important to have a backup:

   docker volume rm cvat_cvat_db
   

6. Update CVAT source code by any preferable way: clone with git or download zip file from GitHub. Check the installation guide for details.

7. Start database container only:

   docker compose up -d cvat_db
   

8. Import PostgreSQL dump into new DB container:

   docker exec -i cvat_db psql -q -d postgres < cvat.db.dump
   

9. Start CVAT:

   docker compose up -d
   

9 - Webhooks

Webhooks are user-defined HTTP callbacks that are triggered by specific events. When an event that triggers a webhook occurs, CVAT makes an HTTP request to the URL configured for the webhook. The request will include a payload with information about the event.

CVAT, webhooks can be triggered by a variety of events, such as the creation, deletion, or modification of tasks, jobs, and so on. This makes it easy to set up automated processes that respond to changes made in CVAT.

For example, you can set up webhooks to alert you when a job’s assignee is changed or when a job/task’s status is updated, for instance, when a job is completed and ready for review or has been reviewed. New task creation can also trigger notifications.

These capabilities allow you to keep track of progress and changes in your CVAT workflow instantly.

In CVAT you can create a webhook for a project or organization. You can use CVAT GUI or direct API calls.

See:

• Create Webhook
  • For project
  • For organization
  • Webhooks forms
  • List of events
• Payloads
  • Create event
  • Update event
  • Delete event
• Webhook secret
• Ping Webhook
• Webhooks with API calls
• Example of setup and use

Create Webhook

For project

To create a webhook for Project, do the following:

1. Create a Project.

2. Go to the Projects and click on the project’s widget.

3. In the top right corner, click Actions > Setup Webhooks.

4. In the top right corner click +

   

5. Fill in the Setup webhook form and click Submit.

For organization

To create a webhook for Organization, do the following:

1. Create Organization
2. Go to the Organization > Settings > Actions > Setup Webhooks.
3. In the top right corner click +

4. Fill in the Setup webhook form and click Submit.

Webhooks forms

The Setup a webhook forms look like the following.

Forms have the following fields:

List of events

The following events are available for webhook alerts.

Payloads

Create event

Webhook payload object for create:<resource> events:

An example of payload for the create:task event:

{
 "event": "create:task",
    "task": {
        "url": "<http://localhost:8080/api/tasks/15>",
        "id": 15,
        "name": "task",
        "project_id": 7,
        "mode": "",
        "owner": {
            "url": "<http://localhost:8080/api/users/1>",
            "id": 1,
            "username": "admin1",
            "first_name": "Admin",
            "last_name": "First"
        },
        "assignee": null,
        "bug_tracker": "",
        "created_date": "2022-10-04T08:05:50.419259Z",
        "updated_date": "2022-10-04T08:05:50.422917Z",
        "overlap": null,
        "segment_size": 0,
        "status": "annotation",
        "labels": \[
            {
                "id": 28,
                "name": "label_0",
                "color": "#bde94a",
                "attributes": [],
                "type": "any",
                "sublabels": [],
                "has_parent": false
            }
        \],
        "segments": [],
        "dimension": "2d",
        "subset": "",
        "organization": null,
        "target_storage": {
            "id": 14,
            "location": "local",
            "cloud_storage_id": null
        },
        "source_storage": {
            "id": 13,
            "location": "local",
            "cloud_storage_id": null
        }
    },
    "webhook_id": 7,
    "sender": {
        "url": "<http://localhost:8080/api/users/1>",
        "id": 1,
        "username": "admin1",
        "first_name": "Admin",
        "last_name": "First"
    }
}

Update event

Webhook payload object for update:<resource> events:

An example of update:<resource> event:

{
    "event": "update:task",
    "task": {
        "url": "<http://localhost:8080/api/tasks/15>",
        "id": 15,
        "name": "new task name",
        "project_id": 7,
        "mode": "annotation",
        "owner": {
            "url": "<http://localhost:8080/api/users/1>",
            "id": 1,
            "username": "admin1",
            "first_name": "Admin",
            "last_name": "First"
        },
        "assignee": null,
        "bug_tracker": "",
        "created_date": "2022-10-04T08:05:50.419259Z",
        "updated_date": "2022-10-04T11:04:51.451681Z",
        "overlap": 0,
        "segment_size": 1,
        "status": "annotation",
        "labels": \[
            {
                "id": 28,
                "name": "label_0",
                "color": "#bde94a",
                "attributes": [],
                "type": "any",
                "sublabels": [],
                "has_parent": false
            }
        \],
        "segments": \[
            {
                "start_frame": 0,
                "stop_frame": 0,
                "jobs": \[
                    {
                        "url": "<http://localhost:8080/api/jobs/19>",
                        "id": 19,
                        "assignee": null,
                        "status": "annotation",
                        "stage": "annotation",
                        "state": "new"
                    }
                \]
            }
        \],
        "data_chunk_size": 14,
        "data_compressed_chunk_type": "imageset",
        "data_original_chunk_type": "imageset",
        "size": 1,
        "image_quality": 70,
        "data": 14,
        "dimension": "2d",
        "subset": "",
        "organization": null,
        "target_storage": {
            "id": 14,
            "location": "local",
            "cloud_storage_id": null
        },
        "source_storage": {
            "id": 13,
            "location": "local",
            "cloud_storage_id": null
        }
    },
    "before_update": {
        "name": "task"
    },
    "webhook_id": 7,
    "sender": {
        "url": "<http://localhost:8080/api/users/1>",
        "id": 1,
        "username": "admin1",
        "first_name": "Admin",
        "last_name": "First"
    }
}

Delete event

Webhook payload object for delete:<resource> events:

Here is an example of the payload for the delete:task event:

{
    "event": "delete:task",
    "task": {
        "url": "<http://localhost:8080/api/tasks/15>",
        "id": 15,
        "name": "task",
        "project_id": 7,
        "mode": "",
        "owner": {
            "url": "<http://localhost:8080/api/users/1>",
            "id": 1,
            "username": "admin1",
            "first_name": "Admin",
            "last_name": "First"
        },
        "assignee": null,
        "bug_tracker": "",
        "created_date": "2022-10-04T08:05:50.419259Z",
        "updated_date": "2022-10-04T08:05:50.422917Z",
        "overlap": null,
        "segment_size": 0,
        "status": "annotation",
        "labels": \[
            {
                "id": 28,
                "name": "label_0",
                "color": "#bde94a",
                "attributes": [],
                "type": "any",
                "sublabels": [],
                "has_parent": false
            }
        \],
        "segments": [],
        "dimension": "2d",
        "subset": "",
        "organization": null,
        "target_storage": {
            "id": 14,
            "location": "local",
            "cloud_storage_id": null
        },
        "source_storage": {
            "id": 13,
            "location": "local",
            "cloud_storage_id": null
        }
    },
    "webhook_id": 7,
    "sender": {
        "url": "<http://localhost:8080/api/users/1>",
        "id": 1,
        "username": "admin1",
        "first_name": "Admin",
        "last_name": "First"
    }
}

Webhook secret

To validate that the webhook requests originate from CVAT, include a secret during the webhook creation process.

When a secret is provided for the webhook, CVAT includes an X-Signature-256 in the request header of the webhook.

CVAT uses the SHA256 hash function to encode the request body for the webhook and places the resulting hash into the header.

The webhook recipient can verify the source of the request by comparing the received X-Signature-256 value with the expected value.

Here’s an example of a header value for a request with an empty body and secret = mykey:

X-Signature-256: e1b24265bf2e0b20c81837993b4f1415f7b68c503114d100a40601eca6a2745f

Here is an example of how you can verify a webhook signature in your webhook receiver service:

# webhook_receiver.py

import hmac
from hashlib import sha256
from flask import Flask, request

app = Flask(__name__)

@app.route("/webhook", methods=["POST"])
def webhook():
    signature = (
        "sha256="
        + hmac.new("mykey".encode("utf-8"), request.data, digestmod=sha256).hexdigest()
    )

    if hmac.compare_digest(request.headers["X-Signature-256"], signature):
        return app.response_class(status=200)

    raise app.response_class(status=500, response="Signatures didn't match!")

Ping Webhook

To confirm the proper configuration of your webhook and ensure that CVAT can establish a connection with the target URL, use the Ping webhook feature.

1. Click the Ping button in the user interface (or send a POST /webhooks/{id}/ping request through API).
2. CVAT will send a webhook alert to the specified target URL with basic information about the webhook.

Ping webhook payload:

Here is an example of a payload for the ping event:

{
   "event": "ping",
    "webhook": {
        "id": 7,
        "url": "<http://localhost:8080/api/webhooks/7>",
        "target_url": "<https://example.com>",
        "description": "",
        "type": "project",
        "content_type": "application/json",
        "is_active": true,
        "enable_ssl": true,
        "created_date": "2022-10-04T08:05:23.007381Z",
        "updated_date": "2022-10-04T08:05:23.007395Z",
        "owner": {
            "url": "<http://localhost:8080/api/users/1>",
            "id": 1,
            "username": "admin1",
            "first_name": "Admin",
            "last_name": "First"
        },
        "project": 7,
        "organization": null,
        "events": \[
            "create:comment",
            "create:issue",
            "create:task",
            "delete:comment",
            "delete:issue",
            "delete:task",
            "update:comment",
            "update:issue",
            "update:job",
            "update:project",
            "update:task"
        \],
        "last_status": 200,
        "last_delivery_date": "2022-10-04T11:04:52.538638Z"
    },
    "sender": {
        "url": "<http://localhost:8080/api/users/1>",
        "id": 1,
        "username": "admin1",
        "first_name": "Admin",
        "last_name": "First"
    }
}

Webhooks with API calls

To create webhook via an API call, see Swagger documentation.

For examples, see REST API tests.

Example of setup and use

This video demonstrates setting up email alerts for a project using Zapier and Gmail.