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Advanced

This section contains advanced documents for system administrators.

1: Installation Analytics
2: Semi-automatic and Automatic Annotation
3: LDAP Backed Authentication
4: Mounting cloud storage
5: Backup guide
6: Upgrade guide
7: IAM: system roles

1 - Installation Analytics

Instructions for deployment and customization of Analytics. This section on GitHub.

It is possible to proxy annotation logs from client to ELK. To do that run the following command below:

Build docker image

# From project root directory
docker-compose -f docker-compose.yml -f components/analytics/docker-compose.analytics.yml build

Run docker container

# From project root directory
docker-compose -f docker-compose.yml -f components/analytics/docker-compose.analytics.yml up -d

At the moment it is not possible to save advanced settings. Below values should be specified manually.

Time picker default

{
"from": "now/d",
"to": "now/d",
"display": "Today",
"section": 0
}

Time picker quick ranges

[
  {
    "from": "now/d",
    "to": "now/d",
    "display": "Today",
    "section": 0
  },
  {
    "from": "now/w",
    "to": "now/w",
    "display": "This week",
    "section": 0
  },
  {
    "from": "now/M",
    "to": "now/M",
    "display": "This month",
    "section": 0
  },
  {
    "from": "now/y",
    "to": "now/y",
    "display": "This year",
    "section": 0
  },
  {
    "from": "now/d",
    "to": "now",
    "display": "Today so far",
    "section": 2
  },
  {
    "from": "now/w",
    "to": "now",
    "display": "Week to date",
    "section": 2
  },
  {
    "from": "now/M",
    "to": "now",
    "display": "Month to date",
    "section": 2
  },
  {
    "from": "now/y",
    "to": "now",
    "display": "Year to date",
    "section": 2
  },
  {
    "from": "now-1d/d",
    "to": "now-1d/d",
    "display": "Yesterday",
    "section": 1
  },
  {
    "from": "now-1w/w",
    "to": "now-1w/w",
    "display": "Previous week",
    "section": 1
  },
  {
    "from": "now-1m/m",
    "to": "now-1m/m",
    "display": "Previous month",
    "section": 1
  },
  {
    "from": "now-1y/y",
    "to": "now-1y/y",
    "display": "Previous year",
    "section": 1
  }
]

2 - Semi-automatic and Automatic Annotation

Information about the installation of components needed for 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 1.8.14. 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
```
Create cvat project inside nuclio dashboard where you will deploy new serverless functions and deploy a couple of DL models. Commands below should be run only after CVAT has been installed using docker-compose because it runs nuclio dashboard which manages all serverless functions.
```
nuctl create project cvat
```
```
nuctl deploy --project-name cvat \
  --path serverless/openvino/dextr/nuclio \
  --volume `pwd`/serverless/common:/opt/nuclio/common \
  --platform local
```
```
nuctl deploy --project-name cvat \
  --path serverless/openvino/omz/public/yolo-v3-tf/nuclio \
  --volume `pwd`/serverless/common:/opt/nuclio/common \
  --platform local
```
Note:
- See deploy_cpu.sh for more examples.
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>
```

3 - LDAP Backed Authentication

Allow users to login with credentials from a central source

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.

version: '3.3'

services:
  cvat:
    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.

4 - Mounting cloud storage

Instructions on how to mount AWS S3 bucket, Microsoft Azure container or Google Drive as a filesystem.

AWS S3 bucket as filesystem

Ubuntu 20.04

Mount

Install s3fs:
```
sudo apt install s3fs
```

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

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

Run s3fs, replace bucket_name, mount_point:

s3fs <bucket_name> <mount_point> -o allow_other

For more details see here.

Automatically mount

Follow the first 3 mounting steps above.

Using fstab

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
```
Give it the execution permission:
```
sudo chmod +x /usr/bin/aws_s3_fuse
```

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

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

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

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

Install blobfuse and fuse:
```
sudo apt-get install blobfuse fuse
```
For more details see here

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>

Create a folder for cache:
```
sudo mkdir -p /mnt/blobfusetmp
```
Make sure the file must be owned by the user who mounts the container:
```
sudo chown <user> /mnt/blobfusetmp
```
Create the mount point, if it doesn’t exists:
```
mkdir -p ${MOUNT_POINT}
```
Uncomment user_allow_other in the /etc/fuse.conf file: sudo nano /etc/fuse.conf

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

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>

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
```
Give it the execution permission:
```
sudo chmod +x /usr/bin/azure_fuse
```

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

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

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:

Install google-drive-ocamlfuse:

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

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).
Create the mount point, if it doesn’t exist(replace mount_point):
```
mountpoint="<mount_point>"
mkdir -p $mountpoint
```
Uncomment user_allow_other in the /etc/fuse.conf file: sudo nano /etc/fuse.conf

Mount the filesystem:

google-drive-ocamlfuse -o allow_other $mountpoint

Automatically mount

Follow the first 4 mounting steps above.

Using fstab

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
```
Give it the execution permission:
```
sudo chmod +x /usr/bin/gdfuse
```

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

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

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

5 - Backup guide

Instructions on how to backup CVAT data with Docker.

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 user ssh keys needed for synchronization with a remote Git repository. 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

6 - Upgrade guide

Instructions for upgrading CVAT deployed with docker compose

Upgrade guide

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/analytics/docker-compose.analytics.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 udgrade CVAT from v1.7.0 to v2.1.0.

Step by step commands how to udgrade CVAT from v1.7.0 to v2.1.0. Let’s assume that you have CVAT v1.7.0 working.

cd cvat
docker-compose down
cd ..
mv cvat cvat_old
wget https://github.com/opencv/cvat/archive/refs/tags/v2.1.0.zip
unzip v2.1.0.zip && mv cvat-2.1.0 cvat
cd cvat
docker pull cvat/server:v2.1.0
docker tag cvat/server:v2.1.0 openvino/cvat_server:latest
docker pull cvat/ui:v2.1.0
docker tag cvat/ui:v2.1.0 openvino/cvat_ui:latest
docker-compose up -d

Advanced

1 - Installation Analytics

Build docker image

Run docker container

Time picker default

Time picker quick ranges

2 - Semi-automatic and Automatic Annotation

GPU Support

3 - LDAP Backed Authentication

The creation of settings.py

Update docker-compose.override.yml

Active Directory Example

FreeIPA Example

Resources

4 - Mounting cloud storage

AWS S3 bucket as filesystem

Microsoft Azure container as filesystem

Google Drive as filesystem

5 - Backup guide

About CVAT data volumes

How to backup all CVAT data

How to restore CVAT from backup

Additional resources

6 - Upgrade guide

Upgrade guide

How to udgrade CVAT from v1.7.0 to v2.1.0.

7 - IAM: system roles

System roles

The creation of `settings.py`

Update `docker-compose.override.yml`