Services launch

This section gives examples for:

• Databases tables creation.
• Buckets creation.
• Launching of containers.

LUNA PLATFORM services must be launched in the following sequence:

• Databases, Balancers, HASP service and other third-party party software.
• Configurator
• Image Store
• Accounts
• Licenses
• Faces
• Events
• Python Matcher
• Python Matcher Proxy. The service is disabled by default
• Remote SDK
• Handlers
• Tasks
• Sender
• Video analytics services
• API
• Admin

The following service are used when emulation of LUNA PLATFORM 3 is required only:

• Backport 3
• User Interface 3

The following service are used when emulation of LUNA PLATFORM 4 is required only:

• Backport 4
• User Interface 4

It is recommended to launch containers one by one and wait for the container status to become "up" (use the docker ps command).

Some of these services are optional and you can disable their use. It is recommended to use Events, Tasks, Sender and Admin services by default. See the "Optional services usage" section for details.

When launching each service, certain parameters are used, for example, --detach, --network, etc. See the section "Launching parameters description" for more detailed information about all launch parameters of LUNA PLATFORM services and databases.

See the "Docker commands" section for details about working with containers.

Third-party services

This section describes the launching of databases in docker containers. They must be launched before LP services.

InfluxDB

Monitoring LUNA PLATFORM services requires running the Influx 2.0.8-alpine database. Below are the commands to launch the InfluxDB container.

For more information, see the "Monitoring" section in the administrator manual.

If necessary, you can configure the visualization of monitoring data using the LUNA Dashboards service, which includes a configured Grafana data visualization system. In addition, you can launch the Grafana Loki tool for advanced work with logs. See the instructions for launching LUNA Dashboards and Grafana Loki in the "Monitoring and logs visualization using Grafana" section.

Note: If necessary, you can use an external InfluxDB 2.0.8-alpine. In this case, you can skip the command below, but you will have to set custom settings for each LUNA PLATFORM service.

Use the docker run command with these parameters:

docker run \
-e DOCKER_INFLUXDB_INIT_MODE=setup \
-e DOCKER_INFLUXDB_INIT_BUCKET=luna_monitoring \
-e DOCKER_INFLUXDB_INIT_USERNAME=luna \
-e DOCKER_INFLUXDB_INIT_PASSWORD=password \
-e DOCKER_INFLUXDB_INIT_ORG=luna \
-e DOCKER_INFLUXDB_INIT_ADMIN_TOKEN=kofqt4Pfqjn6o0RBtMDQqVoJLgHoxxDUmmhiAZ7JS6VmEnrqZXQhxDhad8AX9tmiJH6CjM7Y1U8p5eSEocGzIA== \
-v /etc/localtime:/etc/localtime:ro \
-v /var/lib/luna/influx:/var/lib/influxdb2 \
--restart=always \
--detach=true \
--network=host \
--name influxdb \
dockerhub.visionlabs.ru/luna/influxdb:2.0.8-alpine

PostgreSQL

Note: If necessary, you can use an external PostgreSQL database. In this case, you can skip the command below and perform the actions from the section "External PostgreSQL configuration".

Use the following command to launch PostgreSQL.

docker run \
--env=POSTGRES_USER=luna \
--env=POSTGRES_PASSWORD=luna \
--shm-size=1g \
-v /var/lib/luna/postgresql/data/:/var/lib/postgresql/data/ \
-v /var/lib/luna/current/example-docker/postgresql/entrypoint-initdb.d/:/docker-entrypoint-initdb.d/ \
-v /etc/localtime:/etc/localtime:ro \
--name=postgres \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/postgis-vlmatch:16

-v /var/lib/luna/current/example-docker/postgresql/entrypoint-initdb.d/:/docker-entrypoint-initdb.d/ \ — The "docker-entrypoint-initdb.d" script includes the commands for the creation of services databases. During database creation, a default username and password are automatically used.

-v /var/lib/luna/current/example-docker/postgresql/data/:/var/lib/postgresql/data/ — The volume command enables you to mount the "data" folder to the PostgreSQL container. The folder on the server and the folder in the container will be synchronized. The PostgreSQL data from the container will be saved to this directory.

--network=host — If you need to change the port for PotgreSQL, you should change this string to -p 5440:5432. Where the first port 5440 is the local port and 5432 is the port used inside the container.

You should create all the databases for LP services manually if you are going to use an already installed PostgreSQL.

Redis

Note: If you already have Redis installed, skip this step.

Note: It is recommended that you set a password when starting Redis for increased security. This can be done, for example, using the requirepass parameter in the Redis configuration file, or passing the password via the Redis CLI. Next, you need to specify the password in the corresponding service settings, for example, in the "password" parameter of the "LUNA_ATTRIBUTES_DB" section of the Events service. See the official Redis documentation for more details.

Use the following command to launch Redis.

docker run \
-v /etc/localtime:/etc/localtime:ro \
--name=redis \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/redis:7.2

Configurator

Optional services usage

The listed below services are not mandatory for LP:

• Events
• Image Store
• Tasks
• Sender
• Handlers
• Python Matcher Proxy (disabled by default)
• Lambda (disabled by default)
• Video Manager (disabled by default)

Working with the Lambda service is possible only when deploying LUNA PLATFORM services in Kubernetes. See detailed information in the installation manual using Kubernetes.

You can disable them if their functionality is not required for your tasks.

Use the "ADDITIONAL_SERVICES_USAGE" section in the API service settings in the Configurator service to disable unnecessary services.

You can use the dump file provided in the distribution package to enable/disable services before Configurator launch.

vi /var/lib/luna/current/extras/conf/platform_settings.json

Disabling any of the services has certain consequences. For more information, see the "Disableable services" section of the administrator manual.

Configurator DB tables creation

Use the docker run command with these parameters to create the Configurator database tables.

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /var/lib/luna/current/example-docker/luna_configurator/configs/luna_configurator_postgres.conf:/srv/luna_configurator/configs/config.conf \
-v /var/lib/luna/current/extras/conf/platform_settings.json:/srv/luna_configurator/used_dumps/platform_settings.json \
--network=host \
-v /tmp/logs/configurator:/srv/logs \
--rm \
--entrypoint bash \
dockerhub.visionlabs.ru/luna/luna-configurator:v.2.2.57 \
-c "python3 ./base_scripts/db_create.py; cd /srv/luna_configurator/configs/configs/; python3 -m configs.migrate --config /srv/luna_configurator/configs/config.conf head; cd /srv; python3 ./base_scripts/db_create.py --dump-file /srv/luna_configurator/used_dumps/platform_settings.json"

Here:

• /var/lib/luna/current/extras/conf/platform_settings.json — Enables you to specify the path to the dump file with LP configurations.

• ./base_scripts/db_create.py; — Creates database structure.

• python3 -m configs.migrate head; — Performs settings migrations in Configurator DB and sets revision for migration. The revision will be required during the upgrade to the new LP5 build.

• --dump-file /srv/luna_configurator/used_dumps/platform_settings.json — Updates settings in the Configurator DB with values from the provided file.

Run Configurator container

Use the docker run command with these parameters to launch Configurator:

docker run \
--env=PORT=5070 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /var/lib/luna/current/example-docker/luna_configurator/configs/luna_configurator_postgres.conf:/srv/luna_configurator/configs/config.conf \
-v /tmp/logs/configurator:/srv/logs \
--name=luna-configurator \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-configurator:v.2.2.57 

At this stage, you can activate logging to file if you need to save them on the server (see the "Logging to server" section).

Image Store

Image Store container launch

Note: If you are not going to use the Image Store service, do not launch this container and disable the service utilization in Configurator. See section "Optional services usage".

Use the following command to launch the Image Store service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5020 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /var/lib/luna/image_store/:/srv/local_storage/ \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/image-store:/srv/logs \
--name=luna-image-store \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-image-store:v.3.14.27

Here -v /var/lib/luna/image_store/:/srv/local_storage/ is the data from the specified folder is added to the Docker container when it is launched. All the data from the specified Docker container folder is saved to this directory.

If you already have a directory with LP buckets you should specify it instead of /var/lib/luna/image_store/.

Buckets creation

Buckets are required to store data in Image Store. The Image Store service should be launched before the commands execution.

When upgrading from the previous version, it is recommended to launch the bucket creation commands one more time. Hence you make sure that all the required buckets were created.

If the error with code 13006 appears during launching of the listed above commands, the bucket is already created.

Buckets in LP can be created in two ways:

• Using the lis_bucket_create.py script located in the Image Store service container.
• Using direct requests to the Image Store service.

Use script to create buckets

Run this script to create general buckets:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/api:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-api:v.6.37.3 \
python3 ./base_scripts/lis_bucket_create.py -ii --luna-config http://localhost:5070/1

If you want to specify the storage time of an object in a bucket, you can additionally specify the number of days using the --bucket-ttl argument. See details in the "Object lifecycle" section of the administrator manual.

If you are going to use the Tasks service, use the following command to additionally create the "task-result" in the Image Store service:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/tasks:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-tasks:v.3.22.26 \
python3 ./base_scripts/lis_bucket_create.py -ii --luna-config http://localhost:5070/1

If you are going to use the portraits, use the following command to additionally create the "portraits".

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/api:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-backport3:v.0.11.34 \
python3 ./base_scripts/lis_bucket_create.py -ii --luna-config http://localhost:5070/1

Use direct requests

The curl utility is required for the following requests.

The "visionlabs-samples" bucket is used for face samples storage. The bucket is required for LP utilization.

curl -X POST http://127.0.0.1:5020/1/buckets?bucket=visionlabs-samples

If you want to specify the storage time of an object in a bucket, you can additionally specify the number of days using the ttl query parameter. See details in the "Object lifecycle" section of the administrator manual.

The "portraits" bucket is used for portraits storage. The bucket is required for Backport 3 utilization.

curl -X POST http://127.0.0.1:5020/1/buckets?bucket=portraits

The "visionlabs-bodies-samples" bucket is used for human bodies samples storage. The bucket is required for LP utilization.

curl -X POST http://127.0.0.1:5020/1/buckets?bucket=visionlabs-bodies-samples

The "visionlabs-image-origin" bucket is used for source images storage. The bucket is required for LP utilization.

curl -X POST http://127.0.0.1:5020/1/buckets?bucket=visionlabs-image-origin

The "visionlabs-objects" bucket is used for objects storage. The bucket is required for LP utilization.

curl -X POST http://127.0.0.1:5020/1/buckets?bucket=visionlabs-objects

The "task-result" bucket for the Tasks service. Do not use it if you are not going to use the Tasks service.

curl -X POST http://127.0.0.1:5020/1/buckets?bucket=task-result

Accounts

Accounts database creation

Use the following command to create Accounts DB tables:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/accounts:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-accounts:v.0.3.34 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Accounts container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5170 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/accounts:/srv/logs \
--name=luna-accounts \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-accounts:v.0.3.34

Licenses

Note: To use a trial license, it is required to launch the Licenses service on the same server where trial license is being used.

Specify license settings using Configurator

Follow the steps below to set the settings for HASP-key or Guardant-key.

Note: Do not perform the steps described below if you have already specified the license settings in the sections "Specify HASP license settings using dump file" or "Specify Guardant license settings using dump file".

Specify HASP license settings

Note: Perform these actions only if the HASP key is used. See the "Specify Guardant license settings" section if the Guardant key is used.

To set the license server address, follow these steps:

• Go to the Configurator service interface http://<configurator_server_ip>:5070/.

• Specify the "LICENSE_VENDOR" value in the "Setting name" field and click "Apply Filters".

• Set the IP address of the server with your HASP key in the field "server_address" in the format "127.0.0.1".

• Click "Save".

If the license is activated using the HASP key, then two parameters "vendor" and "server_address" must be specified. If you want to change the HASP protection to Guardant, then you need to add the "license_id" field.

Specify Guardant license settings

Note: Perform these actions only if the Guardant key is used. See the "Specify HASP license settings" section if the HASP key is used.

To set the license server address, follow these steps:

• Go to the Configurator service interface http://<configurator_server_ip>:5070/.

• Enter the value "LICENSE_VENDOR" in the "Setting name" field and click "Apply Filters".

• Set the IP address of the server with your Guardant key in the "server_address" field.

• Set the license ID in the format 0x<your_license_id>, obtained in the section "Save license ID" in the License activation manual, in the "license_id" field.

• Click "Save".

If the license is activated using the Guardant key, then three parameters "vendor", "server_address" and "license_id" must be specified. If you want to change the Guardant protection to HASP, then you need to delete the "license_id" field.

Licenses container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5120 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/licenses:/srv/logs \
--name=luna-licenses \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-licenses:v.0.10.57

Faces

Faces DB tables creation

Use the following command to create the Faces DB tables:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/faces:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-faces:v.4.12.15 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Faces container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5030 \
--env=WORKER_COUNT=2 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/faces:/srv/logs \
--name=luna-faces \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-faces:v.4.12.15

Events

Events DB tables creation

Note: If you are not going to use the Events service, do not launch this container and disable the service utilization in Configurator. See section "Optional services usage".

Use the following command to create the Events DB tables:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/events:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-events:v.4.13.15 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Events container launch

Note: If you are not going to use the Events service, do not launch this container and disable the service utilization in Configurator. See section "Optional services usage".

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5040 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/events:/srv/logs \
--name=luna-events \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-events:v.4.13.15

Python Matcher services

For matching tasks, you can use either only the Python Matcher service, or additionally use the Python Matcher Proxy service, which redirects matching requests to either the Python Matcher service or matching plugins. This section describes how to use Python Matcher without Python Matcher Proxy.

You need to use the Python Matcher Proxy service only if you are going to use matching plugins. Using Python Matcher Proxy and running the corresponding docker container are described in the "Use Python Matcher with Python Matcher Proxy" section.

See the description and usage of matching plugins in the administrator manual.

Use Python Matcher without Python Matcher Proxy

The Python Matcher service with matching by the Faces DB is enabled by default after launching.

The Python Matcher service with matching by the Events is also enabled by default. You can disable it by specifying "USE_LUNA_EVENTS = 0" in the "ADDITIONAL_SERVICES_USAGE" settings of Configurator (see "Optional services usage" section). Thus, the Events service will not be used for LUNA PLATFORM.

The Python Matcher that matches using the matcher library is enabled when "CACHE_ENABLED" is set to "true" in the "DESCRIPTORS_CACHE" setting.

A single image is downloaded for the Python Matcher service and the Python Matcher Proxy service.

Python Matcher container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5100 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/python-matcher:/srv/logs \
--name=luna-python-matcher \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-python-matcher:v.1.10.15

Remote SDK

Remote SDK container launch

You can run the Remote SDK service utilizing CPU (set by default) or GPU.

By default, the Remote SDK service is launched with all estimators and detectors enabled. If necessary, you can disable the use of some estimators or detectors when launching the Remote SDK container. Disabling unnecessary estimators enables you to save RAM or GPU memory, since when the Remote SDK service launches, the possibility of performing these estimates is checked and neural networks are loaded into memory. If you disable the estimator or detector, you can also remove its neural network from the Remote SDK container. See the "Enable/disable several estimators and detectors" section of the administrator manual for more information.

Run the Remote SDK service using one of the following commands according to the utilized processing unit.

Run Remote SDK utilizing CPU

Use the following command to launch the Remote SDK service using CPU:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5220 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/remote-sdk:/srv/logs \
--network=host \
--name=luna-remote-sdk \
--restart=always \
--detach=true \
dockerhub.visionlabs.ru/luna/luna-remote-sdk:v.0.9.2

Run Remote SDK utilizing GPU

The Remote SDK service does not utilize GPU by default. If you are going to use the GPU, then you should enable its use for the Remote SDK service in the Configurator service.

If you need to use the GPU for all estimators and detectors at once, then you need to use the "global_device_class" parameter in the "LUNA_REMOTE_SDK_RUNTIME_SETTINGS" section. All estimators and detectors will use the value of this parameter if the "device_class" parameter of their settings like "LUNA_REMOTE_SDK__SETTINGS.runtime_settings" is set to "global" (by default for all estimators and detectors).

If you need to use the GPU for a specific estimator or detector, then you need to use the "device_class" parameter in sections like "LUNA_REMOTE_SDK__SETTINGS.runtime_settings".

See section "Calculations using GPU" for additional requirements for GPU utilization.

Use the following command to launch the Remote SDK service using GPU:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5220 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
--gpus device=0 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/remote-sdk:/srv/logs \
--network=host \
--name=luna-remote-sdk \
--restart=always \
--detach=true \
dockerhub.visionlabs.ru/luna/luna-remote-sdk:v.0.9.2

Here --gpus device=0 is the parameter specifies the used GPU device and enables GPU utilization. A single GPU can be utilized per Remote SDK instance. Multiple GPU utilization per instance is not available.

Run slim version of Remote SDK

You can run a slim version of the Remote SDK service that contains only configuration files without neural networks. It is assumed that the user himself will add the neural networks he needs to the container.

The launch of the slim version of the Remote SDK service is intended for advanced users.

To successfully launch the Remote SDK container with a custom set of neural networks, you need to perform the following actions:

• Request the required neural networks from VisionLabs.
• Place neural networks in a folder with LUNA PLATFORM installed.
• Assign appropriate rights to neural network files.
• Mount neural network files to the /srv/fsdk/data folder of the Remote SDK container.
• Using the arguments of the variable "EXTEND_CMD" to explicitly specify which of the neural networks should be used.

Using the "enable-all-estimators-by-default" flag for the "EXTEND_CMD" variable, you can disable the use of all neural networks (estimators) by default, and then use special flags to explicitly specify which neural networks should be used. If you do not specify this flag or set the value "--enable-all-estimators-by-default=1", the Remote SDK service will try to find all neural networks in the container. If one of the neural networks is not found, the Remote SDK service will not start.

List of available estimators:

Argument	Description
--enable-all-estimators-by-default	Enable all estimators by default.
--enable-human-detector	Simultaneous detector of bodies and bodies.
--enable-face-detector	Face detector.
--enable-body-detector	Body detector.
--enable-face-landmarks5-estimator	Face landmarks5 estimator.
--enable-face-landmarks68-estimator	Face landmarks68 estimator.
--enable-head-pose-estimator	Head pose estimator.
--enable-liveness-estimator	Liveness estimator.
--enable-fisheye-estimator	FishEye effect estimator.
--enable-face-detection-background-estimator	Image background estimator.
--enable-face-warp-estimator	Face sample estimator.
--enable-body-warp-estimator	Body sample estimator.
--enable-quality-estimator	Image quality estimator.
--enable-image-color-type-estimator	Face color type estimator.
--enable-face-natural-light-estimator	Natural light estimator.
--enable-eyes-estimator	Eyes estimator.
--enable-gaze-estimator	Gaze estimator.
--enable-mouth-attributes-estimator	Mouth attributes estimator.
--enable-emotions-estimator	Emotions estimator.
--enable-mask-estimator	Mask estimator.
--enable-glasses-estimator	Glasses estimator.
--enable-eyebrow-expression-estimator	Eyebrow estimator.
--enable-red-eyes-estimator	Red eyes estimator.
--enable-headwear-estimator	Headwear estimator.
--enable-basic-attributes-estimator	Basic attributes estimator.
--enable-face-descriptor-estimator	Face descriptor extraction estimator.
--enable-body-descriptor-estimator	Body descriptor extraction estimator.
--enable-body-attributes-estimator	Body attributes estimator.
--enable-people-count-estimator	People count estimator.
--enable-deepfake-estimator	Deepfake estimator.

See the detailed information on enabling and disabling certain estimators in the section "Enable/disable several estimators and detectors " of the administrator manual.

Below is an example of a command to assign rights to a neural network file:

chown -R 1001:0 /var/lib/luna/current/<neural_network_name>.plan

Example of a command to run Remote SDK container with mounting neural networks for face detection and face descriptor extraction:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5220 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
--env=EXTEND_CMD="--enable-all-estimators-by-default=0 --enable-face-detector=1 --enable-face-descriptor-estimator=1" \
-v /var/lib/luna/current/cnn59b_cpu-avx2.plan:/srv/fsdk/data/cnn59b_cpu-avx2.plan \
-v /var/lib/luna/current/FaceDet_v3_a1_cpu-avx2.plan:/srv/fsdk/data/FaceDet_v3_a1_cpu-avx2.plan \
-v /var/lib/luna/current/FaceDet_v3_redetect_v3_cpu-avx2.plan:/srv/fsdk/data/FaceDet_v3_redetect_v3_cpu-avx2.plan \
-v /var/lib/luna/current/slnet_v3_cpu-avx2.plan:/srv/fsdk/data/slnet_v3_cpu-avx2.plan \
-v /var/lib/luna/current/LNet_precise_v2_cpu-avx2.plan:/srv/fsdk/data/LNet_precise_v2_cpu-avx2.plan \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/remote-sdk:/srv/logs \
--network=host \
--name=luna-remote-sdk \
--restart=always \
--detach=true \
dockerhub.visionlabs.ru/luna/luna-remote-sdk:v.0.9.2

Handlers

Note: If you are not going to use the Handlers service, do not launch this container and disable the service utilization in Configurator. See section "Optional services usage".

Handlers DB tables creation

Use the following command to create the Handlers DB tables:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/handlers:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-handlers:v.3.10.4 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Handlers container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5090 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/handlers:/srv/logs \
--name=luna-handlers \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-handlers:v.3.10.4

Tasks

Note: If you are not going to use the Tasks service, do not launch the Tasks container and the Tasks Worker container. Disable the service utilization in Configurator. See section "Optional services usage".

Tasks DB tables creation

Use the following command to create Tasks DB tables:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/tasks:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-tasks:v.3.22.26 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Tasks and Tasks Worker containers launch

Tasks service image includes the Tasks service and the Tasks Worker. They both must be launched.

The "task-result" bucket should be created for the Tasks service before the service launch. The buckets creation is described in the "Buckets creation".

If it is necessary to use the Estimator task using a network disk, then you should first mount the directory with images from the network disk into special directories of Tasks and Tasks Worker containers. See the "Estimator task" section in the administrator manual for details.

Tasks Worker launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5051 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
--env=SERVICE_TYPE="tasks_worker" \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/tasks-worker:/srv/logs \
--name=luna-tasks-worker \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-tasks:v.3.22.26

Tasks launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5050 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/tasks:/srv/logs \
--name=luna-tasks \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-tasks:v.3.22.26

Sender

Sender container launch

Note: If you are not going to use the Sender service, do not launch this container and disable the service utilization in Configurator. See section "Optional services usage".

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5080 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/sender:/srv/logs \
--name=luna-sender \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-sender:v.2.12.19

Video analytics services

Note: If you are not going to use the video analytics services, do not launch the Video Agent container and the Video Manager container. Disable the service utilization in Configurator. See section "Optional services usage".

Video Manager DB tables creation

Use the following command to create Video Manager DB tables:

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/video-manager:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-video-manager:v.0.2.2 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Video Manager launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5230 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/video-manager:/srv/logs \
--name=luna-video-manager \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-video-manager:v.0.2.2

Video Agent launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5250 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/video-agent:/srv/logs \
--name=luna-video-agent \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-video-agent:v.0.1.2

API

API container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5000 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
--name=luna-api \
--restart=always \
--detach=true \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/api:/srv/logs \
--network=host \
dockerhub.visionlabs.ru/luna/luna-api:v.6.37.3

Account creation

The account is created using an HTTP request to the "create account" resource of the API service.

You can also create an account using the Admin service. This method requires an existing login and password (or the default login and password) and enables you to create an "admin" account. See the "Admin service" section of the administrator manual for details.

To create the account using a request to the API service, you need to provide the following mandatory data:

• "login" — Email address.
• "password" — Password.
• "account_type" — Account type ("user" or "advanced_user").

Create the account using your authentication details.

Example of CURL-request to the "create account" resource:

curl --location --request POST 'http://127.0.0.1:5000/6/accounts' \
--header 'Content-Type: application/json' \
--data '{
  "login": "user@mail.com",
  "password": "password",
  "account_type": "user",
  "description": "description"
}'

It is necessary to replace the authentication data from the example with your own.

To work with tokens, you must have an account.

Create GC task schedule

Before you start working with the LUNA PLATFORM, you can create a schedule for the Garbage collection task.

To do this, make a "create tasks schedule" request to the API service, specifying the necessary rules for the schedule.

An example of a schedule creation command for an account created in section "Account creation" is given below.

The example sets a schedule for the Garbage collection task for events older than 30 days with the removal of the samples and the source images. The task will be repeated once a day at 05:30 am.

curl --location --request POST 'http://127.0.0.1:5000/6/tasks/schedules' \
--header 'Authorization: Basic dXNlckBtYWlsLmNvbTpwYXNzd29yZA==' \
--header 'Content-Type: application/json' \
--data '{
    "task": {
        "task_type": 4,
        "content": {
            "target": "events",
            "filters": {
                "create_time__lt": "now-30d"
            },
            "remove_samples": true,
            "remove_image_origins": true
        }
    },
    "trigger": {"cron": "30 5 * * *", "cron_timezone": "utc"},
    "behaviour": {"start_immediately": false, "create_stopped": false}
}'

If necessary, you can create a schedule without automatically activating it. To do this, specify the parameter "create_stopped": "true". In this case, after creating the schedule, it must be activated manually using the "action" = "start" parameter of the "patch tasks schedule" request.

For more information, see the "Running scheduled tasks" section of the administrator manual.

Admin

Admin container launch

Note: If you are not going to use the Admin service, do not launch this container.

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5010 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/admin:/srv/logs \
--name=luna-admin \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-admin:v.5.7.26 

Monitoring data about the number of executed requests is saved in the luna-admin bucket of the InfluxDB. To enable data saving use the following command:

docker exec -it luna-admin python3 ./base_scripts/influx2_cli.py create_usage_task --luna-config http://127.0.0.1:5070/1

Backport 3

The section describes launching of Backport 3 service.

The service is not mandatory for utilizing LP5 and is required for emulation of LP 3 API only.

Backport 3 bucket creation

Use the following command to create the "portraits" bucket for Backport 3:

curl -X POST http://127.0.0.1:5020/1/buckets?bucket=portraits

Backport 3 DB tables creation

Use the following command to create DB tables for Backport 3:

docker run \
-v /etc/localtime:/etc/localtime:ro  \
-v /tmp/logs/backport3:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/luna-backport3:v.0.11.34 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Backport 3 container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5140 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
--name=luna-backport3 \
--restart=always \
--detach=true \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/backport3:/srv/logs \
--network=host \
dockerhub.visionlabs.ru/luna/luna-backport3:v.0.11.34

User Interface 3

The User Interface 3 is used with the Backport 3 service only.

User Interface 3 container launch

Use the following command to launch the service:

docker run \
--env=PORT=4100 \
--env=LUNA_API_URL=http://127.0.0.1:5140 \
--name=luna-ui-3 \
--restart=always \
--detach=true \
--network=host \
-v /etc/localtime:/etc/localtime:ro \
dockerhub.visionlabs.ru/luna/luna3-ui:v.0.5.13

Here:

• --env=LUNA_API_URL — Specifies the URL of the Backport 3 service.

• --env=PORT — Specifies the port of the User Interface 3 service.

Backport 4

The section describes launching of Backport 4 service.

The service is not mandatory for utilizing LP5 and is required for emulation of LP 4 API only.

Backport 4 container launch

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5130 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
--name=luna-backport4 \
--restart=always \
--detach=true \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/backport4:/srv/logs \
--network=host \
dockerhub.visionlabs.ru/luna/luna-backport4:v.1.5.34

User Interface 4

The User Interface 4 is used with the Backport 4 service only.

User Interface 4 container launch

Note: You should have the account with account type user before launching the User Interface 4 container. Its login and password in Base64 format will be used to work with the user interface.

Use the following command to launch the service:

docker run \
--env=PORT=4200 \
--env=LUNA_API_URL=http://<server_external_ip>:5130 \
--env=BASIC_AUTH=dXNlckBtYWlsLmNvbTpwYXNzd29yZA== \
--name=luna-ui-4 \
--restart=always \
--detach=true \
--network=host \
-v /etc/localtime:/etc/localtime:ro \
dockerhub.visionlabs.ru/luna/luna4-ui:v.0.1.9

Here:

• --env=PORT — Sets the port for running User Interface 4.

• --env=BASIC_AUTH — Sets the Basic authorization for the account which data is displayed in the user interface. It is necessary to convert login:password created in the section "Account creation" to Base64 format. The account type should be set to user.

• --env=LUNA_API_URL — Sets the URL of the Backport 4 service.

You should use the external IP of the service, not localhost.

You should specify the Backport 4 service port (5130 is set by default).