clearml-docs/docs/deploying_clearml/enterprise_deploy/appgw_install_compose_hosted.md

title
Docker-Compose - Hosted Server

:::important Enterprise Feature The AI Application Gateway is available under the ClearML Enterprise plan. :::

The AI Application Gateway enables external access to ClearML tasks, and applications running on workload nodes that require HTTP or TCP access. The gateway is configured with an endpoint or external address, making these services accessible from the user's machine, outside the workload nodes’ network.

This guide details the installation of the App Gateway Router for ClearML users who use ClearML's hosted control plane while hosting their own workload nodes.

Requirements

• Linux OS (x86) machine with root access
• The machine needs to be reachable from your user network
• The machine needs to have network reachability to workload nodes
• Credentials for the ClearML docker repository
• A valid ClearML Server installation

Additionally, for a secure connection, it is recommended to have a DNS entry and a valid SSL Certificate assigned to the machine IP.

Host Configuration

Docker Installation

Installing docker and docker-compose might vary depending on the specific operating system you're using. Here is an example for AmazonLinux:

sudo dnf -y install docker
DOCKER_CONFIG="/usr/local/lib/docker"
sudo mkdir -p $DOCKER_CONFIG/cli-plugins
sudo curl -SL https://github.com/docker/compose/releases/download/v2.17.3/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose
sudo chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose
sudo systemctl enable docker
sudo systemctl start docker
 
sudo docker login

Use the ClearML docker hub credentials when prompted by docker login.

Docker-compose File

This is an example of the docker-compose file you will need to create:

version: '3.5'
services:
  task_traffic_webserver:
    image: clearml/ai-gateway-proxy:${PROXY_TAG:?err}
    network_mode: "host"
    restart: unless-stopped
    container_name: task_traffic_webserver
    volumes:
    - ./task_traffic_router/config/nginx:/etc/nginx/conf.d:ro
    - ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:ro
  task_traffic_router:
    image: clearml/ai-gateway-router:${ROUTER_TAG:?err}
    restart: unless-stopped
    container_name: task_traffic_router
    volumes:
    - /var/run/docker.sock:/var/run/docker.sock
    - ./task_traffic_router/config/nginx:/etc/nginx/conf.d:rw
    - ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:rw
    environment:
    - LOGGER_LEVEL=INFO
    - ROUTER__WEBSERVER__SERVER_PORT="8010"
    - ROUTER_NAME=${ROUTER_NAME:?err}
    - ROUTER_URL=${ROUTER_URL:?err}
    - CLEARML_API_HOST=${CLEARML_API_HOST:?err}
    - CLEARML_API_ACCESS_KEY=${CLEARML_API_ACCESS_KEY:?err}
    - CLEARML_API_SECRET_KEY=${CLEARML_API_SECRET_KEY:?err}
    - AUTH_COOKIE_NAME=${AUTH_COOKIE_NAME:?err}
    - AUTH_SECURE_ENABLED=${AUTH_SECURE_ENABLED}
    - TCP_ROUTER_ADDRESS=${TCP_ROUTER_ADDRESS}
    - TCP_PORT_START=${TCP_PORT_START}
    - TCP_PORT_END=${TCP_PORT_END}

Configuration File

You will be provided with a prefilled runtime.env file containing the following entries:

# PREFILLED SECTION, PROVIDED BY CLEARML
PROXY_TAG=
ROUTER_TAG=
CLEARML_API_HOST=https://api.
AUTH_COOKIE_NAME=

# TO BE FILLED BY USER
ROUTER_NAME=main-router
ROUTER_URL=http://<ROUTER-HOST-PUBLIC-IP>:8010
CLEARML_API_ACCESS_KEY=
CLEARML_API_SECRET_KEY=
AUTH_SECURE_ENABLED=true
TCP_ROUTER_ADDRESS=<ROUTER-HOST-PUBLIC-IP>
TCP_PORT_START=
TCP_PORT_END=

Configuration Options:

• ROUTER_NAME: In the case of multiple routers on the same tenant, each router needs to have a unique name.
• CLEARML_API_ACCESS_KEY, CLEARML_API_SECRET_KEY: API credentials for Admin user or Service Account with admin privileges created in the ClearML web UI. Make sure to label these credentials clearly, so that they will not be revoked by mistake.
• ROUTER_URL: External address to access the router. This can be the IP address or DNS of the node where the router is running, or the address of a load balancer if the router operates behind a proxy/load balancer. This URL is used to access AI workload applications (e.g. remote IDE, model deployment, etc.), so it must be reachable and resolvable for them.
• TCP_ROUTER_ADDRESS: Router external address, can be an IP or the host machine or a load balancer hostname, depends on network configuration.
• TCP_PORT_START: Start port for the TCP Tasks, chosen by the customer. Ensure that ports are open and can be allocated on the host.
• TCP_PORT_END: End port for the TCP Tasks, chosen by the customer. Ensure that ports are open and can be allocated on the host.

Installation

Run the following command to start the router:

sudo docker compose --env-file runtime.env up -d

Advanced Configuration

Using Open HTTP

To deploy the App Gateway Router on open HTTP (without a certificate), set the AUTH_SECURE_ENABLED entry to false in the runtime.env file.

Multiple Router in the Same Tenant

If you have workloads running in separate networks that cannot communicate with each other, you need to deploy multiple routers, one for each isolated environment. Each router will only process tasks from designated queues, ensuring that tasks are correctly routed to agents within the same network.

For example:

• If Agent A and Agent B are in separate networks, each must have its own router to receive tasks.
• Router A will handle tasks from Agent A’s queues. Router B will handle tasks from Agent B’s queues.

To achieve this, each router must be configured with:

• A unique ROUTER_NAME
• A distinct set of queues defined in LISTEN_QUEUE_NAME.

Example Configuration

Each router's runtime.env file should include:

• Router A:

  ROUTER_NAME=router-a  
  LISTEN_QUEUE_NAME=queue1,queue2  
  

• Router B:

  ROUTER_NAME=router-b  
  LISTEN_QUEUE_NAME=queue3,queue4  
  

Make sure LISTEN_QUEUE_NAME is set in the docker-compose environment variables for each router instance.