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7
docs/deploying_clearml/enterprise_deploy/appgw_install_compose.md
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@ -93,10 +93,11 @@ Edit it according to the following guidelines:
specified during installation. This tag is provided by ClearML to ensure compatibility with the recommended version.
* `ROUTER_TAG`: App Gateway Router tag. The Docker image tag for the router component. It defines the specific version
to be installed and is provided by ClearML as part of the setup process.
* `ROUTER_NAME`: Each router needs to have a unique name across ClearML server tenant (in case of [multiple routers on the same tenant](#multiple-router-in-the-same-tenant)).
* `ROUTER_NAME`: In the case of [multiple routers on the same tenant](#multiple-router-in-the-same-tenant), each router
needs to have a unique name.
* `ROUTER__WEBSERVER__SERVER_PORT`: Webserver port. The default port is 8080, but it can be adjusted to meet specific network requirements.
* `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. Clients use this URL
* `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.
* `CLEARML_API_HOST`: ClearML API server URL starting with `https://api.`
* `CLEARML_API_ACCESS_KEY`: ClearML server API key.

9
docs/deploying_clearml/enterprise_deploy/appgw_install_k8s.md
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@ -7,7 +7,7 @@ The AI Application Gateway is available under the ClearML Enterprise plan.
:::
This guide details the installation of the ClearML App Gateway Router.
The App Gateway Router enables access to your AI workloads (e.g. remote IDEs like VSCode and Jupyter, model API interface, etc.).
The App Gateway Router enables access to your AI workload applications (e.g. remote IDEs like VSCode and Jupyter, model API interface, etc.).
It acts as a proxy, identifying ClearML Tasks running within its [K8s namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/)
and making them available for network access.
@ -106,13 +106,12 @@ Replace the placeholders with the following values:
* `<RELEASE_NAME>` - Unique name for the App Gateway Router within the K8s namespace. This is a required parameter in
Helm, which identifies a specific installation of the chart. The release name also defines the router’s name and
appears in the UI within session app URLs in the IDE. While we typically recommend using a fixed string (e.g.
`clearml-enterprise` or `clearml-agent`), it can be customized to support multiple installations within the same
appears in the UI within AI workload application URLs (e.g. Remote IDE URLs). This can be customized to support multiple installations within the same
namespace by assigning different release names.
* `<WORKLOAD_NAMESPACE>` - [Kubernetes Namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/)
where workloads will be executed. This namespace must be shared between a dedicated ClearML Agent and an App
Gateway Router. The agent is responsible for monitoring its assigned task queues and spawning workloads within this
namespace (unless otherwise configured). Meanwhile, the router monitors the same namespace for AI workloads, such as
session-based tasks. The router has a namespace-limited scope, meaning it can only detect and manage tasks within its
namespace. The router monitors the same namespace for AI workloads (e.g. remote IDE applications). The router has a
namespace-limited scope, meaning it can only detect and manage tasks within its
assigned namespace.
* `<CHART_VERSION>` - Version recommended by the ClearML Support Team.