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docs/deploying_clearml/enterprise_deploy/appgw_install_k8s.md
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@@ -6,13 +6,14 @@ title: Kubernetes Deployment
The AI Application Gateway is available under the ClearML Enterprise plan.
:::
This guide details the installation of the ClearML AI App Gateway Router.
The AI App Gateway Router enables access to session-based applications like VSCode and Jupyter.
It acts as a proxy, discovering ClearML Tasks running within its namespace and configuring them for user access.
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.).
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.
:::important
The AI App Gateway Router must be installed in the same namespace as a dedicated ClearML Agent.
It can only discover ClearML Tasks within its own namespace.
The App Gateway Router must be installed in the same K8s namespace as a dedicated ClearML Agent.
It can only configure access for ClearML Tasks within its own namespace.
:::
@@ -44,7 +45,7 @@ Replace `<GITHUB_TOKEN>` with your valid GitHub token that has access to the Cle
### Prepare Values
Before installing the AI App Gateway Router, create a Helm override file:
Before installing the App Gateway Router, create a Helm override file:
```
imageCredentials:
@@ -67,13 +68,13 @@ tcpSession:
end:
```
Edit it according to these guidelines:
Configuration options:
* `imageCredentials.password`: ClearML DockerHub Access Token.
* `clearml.apiServerKey`: ClearML server API key.
* `clearml.apiServerSecret`: ClearML server secret key.
* `clearml.apiServerUrlReference`: ClearML API server URL usually starting with `https://api.`.
* `clearml.authCookieName`: Cookie name used by the ClearML server to store the ClearML authentication cookie.
* `clearml.apiServerUrlReference`: ClearML API server URL starting with `https://api.`.
* `clearml.authCookieName`: Cookie used by the ClearML server to store the ClearML authentication cookie.
* `clearml.sslVerify`: Enable or disable SSL certificate validation on `apiserver` calls check.
* `ingress.hostName`: Hostname of router used by the ingress controller to access it.
* `tcpSession.routerAddress`: The external router address (can be an IP, hostname, or load balancer address) depending on your network setup. Ensure this address is accessible for TCP connections.
@@ -82,7 +83,7 @@ Edit it according to these guidelines:
* `tcpSession.portRange.end`: End port for the TCP Session feature.
The whole list of supported configuration is available with the command:
The full list of supported configuration is available with the command:
```
helm show readme allegroai-enterprise/clearml-enterprise-task-traffic-router
@@ -103,6 +104,15 @@ allegroai-enterprise/clearml-enterprise-task-traffic-router \
Replace the placeholders with the following values:
* `<RELEASE_NAME>` - Unique name for the AI App Gateway Router within the namespace. This name will appear in the UI and be used for the redirection URL.
* `<WORKLOAD_NAMESPACE>` - Namespace that will be shared with a dedicated ClearML Agent.
* `<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
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
assigned namespace.
* `<CHART_VERSION>` - Version recommended by the ClearML Support Team.