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Noam Wasersprung
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commit
acf5bc08d1
@ -30,12 +30,12 @@ their instances:
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* [Embedding Model Deployment](../../webapp/applications/apps_embed_model_deployment.md)
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* [Llama.cpp Model Deployment](../../webapp/applications/apps_llama_deployment.md)
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The AI Application Gateway is provided through an additional component to the ClearML Server deployment: The ClearML Task Traffic Router.
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If your ClearML Deployment does not have the Task Traffic Router properly installed, these application instances may not be accessible.
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The AI Application Gateway requires an additional component to the ClearML Server deployment: the **ClearML App Gateway Router**.
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If your ClearML Deployment does not have the App Gateway Router properly installed, these application instances may not be accessible.
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#### Installation
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The Task Traffic Router supports two deployment options:
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The App Gateway Router supports two deployment options:
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* [Docker Compose](appgw_install_compose.md)
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* [Kubernetes](appgw_install_k8s.md)
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@ -40,77 +40,72 @@ This is an example of the `docker-compose` file you will need:
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```
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version: '3.5'
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services:
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task_traffic_webserver:
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image: allegroai/task-traffic-router-webserver:${TASK-TRAFFIC-ROUTER-WEBSERVER-TAG}
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ports:
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- "80:8080"
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restart: unless-stopped
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container_name: task_traffic_webserver
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volumes:
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- ./task_traffic_router/config/nginx:/etc/nginx/conf.d:ro
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- ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:ro
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task_traffic_router:
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image: allegroai/task-traffic-router:${TASK-TRAFFIC-ROUTER-TAG}
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restart: unless-stopped
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container_name: task_traffic_router
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volumes:
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- /var/run/docker.sock:/var/run/docker.sock
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- ./task_traffic_router/config/nginx:/etc/nginx/conf.d:rw
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- ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:rw
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environment:
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- LOGGER_LEVEL=INFO
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- CLEARML_API_HOST=${CLEARML_API_HOST:?err}
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- CLEARML_API_ACCESS_KEY=${CLEARML_API_ACCESS_KEY:?err}
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- CLEARML_API_SECRET_KEY=${CLEARML_API_SECRET_KEY:?err}
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- ROUTER_URL=${ROUTER_URL:?err}
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- ROUTER_NAME=${ROUTER_NAME:?err}
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- AUTH_ENABLED=${AUTH_ENABLED:?err}
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- SSL_VERIFY=${SSL_VERIFY:?err}
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- AUTH_COOKIE_NAME=${AUTH_COOKIE_NAME:?err}
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- AUTH_BASE64_JWKS_KEY=${AUTH_BASE64_JWKS_KEY:?err}
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- LISTEN_QUEUE_NAME=${LISTEN_QUEUE_NAME}
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- EXTRA_BASH_COMMAND=${EXTRA_BASH_COMMAND}
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- TCP_ROUTER_ADDRESS=${TCP_ROUTER_ADDRESS}
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- TCP_PORT_START=${TCP_PORT_START}
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- TCP_PORT_END=${TCP_PORT_END}
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task_traffic_webserver:
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image: clearml/ai-gateway-proxy:${PROXY_TAG:?err}
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network_mode: "host"
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restart: unless-stopped
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container_name: task_traffic_webserver
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volumes:
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- ./task_traffic_router/config/nginx:/etc/nginx/conf.d:ro
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- ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:ro
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task_traffic_router:
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image: clearml/ai-gateway-router:${ROUTER_TAG:?err}
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restart: unless-stopped
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container_name: task_traffic_router
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volumes:
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- /var/run/docker.sock:/var/run/docker.sock
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- ./task_traffic_router/config/nginx:/etc/nginx/conf.d:rw
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- ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:rw
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environment:
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- ROUTER_NAME=${ROUTER_NAME:?err}
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- ROUTER__WEBSERVER__SERVER_PORT=${ROUTER__WEBSERVER__SERVER_PORT:?err}
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- ROUTER_URL=${ROUTER_URL:?err}
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- CLEARML_API_HOST=${CLEARML_API_HOST:?err}
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- CLEARML_API_ACCESS_KEY=${CLEARML_API_ACCESS_KEY:?err}
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- CLEARML_API_SECRET_KEY=${CLEARML_API_SECRET_KEY:?err}
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- AUTH_COOKIE_NAME=${AUTH_COOKIE_NAME:?err}
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- AUTH_SECURE_ENABLED=${AUTH_SECURE_ENABLED}
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- TCP_ROUTER_ADDRESS=${TCP_ROUTER_ADDRESS}
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- TCP_PORT_START=${TCP_PORT_START}
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- TCP_PORT_END=${TCP_PORT_END}
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```
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Create a *runtime.env* file containing the following entries:
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Create a `runtime.env` file containing the following entries:
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```
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TASK-TRAFFIC-ROUTER-WEBSERVER-TAG=
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TASK-TRAFFIC-ROUTER-TAG=
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CLEARML_API_HOST=https://api.
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PROXY_TAG=
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ROUTER_TAG=
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ROUTER_NAME=main-router
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ROUTER__WEBSERVER__SERVER_PORT=8010
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ROUTER_URL=
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CLEARML_API_HOST=
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CLEARML_API_ACCESS_KEY=
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CLEARML_API_SECRET_KEY=
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ROUTER_URL=
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ROUTER_NAME=main-router
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AUTH_ENABLED=true
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SSL_VERIFY=true
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AUTH_COOKIE_NAME=
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AUTH_BASE64_JWKS_KEY=
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LISTEN_QUEUE_NAME=
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EXTRA_BASH_COMMAND=
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AUTH_SECURE_ENABLED=true
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TCP_ROUTER_ADDRESS=
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TCP_PORT_START=
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TCP_PORT_END=
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```
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Edit it according to the following guidelines:
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* `CLEARML_API_HOST`: URL usually starting with `https://api.`
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* `CLEARML_API_ACCESS_KEY`: ClearML server api key
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* `CLEARML_API_SECRET_KEY`: ClearML server secret key
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* `ROUTER_URL`: URL for this router that was previously configured in the load balancer starting with `https://`
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* `ROUTER_NAME`: Unique name for this router
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* `AUTH_ENABLED`: Enable or disable http calls authentication when the router is communicating with the ClearML server
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* `SSL_VERIFY`: Enable or disable SSL certificate validation when the router is communicating with the ClearML server
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* `AUTH_COOKIE_NAME`: Cookie name used by the ClearML server to store the ClearML authentication cookie. This can usually be found in the `value_prefix` key starting with `allegro_token` in `envoy.yaml` file in the ClearML server installation (`/opt/allegro/config/envoy/envoy.yaml`) (see below)
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* `AUTH_SECURE_ENABLED`: Enable the Set-Cookie `secure` parameter
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* `AUTH_BASE64_JWKS_KEY`: Value form `k` key in the `jwks.json` file in the ClearML server installation
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* `LISTEN_QUEUE_NAME`: (*optional*) Name of queue to check for tasks (if none, every task is checked)
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* `EXTRA_BASH_COMMAND`: Command to be launched before starting the router
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* `PROXY_TAG`: AI Application Gateway proxy tag. The Docker image tag for the proxy component, which needs to be
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specified during installation. This tag is provided by ClearML to ensure compatibility with the recommended version.
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* `ROUTER_TAG`: App Gateway Router tag. The Docker image tag for the router component. It defines the specific version
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to be installed and is provided by ClearML as part of the setup process.
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* `ROUTER_NAME`: In the case of [multiple routers on the same tenant](#multiple-router-in-the-same-tenant), each router
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needs to have a unique name.
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* `ROUTER__WEBSERVER__SERVER_PORT`: Webserver port. The default port is 8080, but it can be adjusted to meet specific network requirements.
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* `ROUTER_URL`: External address to access the router. This can be the IP address or DNS of the node where the router
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is running, or the address of a load balancer if the router operates behind a proxy/load balancer. This URL is used
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to access AI workload applications (e.g. remote IDE, model deployment, etc.), so it must be reachable and resolvable for them.
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* `CLEARML_API_HOST`: ClearML API server URL starting with `https://api.`
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* `CLEARML_API_ACCESS_KEY`: ClearML server API key.
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* `CLEARML_API_SECRET_KEY`: ClearML server secret key.
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* `AUTH_COOKIE_NAME`: Cookie used by the ClearML server to store the ClearML authentication cookie. This can usually be
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found in the `envoy.yaml` file in the ClearML server installation (`/opt/allegro/config/envoy/envoy.yaml`), under the
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`value_prefix` key starting with `allegro_token`
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* `AUTH_SECURE_ENABLED`: Enable the Set-Cookie `secure` parameter. Set to `false` in case services are exposed with `http`.
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* `TCP_ROUTER_ADDRESS`: Router external address, can be an IP or the host machine or a load balancer hostname, depends on network configuration
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* `TCP_PORT_START`: Start port for the TCP Session feature
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* `TCP_PORT_END`: End port for the TCP Session feature
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@ -121,12 +116,42 @@ Run the following command to start the router:
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sudo docker compose --env-file runtime.env up -d
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```
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:::note How to find my jwkskey
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### Advanced Configuration
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The *JSON Web Key Set* (*JWKS*) is a set of keys containing the public keys used to verify any JSON Web Token (JWT).
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#### Using Open HTTP
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In a `docker-compose` server installation, this can be found in the `CLEARML__secure__auth__token_secret` env var in the apiserver server component.
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To deploy the App Gateway Router on open HTTP (without a certificate), set the `AUTH_SECURE_ENABLED` entry
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to `false` in the `runtime.env` file.
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:::
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#### Multiple Router in the Same Tenant
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If you have workloads running in separate networks that cannot communicate with each other, you need to deploy multiple
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routers, one for each isolated environment. Each router will only process tasks from designated queues, ensuring that
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tasks are correctly routed to agents within the same network.
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For example:
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* If Agent A and Agent B are in separate networks, each must have its own router to receive tasks.
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* Router A will handle tasks from Agent A’s queues. Router B will handle tasks from Agent B’s queues.
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To achieve this, each router must be configured with:
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* A unique `ROUTER_NAME`
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* A distinct set of queues defined in `LISTEN_QUEUE_NAME`.
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##### Example Configuration
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Each router's `runtime.env` file should include:
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* Router A:
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```
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ROUTER_NAME=router-a
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LISTEN_QUEUE_NAME=queue1,queue2
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```
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* Router B:
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```
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ROUTER_NAME=router-b
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LISTEN_QUEUE_NAME=queue3,queue4
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```
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Make sure `LISTEN_QUEUE_NAME` is set in the [`docker-compose` environment variables](#docker-compose-file) for each router instance.
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@ -3,17 +3,26 @@ title: Kubernetes Deployment
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---
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:::important Enterprise Feature
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The Application Gateway is available under the ClearML Enterprise plan.
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The AI Application Gateway is available under the ClearML Enterprise plan.
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:::
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This guide details the installation of the ClearML App Gateway Router.
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The App Gateway Router enables access to your AI workload applications (e.g. remote IDEs like VSCode and Jupyter, model API interface, etc.).
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It acts as a proxy, identifying ClearML Tasks running within its [K8s namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/)
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and making them available for network access.
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:::important
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The App Gateway Router must be installed in the same K8s namespace as a dedicated ClearML Agent.
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It can only configure access for ClearML Tasks within its own namespace.
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:::
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This guide details the installation of the ClearML AI Application Gateway, specifically the ClearML Task Router Component.
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## Requirements
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* Kubernetes cluster: `>= 1.21.0-0 < 1.32.0-0`
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* Helm installed and configured
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* Helm token to access `allegroai` helm-chart repo
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* Credentials for `allegroai` docker repo
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* Helm token to access `clearml` helm-chart repo
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* Credentials for `clearml` docker repo
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* A valid ClearML Server installation
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## Optional for HTTPS
|
||||
@ -26,62 +35,55 @@ This guide details the installation of the ClearML AI Application Gateway, speci
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||||
### Login
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||||
|
||||
```
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||||
helm repo add allegroai-enterprise \
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helm repo add clearml-enterprise \
|
||||
https://raw.githubusercontent.com/clearml/clearml-enterprise-helm-charts/gh-pages \
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||||
--username <GITHUB_TOKEN> \
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||||
--password <GITHUB_TOKEN>
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```
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Replace `<GITHUB_TOKEN>` with your valid GitHub token that has access to the ClearML Enterprise Helm charts repository.
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||||
### Prepare Values
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||||
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Before installing the TTR, create a `helm-override` files named `task-traffic-router.values-override.yaml`:
|
||||
Before installing the App Gateway Router, create a Helm override file:
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||||
|
||||
```
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||||
imageCredentials:
|
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password: "<DOCKERHUB_TOKEN>"
|
||||
password: ""
|
||||
clearml:
|
||||
apiServerKey: ""
|
||||
apiServerSecret: ""
|
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apiServerUrlReference: "https://api."
|
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jwksKey: ""
|
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authCookieName: ""
|
||||
apiServerKey: ""
|
||||
apiServerSecret: ""
|
||||
apiServerUrlReference: ""
|
||||
authCookieName: ""
|
||||
sslVerify: true
|
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ingress:
|
||||
enabled: true
|
||||
hostName: "task-router.dev"
|
||||
enabled: true
|
||||
hostName: ""
|
||||
tcpSession:
|
||||
routerAddress: ""
|
||||
portRange:
|
||||
start:
|
||||
end:
|
||||
routerAddress: ""
|
||||
service:
|
||||
type: LoadBalancer
|
||||
portRange:
|
||||
start:
|
||||
end:
|
||||
```
|
||||
|
||||
Edit it accordingly to these guidelines:
|
||||
Configuration options:
|
||||
|
||||
* `clearml.apiServerUrlReference`: URL usually starting with `https://api.`
|
||||
* `clearml.apiServerKey`: ClearML server api key
|
||||
* `clearml.apiServerSecret`: ClearML server secret key
|
||||
* `ingress.hostName`: URL of router we configured previously for load balancer starting with `https://`
|
||||
* `clearml.sslVerify`: Enable or disable SSL certificate validation on apiserver calls check
|
||||
* `clearml.authCookieName`: Value from `value_prefix` key starting with `allegro_token` in `envoy.yaml` file in ClearML server installation.
|
||||
* `clearml.jwksKey`: Value form `k` key in `jwks.json` file in ClearML server installation (see below)
|
||||
* `tcpSession.routerAddress`: Router external address can be an IP or the host machine or a load balancer hostname, depends on the network configuration
|
||||
* `tcpSession.portRange.start`: Start port for the TCP Session feature
|
||||
* `tcpSession.portRange.end`: End port for the TCP Session feature
|
||||
|
||||
:::note How to find my jwkskey
|
||||
|
||||
The *JSON Web Key Set* (*JWKS*) is a set of keys containing the public keys used to verify any JSON Web Token (JWT).
|
||||
|
||||
```
|
||||
kubectl -n clearml get secret clearml-conf \
|
||||
-o jsonpath='{.data.secure_auth_token_secret}' \
|
||||
| base64 -d && echo
|
||||
```
|
||||
|
||||
:::
|
||||
* `imageCredentials.password`: ClearML DockerHub Access Token.
|
||||
* `clearml.apiServerKey`: ClearML server API key.
|
||||
* `clearml.apiServerSecret`: ClearML server secret key.
|
||||
* `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.
|
||||
* `tcpSession.service.type`: Service type used to expose TCP functionality, default is `NodePort`.
|
||||
* `tcpSession.portRange.start`: Start port for the TCP Session feature.
|
||||
* `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
|
||||
@ -94,9 +96,22 @@ To install the TTR component via Helm use the following command:
|
||||
```
|
||||
helm upgrade --install \
|
||||
<RELEASE_NAME> \
|
||||
-n <NAME_SPACE> \
|
||||
-n <WORKLOAD_NAMESPACE> \
|
||||
allegroai-enterprise/clearml-enterprise-task-traffic-router \
|
||||
--version <CURRENT CHART VERSION> \
|
||||
-f task-traffic-router.values-override.yaml
|
||||
--version <CHART_VERSION> \
|
||||
-f override.yaml
|
||||
```
|
||||
|
||||
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 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. 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.
|
||||
@ -513,31 +513,30 @@ Create a `NetworkPolicy` in the tenant namespace with the following configuratio
|
||||
- podSelector: {}
|
||||
```
|
||||
|
||||
### Install Task Traffic Router Chart
|
||||
### Install the App Gateway Router Chart
|
||||
|
||||
Install the [Task Traffic Router](appgw.md) in your Kubernetes cluster, allowing it to manage and route tasks:
|
||||
Install the App Gateway Router in your Kubernetes cluster, allowing it to manage and route tasks:
|
||||
|
||||
1. Prepare the `overrides.yaml` file with the following content:
|
||||
|
||||
```
|
||||
imageCredentials:
|
||||
password: "<allegroaienterprise_DockerHub_TOKEN>"
|
||||
password: "<clearmlenterprise_DockerHub_TOKEN>"
|
||||
clearml:
|
||||
apiServerUrlReference: "<http://clearml-enterprise-apiserver.clearml:8008>"
|
||||
apiserverKey: "<TENANT_KEY>"
|
||||
apiserverSecret: "<TENANT_SECRET>"
|
||||
jwksKey: "<JWKS_KEY>"
|
||||
ingress:
|
||||
enabled: true
|
||||
hostName: "<unique url in same domain as apiserver/webserver>"
|
||||
```
|
||||
|
||||
2. Install Task Traffic Router in the specified tenant namespace:
|
||||
2. Install App Gateway Router in the specified tenant namespace:
|
||||
|
||||
```
|
||||
helm install -n <TENANT_NAMESPACE> \\
|
||||
clearml-ttr \\
|
||||
allegroai-enterprise/clearml-task-traffic-router \\
|
||||
clearml-enterprise/clearml-task-traffic-router \\
|
||||
--create-namespace \\
|
||||
-f overrides.yaml
|
||||
```
|
||||
|
||||
@ -13,7 +13,7 @@ running, it serves your embedding model through a secure, publicly accessible ne
|
||||
endpoint activity and shuts down if the model remains inactive for a specified maximum idle time.
|
||||
|
||||
:::info AI Application Gateway
|
||||
The Embedding Model Deployment app makes use of the ClearML Traffic Router which implements a secure, authenticated
|
||||
The Embedding Model Deployment app makes use of the App Gateway Router which implements a secure, authenticated
|
||||
network endpoint for the model.
|
||||
|
||||
If the ClearML AI application Gateway is not available, the model endpoint might not be accessible.
|
||||
|
||||
@ -16,7 +16,7 @@ The Gradio launcher monitors the Gradio app activity and shuts down if it is ina
|
||||
<a id="traffic_router"/>
|
||||
|
||||
:::important AI Application Gateway
|
||||
The Gradio Launcher relies on the ClearML Traffic Router which implements user authentication, and redirects requests
|
||||
The Gradio Launcher relies on the ClearML App Gateway Router which implements user authentication, and redirects requests
|
||||
to the IP/port served by the Gradio app.
|
||||
|
||||
If the ClearML AI application Gateway is not available, the Gradio app might not be accessible.
|
||||
|
||||
@ -12,7 +12,7 @@ running, it serves your model through a secure, publicly accessible network endp
|
||||
and shuts down if the model remains inactive for a specified maximum idle time.
|
||||
|
||||
:::important AI Application Gateway
|
||||
The llama.cpp Model Deployment app makes use of the ClearML Traffic Router which implements a secure, authenticated
|
||||
The llama.cpp Model Deployment app makes use of the App Gateway Router which implements a secure, authenticated
|
||||
network endpoint for the model.
|
||||
|
||||
If the ClearML AI application Gateway is not available, the model endpoint might not be accessible.
|
||||
|
||||
@ -13,7 +13,7 @@ it serves your model through a secure, publicly accessible network endpoint. The
|
||||
shuts down if the model remains inactive for a specified maximum idle time.
|
||||
|
||||
:::info AI Application Gateway
|
||||
The vLLM Model Deployment app makes use of the ClearML Traffic Router which implements a secure, authenticated
|
||||
The vLLM Model Deployment app makes use of the App Gateway Router which implements a secure, authenticated
|
||||
network endpoint for the model.
|
||||
|
||||
If the ClearML AI application Gateway is not available, the model endpoint might not be accessible.
|
||||
|
||||
@ -17,7 +17,7 @@ time.
|
||||
<a id="traffic_router"/>
|
||||
|
||||
:::important AI Application Gateway
|
||||
The Streamlit Launcher relies on the ClearML Traffic Router which implements user authentication, and redirects requests
|
||||
The Streamlit Launcher relies on the ClearML App Gateway Router which implements user authentication, and redirects requests
|
||||
to the IP/port served by the Streamlit app.
|
||||
|
||||
If the ClearML AI application Gateway is not available, the Streamlit app might not be accessible.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user