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Update Enterprise Server deployment on K8s
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title: Kubernetes
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title: Kubernetes
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---
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---
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This guide provides step-by-step instructions for installing the ClearML Enterprise control-plane setup in a Kubernetes cluster.
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This guide provides step-by-step instructions for installing the ClearML Enterprise setup in a Kubernetes cluster.
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ClearML Enterprise is the main ClearML Server, comprising the ClearML `apiserver`, `fileserver`, and `webserver` components.
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The package also includes MongoDB, ElasticSearch, and Redis as Helm dependencies.
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## Prerequisites
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## Prerequisites
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To deploy ClearML Enterprise, ensure the following components and configurations are in place:
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* A Kubernetes cluster
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- Kubernetes Cluster: A vanilla Kubernetes cluster is preferred for optimal GPU support.
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* An ingress controller (e.g. `nginx-ingress`) and the ability to create LoadBalancer services (e.g. MetalLB) if needed
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- CLI Tools: `kubectl` and `helm` must be installed and configured.
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to expose ClearML
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- Ingress Controller: An Ingress controller (e.g., `nginx-ingress`) is required. If exposing services externally, a
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* Credentials for ClearML Enterprise GitHub Helm chart repository
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LoadBalancer-capable solution (e.g. `MetalLB`) should also be configured.
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* Credentials for ClearML Enterprise DockerHub repository
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- Server and workers that communicate on HTTP/S (ports 80 and 443). Additionally, the TCP session feature requires a
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* URL for downloading the ClearML Enterprise applications configuration
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range of ports for TCP traffic based on your configuration (see [AI App Gateway installation](appgw_install_k8s.md)).
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- DNS Configuration: A domain with subdomain support is required, ideally with trusted TLS certificates. All entries must
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be resolvable by the Ingress controller. Example subdomains:
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- Control Plane:
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- `api.<BASE_DOMAIN>`
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- `app.<BASE_DOMAIN>`
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- `files.<BASE_DOMAIN>`
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- Worker:
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- `router.<BASE_DOMAIN>`
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- `tcp-router.<BASE_DOMAIN>` (optional, for TCP sessions)
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- Storage: A configured StorageClass and an accessible storage backend.
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- ClearML Enterprise Access:
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- Helm repository credentials (`<HELM_REPO_TOKEN>`)
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- DockerHub registry credentials (`<CLEARML_DOCKERHUB_TOKEN>`)
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### Recommended Cluster Specifications
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## Control Plane Installation
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For optimal performance, a Kubernetes cluster with at least 3 nodes is recommended, each having:
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- 8 vCPUs
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- 32 GB RAM
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- 500 GB storage
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The following steps cover installing the control plane (server and required charts) and will
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## Installation
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require some or all of the tokens/deliverables mentioned above.
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### Add the Helm Repo Locally
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### Requirements
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Add the ClearML Helm repository:
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``` bash
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helm repo add clearml-enterprise https://raw.githubusercontent.com/clearml/clearml-enterprise-helm-charts/gh-pages --username <HELM_REPO_TOKEN> --password <HELM_REPO_TOKEN>
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```
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* Add the ClearML Enterprise repository:
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Update the repository locally:
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``` bash
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helm repo update
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```
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### Prepare Values
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```
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Create a `clearml-values.override.yaml` file with the following content:
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helm repo add clearml-enterprise https://raw.githubusercontent.com/clearml/clearml-enterprise-helm-charts/gh-pages --username <clearmlenterprise_GitHub_TOKEN> --password <clearmlenterprise_GitHub_TOKEN>
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```
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* Update the repository locally:
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```
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helm repo update
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```
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### Install ClearML Enterprise Chart
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#### Configuration
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The Helm Chart must be installed with an `overrides.yaml` overriding values as follows:
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:::note
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:::note
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In the following configuration, replace `<BASE_DOMAIN>` with a valid domain
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In the following configuration, replace the `<BASE_DOMAIN>` placeholders with a valid domain that will have records
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that will have records pointing to the cluster’s ingress controller (see ingress details in the values below).
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pointing to the cluster's Ingress Controller. This will be the base domain for reaching your ClearML installation.
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:::
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:::
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``` yaml
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```yaml
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imageCredentials:
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imageCredentials:
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password: "<clearml_enterprise_DockerHub_TOKEN>"
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password: "<CLEARML_DOCKERHUB_TOKEN>"
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clearml:
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clearml:
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cookieDomain: "<BASE_DOMAIN>"
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cookieDomain: "<BASE_DOMAIN>"
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# Set values for improved security
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apiserverKey: "<GENERATED_API_SERVER_KEY>"
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apiserverSecret: "<GENERATED_API_SERVER_SECRET>"
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fileserverKey: "<GENERATED_FILE_SERVER_KEY>"
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fileserverSecret: "<GENERATED_FILE_SERVER_SECRET>"
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secureAuthTokenSecret: "<GENERATED_AUTH_TOKEN_SECRET>"
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testUserKey: "<GENERATED_TEST_USER_KEY>"
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testUserSecret: "<GENERATED_TEST_USER_SECRET>"
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apiserver:
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apiserver:
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ingress:
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ingress:
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enabled: true
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enabled: true
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hostName: "api.<BASE_DOMAIN>"
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hostName: "api.<BASE_DOMAIN>"
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service:
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service:
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type: ClusterIP
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type: ClusterIP
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fileserver:
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fileserver:
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ingress:
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ingress:
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enabled: true
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enabled: true
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hostName: "file.<BASE_DOMAIN>"
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hostName: "files.<BASE_DOMAIN>"
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service:
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service:
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type: ClusterIP
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type: ClusterIP
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webserver:
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webserver:
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ingress:
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ingress:
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enabled: true
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enabled: true
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hostName: "app.<BASE_DOMAIN>"
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hostName: "app.<BASE_DOMAIN>"
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service:
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service:
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type: ClusterIP
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type: ClusterIP
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clearmlApplications:
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clearmlApplications:
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enabled: true
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enabled: true
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```
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```
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#### Additional Configuration Options
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### Install the Chart
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##### Fixed Users (Simple Login)
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Enable static login with username and password in `overrides.yaml`.
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This is an optional step in case SSO (Identity provider) configuration will not be performed.
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Install the ClearML Enterprise Helm chart using the previous values override file.
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``` bash
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helm upgrade -i -n clearml clearml clearml-enterprise/clearml-enterprise --create-namespace -f clearml-values.override.yaml
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```
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```
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## Additional Configuration Options
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:::note
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You can view the full set of available and documented values of the chart by running the following command:
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```bash
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helm show readme clearml-enterprise/clearml-enterprise
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# or
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helm show values clearml-enterprise/clearml-enterprise
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```
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:::
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### Default Secret Values
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For improved security, all the internal credentials are auto-generated randomly and stored in a Secret in
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Kubernetes.
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If you need to define your own credentials to be used instead, replace the default key and secret values in `clearml-values.override.yaml`.
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``` yaml
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clearml:
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# Replace the following values to use custom internal credentials.
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apiserverKey: ""
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apiserverSecret: ""
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fileserverKey: ""
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fileserverSecret: ""
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secureAuthTokenSecret: ""
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testUserKey: ""
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testUserSecret: ""
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```
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In a shell, if `openssl` is installed, you can use this simple command to generate random strings suitable as keys and secrets:
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``` bash
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openssl rand -hex 16
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```
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### Fixed Users
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Enable and configure simple login with username and password in `clearml-values.override.yaml`. This is useful for simple PoC
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installations. This is an optional step in case the SSO (Identity provider) configuration is not performed.
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Please note that this setup is not ideal for multi-tenant setups as fixed users will only be associated with the default tenant.
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``` yaml
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apiserver:
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apiserver:
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additionalConfigs:
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additionalConfigs:
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apiserver.conf: |
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apiserver.conf: |
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@ -128,282 +163,7 @@ apiserver:
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}
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}
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```
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```
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## Next Steps
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##### SSO (Identity Provider)
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Once the ClearML Enterprise control-plane is up and running, proceed with installing the ClearML Enterprise Agent and
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[AI App Gateway](appgw_install_k8s.md).
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The following examples (Auth0 and Keycloak) show how to configure an identity provider on the ClearML server.
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Add the following values configuring `extraEnvs` for `apiserver` in the `clearml-enterprise` values `override.yaml` file.
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Substitute all `<PLACEHOLDER>`s with the correct value for your configuration.
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##### Auth0 Identity Provider
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```yaml
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apiserver:
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extraEnvs:
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- name: CLEARML__secure__login__sso__oauth_client__auth0__client_id
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value: "<SSO_CLIENT_ID>"
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- name: CLEARML__secure__login__sso__oauth_client__auth0__client_secret
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value: "<SSO_CLIENT_SECRET>"
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- name: CLEARML__services__login__sso__oauth_client__auth0__base_url
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value: "<SSO_CLIENT_URL>"
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- name: CLEARML__services__login__sso__oauth_client__auth0__authorize_url
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value: "<SSO_CLIENT_AUTHORIZE_URL>"
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- name: CLEARML__services__login__sso__oauth_client__auth0__access_token_url
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value: "<SSO_CLIENT_ACCESS_TOKEN_URL>"
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- name: CLEARML__services__login__sso__oauth_client__auth0__audience
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value: "<SSO_CLIENT_AUDIENCE>"
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```
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##### Keycloak Identity Provider
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```yaml
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apiserver:
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extraEnvs:
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- name: CLEARML__secure__login__sso__oauth_client__keycloak__client_id
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value: "<KC_CLIENT_ID>"
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- name: CLEARML__secure__login__sso__oauth_client__keycloak__client_secret
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value: "<KC_SECRET_ID>"
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- name: CLEARML__services__login__sso__oauth_client__keycloak__base_url
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value: "<KC_URL>/realms/<REALM_NAME>/"
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- name: CLEARML__services__login__sso__oauth_client__keycloak__authorize_url
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value: "<KC_URL>/realms/<REALM_NAME>/protocol/openid-connect/auth"
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- name: CLEARML__services__login__sso__oauth_client__keycloak__access_token_url
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value: "<KC_URL>/realms/<REALM_NAME>/protocol/openid-connect/token"
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- name: CLEARML__services__login__sso__oauth_client__keycloak__idp_logout
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value: "true"
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```
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#### Installing the Chart
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```
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helm install -n clearml \
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clearml \
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clearml-enterprise/clearml-enterprise \
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--create-namespace \
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-f overrides.yaml
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```
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### Install ClearML Agent Chart
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#### Configuration
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To configure the agent you will need to choose a Redis password and use that when setting up Redis as well
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(see [Shared Redis installation](multi_tenant_k8s.md#shared-redis-installation)).
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The Helm Chart must be installed with `overrides.yaml`:
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```yaml
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imageCredentials:
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password: "<CLEARML_DOCKERHUB_TOKEN>"
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clearml:
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agentk8sglueKey: "<ACCESS_KEY>"
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agentk8sglueSecret: "<SECRET_KEY>"
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agentk8sglue:
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apiServerUrlReference: "https://api.<BASE_DOMAIN>"
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fileServerUrlReference: "https://files.<BASE_DOMAIN>"
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webServerUrlReference: "https://app.<BASE_DOMAIN>"
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defaultContainerImage: "python:3.9"
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```
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#### Installing the Chart
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```bash
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helm install -n <WORKLOAD_NAMESPACE> \
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clearml-agent \
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clearml-enterprise/clearml-enterprise-agent \
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--create-namespace \
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-f overrides.yaml
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```
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To create a queue by API:
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```bash
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curl $APISERVER_URL/queues.create \
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-H "Content-Type: application/json" \
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-H "X-Clearml-Impersonate-As:<USER_ID>" \
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-u $APISERVER_KEY:$APISERVER_SECRET \
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-d '{"name":"default"}'
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```
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## ClearML AI Application Gateway Installation
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### Configuring Chart
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The Helm Chart must be installed with `overrides.yaml`:
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```yaml
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imageCredentials:
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password: "<DOCKERHUB_TOKEN>"
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clearml:
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apiServerKey: ""
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apiServerSecret: ""
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apiServerUrlReference: "https://api."
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authCookieName: ""
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ingress:
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enabled: true
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hostName: "task-router.dev"
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tcpSession:
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routerAddress: "<NODE_IP OR EXTERNAL_NAME>"
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portRange:
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start: <START_PORT>
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end: <END_PORT>
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```
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||||||
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**Configuration options:**
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||||||
|
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* **`clearml.apiServerUrlReference`:** URL usually starting with `https://api.`
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* **`clearml.apiServerKey`:** ClearML server API key
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* **`clearml.apiServerSecret`:** ClearML server secret key
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* **`ingress.hostName`:** URL of the router we configured previously for load balancer starting with `https://`
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* **`clearml.sslVerify`:** Enable or disable SSL certificate validation on apiserver calls check
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* **`clearml.authCookieName`:** Value from `value_prefix` key starting with `allegro_token` in `envoy.yaml` file in ClearML server installation.
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* **`tcpSession.routerAddress`**: Router external address can be an IP or the host machine or a load balancer hostname, depends on the network configuration
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* **`tcpSession.portRange.start`**: Start port for the TCP Session feature
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* **`tcpSession.portRange.end`**: End port for the TCP Session feature
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|
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|
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### Installing the Chart
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||||||
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||||||
|
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||||||
```bash
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helm install -n <WORKLOAD_NAMESPACE> \
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clearml-ttr \
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clearml-enterprise/clearml-enterprise-task-traffic-router \
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||||||
--create-namespace \
|
|
||||||
-f overrides.yaml
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|
||||||
```
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||||||
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||||||
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||||||
|
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||||||
|
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||||||
## Applications Installation
|
|
||||||
|
|
||||||
|
|
||||||
To install the ClearML Applications on the newly installed ClearML Enterprise control-plane, download the applications
|
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||||||
package using the URL provided by the ClearML staff.
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||||||
|
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||||||
|
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||||||
|
|
||||||
|
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||||||
### Download and Extract
|
|
||||||
|
|
||||||
|
|
||||||
```
|
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||||||
wget -O apps.zip "<ClearML enterprise applications configuration download url>"
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|
||||||
unzip apps.zip
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
### Adjust Application Docker Images Location (Air-Gapped Systems)
|
|
||||||
|
|
||||||
|
|
||||||
ClearML applications use pre-built docker images provided by ClearML on the ClearML DockerHub
|
|
||||||
repository. If you are using an air-gapped system, these images must be available as part of your internal docker
|
|
||||||
registry, and the correct docker images location must be specified before installing the applications.
|
|
||||||
|
|
||||||
|
|
||||||
Use the following script to adjust the applications packages accordingly before installing the applications:
|
|
||||||
|
|
||||||
|
|
||||||
```
|
|
||||||
python convert_image_registry.py \
|
|
||||||
--apps-dir /path/to/apps/ \
|
|
||||||
--repo local_registry/clearml-apps
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
The script will change the application zip files to point to the new registry, and will output the list of containers
|
|
||||||
that need to be copied to the local registry. For example:
|
|
||||||
|
|
||||||
|
|
||||||
```
|
|
||||||
make sure allegroai/clearml-apps:hpo-1.10.0-1062 was added to local_registry/clearml-apps
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
### Install Applications
|
|
||||||
|
|
||||||
|
|
||||||
Use the `upload_apps.py` script to upload the application packages to the ClearML server:
|
|
||||||
|
|
||||||
|
|
||||||
```
|
|
||||||
python upload_apps.py \
|
|
||||||
--host $APISERVER_ADDRESS \
|
|
||||||
--user $APISERVER_USER --password $APISERVER_PASSWORD \
|
|
||||||
--dir apps -ml
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
## Configuring Shared Memory for Large Model Deployment
|
|
||||||
|
|
||||||
|
|
||||||
Deploying large models may fail due to shared memory size limitations. This issue commonly arises when the allocated
|
|
||||||
`/dev/shm` space is insufficient.:
|
|
||||||
|
|
||||||
|
|
||||||
```
|
|
||||||
> 3d3e22c3066f:168:168 [0] misc/shmutils.cc:72 NCCL WARN Error: failed to extend /dev/shm/nccl-UbzKZ9 to 9637892 bytes
|
|
||||||
> 3d3e22c3066f:168:168 [0] misc/shmutils.cc:113 NCCL WARN Error while creating shared memory segment /dev/shm/nccl-UbzKZ9 (size 9637888)
|
|
||||||
> 3d3e22c3066f:168:168 [0] NCCL INFO transport/shm.cc:114 -> 2
|
|
||||||
> 3d3e22c3066f:168:168 [0] NCCL INFO transport.cc:33 -> 2
|
|
||||||
> 3d3e22c3066f:168:168 [0] NCCL INFO transport.cc:113 -> 2
|
|
||||||
> 3d3e22c3066f:168:168 [0] NCCL INFO init.cc:1263 -> 2
|
|
||||||
> 3d3e22c3066f:168:168 [0] NCCL INFO init.cc:1548 -> 2
|
|
||||||
> 3d3e22c3066f:168:168 [0] NCCL INFO init.cc:1799 -> 2
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
To configure a proper SHM size you can use the following configuration in the agent `overrides.yaml`.
|
|
||||||
|
|
||||||
|
|
||||||
Replace `<SIZE>` with the desired memory allocation in GiB, based on your model requirements.
|
|
||||||
|
|
||||||
|
|
||||||
This example configures a specific queue, but you can include this setting in the `basePodTemplate` if you need to
|
|
||||||
apply it to all tasks.
|
|
||||||
|
|
||||||
|
|
||||||
```yaml
|
|
||||||
agentk8sglue:
|
|
||||||
queues:
|
|
||||||
GPUshm:
|
|
||||||
templateOverrides:
|
|
||||||
env:
|
|
||||||
- name: VLLM_SKIP_P2P_CHECK
|
|
||||||
value: "1"
|
|
||||||
volumeMounts:
|
|
||||||
- name: dshm
|
|
||||||
mountPath: /dev/shm
|
|
||||||
volumes:
|
|
||||||
- name: dshm
|
|
||||||
emptyDir:
|
|
||||||
medium: Memory
|
|
||||||
sizeLimit: <SIZE>Gi
|
|
||||||
```
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user