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Add ClearML Enterprise Server on k8s overview and ToC
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---
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title: Complete ClearML Enterprise K8s Installation and Configuration
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title: ClearML Enterprise K8s Installation and Configuration
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---
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This guides walks you through installing and configuring ClearML Enterprise on Kubernetes, from basic installation
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to advanced configuration options.
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This guides walks you through the complete installation and configuration of ClearML Enterprise Server on Kubernetes
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from initial setup to advanced configuration options.
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Follow the steps in the order presented for a smooth setup process.
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## Prerequisites
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Before installing ClearML Enterprise, verify that the following components are in place:
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Before you begin, ensure the following requirements are met:
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- Kubernetes Cluster: A vanilla Kubernetes cluster is recommended for optimal GPU support.
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- Kubernetes Cluster: A standard Kubernetes (vanilla) cluster is recommended, especially for optimal GPU support.
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- CLI Tools: `kubectl` and `helm` must be installed and configured.
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- Ingress Controller: Required to expose services via HTTP/S (e.g., `nginx-ingress`). If you need external access, also
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- Ingress Controller: Required to expose services via HTTP/S (e.g., `nginx-ingress`). For external access,
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configure a LoadBalancer (e.g., `MetalLB`).
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- Server and workers communicating on HTTP/S (ports 80 and 443). Additionally, the TCP session feature requires a range
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of ports for TCP traffic based on your configuration (see [AI App Gateway installation](appgw_install_k8s.md)).
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- Network Ports:
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- HTTP/S communication (ports 80 and 443) must be available between the server and agents.
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- TCP session support (for interactive apps) requires an additional port range (see the [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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- Server:
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@ -50,54 +51,50 @@ See the [ClearML Server on Kubernetes installation guide](k8s.md).
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### ClearML Applications
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ClearML Applications are like plugins that allow you to manage ML workloads and automatically run recurring workflows
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ClearML Applications are plugin services that automate ML workloads
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without any coding. Applications are installed on top of the ClearML Server and are provided by the ClearML team.
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See the [Application Installation guide](extra_configs/apps.md).
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### ClearML Enterprise Agent
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The ClearML Enterprise Agent Enables scheduling and execution of distributed workloads (Tasks) on your Kubernetes cluster.
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The ClearML Enterprise Agent enables scheduling and execution of distributed workloads (Tasks) on your Kubernetes cluster.
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See the [ClearML Agent installation guide](agent_k8s.md).
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### AI Application Gateway
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The ClearML AI Application Gateway provides secure and authenticated routing of HTTPS connections from a
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user's browser running the ClearML WebApp to pods running interactive ClearML applications.
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Some ClearML applications (e.g., JupyterLab, Streamlit) may require users to access running ClearML tasks in a secure
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and authenticated manner, based on ClearML user permissions. To provide access to these tasks running inside pods, an AI
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App Gateway service must run on the same network as the agents and pods running the tasks.
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The AI App Gateway enables secure, authenticated access to interactive ClearML applications (e.g., JupyterLab, Streamlit)
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based on ClearML user permissions. It routes HTTPS traffic from users to running pods on the cluster.
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See the [AI Application Gateway installation guide](appgw_install_k8s.md).
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## Additional Configurations
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## Additional Configuration Options
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### Setup GPUs
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### GPU Operator
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#### GPU Operator
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Deploy the NVIDIA GPU Operator in order to use Nvidia GPUs in ClearML.
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$$$$$$$$$$$$$$$$$$$$In order to use Nvidia GPUs in ClearML.
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See the [guide for deploying the NVIDIA GPU Operator alongside ClearML Enterprise](extra_configs/gpu_operator.md)
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See the [GPU Operator Basic Deployment guide](extra_configs/gpu_operator.md)
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### Fractional GPU Support
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Enable allocating a fraction of the available GPU cores and memory for better utilization of shared GPU nodes.
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To optimize GPU utilization:
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$$$$$$TODO link
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* ClearML Dynamic MIG Orchestrator (CDMO): Manage GPU fractions using NVIDIA MIGs. See the [CDMO guide](fractional_gpus/cdmo.md)
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* ClearML Fractional GPU Injector (CFGI): Use fractional (non-MIG) GPU slices for efficient resource sharing. See the [CFGI guide](fractional_gpus/cfgi.md)
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* Mixed Deployments: Deploy both CDMO and CFGI in clusters with diverse GPU types. Use the NVIDIA GPU Operator to handle
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mixed hardware setups. See the [CDMO and CFGI guide](fractional_gpus/cdmo_cfgi_same_cluster.md).
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### Multi-Tenant Setup
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Enable isolated tenants within the same ClearML Server, each with separate configuration, users, and project namespaces.
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Run multiple isolated tenants on a single ClearML Server instance, each with its own configuration and user namespaces.
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See the [multi-tenant service installation guide](multi_tenant_k8s.md).
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See the [Multi-Tenant Service guide](multi_tenant_k8s.md).
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### SSO (Identity Provider) Setup
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Configure Single sign-on identity providers on ClearML Enterprise.
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Integrate identity providers to enable SSO login for ClearML Enterprise users.
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See the [SSO Setup guide](extra_configs/sso_login.md).
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@ -109,12 +106,11 @@ See the [Custom Event guide](extra_configs/custom_events.md).
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### ClearML Presign Service
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The ClearML Presign Service is a secure component for generating and redirecting pre-signed storage URLs for
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authenticated users.
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The ClearML Presign Service securely generates pre-signed storage URLs for authenticated users.
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See the [ClearML Presign Service guide](extra_configs/presign_service.md).
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### Install with a non-root user
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## Install with a Non-Root User
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In some Helm charts, you will find a values file called `values-enterprise-non-root-privileged.yaml` to be used for a
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non-root installation.
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71
sidebars.js
71
sidebars.js
@ -664,7 +664,7 @@ module.exports = {
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{'ClearML Application Gateway': [
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'deploying_clearml/enterprise_deploy/appgw_install_compose',
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'deploying_clearml/enterprise_deploy/appgw_install_compose_hosted',
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'deploying_clearml/enterprise_deploy/appgw_install_k8s',
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'deploying_clearml/enterprise_deploy/appgw_install_k8s',
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]
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},
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'deploying_clearml/enterprise_deploy/custom_billing',
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@ -719,5 +719,74 @@ module.exports = {
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},
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],
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},
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],
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enterpriseDeploy: [
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{
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type: 'category',
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collapsible: true,
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label: 'ClearML Enterprise K8s Installation and Configuration',
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link: {type: 'doc', id: 'deploying_clearml/enterprise_deploy/k8s_overview'},
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items: [
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'deploying_clearml/enterprise_deploy/agent_k8s',
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'deploying_clearml/enterprise_deploy/extra_configs/apps',
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{
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type: 'category',
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collapsible: true,
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label: 'Extra Configuration',
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items: [
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{
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type: 'doc',
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label: 'GPU Operator Basic Deployment',
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id: 'deploying_clearml/enterprise_deploy/extra_configs/gpu_operator'
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}, {
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type: 'doc',
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id: 'deploying_clearml/enterprise_deploy/extra_configs/custom_events'
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},
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{
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type: 'doc',
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id: 'deploying_clearml/enterprise_deploy/extra_configs/presign_service'
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},
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{
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type: 'doc',
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id: 'deploying_clearml/enterprise_deploy/extra_configs/dynamic_edit_task_pod_template'
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},
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{
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type: 'doc',
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id: 'deploying_clearml/enterprise_deploy/extra_configs/multi_node_training'
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},
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{
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type: 'doc',
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label: 'K8s Deployment with Self-Signed Certificates',
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id: 'deploying_clearml/enterprise_deploy/extra_configs/self_signed_certificates'
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},
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{
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type: 'doc',
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id: 'deploying_clearml/enterprise_deploy/extra_configs/sso_login'
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},
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],
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},
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{
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type: 'category',
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collapsible: true,
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label: 'Fractional GPUs',
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items: [
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{
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type: 'doc',
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label: 'ClearML Dynamic MIG Operator (CDMO)',
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id: 'deploying_clearml/enterprise_deploy/fractional_gpus/cdmo'
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},
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{
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type: 'doc',
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id: 'deploying_clearml/enterprise_deploy/fractional_gpus/cfgi'
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},
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{
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type: 'doc',
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id: 'deploying_clearml/enterprise_deploy/fractional_gpus/cdmo_cfgi_same_cluster'
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},
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],
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},
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]
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}
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]
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};
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