clearml-docs/docs/deploying_clearml/clearml_server_kubernetes_helm.md

title
Kubernetes Using Helm

:::important This documentation page is being updated. Please see the new and improved ClearML Helm Charts repository for the most updated instructions. :::

:::note This documentation page applies to deploying your own open source ClearML Server. It does not apply to ClearML Hosted Service users. :::

:::warning If ClearML Server is being reinstalled, we recommend clearing browser cookies for ClearML Server. For example, for Firefox, go to Developer Tools > Storage > Cookies, and for Chrome, go to Developer Tools > Application > Cookies, and delete all cookies under the ClearML Server URL. :::

For information about upgrading ClearML Server in Kubernetes Clusters using Help, see here.

Prerequisites

• A Kubernetes cluster.
• kubectl installed and configured (see Install and Set Up kubectl in the Kubernetes documentation).
• helm is installed (see Installing Helm in the Helm documentation).
• One node labeled app=clearml.

:::warning ClearML Server deployment uses node storage. If more than one node is labeled as app=clearml, and the server is later redeployed or updated, then ClearML Server may not locate all the data. :::

Deploying

:::warning By default, ClearML Server launches with unrestricted access. To restrict ClearML Server access, follow the instructions in the Security page. :::

Step 1: Modify Elasticsearch Default Values in the Docker Configuration File

Before deploying ClearML Server in a Kubernetes cluster, modify several Elasticsearch settings in the Docker configuration. For more information, see Install Elasticsearch with Docker in the Elasticsearch documentation and Daemon configuration file in the Docker documentation.

To modify Elasticsearch default values in the Docker configuration file:

1. Connect to the node in the Kubernetes cluster labeled app=clearml.

2. Create or edit (if one exists) the /etc/docker/daemon.json file, and add or modify the defaults-ulimits section as the following example shows:

    {
        "default-ulimits": {
            "nofile": {
                "name": "nofile",
                "hard": 65536,
                "soft": 1024
            },
            "memlock":
            {
                "name": "memlock",
                "soft": -1,
                "hard": -1
            }
        }
    }
   

3. Elasticsearch requires that the vm.max_map_count kernel setting, which is the maximum number of memory map areas a process can use, be set to at least 262144.

   For CentOS 7, Ubuntu 16.04, Mint 18.3, Ubuntu 18.04 and Mint 19.x, use the following commands to set vm.max_map_count:

    echo "vm.max_map_count=262144" > /tmp/99-clearml.conf
    sudo mv /tmp/99-clearml.conf /etc/sysctl.d/99-clearml.conf
    sudo sysctl -w vm.max_map_count=262144
   

4. Restart docker:

    sudo service docker restart
   

Step 2. Deploy ClearML Server in the Kubernetes Using Helm

After modifying several Elasticsearch settings in the Docker configuration (see Step 1 above), deploy ClearML Server.

To deploy ClearML Server in Kubernetes using Helm:

1. Add the clearml-server repository to Helm:

    helm repo add allegroai https://allegroai.github.io/clearml-helm-charts
   

2. Confirm the clearml repository is now in Helm:

    helm search repo allegroai
   

   The helm search results must include allegroai/clearml.

3. Install clearml on your cluster:

    helm install clearml-server allegroai/clearml -n clearml --create-namespace
   

   A clearml namespace is created in the cluster and clearml-server is deployed in it.

Port Mapping

After ClearML Server is deployed, the services expose the following node ports:

• API server on 30008.
• Web server on 30080.
• File server on 30081.

The node ports map to the following container ports:

• 30080 maps to clearml-webserver container on port 8080
• 30008 maps to clearml-apiserver container on port 8008
• 30081 maps to clearml-fileserver container on port 8081

:::important We recommend using the container ports (8080, 8008, and 8081), or a load balancer (see the next section, Accessing ClearML Server). :::

Accessing ClearML Server

To access ClearML Server:

• Create a load balancer and domain with records pointing to ClearML Server using the following rules, which ClearML uses to translate domain names:

  • The record to access the ClearML Web UI:

      *app.<your domain name>.* 
    

  For example, clearml.app.mydomainname.com points to your node on port 30080.

  • The record to access the ClearML API:

      *api.<your domain name>.* 
    

  For example, clearml.api.mydomainname.com points to your node on port 30008.

  • The record to access the ClearML file server:

      *files.<your domain name>.*
    

    For example, clearmlfiles.mydomainname.com points to your node on port 30081.

Next Step

• Configuring ClearML for ClearML Server.