This repository is being deprecated

🎉 Please see the new and improved ClearML Helm Charts repository 🎉

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ClearML Server for Kubernetes Clusters Using Helm

Cloud Ready Version (Advanced)

Auto-Magical Experiment Manager & Version Control for AI

Introduction

The clearml-server is the backend service infrastructure for ClearML. It allows multiple users to collaborate and manage their experiments. By default, *ClearML is set up to work with the ClearML Demo Server, which is open to anyone and resets periodically. In order to host your own server, you will need to install clearml-server and point ClearML to it.

clearml-server contains the following components:

• The ClearML Web-App, a single-page UI for experiment management and browsing
• RESTful API for:
  • Documenting and logging experiment information, statistics and results
  • Querying experiments history, logs and results
• Locally-hosted file server for storing images and models making them easily accessible using the Web-App

Use this repository to add clearml-server to your Helm and then deploy clearml-server on Kubernetes clusters using Helm.

Deploying Your Own Elasticsearch, Redis and Mongodb

ClearML Server requires that you have elasticsearch, redis and mongodb services. This chart default templates contains bitnami charts for redis and mongodb, and the official chart for elasticsearch (which is currently still beta). You can either use the default ones, or use your own deployments and set their name and ports in the appropriate sections of this chart. In order to use your own deployment, make sure to disable the existing one in the values.yaml (for example, in order to disable elastic set elasticsearch.enabled = false)

Prerequisites

1. a Kubernetes cluster
2. Persistent Volumes for pvc-apiserver.yaml, pvc-fileserver.yaml, and pvc-agentservices.yaml.
3. Persistent volumes for elasticsearch, mongodb and redis (redis is optional). See relevant information for each chart:
   • elasticsearch
   • mongodb
   • redis Make sure to define the following values for each PV:
   • elasticsearch - in the values.yaml set elasticsearch.persistence.enabled=true and set elasticsearch.volumeClaimTemplate.storageClassName to the storageClassName used in your elasticsearch PV.
   • mongodb - in order to define a persistent volume for mongodb, in the values.yaml set mongodb.persistence.enabled=true and set mongodb.persistence.storageClass to the storageClassName used in your mongodb PV. Read here for more details.
   • redis - in order to define a persistent volume for redis, in the values.yaml set redis.master.persistence.enabled=true and set redis.master.persistence.storageClass to the storageClassName used in your redis PV. Read here for more details.
4. kubectl is installed and configured (see Install and Set Up kubectl in the Kubernetes documentation)
5. helm installed (see Installing Helm in the Helm documentation)

Deploying ClearML Server in Kubernetes Clusters Using Helm

1. Add the clearml-server repository to your Helm:

    helm repo add allegroai https://allegroai.github.io/clearml-server-helm-cloud-ready/
   

2. Confirm the clearml-server repository is now in Helm:

    helm search repo clearml
   

   The helm search results must include allegroai/clearml-server-cloud-ready.

3. Install clearml-server-cloud-ready on your cluster:

    helm install clearml-server allegroai/clearml-server-cloud-ready --namespace=clearml --create-namespace
   

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

Updating ClearML Server application using Helm

1. If you are upgrading from the single node version of ClearML Server helm charts, follow these steps first:

   1. Log in to the node previously labeled as app=trains
   2. Copy each folder under /opt/clearml/data to it's persistent volume.
   3. Follow the Deploying ClearML Server instructions to deploy Clearml

2. Update using new or updated values.yaml

    helm upgrade clearml-server allegroai/clearml-server-cloud-ready -f new-values.yaml
   

3. If there are no breaking changes, you can update your deployment to match repository version:

    helm upgrade clearml-server allegroai/clearml-server-cloud-ready
   

   Important:

   • If you previously deployed a clearml-server, you may encounter errors. If so, you must first delete old deployment using the following command:

       helm delete --purge clearml-server
     

     After running the helm delete command, you can run the helm install command.

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

Accessing ClearML Server

Access clearml-server by creating a load balancer and domain name with records pointing to the load balancer.

Once you have a load balancer and domain name set up, follow these steps to configure access to clearml-server on your k8s cluster:

1. Create domain records

   • Create 3 records to be used for Web-App, File server and API access using the following rules:

     • app.<your domain name>
     • files.<your domain name>
     • api.<your domain name>

     (for example, app.clearml.mydomainname.com, files.clearml.mydomainname.com and api.clearml.mydomainname.com)

2. Point the records you created to the load balancer

3. Configure the load balancer to redirect traffic coming from the records you created:

   • app.<your domain name> should be redirected to k8s cluster nodes on port 30080
   • files.<your domain name> should be redirected to k8s cluster nodes on port 30081
   • api.<your domain name> should be redirected to k8s cluster nodes on port 30008

Additional Configuration for ClearML Server

You can also configure the clearml-server for:

• fixed users (users with credentials)
• non-responsive experiment watchdog settings

For detailed instructions, see the Optional Configuration section in the clearml-server repository README file.