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

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.

Prerequisites

• a Kubernetes cluster

• kubectl is installed and configured (see Install and Set Up kubectl in the Kubernetes documentation)

• helm installed (see Installing Helm in the Helm documentation)

• one node labeled app: clearml

  Important: ClearML Server deployment uses node storage. If more than one node is labeled as app: clearml and you redeploy or update later, then ClearML Server may not locate all of your data.

Modify the default values required by Elastic in Docker configuration file (see Notes for production use and defaults)

1 - Connect to node you labeled as app=clearml

Edit /etc/docker/daemon.json (if it exists) or create it (if it does not exist).

Add or modify the defaults-ulimits section as shown below. Be sure the defaults-ulimits section contains the nofile and memlock sub-sections and values shown.

Note: Your configuration file may contain other sections. If so, confirm that the sections are separated by commas (valid JSON format). For more information about Docker configuration files, see Daemon configuration file, in the Docker documentation.

The clearml-server required defaults values are (json):

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

2 - Set the Maximum Number of Memory Map Areas

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

For CentOS 7, Ubuntu 16.04, Mint 18.3, Ubuntu 18.04 and Mint 19.x, we tested 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

For information about setting this parameter on other systems, see the elastic documentation.

3 - Restart docker:

sudo service docker restart

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/
   

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

    helm search clearml
   

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

3. By default, the clearml-server deployment uses storage on a single node (labeled app=clearml). To change the type of storage used (for example NFS), see Configuring ClearML Server storage for NFS.

4. If you'd like clearml-agent support in your clearml Kubernetes cluster, create a local values.yaml, as specified in Configuring ClearML Agent instances in your cluster.

5. Install clearml-server-chart on your cluster:

    helm install allegroai/clearml-server-chart --namespace=clearml --name clearml-server
   

   Alternatively, in case you've created a local values.yaml file, use:

    helm install allegroai/clearml-server-chart --namespace=clearml --name clearml-server --values values.yaml
   

   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 Trains Server version 0.15 or older to ClearML Server, a data migration is required before you upgrade. Stay tuned, we'll update the required steps for the upgrade soon!

2. If you are upgrading from Trains Server to ClearML Server, follow these steps first:

   1. Log in to the node labeled as app=trains

   2. Rename /opt/trains and its subdirectories to /opt/clearml the following way:

      sudo mv /opt/trains /opt/clearml
      

   3. Label the node as app=clearml

   4. Follow the Deploying ClearML Server instructions to deploy Clearml

3. Update using new or updated values.yaml

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

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

    helm upgrade clearml-server allegroai/clearml-server-chart
   

   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

Configuring ClearML Agent instances in your cluster

In order to create clearml-agent instances as part of your deployment, create or update your local values.yaml file. This values.yaml file should be used in your helm install command (see Deploying ClearML Server in Kubernetes Clusters Using Helm) or helm upgrade command (see Updating ClearML Server application using Helm)

The file must contain the following values in the agent section:

• numberOfClearmlAgents: controls the number of clearml-agent pods to be deployed. Each agent pod will listen for and execute experiments from the clearml-server
• nvidiaGpusPerAgent: defines the number of GPUs required by each agent pod
• clearmlApiHost: the URL used to access the ClearML API server, as defined in your load-balancer (usually https://api.<your domain name>, see Accessing ClearML Server)
• clearmlWebHost: the URL used to access the ClearML webservice, as defined in your load-balancer (usually https://app.<your domain name>, see Accessing ClearML Server)
• clearmlFilesHost: the URL used to access the ClearML fileserver, as defined in your load-balancer (usually https://files.<your domain name>, see Accessing ClearML Server)

Additional optional values in the agent section include:

• defaultBaseDocker: the default docker image used by the agent running in the agent pod in order to execute an experiment. Default is nvidia/cuda.
• agentVersion: determines the specific agent version to be used in the deployment, for example "==0.13.3". Default is null (use latest version)
• clearmlGitUser / clearmlGitPassword: GIT credentials used by clearml-agent running the experiment when cloning the GIT repository defined in the experiment, if defined. Default is null (not used)
• awsAccessKeyId / awsSecretAccessKey / awsDefaultRegion: AWS account info used by ClearML when uploading files to an AWS S3 buckets (not required if only using the default clearml-fileserver). Default is null (not used)
• azureStorageAccount / azureStorageKey: Azure account info used by ClearML when uploading files to MS Azure Blob Service (not required if only using the default clearml-fileserver). Default is null (not used)

For example, the following values.yaml file requests 4 agent instances in your deployment (see chart-example-values.yaml):

agent:
  numberOfClearmlAgents: 4
  nvidiaGpusPerAgent: 1
  defaultBaseDocker: "nvidia/cuda"
  clearmlApiHost: "https://api.clearml.mydomain.com"
  clearmlWebHost: "https://app.clearml.mydomain.com"
  clearmlFilesHost: "https://files.clearml.mydomain.com"
  clearmlGitUser: null
  clearmlGitPassword: null
  awsAccessKeyId: null
  awsSecretAccessKey: null
  awsDefaultRegion: null
  azureStorageAccount: null
  azureStorageKey: null

Configuring ClearML Server storage for NFS

The clearml-server deployment uses a PersistentVolume of type HostPath, which uses a fixed path on the node labeled app: clearml.

The existing chart supports changing the volume type to NFS, by setting the use_nfs value and configuring the NFS persistent volume using additional values in your local values.yaml file:

storage:
  use_nfs: true
  nfs:
    server: "<nfs-server-ip-address>"
    base_path: "/nfs/path/for/clearml/data"

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.