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AWS Deployment

Learn how to deploy to the cloud with AWS.

December 4, 2023

Before You Start #

This guide assumes that you have already tried HPE ML Data Management locally and have all of the following installed:


1. Create an EKS Cluster #

  1. Use the eksctl tool to deploy an EKS Cluster:
eksctl create cluster --name pachyderm-cluster --region <region> -profile <your named profile>
  1. Verify deployment:
kubectl get all

2. Create an S3 Bucket #

  1. Run the following command:
aws s3api create-bucket --bucket ${BUCKET_NAME} --region ${AWS_REGION}
  1. Verify.
aws s3 ls

3. Enable Persistent Volumes Creation #

  1. Create an IAM OIDC provider for your cluster.
  2. Install the Amazon EBS Container Storage Interface (CSI) driver on your cluster.
  3. Create a gp3 storage class manifest file (e.g., gp3-storageclass.yaml)
    kind: StorageClass
    apiVersion: storage.k8s.io/v1
    metadata:
      name: gp3
      annotations:
        storageclass.kubernetes.io/is-default-class: "true"
    provisioner: kubernetes.io/aws-ebs
    parameters:
      type: gp3
      fsType: ext4
  4. Set gp3 to your default storage class.
    kubectl apply -f gp3-storageclass.yaml
  5. Verify that it has been set as your default.
    kubectl get storageclass

4. Set up an RDS PostgreSQL Instance #

By default, HPE ML Data Management runs with a bundled version of PostgreSQL. For production environments, it is strongly recommended that you disable the bundled version and use an RDS PostgreSQL instance.

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  1. In the RDS console, create a database in the region matching your HPE ML Data Management cluster.
  2. Choose the PostgreSQL engine.
  3. Select a PostgreSQL version >= 13.3.
  4. Configure your DB instance as follows:
SETTINGRecommended value
DB instance identifierFill in with a unique name across all of your DB instances in the current region.
Master usernameChoose your Admin username.
Master passwordChoose your Admin password.
DB instance classThe standard default should work. You can change the instance type later on to optimize your performances and costs.
Storage type and Allocated storageIf you select io1, keep the 100 GiB default size.
Read more information on Storage for RDS on Amazon’s website.
Storage autoscalingIf your workload is cyclical or unpredictable, enable storage autoscaling to allow RDS to scale up your storage when needed.
Standby instanceWe highly recommend creating a standby instance for production environments.
VPCSelect the VPC of your Kubernetes cluster. Attention: After a database is created, you can’t change its VPC.
Read more on VPCs and RDS on Amazon documentation.
Subnet groupPick a Subnet group or Create a new one.
Read more about DB Subnet Groups on Amazon documentation.
Public accessSet the Public access to No for production environments.
VPC security groupCreate a new VPC security group and open the postgreSQL port or use an existing one.
Password authentication or Password and IAM database authenticationChoose one or the other.
Database nameIn the Database options section, enter HPE ML Data Management’s Database name (We are using HPE ML Data Managementin this example.) and click Create database to create your PostgreSQL service. Your instance is running.
Warning: If you do not specify a database name, Amazon RDS does not create a database.
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Standalone Clusters

If you are deploying a standalone cluster, you must create a second database named dex in your RDS instance for HPE ML Data Management’s authentication service. Read more about dex on PostgreSQL in Dex’s documentation.

Multi-cluster setups use Enterprise Server to handle authentication, so you do not need to create a dex database.

  1. Create a new user account and grant it full CRUD permissions to both HPE ML Data Managementand (when applicable) dex databases. Read about managing PostgreSQL users and roles in this blog. HPE ML Data Management will use the same username to connect to HPE ML Data Managementas well as to dex.

5. Create a Values.yaml #

Version:

6. Configure Helm #

Run the following to add the HPE ML Data Management repo to Helm:

helm repo add pachyderm https://helm.pachyderm.com
helm repo update
helm install pachyderm pachyderm/pachyderm -f my_pachyderm_values.yaml 

7. Verify Installation #

  1. In a new terminal, run the following command to check the status of your pods:
kubectl get pods
NAME                                           READY   STATUS      RESTARTS   AGE
pod/console-5b67678df6-s4d8c                   1/1     Running     0          2m8s
pod/etcd-0                                     1/1     Running     0          2m8s
pod/pachd-c5848b5c7-zwb8p                      1/1     Running     0          2m8s
pod/pg-bouncer-7b855cb797-jqqpx                1/1     Running     0          2m8s
pod/postgres-0                                 1/1     Running     0          2m8s
  1. Re-run this command after a few minutes if pachd is not ready.

8. Connect to Cluster #

You’ll need your organization’s cluster URL (proxy.host) value to connect.

  1. Run the following command to get your cluster URL:
kubectl get services | grep pachyderm-proxy | awk '{print $4}'
  1. Connect to your cluster:

    Method:
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If the connection commands did not work together, run each separately.

Optionally open your browser and navigate to the Console UI.

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You can check your HPE ML Data Management version and connection to pachd at any time with the following command:

pachctl version
COMPONENT           VERSION  

pachctl             2.8.1  
pachd               2.8.1