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v0.24.2
v0.24.2
  • ☂️Introduction
  • Model Monitoring
    • Quickstart
    • Data Preparation
      • How to get data ready for NannyML
    • Tutorials
      • Monitoring a tabular data model
      • Monitoring with segmentation
      • Monitoring a text classification model
      • Monitoring a computer vision model
    • How it works
      • Probabilistic Adaptive Performance Estimation (PAPE)
      • Reverse Concept Drift (RCD)
    • Custom Metrics
      • Creating Custom Metrics
        • Writing Functions for Binary Classification
        • Writing Functions for Multiclass Classification
        • Writing Functions for Regression
        • Handling Missing Values
        • Advanced Tutorial: Creating a MTBF Custom Metric
      • Adding a Custom Metric through NannyML SDK
    • Reporting
      • Creating a new report
      • Report structure
      • Exporting a report
      • Managing reports
      • Report template
      • Add to report feature
  • Product tour
    • Navigation
    • Adding a model
    • Model overview
    • Deleting a model
    • Model side panel
      • Summary
      • Performance
      • Concept drift
      • Covariate shift
      • Data quality
      • Logs
      • Model settings
        • General
        • Data
        • Performance settings
        • Concept Drift settings
        • Covariate Shift settings
        • Descriptive Statistics settings
        • Data Quality settings
    • Account settings
  • Deployment
    • Azure
      • Azure Managed Application
        • Finding the URL to access managed NannyML Cloud
        • Enabling access to storage
      • Azure Software-as-a-Service (SaaS)
    • AWS
      • AMI with CFT
        • Architecture
      • EKS
        • Quick start cluster setup
      • S3 Access
    • Application setup
      • Authentication
      • Notifications
      • Webhooks
      • Permissions
  • NannyML Cloud SDK
    • Getting Started
    • Example
      • Authentication & loading data
      • Setting up the model schema
      • Creating the monitoring model
      • Customizing the monitoring model settings
      • Setting up continuous monitoring
      • Add delayed ground truth (optional)
    • API Reference
  • Probabilistic Model Evaluation
    • Introduction
    • Tutorials
      • Evaluating a binary classification model
      • Data Preparation
    • How it works
      • HDI+ROPE (with minimum precision)
      • Getting Probability Distribution of a Performance Metric with targets
      • Getting Probability Distribution of Performance Metric without targets
      • Getting Probability Distribution of Performance Metric when some observations have labels
      • Defaults for ROPE and estimation precision
  • Experiments Module
    • Introduction
    • Tutorials
      • Running an A/B test
      • Data Preparation
    • How it works
      • Getting probability distribution of the difference of binary downstream metrics
  • miscellaneous
    • Engineering
    • Usage logging in NannyNL
    • Versions
      • Version 0.24.2
      • Version 0.24.1
      • Version 0.24.0
      • Version 0.23.0
      • Version 0.22.0
      • Version 0.21.0
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On this page
  • Install eksctl
  • Define cluster name
  • Create cluster with EBS CSI driver
  • Delete cluster
  1. Deployment
  2. AWS
  3. EKS

Quick start cluster setup

Instructions for quickly setting up an EKS cluster

PreviousEKSNextS3 Access

This page provides instructions for setting up an Amazon EKS cluster that supports NannyML Cloud. It is intended as a quick start when you just want to get going. For configuration options and explanation please consult the .

You will need to have configured. The account used needs to manage EKS clusters and IAM roles.

Install eksctl

# for ARM systems, set ARCH to: `arm64`, `armv6` or `armv7`
ARCH=amd64
PLATFORM=$(uname -s)_$ARCH

curl -sLO "https://github.com/eksctl-io/eksctl/releases/latest/download/eksctl_$PLATFORM.tar.gz"

# (Optional) Verify checksum
curl -sL "https://github.com/eksctl-io/eksctl/releases/latest/download/eksctl_checksums.txt" | grep $PLATFORM | sha256sum --check

tar -xzf eksctl_$PLATFORM.tar.gz -C /tmp && rm eksctl_$PLATFORM.tar.gz

sudo mv /tmp/eksctl /usr/local/bin

Define cluster name

CLUSTER_NAME=nannyml-cloud-demo

Create cluster with EBS CSI driver

eksctl create cluster --name $CLUSTER_NAME --version=1.29 && \
eksctl utils associate-iam-oidc-provider --cluster $CLUSTER_NAME --approve && \
eksctl create iamserviceaccount \
    --name ebs-csi-controller-sa \
    --namespace kube-system \
    --cluster $CLUSTER_NAME \
    --role-name AmazonEKS_EBS_CSI_DriverRole \
    --role-only \
    --attach-policy-arn arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy \
    --approve && \
eksctl create addon \
    --name aws-ebs-csi-driver \
    --cluster $CLUSTER_NAME \
    --service-account-role-arn arn:aws:iam::$(aws sts get-caller-identity --query "Account" --output text):role/AmazonEKS_EBS_CSI_DriverRole \
    --force

Delete cluster

At some point you may decide to delete your cluster, e.g. after initial testing you want to create a cluster in your AWS organisation account.

Deleting a cluster containing a NannyML Cloud instance will cause models and configuration to be lost.

eksctl delete cluster --name $CLUSTER_NAME

This deletes all cluster resources, including EC2 instances and IAM roles associated with cluster. However, it does not delete Elastic Block Store (EBS) volumes. If desired, you can delete these manually from .

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AWS API credentials
permissions
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