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MLOps on Amazon EKS - Comprehensive Guide

Overview

This project provides a prototypical MLOps solution on Amazon EKS using a modular, Helm-based approach. It supports comprehensive ML workflows including distributed training, and inference serving. Comprehensive support for agentic applications is under development.

The solution deploys a complete MLOps platform using Terraform on Amazon EKS with VPC networking, shared file systems (EFS and FSx for Lustre), auto-scaling infrastructure (Karpenter and Cluster Autoscaler), service mesh (Istio), authentication (Dex/OAuth2), and optional ML platform components (Kubeflow, MLFlow, Airflow, KServe, etc.).

Key Features

  • Modular Architecture: Enable/disable MLOps components as needed (Airflow, Kubeflow, KServe, Kueue, MLFlow, Slinky Slurm)
  • Helm-Based Pipelines: Execute ML pipeline steps using Helm charts with YAML recipes
  • Multi-Accelerator Support: Nvidia GPUs, AWS Trainium, AWS Inferentia with EFA
  • Distributed Training & Inference: Multi-node training and inference with popular frameworks
  • Shared Storage: EFS and FSx for Lustre with automatic S3 backup
  • Auto-Scaling: Karpenter for GPU/AI chips, Cluster Autoscaler for CPU
  • Service Mesh & Security: Istio ingress gateway, cert-manager, OAuth2 authentication

Prerequisites

  • AWS Account with Administrator access
  • AWS Region selection (examples use us-west-2)
  • EC2 Service Limits increased for:
    • p4d.24xlarge, g6.xlarge, g6.2xlarge, g6.48xlarge (8+ each)
    • inf2.xlarge, inf2.48xlarge, trn1.32xlarge (8+ each)
    • Increase EC2 Service Limits for additional EC2 instance types you plan to use
  • EC2 Key Pair
  • S3 Bucket for Terraform state and FSx Lustre data (see below)
  • Your public IP address (from AWS check ip)

Create S3 Bucket

Create an S3 bucket before proceeding. This bucket stores Terraform state and FSx Lustre data. The bucket name must be globally unique.

# Replace <YOUR_S3_BUCKET> with a unique name (e.g., my-mlops-bucket-12345)
aws s3 mb s3://<YOUR_S3_BUCKET> --region us-west-2

Getting Started

1. Launch Build Machine

Use CloudFormation template ml-ops-desktop.yaml to create build machine:

# Via AWS Console or CLI with --capabilities CAPABILITY_NAMED_IAM
  • Once the stack status in CloudFormation console is CREATE_COMPLETE, find the ML Ops desktop instance launched in your stack in the Amazon EC2 console, and connect to the instance using SSH as user ubuntu, using your SSH key pair.
  • When you connect using SSH, and you see the message "Cloud init in progress! Logs: /var/log/cloud-init-output.log", disconnect and try later after about 15 minutes. The desktop installs the Amazon DCV server on first-time startup, and reboots after the install is complete.
  • If you see the message ML Ops desktop is enabled!, run the command sudo passwd ubuntu to set a new password for user ubuntu. Now you are ready to connect to the desktop using the Amazon DCV client
  • The build machine desktop uses EC2 user-data to initialize the desktop. Most transient failures in the desktop initialization can be fixed by rebooting the desktop.

2. Clone Repository

cd ~
git clone https://github.com/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow.git
cd amazon-eks-machine-learning-with-terraform-and-kubeflow

3. Install kubectl

./eks-cluster/utils/install-kubectl-linux.sh

4. Configure Terraform Backend

Configure S3 backend for Terraform state storage. Replace <YOUR_S3_BUCKET> with the S3 bucket you created in Prerequisites. The <S3_PREFIX> is a folder path in your bucket (e.g., eks-ml-platform) - it will be created automatically.

# Example: ./eks-cluster/utils/s3-backend.sh my-mlops-bucket-12345 eks-ml-platform
./eks-cluster/utils/s3-backend.sh <YOUR_S3_BUCKET> <S3_PREFIX>

5. Initialize and Apply Terraform

  • Logout from AWS Public ECR as otherwise terraform apply command may fail.
  • Specify at least three AWS Availability Zones from your AWS Region in the azs terraform variable.
  • Replace <YOUR_S3_BUCKET> with the S3 bucket you created in Prerequisites.
  • To use Amazon EC2 P4, and P5 instances, set cuda_efa_az terrraform variable to a zone in azs list that supports P-family instances.
  • To use Amazon EC2 AWS Inferentia and Trainium instance types instances, set neuron_az terraform variable to an Availability Zone in your azs list that supports these instance types.

The command below is just an example: You will need to adapt it specify the azs that support the P-family, and AWS Inferentia and Trainium instances within your AWS Region.

docker logout public.ecr.aws
cd eks-cluster/terraform/aws-eks-cluster-and-nodegroup
terraform init

# Replace <YOUR_S3_BUCKET> with your actual S3 bucket name
terraform apply -var="profile=default" -var="region=us-west-2" \
  -var="cluster_name=my-eks-cluster" \
  -var='azs=["us-west-2a","us-west-2b","us-west-2c"]' \
  -var="import_path=s3://<YOUR_S3_BUCKET>/eks-ml-platform/" \
  -var="cuda_efa_az=us-west-2c" \
  -var="neuron_az=us-west-2c"

Alternatively:

docker logout public.ecr.aws
cd eks-cluster/terraform/aws-eks-cluster-and-nodegroup
terraform init

cp terraform.tfvars.example terraform.tfvars # Add/modify variables as needed
./create_eks.sh

Using On-Demand Capacity Reservations (ODCR)

To use ODCR with Karpenter for guaranteed capacity on cudaefa NodePool (p4d, p5 instances), first create capacity reservations in the AWS Console or CLI, then add the ODCR variables:

terraform apply -var="profile=default" -var="region=us-west-2" \
  -var="cluster_name=my-eks-cluster" \
  -var='azs=["us-west-2a","us-west-2b","us-west-2c"]' \
  -var="import_path=s3://<YOUR_S3_BUCKET>/eks-ml-platform/" \
  -var="cuda_efa_az=us-west-2c" \
  -var="neuron_az=us-west-2c" \
  -var="karpenter_odcr_enabled=true" \
  -var='karpenter_odcr_capacity_types=["reserved","on-demand"]' \
  -var='karpenter_odcr_cudaefa_ids=["cr-xxxxx","cr-yyyyy"]'

Verify nodes launch with reserved capacity:

kubectl get nodes -l karpenter.sh/nodepool=cudaefa -o jsonpath='{range .items[*]}{.metadata.name}: {.metadata.labels.karpenter\.sh/capacity-type}{"\n"}{end}'

6. Create Home Folders on Shared Storage

cd ~/amazon-eks-machine-learning-with-terraform-and-kubeflow/
kubectl apply -f eks-cluster/utils/attach-pvc.yaml -n kubeflow
# wait for the `attach-pvc` Pod to be in "ready" Status. 
# check status of the Pod by running
kubectl get pods -n kubeflow -w
# Once the attach-pvc Pod is Ready, run the following commands
kubectl exec -it -n kubeflow attach-pvc -- /bin/bash

# Inside pod
cd /efs && mkdir home && chown 1000:100 home
cd /fsx && mkdir home && chown 1000:100 home
exit

System Architecture

The solution uses Terraform to deploy a comprehensive MLOps platform on Amazon EKS with the following architecture:

Architecture Schematic

┌─────────────────────────────────────────────────────────────────────────────┐
│                              AWS Cloud (Region)                             │
│                                                                             │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                          VPC (192.168.0.0/16)                          │ │
│  │                                                                        │ │
│  │  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐      │ │
│  │  │  Public Subnet   │  │  Public Subnet   │  │  Public Subnet   │      │ │
│  │  │   (AZ-1)         │  │   (AZ-2)         │  │   (AZ-3)         │      │ │
│  │  └────────┬─────────┘  └────────┬─────────┘  └────────┬─────────┘      │ │
│  │           │                     │                     │                │ │
│  │  ┌────────▼─────────────────────▼──────────────────---▼────────┐       │ │
│  │  │            Internet Gateway + NAT Gateway                   │       │ │
│  │  └─────────────────────────────────────────────────────────────┘       │ │
│  │                                                                        │ │
│  │  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐      │ │
│  │  │ Private Subnet   │  │ Private Subnet   │  │ Private Subnet   │      │ │
│  │  │   (AZ-1)         │  │   (AZ-2)         │  │   (AZ-3)         │      │ │
│  │  │                  │  │                  │  │                  │      │ │
│  │  │ ┌──────────────┐ │  │ ┌──────────────┐ │  │ ┌──────────────┐ │      │ │
│  │  │ │ EKS Cluster  │ │  │ │ EKS Cluster  │ │  │ │ EKS Cluster  │ │      │ │
│  │  │ │              │ │  │ │              │ │  │ │              │ │      │ │
│  │  │ │ System Nodes │ │  │ │ GPU Nodes    │ │  │ │ Neuron Nodes │ │      │ │
│  │  │ │ (Cluster AS) │ │  │ │ (Karpenter)  │ │  │ │ (Karpenter)  │ │      │ │
│  │  │ └──────────────┘ │  │ └──────────────┘ │  │ └──────────────┘ │      │ │
│  │  └──────────────────┘  └──────────────────┘  └──────────────────┘      │ │
│  │                                                                        │ │
│  └──────────────────────────────────────────────────────────────────────--┘ │
│                                                                             │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                        Shared Storage Layer                            │ │
│  │                                                                        │ │
│  │  ┌─────────────────────────┐      ┌──────────────────────────┐         │ │
│  │  │  Amazon EFS             │      │  FSx for Lustre          │         │ │
│  │  │  (Code, Logs, Configs)  │      │  (Data, Models)          │         │ │
│  │  │  /efs mount             │      │  /fsx mount              │         │ │
│  │  └─────────────────────────┘      └──────────-┬──────────────┘         │ │
│  │                                               │                        │ │
│  │                                               │ Auto Import/Export     │ │
│  │                                               ▼                        │ │
│  │                                    ┌──────────────────────┐            │ │
│  │                                    │   Amazon S3 Bucket   │            │ │
│  │                                    │   (ml-platform/)     │            │ │
│  │                                    └──────────────────────┘            │ │
│  └────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                        EKS Cluster Components                               │
│                                                                             │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                      Core Infrastructure                               │ │
│  │  • Istio Service Mesh (ingress gateway, mTLS)                          │ │
│  │  • Cert Manager (TLS certificates)                                     │ │
│  │  • AWS Load Balancer Controller                                        │ │
│  │  • EBS/EFS/FSx CSI Drivers                                             │ │
│  │  • Cluster Autoscaler (CPU nodes)                                      │ │
│  │  • Karpenter (GPU/Neuron nodes)                                        │ │
│  │  • AWS EFA Device Plugin                                               │ │
│  │  • Nvidia Device Plugin / Neuron Device Plugin                         │ │
│  │  • Neuron Scheduler                                                    │ │
│  └────────────────────────────────────────────────────────────────────────┘ │
│                                                                             │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                    Authentication & Security                           │ │
│  │  • Dex (OIDC provider)                                                 │ │
│  │  • OAuth2 Proxy (authentication proxy)                                 │ │
│  │  • IRSA (IAM Roles for Service Accounts)                               │ │
│  └────────────────────────────────────────────────────────────────────────┘ │
│                                                                             │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                    ML Platform Components                              │ │
│  │  • MPI Operator (distributed training)                                 │ │
│  │  • Kubeflow Training Operator (PyTorchJob, TFJob, etc.)                │ │
│  │  • KubeRay Operator (Ray clusters)                                     │ │
│  │  • LeaderWorkerSet (LWS) for multi-node inference                      │ │
│  └────────────────────────────────────────────────────────────────────────┘ │
│                                                                             │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │              Optional Modular Components (Helm)                        │ │
│  │  • Kubeflow Platform (Notebooks, Pipelines, Katib, Dashboard)          │ │
│  │  • MLFlow (experiment tracking)                                        │ │
│  │  • Airflow (workflow orchestration)                                    │ │
│  │  • KServe (model serving)                                              │ │
│  │  • Kueue (job queueing)                                                │ │
│  │  • Slurm (HPC workload manager)                                        │ │
│  │  • Prometheus Stack (monitoring)                                       │ │
│  │  • DCGM Exporter (GPU metrics)                                         │ │
│  │  • ACK SageMaker Controller                                            │ │
│  └────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘

Key Architecture Components

Networking Layer

  • VPC: Custom VPC with public and private subnets across 3 availability zones
  • Internet Gateway: Outbound internet access for public subnets
  • NAT Gateway: Outbound internet access for private subnets
  • Security Groups: EFS, FSx for Lustre, and EKS cluster security groups

Compute Layer

  • EKS Control Plane: Managed Kubernetes control plane (v1.33)
  • System Node Group: CPU-only nodes for system workloads (Cluster Autoscaler managed)
  • GPU Node Groups: Nvidia GPU nodes with EFA support (Karpenter managed)
  • Neuron Node Groups: AWS Trainium/Inferentia nodes with EFA (Karpenter managed)
  • Launch Templates: Custom AMIs with EFA network interfaces and EBS volumes

Storage Layer

  • Amazon EFS: Shared file system for code, configurations, logs, and checkpoints
  • FSx for Lustre: High-performance file system for datasets and models
  • S3 Integration: FSx auto-imports/exports to S3 bucket (eventual consistency)
  • EBS Volumes: Optional per-pod EBS volumes via CSI driver

Auto-Scaling

  • Cluster Autoscaler: Scales CPU-only managed node groups (0 to max)
  • Karpenter: Scales GPU and Neuron nodes dynamically based on pod requirements
  • Node Provisioners: Separate provisioners for CUDA, CUDA+EFA, and Neuron workloads

Service Mesh & Ingress

  • Istio: Service mesh with mTLS, traffic management, and observability
  • Ingress Gateway: ClusterIP service with HTTP/HTTPS/TCP ports
  • Virtual Services: Route traffic to ML platform components

Authentication & Authorization

  • Dex: OpenID Connect (OIDC) identity provider
  • OAuth2 Proxy: Authentication proxy for web applications
  • IRSA: IAM roles for Kubernetes service accounts (S3, ECR, SageMaker access)
  • Kubeflow Profiles: Multi-tenant user namespaces with RBAC

ML Operators

  • MPI Operator: Distributed training with MPI (Horovod, etc.)
  • Training Operator: PyTorchJob, TFJob, MPIJob, etc.
  • KubeRay Operator: Ray cluster management for distributed compute
  • LeaderWorkerSet: Multi-node inference with leader election

Device Plugins

  • Nvidia Device Plugin: GPU resource management and scheduling
  • Neuron Device Plugin: Neuron core/device resource management
  • EFA Device Plugin: EFA network interface management
  • Neuron Scheduler: Custom scheduler for Neuron workloads

Infrastructure as Code

All infrastructure is defined in Terraform with modular components:

  • Main Module: VPC, EKS cluster, node groups, storage, IAM roles
  • Istio Module: Service mesh configuration
  • Kubeflow Module: ML platform components (optional)
  • MLFlow Module: Experiment tracking (optional)
  • Slurm Module: HPC workload manager (optional)

Deployment Flow

  1. Terraform Init: Initialize S3 backend for state management
  2. Terraform Apply: Deploy VPC, EKS, storage, and core components
  3. Helm Releases: Deploy ML operators, device plugins, and optional modules
  4. User Namespaces: Create user profiles with IRSA and PVCs
  5. ML Workloads: Deploy training/inference jobs via Helm charts

Modular Components

Enable optional components via Terraform variables:

Component Variable Default
Airflow airflow_enabled false
kagent kagent_enabled false
Karpenter ODCR (cudaefa only) karpenter_odcr_enabled false
Kubeflow kubeflow_platform_enabled false
KServe kserve_enabled false
Kueue kueue_enabled false
MLFlow mlflow_enabled false
Nvidia DCGM Exporter dcgm_exporter_enabled false
SageMaker Controller ack_sagemaker_enabled false
SageMaker HyperPod hyperpod_enabled false
Slinky Slurm slurm_enabled false

Inference Examples

For comprehensive inference examples across multiple serving platforms and accelerators, see Inference Examples README.

Supported serving platforms:

  • Ray Serve with vLLM
  • Triton Inference Server (vLLM, TensorRT-LLM, Python, Ray vLLM backends)
  • DJL Serving

Training Examples

For comprehensive training examples across multiple frameworks and accelerators, see Training Examples README.

Supported frameworks:

  • Hugging Face Accelerate
  • Nemo Megatron
  • Neuronx Distributed
  • Neuronx Distributed Training
  • Megatron-DeepSpeed
  • RayTrain

MCP Gateway Registry

Deploy Model Context Protocol (MCP) Gateway Registry for agentic AI applications:

Features:

  • OAuth authentication (GitHub, AWS Cognito)
  • MCP server registry and gateway
  • Financial information, time, and custom tool servers
  • Shared EFS storage for persistent state

kagent - Kubernetes Native AI Agents

kagent is a Kubernetes-native framework for building AI agents with tool capabilities and LLM integration.

Enable kagent:

terraform apply \
  -var="kagent_enabled=true"

Configuration Options:

  • kagent_version: Helm chart version (default: "0.7.11", pinned for stability - override to upgrade)
  • kagent_database_type: Choose "sqlite" (default, single replica) or "postgresql" (HA, multi-replica)
  • kagent_enable_ui: Enable web UI (default: true)
  • kagent_enable_istio_ingress: Expose UI via Istio ingress (default: false)
  • kagent_enable_bedrock_access: Enable IRSA for Amazon Bedrock access (default: false)

Access kagent UI:

# Port-forward (default)
kubectl port-forward -n kagent svc/kagent-ui 8080:8080

# Or via Terraform output
$(terraform output -raw kagent_ui_access_command)

LLM Integration Options:

kagent supports multiple LLM providers. You can use self-hosted models in EKS or cloud-based services.

Option 1: Self-Hosted Models in EKS (Recommended)

Deploy LLM serving solutions within the same EKS cluster:

# Example: Using vLLM for self-hosted models
apiVersion: kagent.dev/v1alpha2
kind: ModelConfig
metadata:
  name: llama-3-8b
  namespace: kagent
spec:
  provider: OpenAI  # vLLM provides OpenAI-compatible API
  model: meta-llama3-8b-instruct
  apiKeySecret: kagent-openai
  apiKeySecretKey: OPENAI_API_KEY
  openAI:
    baseUrl: http://vllm-service.inference.svc.cluster.local:8000/v1

See the examples/inference/ directory for deploying vLLM, Ray Serve, or Triton in EKS.

Option 2: OpenAI or Compatible APIs

A placeholder kagent-openai secret is automatically created. Update it with your OpenAI API key:

kubectl create secret generic kagent-openai \
  --from-literal=OPENAI_API_KEY=<your-openai-api-key> \
  -n kagent \
  --dry-run=client -o yaml | kubectl apply -f -

Then create a ModelConfig:

apiVersion: kagent.dev/v1alpha2
kind: ModelConfig
metadata:
  name: gpt-4
  namespace: kagent
spec:
  provider: OpenAI
  model: gpt-4
  apiKeySecret: kagent-openai
  apiKeySecretKey: OPENAI_API_KEY
  openAI:
    baseUrl: https://api.openai.com/v1

Option 3: Amazon Bedrock (Optional)

For AWS Bedrock integration, enable IRSA:

terraform apply \
  -var="kagent_enabled=true" \
  -var="kagent_enable_bedrock_access=true"

When enabled, an IAM role with Bedrock permissions is automatically created and attached to the kagent controller via IRSA.

apiVersion: kagent.dev/v1alpha2
kind: ModelConfig
metadata:
  name: claude-sonnet
  namespace: kagent
spec:
  provider: Bedrock
  model: anthropic.claude-3-5-sonnet-20241022-v2:0
  region: us-west-2

Note: The module automatically configures controller.serviceAccount.name=kagent-sa and controller.serviceAccount.create=false in the Helm values when Bedrock access is enabled.

High Availability:

For production deployments with multiple controller replicas:

terraform apply \
  -var="kagent_enabled=true" \
  -var="kagent_database_type=postgresql" \
  -var="kagent_controller_replicas=3"

Legacy Examples

See README.md

Architecture Concepts

Helm-Based Pipeline Execution

The solution uses Helm charts to execute discrete MLOps pipeline steps:

  1. Each pipeline step is a Helm chart installation with a YAML recipe (Helm values file)
  2. Steps execute within a single Helm Release for atomicity
  3. Charts are installed, executed, then uninstalled before the next step
  4. Can be orchestrated manually (Helm CLI), via Kubeflow Pipelines, or Apache Airflow

YAML Recipes

Common fields in Helm values files:

  • image: Docker container image
  • resources: Infrastructure requirements (CPU, memory, GPUs)
  • git: Code repository to clone (available in $GIT_CLONE_DIR)
  • inline_script: Arbitrary script definitions
  • pre_script: Executed after git clone, before job launch
  • train/process: Launch commands and arguments
  • post_script: Optional post-execution script
  • pvc: Persistent volume mounts (EFS at /efs, FSx at /fsx)
  • ebs: Optional EBS volume configuration

Storage Architecture

  • EFS: Code, configs, logs, training checkpoints
  • FSx for Lustre: Data, pre-trained models (auto-syncs with S3)
  • S3: Automatic backup of FSx content under ml-platform/ prefix
  • Eventual Consistency: FSx maintains eventual consistency with S3

Infrastructure Management

  • Karpenter: Auto-scales GPU and AI chip nodes (Nvidia, Trainium, Inferentia)
  • Cluster Autoscaler: Auto-scales CPU-only nodes
  • EKS Managed Node Groups: Automatic scaling from zero to required capacity

Kubeflow Platform (Optional)

When enabled, includes:

  • Kubeflow Notebooks
  • Kubeflow Tensorboard
  • Kubeflow Pipelines
  • Kubeflow Katib
  • Kubeflow Central Dashboard (v1.9.2)

Access dashboard via port-forwarding:

sudo kubectl port-forward svc/istio-ingressgateway -n ingress 443:443

Add to /etc/hosts:

127.0.0.1 istio-ingressgateway.ingress.svc.cluster.local

Login: [email protected] with static password from Terraform output

Cleanup

Before Destroying Infrastructure

  1. Verify S3 backup of important data
  2. Uninstall all Helm releases:
for x in $(helm list -q -n kubeflow-user-example-com); do 
  helm uninstall $x -n kubeflow-user-example-com
done
  1. Wait 5 minutes, then delete remaining pods:
kubectl delete --all pods -n kubeflow-user-example-com
  1. Delete attach-pvc pod:
kubectl delete -f eks-cluster/utils/attach-pvc.yaml -n kubeflow
  1. Wait 15 minutes for auto-scaling to zero

Destroy Infrastructure

This command should mirror your Apply Terraform command:

cd eks-cluster/terraform/aws-eks-cluster-and-nodegroup
# Replace <YOUR_S3_BUCKET> with your actual S3 bucket name
terraform destroy -var="profile=default" -var="region=us-west-2" \
  -var="cluster_name=my-eks-cluster" \
  -var='azs=["us-west-2a","us-west-2b","us-west-2c"]' \
  -var="import_path=s3://<YOUR_S3_BUCKET>/ml-platform/"

Supported Technologies

ML Frameworks

  • Hugging Face Accelerate
  • PyTorch Lightning
  • Nemo Megatron-LM
  • Megatron-DeepSpeed
  • Neuronx Distributed
  • Ray Train
  • TensorFlow

Inference Engines

  • vLLM (GPU & Neuron)
  • Ray Serve
  • Triton Inference Server
  • TensorRT-LLM
  • DJL Serving

Accelerators

  • Nvidia GPUs (P4d, G6, etc.)
  • AWS Trainium (Trn1)
  • AWS Inferentia (Inf2)
  • AWS EFA for distributed training

Utilities

Helper Scripts

# Install kubectl
./eks-cluster/utils/install-kubectl-linux.sh

# Configure S3 backend (replace <YOUR_S3_BUCKET> and <S3_PREFIX> with your values)
./eks-cluster/utils/s3-backend.sh <YOUR_S3_BUCKET> <S3_PREFIX>

# Prepare S3 bucket with COCO dataset (replace <YOUR_S3_BUCKET> with your value)
./eks-cluster/utils/prepare-s3-bucket.sh <YOUR_S3_BUCKET>

# Create EKS cluster with logging
./eks-cluster/terraform/aws-eks-cluster-and-nodegroup/create_eks.sh

# Destroy with retries
./eks-cluster/terraform/aws-eks-cluster-and-nodegroup/terraform_destroy_retry.sh

Resources

Contributing

See CONTRIBUTING.md for guidelines.

Security

See CONTRIBUTING.md for security issue notifications.

License

See LICENSE file.

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