From 33bbed95586037778708b869bf1afbb9acac6f4f Mon Sep 17 00:00:00 2001 From: shivkumr Date: Mon, 15 Jul 2024 21:11:43 -0400 Subject: [PATCH 1/8] deploy-monitor-genai-model lab first commit --- manifests/.workshop/terraform/outputs.tf | 3 +- manifests/manifests | 1 + .../.workshop/cleanup.sh | 29 + .../.workshop/terraform/main.tf | 335 +++++++ .../.workshop/terraform/output.tf | 12 + .../.workshop/terraform/values.yaml | 5 + .../.workshop/terraform/vars.tf | 42 + .../aiml/ingress.yaml | 28 + .../aiml/kustomization.yaml | 7 + .../aiml/namespace.yaml | 6 + .../aiml/service.yaml | 62 ++ .../genai-eks/kuberay-values.yaml | 2 + .../nvidia-dcgm-exporter-dashboard.json | 840 ++++++++++++++++++ .../ingress-values.yaml | 11 + .../jupyterhub/jupyter.ipynb | 220 +++++ .../jupyterhub/jupyter.py | 84 ++ .../jupyterhub/jupyterhub-configmap.yaml | 282 ++++++ .../jupyterhub/jupyterhub-values.yaml | 53 ++ .../karpenter/g5-gpu-nodepool-nodeclass.yaml | 60 ++ .../karpenter/kustomization.yaml | 5 + .../karpenter/x86-cpu-nodepool-nodeclass.yaml | 55 ++ .../otel/clusterrole.yaml | 29 + .../otel/clusterrolebinding.yaml | 12 + .../otel/kustomization.yaml | 7 + .../otel/namespace.yaml | 6 + .../otel/opentelemetrycollector.yaml | 114 +++ .../otel/serviceaccount.yaml | 6 + .../rayservice/ingress-nginx-values.yaml | 11 + .../rayservice/rayservice-ingress.yaml | 28 + .../rayservice/rayservice.yaml | 99 +++ policy.json | 10 + .../_category_.json | 3 + .../assets/GenAI-on-EKS.png | Bin 0 -> 153329 bytes .../assets/gpu-metrics-grafana.png | Bin 0 -> 165127 bytes .../assets/gpu-metrics-monitor.png | Bin 0 -> 123066 bytes .../assets/grafana-login.png | Bin 0 -> 177264 bytes .../assets/happy-pup.jpeg | Bin 0 -> 48795 bytes .../assets/huggingface-token.png | Bin 0 -> 114300 bytes .../assets/jupyterhub-login.png | Bin 0 -> 195544 bytes .../assets/upload-nb.png | Bin 0 -> 321695 bytes .../depoly-prerequesite.md | 132 +++ .../aiml/deploy-monitor-genai-model/index.md | 23 + .../jupyterhubsetup.md | 43 + .../modelinference.md | 93 ++ .../modeltraining.md | 61 ++ .../deploy-monitor-genai-model/monitorGPU.md | 106 +++ .../tests/hook-suite.sh | 11 + 47 files changed, 2935 insertions(+), 1 deletion(-) create mode 120000 manifests/manifests create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/output.tf create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/values.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/aiml/ingress.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/aiml/kustomization.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/aiml/namespace.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/aiml/service.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/genai-eks/kuberay-values.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/grafana/nvidia-dcgm-exporter-dashboard.json create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/ingress-values.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-configmap.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/karpenter/kustomization.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/rayservice/ingress-nginx-values.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice-ingress.yaml create mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml create mode 100644 policy.json create mode 100644 website/docs/aiml/deploy-monitor-genai-model/_category_.json create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/GenAI-on-EKS.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/gpu-metrics-grafana.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/gpu-metrics-monitor.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/grafana-login.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/happy-pup.jpeg create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/huggingface-token.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/jupyterhub-login.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/upload-nb.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/depoly-prerequesite.md create mode 100644 website/docs/aiml/deploy-monitor-genai-model/index.md create mode 100644 website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md create mode 100644 website/docs/aiml/deploy-monitor-genai-model/modelinference.md create mode 100644 website/docs/aiml/deploy-monitor-genai-model/modeltraining.md create mode 100644 website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-suite.sh diff --git a/manifests/.workshop/terraform/outputs.tf b/manifests/.workshop/terraform/outputs.tf index 7beb9d432..e119ce585 100644 --- a/manifests/.workshop/terraform/outputs.tf +++ b/manifests/.workshop/terraform/outputs.tf @@ -5,4 +5,5 @@ output "environment" { export ${k}='${v}' %{endfor} EOF -} +sensitive = true +} \ No newline at end of file diff --git a/manifests/manifests b/manifests/manifests new file mode 120000 index 000000000..c112814e8 --- /dev/null +++ b/manifests/manifests @@ -0,0 +1 @@ +/manifests \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh new file mode 100644 index 000000000..91d7d399e --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +kubectl delete ingress dogbooth -n dogbooth --ignore-not-found +kubectl delete rayservice dogbooth -n dogbooth --ignore-not-found +kubectl delete ns dogbooth --ignore-not-found + +helm uninstall jupyterhub -n jupyterhub +helm uninstall nginx-ingress +helm uninstall kuberay-operator + +# Uninstall gpu-operator +GPU_OPERATOR_RELEASE_NAME=$(helm list -n gpu-operator -q) +helm uninstall $GPU_OPERATOR_RELEASE_NAME -n gpu-operator + +delete-all-if-crd-exists nodepools.karpenter.sh +delete-all-if-crd-exists ec2nodeclasses.karpenter.k8s.aws + +EXIT_CODE=0 + +timeout --foreground -s TERM 30 bash -c \ + 'while [[ $(kubectl get nodes --selector=type=karpenter -o json | jq -r ".items | length") -gt 0 ]];\ + do sleep 5;\ + done' || EXIT_CODE=$? + +if [ $EXIT_CODE -ne 0 ]; then + logmessage "Warning: Karpenter nodes did not clean up" +fi + +uninstall-helm-chart karpenter karpenter diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf new file mode 100644 index 000000000..8a4ba41ea --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf @@ -0,0 +1,335 @@ +provider "aws" { + region = "us-east-1" + alias = "virginia" +} + +data "aws_caller_identity" "current" {} +data "aws_region" "current" {} +data "aws_ecrpublic_authorization_token" "token" { provider = aws.virginia } + +module "ebs_csi_driver_irsa" { + source = "terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks" + version = "~> 5.20" + + role_name_prefix = "${var.addon_context.eks_cluster_id}-ebs-csi-" + + attach_ebs_csi_policy = true + + oidc_providers = { + main = { + provider_arn = var.addon_context.eks_oidc_provider_arn + namespace_service_accounts = ["kube-system:ebs-csi-controller-sa"] + } + } + + tags = var.tags +} + +module "eks_blueprints_addons" { + source = "aws-ia/eks-blueprints-addons/aws" + version = "1.16.3" + + cluster_name = var.addon_context.eks_cluster_id + cluster_endpoint = var.addon_context.aws_eks_cluster_endpoint + cluster_version = var.eks_cluster_version + oidc_provider_arn = var.addon_context.eks_oidc_provider_arn + + eks_addons = { + aws-ebs-csi-driver = { + most_recent = true + service_account_role_arn = module.ebs_csi_driver_irsa.iam_role_arn + preserve = false + } + } + + enable_aws_load_balancer_controller = true + aws_load_balancer_controller = { + wait = true + } +} + +resource "aws_eks_addon" "pod_identity" { + cluster_name = var.addon_context.eks_cluster_id + addon_name = "eks-pod-identity-agent" + resolve_conflicts_on_create = "OVERWRITE" + preserve = false +} + +module "karpenter" { + source = "terraform-aws-modules/eks/aws//modules/karpenter" + version = "20.13.1" + + cluster_name = var.addon_context.eks_cluster_id + enable_pod_identity = true + create_pod_identity_association = true + namespace = "karpenter" + + node_iam_role_additional_policies = { + AmazonSSMManagedInstanceCore = "arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" + } +} + +resource "kubernetes_annotations" "disable_gp2" { + annotations = { + "storageclass.kubernetes.io/is-default-class" : "false" + } + api_version = "storage.k8s.io/v1" + kind = "StorageClass" + metadata { + name = "gp2" + } + force = true + +} + +resource "kubernetes_storage_class" "default_gp3" { + metadata { + name = "gp3" + annotations = { + "storageclass.kubernetes.io/is-default-class" : "true" + } + } + + storage_provisioner = "ebs.csi.aws.com" + reclaim_policy = "Delete" + allow_volume_expansion = true + volume_binding_mode = "WaitForFirstConsumer" + parameters = { + fsType = "ext4" + encrypted = true + type = "gp3" + } + + depends_on = [kubernetes_annotations.disable_gp2] +} + + +resource "aws_prometheus_workspace" "eks_workshop_v2_amp" { + alias = var.addon_context.eks_cluster_id +} + +resource "time_sleep" "blueprints_addons_sleep" { + depends_on = [ + module.eks_blueprints_addons + ] + create_duration = "10s" +} + + + + + +data "aws_subnets" "private" { + tags = { + created-by = "eks-workshop-v2" + env = var.addon_context.eks_cluster_id + } + filter { + name = "tag:Name" + values = ["*Private*"] + } +} + +resource "aws_prometheus_scraper" "agentless_scraper" { + source { + eks { + cluster_arn = "arn:aws:eks:${data.aws_region.current.name}:${data.aws_caller_identity.current.account_id}:cluster/${var.eks_cluster_id}" + subnet_ids = data.aws_subnets.private.ids + } + } + destination { + amp { + workspace_arn = aws_prometheus_workspace.eks_workshop_v2_amp.arn + } + } + scrape_configuration = < id + }) + sensitive = true +} \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/values.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/values.yaml new file mode 100644 index 000000000..1a9d9d6eb --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/values.yaml @@ -0,0 +1,5 @@ +service: + type: LoadBalancer + annotations: + service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing + service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: instance diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf new file mode 100644 index 000000000..2d0a20b31 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf @@ -0,0 +1,42 @@ +# tflint-ignore: terraform_unused_declarations +variable "eks_cluster_id" { + description = "EKS cluster name" + type = string +} + +# tflint-ignore: terraform_unused_declarations +variable "eks_cluster_version" { + description = "EKS cluster version" + type = string +} + +# tflint-ignore: terraform_unused_declarations +variable "cluster_security_group_id" { + description = "EKS cluster security group ID" + type = any +} + +# tflint-ignore: terraform_unused_declarations +variable "addon_context" { + description = "Addon context that can be passed directly to blueprints addon modules" + type = any +} + +# tflint-ignore: terraform_unused_declarations +variable "tags" { + description = "Tags to apply to AWS resources" + type = any +} + +# tflint-ignore: terraform_unused_declarations +variable "resources_precreated" { + description = "Have expensive resources been created already" + type = bool +} + +variable "karpenter_version" { + description = "The version of Karpenter to use" + type = string + # renovate: datasource=github-releases depName=aws/karpenter-provider-aws + default = "0.35.4" +} \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/aiml/ingress.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/ingress.yaml new file mode 100644 index 000000000..a0159241a --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/ingress.yaml @@ -0,0 +1,28 @@ +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: dogbooth + namespace: dogbooth + annotations: + nginx.ingress.kubernetes.io/rewrite-target: "/$1" +spec: + ingressClassName: nginx + rules: + - http: + paths: + # Ray Dashboard + - path: /dogbooth/(.*) + pathType: ImplementationSpecific + backend: + service: + name: dogbooth-head-svc + port: + number: 8265 + # Ray Serve + - path: /dogbooth/serve/(.*) + pathType: ImplementationSpecific + backend: + service: + name: dogbooth-head-svc + port: + number: 8000 \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/aiml/kustomization.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/kustomization.yaml new file mode 100644 index 000000000..472526122 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/kustomization.yaml @@ -0,0 +1,7 @@ +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +namespace: dogbooth +resources: + - namespace.yaml + - service.yaml + - ingress.yaml diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/aiml/namespace.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/namespace.yaml new file mode 100644 index 000000000..85c9f576c --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/namespace.yaml @@ -0,0 +1,6 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: dogbooth + labels: + app.kubernetes.io/created-by: eks-workshop diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/aiml/service.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/service.yaml new file mode 100644 index 000000000..e87fdd204 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/aiml/service.yaml @@ -0,0 +1,62 @@ +apiVersion: ray.io/v1alpha1 +kind: RayService +metadata: + name: dogbooth + namespace: dogbooth +spec: + serviceUnhealthySecondThreshold: 600 + deploymentUnhealthySecondThreshold: 600 + serveConfig: + importPath: dogbooth:entrypoint + runtimeEnv: | + env_vars: {"MODEL_ID": "sguruvar/dogbooth"} + rayClusterConfig: + rayVersion: '2.6.0' + headGroupSpec: + rayStartParams: + dashboard-host: '0.0.0.0' + template: + spec: + containers: + - name: ray-head + image: public.ecr.aws/data-on-eks/dogbooth:0.0.1-gpu + resources: + limits: + cpu: 2 + memory: 16Gi + requests: + cpu: 2 + memory: 16Gi + ports: + - containerPort: 6379 + name: gcs-server + - containerPort: 8265 + name: dashboard + - containerPort: 10001 + name: client + - containerPort: 8000 + name: serve + workerGroupSpecs: + - replicas: 1 + minReplicas: 1 + maxReplicas: 5 + rayStartParams: {} + groupName: small-group + template: + spec: + containers: + - name: ray-worker + image: public.ecr.aws/data-on-eks/dogbooth:0.0.1-gpu + lifecycle: + preStop: + exec: + command: ["/bin/sh","-c","ray stop"] + resources: + limits: + cpu: "2" + memory: "16Gi" + nvidia.com/gpu: 1 + requests: + cpu: "2" + memory: "16Gi" + nvidia.com/gpu: 1 \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/genai-eks/kuberay-values.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/genai-eks/kuberay-values.yaml new file mode 100644 index 000000000..c839d3047 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/genai-eks/kuberay-values.yaml @@ -0,0 +1,2 @@ +batchScheduler: + enabled: true diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/grafana/nvidia-dcgm-exporter-dashboard.json b/manifests/modules/aiml/deploy-monitor-genai-model/grafana/nvidia-dcgm-exporter-dashboard.json new file mode 100644 index 000000000..9b28450be --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/grafana/nvidia-dcgm-exporter-dashboard.json @@ -0,0 +1,840 @@ +{ + "__inputs": [ + { + "name": "DS_PROMETHEUS", + "label": "Prometheus", + "description": "", + "type": "datasource", + "pluginId": "prometheus", + "pluginName": "Prometheus" + } + ], + "__requires": [ + { + "type": "panel", + "id": "gauge", + "name": "Gauge", + "version": "" + }, + { + "type": "grafana", + "id": "grafana", + "name": "Grafana", + "version": "6.7.3" + }, + { + "type": "panel", + "id": "graph", + "name": "Graph", + "version": "" + }, + { + "type": "datasource", + "id": "prometheus", + "name": "Prometheus", + "version": "1.0.0" + } + ], + "annotations": { + "list": [ + { + "$$hashKey": "object:192", + "builtIn": 1, + "datasource": "-- Grafana --", + "enable": true, + "hide": true, + "iconColor": "rgba(0, 211, 255, 1)", + "name": "Annotations & Alerts", + "type": "dashboard" + } + ] + }, + "description": "This dashboard is to display the metrics from DCGM 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gpu=~\"${gpu}\"}", + "instant": false, + "interval": "", + "legendFormat": "GPU {{gpu}}", + "refId": "A" + } + ], + "thresholds": [], + "timeFrom": null, + "timeRegions": [], + "timeShift": null, + "title": "GPU Temperature", + "tooltip": { + "shared": true, + "sort": 0, + "value_type": "individual" + }, + "type": "graph", + "xaxis": { + "buckets": null, + "mode": "time", + "name": null, + "show": true, + "values": [] + }, + "yaxes": [ + { + "format": "celsius", + "label": null, + "logBase": 1, + "max": null, + "min": null, + "show": true + }, + { + "format": "short", + "label": null, + "logBase": 1, + "max": null, + "min": null, + "show": true + } + ], + "yaxis": { + "align": false, + "alignLevel": null + } + }, + { + "datasource": { + "type": "prometheus", + "uid": "PBFA97CFB590B2093" + }, + "gridPos": { + "h": 8, + "w": 6, + "x": 18, + "y": 0 + }, + "id": 14, + "options": { + "fieldOptions": { + "calcs": [ + "mean" + ], + "defaults": { + "color": { + "mode": "thresholds" + }, + 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\ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb new file mode 100644 index 000000000..21b942b1e --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "54217ee5-4e5a-418f-a850-7a096183f85c", + "metadata": {}, + "outputs": [], + "source": [ + "# Verify NVIDIA GPU is visible\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f2855eb-4cc0-42ca-b24e-79403344f82d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir(\"/home/jovyan\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8526f6cf-d94e-4ef6-a870-b5cf2b7b3bbd", + "metadata": {}, + "outputs": [], + "source": [ + "# Clone the diffusers repo\n", + "!git clone https://github.com/huggingface/diffusers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "146c939b-9f67-4f9b-ab0a-d56f579ef574", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the directory\n", + "os.chdir(\"diffusers\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3114141-0dfe-4521-8f74-10210e307ade", + "metadata": {}, + "outputs": [], + "source": [ + "# Install requirements\n", + "! pip install -e .\n", + "! pip install xformers==0.0.16 diffusers[torch]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40923ba1-5e9c-452a-9375-82e87e986c8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Fix for bitsandbytes https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md\n", + "! wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh\n", + "! bash install_cuda.sh 117 ~/local 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b355fa8f-e2cb-4ef4-bce2-64739daef7db", + "metadata": {}, + "outputs": [], + "source": [ + "# Install bitsandbytes for optimizations\n", + "! pip install bitsandbytes==0.41.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "871f97d8-ffeb-4b3d-8f69-ef8590ca3c16", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the newly installed CUDA version for bitsandbytes\n", + "os.environ[\"BNB_CUDA_VERSION\"] = \"117\"\n", + "os.environ[\"LD_LIBRARY_PATH\"] = os.getenv(\"LD_LIBRARY_PATH\") + \":/home/jovyan/local/cuda-11.7\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0271afa3-1149-47f2-8269-3c28913659db", + "metadata": {}, + "outputs": [], + "source": [ + "# Validate successful install of bitsandbytes\n", + "! python -m bitsandbytes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263054f2-aeae-4efb-8143-6e58a59581e8", + "metadata": {}, + "outputs": [], + "source": [ + "# Install requirements for dreambooth\n", + "os.chdir(\"examples/dreambooth\")\n", + "! pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815e74fa-24a5-47c7-8b4f-63cba3fc1fc9", + "metadata": {}, + "outputs": [], + "source": [ + "# Setup default configuration for accelerate\n", + "! accelerate config default" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d67262c-8c11-4f32-951c-d4a05347b94a", + "metadata": {}, + "outputs": [], + "source": [ + "# Login to huggingface associated with your account (please create one if it doesn't exist)\n", + "! huggingface-cli login --token " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7aa7aecf-98c5-43b9-adce-5ffbe697dbfa", + "metadata": {}, + "outputs": [], + "source": [ + "# Download sample dataset of the subject. See the sample images here https://huggingface.co/datasets/diffusers/dog-example\n", + "from huggingface_hub import snapshot_download\n", + "local_dir = \"./dog\"\n", + "snapshot_download(\n", + " \"diffusers/dog-example\",\n", + " local_dir=local_dir, repo_type=\"dataset\",\n", + " ignore_patterns=\".gitattributes\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "712c8d7b-c316-411c-b7b8-e3d20db87a9b", + "metadata": {}, + "outputs": [], + "source": [ + "# Export environment variables to provide input model, dataset directory and output directory for the tuned model\n", + "os.environ[\"MODEL_NAME\"] = \"stabilityai/stable-diffusion-2-1\"\n", + "os.environ[\"INSTANCE_DIR\"] = \"dog\"\n", + "os.environ[\"OUTPUT_DIR\"] = \"dogbooth\"\n", + "os.environ[\"RESOLUTION\"] = \"768\"\n", + "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"garbage_collection_threshold:0.6,max_split_size_mb:128\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef9da088-63ba-4e0b-9537-abb1bdacd6c2", + "metadata": {}, + "outputs": [], + "source": [ + "# Launch the training and push the output model to huggingface\n", + "! accelerate launch train_dreambooth.py \\\n", + " --pretrained_model_name_or_path=$MODEL_NAME \\\n", + " --instance_data_dir=$INSTANCE_DIR \\\n", + " --output_dir=$OUTPUT_DIR \\\n", + " --instance_prompt=\"a photo of [v]dog\" \\\n", + " --resolution=768 \\\n", + " --train_batch_size=1 \\\n", + " --gradient_accumulation_steps=1 \\\n", + " --gradient_checkpointing \\\n", + " --learning_rate=1e-6 \\\n", + " --lr_scheduler=\"constant\" \\\n", + " --enable_xformers_memory_efficient_attention \\\n", + " --use_8bit_adam \\\n", + " --lr_warmup_steps=0 \\\n", + " --max_train_steps=800 \\\n", + " --push_to_hub\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py new file mode 100644 index 000000000..1ea36237c --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py @@ -0,0 +1,84 @@ +# Verify NVIDIA GPU is visible +!nvidia-smi + + +import os +os.chdir("/home/jovyan") + + +# Clone the diffusers repo +!git clone https://github.com/huggingface/diffusers + +# Change the directory +os.chdir("diffusers") + +# Install requirements +! pip install -e . +! pip install xformers==0.0.16 diffusers[torch] + + +# Fix for bitsandbytes https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md +! wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/cuda_install.sh +! bash cuda_install.sh 117 ~/local 1 + + +# Install bitsandbytes for optimizations +! pip install bitsandbytes==0.41.0 + +# Use the newly installed CUDA version for bitsandbytes +os.environ["BNB_CUDA_VERSION"] = "117" +os.environ["LD_LIBRARY_PATH"] = os.getenv("LD_LIBRARY_PATH") + ":/home/jovyan/local/cuda-11.7" + +# Validate successful install of bitsandbytes +! python -m bitsandbytes + +# Install requirements for dreambooth +os.chdir("examples/dreambooth") +! pip install -r requirements.txt + + + +# Setup default configuration for accelerate +! accelerate config default + +# Login to huggingface associated with your account (please create one if it doesn't exist) +! huggingface-cli login --token + +# Download sample dataset of the subject. See the sample images here https://huggingface.co/datasets/diffusers/dog-example +from huggingface_hub import snapshot_download + +local_dir = "./dog" +snapshot_download( + "diffusers/dog-example", + local_dir=local_dir, repo_type="dataset", + ignore_patterns=".gitattributes", +) + +# Export environment variables to provide input model, dataset directory and output directory for the tuned model +os.environ["MODEL_NAME"] = "stabilityai/stable-diffusion-2-1" +os.environ["INSTANCE_DIR"] = "dog" +os.environ["OUTPUT_DIR"] = "dogbooth" +os.environ["RESOLUTION"] = "768" +os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "garbage_collection_threshold:0.6,max_split_size_mb:128" + + + +# Launch the training and push the output model to huggingface +! accelerate launch train_dreambooth.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --output_dir=$OUTPUT_DIR \ + --instance_prompt="a photo of [v]dog" \ + --resolution=768 \ + --train_batch_size=1 \ + --gradient_accumulation_steps=1 \ + --gradient_checkpointing \ + --learning_rate=1e-6 \ + --lr_scheduler="constant" \ + --enable_xformers_memory_efficient_attention \ + --use_8bit_adam \ + --lr_warmup_steps=0 \ + --max_train_steps=800 \ + --push_to_hub + + diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-configmap.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-configmap.yaml new file mode 100644 index 000000000..e090a87d2 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-configmap.yaml @@ -0,0 +1,282 @@ +apiVersion: v1 +kind: ConfigMap +metadata: + name: notebook + namespace: jupyterhub +data: + jupyterhub-notebook.ipynb: | + { + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Verify NVIDIA GPU is visible\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "import os\n", + "os.chdir(\"/home/jovyan\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Clone the diffusers repo\n", + "!git clone https://github.com/huggingface/diffusers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Change the directory\n", + "os.chdir(\"diffusers\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Install requirements\n", + "! pip install -e .\n", + "! pip install xformers==0.0.16 diffusers[torch]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Fix for bitsandbytes https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md\n", + "! wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh\n", + "! bash install_cuda.sh 117 ~/local 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Install bitsandbytes for optimizations\n", + "! pip install bitsandbytes==0.41.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Use the newly installed CUDA version for bitsandbytes\n", + "os.environ[\"BNB_CUDA_VERSION\"] = \"117\"\n", + "os.environ[\"LD_LIBRARY_PATH\"] = os.getenv(\"LD_LIBRARY_PATH\") + \":/home/jovyan/local/cuda-11.7\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Validate successful install of bitsandbytes\n", + "! python -m bitsandbytes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Install requirements for dreambooth\n", + "os.chdir(\"examples/dreambooth\")\n", + "! pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Setup default configuration for accelerate\n", + "! accelerate config default" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Login to huggingface associated with your account (please create one if it doesn't exist)\n", + "! huggingface-cli login --token " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Download sample dataset of the subject. See the sample images here https://huggingface.co/datasets/diffusers/dog-example\n", + "from huggingface_hub import snapshot_download\n", + "\n", + "local_dir = \"./dog\"\n", + "snapshot_download(\n", + " \"diffusers/dog-example\",\n", + " local_dir=local_dir, repo_type=\"dataset\",\n", + " ignore_patterns=\".gitattributes\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Export environment variables to provide input model, dataset directory and output directory for the tuned model\n", + "os.environ[\"MODEL_NAME\"] = \"stabilityai/stable-diffusion-2-1\"\n", + "os.environ[\"INSTANCE_DIR\"] = \"dog\"\n", + "os.environ[\"OUTPUT_DIR\"] = \"dogbooth\"\n", + "os.environ[\"RESOLUTION\"] = \"768\"\n", + "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"garbage_collection_threshold:0.6,max_split_size_mb:128\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Launch the training and push the output model to huggingface\n", + "! accelerate launch train_dreambooth.py \\\n", + " --pretrained_model_name_or_path=$MODEL_NAME \\\n", + " --instance_data_dir=$INSTANCE_DIR \\\n", + " --output_dir=$OUTPUT_DIR \\\n", + " --instance_prompt=\"a photo of [v]dog\" \\\n", + " --resolution=768 \\\n", + " --train_batch_size=1 \\\n", + " --gradient_accumulation_steps=1 \\\n", + " --gradient_checkpointing \\\n", + " --learning_rate=1e-6 \\\n", + " --lr_scheduler=\"constant\" \\\n", + " --enable_xformers_memory_efficient_attention \\\n", + " --use_8bit_adam \\\n", + " --lr_warmup_steps=0 \\\n", + " --max_train_steps=800 \\\n", + " --push_to_hub" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Run a sample inference\n", + "from diffusers import StableDiffusionPipeline\n", + "import torch\n", + "\n", + "model_id = \"./dogbooth\"\n", + "pipe = StableDiffusionPipeline.from_pretrained(model_id).to(\"cuda\")\n", + "\n", + "prompt = \"a photo of [v]dog on the moon\"\n", + "image = pipe(prompt, num_inference_steps=100, guidance_scale=7.5).images[0]\n", + "\n", + "image.save(\"dog-bucket.png\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 + } diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml new file mode 100644 index 000000000..b30032a62 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml @@ -0,0 +1,53 @@ +hub: + config: + Authenticator: + admin_users: + - admin1 + allowed_users: + - user1 + # testing only - do not do this for production + DummyAuthenticator: + password: never-do-this + JupyterHub: + authenticator_class: dummy +proxy: + service: + annotations: + service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip + service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing # Private Load Balancer can only be accessed within the VPC + service.beta.kubernetes.io/aws-load-balancer-type: external + service.beta.kubernetes.io/aws-load-balancer-cross-zone-load-balancing-enabled: 'true' + service.beta.kubernetes.io/aws-load-balancer-ip-address-type: ipv4 +singleuser: + image: + name: public.ecr.aws/h3o5n2r0/gpu-jupyter + tag: v1.5_cuda-11.6_ubuntu-20.04_python-only + pullPolicy: Always + cmd: null + startTimeout: 600 + memory: + guarantee: 12G + extraResource: + limits: + nvidia.com/gpu: "1" + storage: + capacity: 100Gi + extraVolumes: + - name: shm-volume + emptyDir: + medium: Memory + - name: notebook + configMap: + name: notebook + extraVolumeMounts: + - name: shm-volume + mountPath: /dev/shm + - name: notebook + mountPath: /home/jovyan/jupyterhub-notebook.ipynb + extraTolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule +scheduling: + userScheduler: + enabled: false \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml new file mode 100644 index 000000000..d1bf39511 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml @@ -0,0 +1,60 @@ +apiVersion: karpenter.sh/v1beta1 +kind: NodePool +metadata: + name: g5-gpu-karpenter + labels: + type: karpenter + NodeGroupType: g5-gpu-karpenter +spec: + template: + spec: + nodeClassRef: + apiVersion: karpenter.k8s.aws/v1beta1 + kind: EC2NodeClass + name: g5-gpu-karpenter + taints: + - key: "nvidia.com/gpu" + value: "true" + effect: "NoSchedule" + requirements: + - key: "karpenter.k8s.aws/instance-family" + operator: In + values: ["g5"] + - key: "karpenter.k8s.aws/instance-size" + operator: In + values: [ "2xlarge", "4xlarge", "8xlarge" ] + - key: "kubernetes.io/arch" + operator: In + values: ["amd64"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["on-demand"] + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmpty + consolidateAfter: 300s + expireAfter: 720h + weight: 100 + + +--- + +apiVersion: karpenter.k8s.aws/v1beta1 +kind: EC2NodeClass +metadata: + name: g5-gpu-karpenter +spec: + amiFamily: Ubuntu + role: "${KARPENTER_ROLE}" + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: "eks-workshop" + securityGroupSelectorTerms: + - tags: + karpenter.sh/discovery: "eks-workshop" + blockDeviceMappings: + - deviceName: /dev/sda1 + ebs: + volumeSize: 100Gi + volumeType: gp3 \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/kustomization.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/kustomization.yaml new file mode 100644 index 000000000..6d99e5699 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/kustomization.yaml @@ -0,0 +1,5 @@ +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +resources: + - g5-gpu-nodepool-nodeclass.yaml + - x86-cpu-nodepool-nodeclass.yaml diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml new file mode 100644 index 000000000..b80b9b1ac --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml @@ -0,0 +1,55 @@ +apiVersion: karpenter.sh/v1beta1 +kind: NodePool +metadata: + name: x86-cpu-karpenter + labels: + type: karpenter + NodeGroupType: x86-cpu-karpenter +spec: + template: + spec: + nodeClassRef: + apiVersion: karpenter.k8s.aws/v1beta1 + kind: EC2NodeClass + name: x86-cpu-karpenter + requirements: + - key: "karpenter.k8s.aws/instance-family" + operator: In + values: ["m5"] + - key: "karpenter.k8s.aws/instance-size" + operator: In + values: [ "xlarge", "2xlarge", "4xlarge", "8xlarge"] + - key: "kubernetes.io/arch" + operator: In + values: ["amd64"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["on-demand"] + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmpty + consolidateAfter: 300s + expireAfter: 720h + weight: 100 + +--- + +apiVersion: karpenter.k8s.aws/v1beta1 +kind: EC2NodeClass +metadata: + name: x86-cpu-karpenter +spec: + amiFamily: AL2 # Amazon Linux 2 + role: ${KARPENTER_ROLE} + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: "eks-workshop" + securityGroupSelectorTerms: + - tags: + karpenter.sh/discovery: "eks-workshop" + blockDeviceMappings: + - deviceName: /dev/xvda + ebs: + volumeSize: 100Gi + volumeType: gp3 \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml new file mode 100644 index 000000000..4bb1fb762 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml @@ -0,0 +1,29 @@ +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRole +metadata: + name: otel-prometheus-role +rules: + - apiGroups: + - "" + resources: + - nodes + - nodes/proxy + - services + - endpoints + - pods + verbs: + - get + - list + - watch + - apiGroups: + - extensions + resources: + - ingresses + verbs: + - get + - list + - watch + - nonResourceURLs: + - /metrics + verbs: + - get diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml new file mode 100644 index 000000000..a5c187a1f --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml @@ -0,0 +1,12 @@ +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRoleBinding +metadata: + name: otel-prometheus-role-binding +roleRef: + apiGroup: rbac.authorization.k8s.io + kind: ClusterRole + name: otel-prometheus-role +subjects: + - kind: ServiceAccount + name: adot-collector + namespace: other diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml new file mode 100644 index 000000000..f3e3283c3 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml @@ -0,0 +1,7 @@ +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +namespace: checkout +resources: + - namespace.yaml + - service.yaml + - ingress.yaml \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml new file mode 100644 index 000000000..85c9f576c --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml @@ -0,0 +1,6 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: dogbooth + labels: + app.kubernetes.io/created-by: eks-workshop diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml new file mode 100644 index 000000000..2e9822c73 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml @@ -0,0 +1,114 @@ +apiVersion: opentelemetry.io/v1alpha1 +kind: OpenTelemetryCollector +metadata: + name: adot + namespace: adot-col +spec: + image: public.ecr.aws/aws-observability/aws-otel-collector:v0.21.0 + mode: deployment + serviceAccount: adot-collector + config: | + receivers: + prometheus: + config: + global: + scrape_interval: 60s + scrape_timeout: 15s + external_labels: + cluster: ${EKS_CLUSTER_NAME} + account_id: ${AWS_ACCOUNT_ID} + region: ${AWS_REGION} + scrape_configs: + - job_name: 'kubernetes-kubelet' + scrape_interval: 60s + scrape_timeout: 15s + scheme: https + tls_config: + ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt + insecure_skip_verify: true + bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token + kubernetes_sd_configs: + - role: node + relabel_configs: + - action: labelmap + regex: __meta_kubernetes_node_label_(.+) + - target_label: __address__ + replacement: kubernetes.default.svc.cluster.local:443 + - source_labels: [__meta_kubernetes_node_name] + regex: (.+) + target_label: __metrics_path__ + replacement: /api/v1/nodes/$${1}/proxy/metrics + - job_name: 'kubelet' + scheme: https + tls_config: + ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt + insecure_skip_verify: true + bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token + kubernetes_sd_configs: + - role: node + relabel_configs: + - action: labelmap + regex: __meta_kubernetes_node_label_(.+) + - target_label: __address__ + replacement: kubernetes.default.svc.cluster.local:443 + - source_labels: [__meta_kubernetes_node_name] + regex: (.+) + target_label: __metrics_path__ + replacement: /api/v1/nodes/$${1}/proxy/metrics/cadvisor + - job_name: 'kubernetes-pods' + honor_labels: true + + kubernetes_sd_configs: + - role: pod + + relabel_configs: + - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape] + action: keep + regex: true + - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape_slow] + action: drop + regex: true + - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scheme] + action: replace + regex: (https?) + target_label: __scheme__ + - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path] + action: replace + target_label: __metrics_path__ + regex: (.+) + - action: labelmap + regex: __meta_kubernetes_pod_annotation_prometheus_io_param_(.+) + replacement: __param_$1 + - action: labelmap + regex: __meta_kubernetes_pod_label_(.+) + - source_labels: [__meta_kubernetes_namespace] + action: replace + target_label: namespace + - source_labels: [__meta_kubernetes_pod_name] + action: replace + target_label: pod + - source_labels: [__meta_kubernetes_pod_phase] + regex: Pending|Succeeded|Failed|Completed + action: drop + exporters: + prometheusremotewrite: + endpoint: ${AMP_ENDPOINT}api/v1/remote_write + auth: + authenticator: sigv4auth + logging: + loglevel: info + extensions: + sigv4auth: + region: ${AWS_REGION} + service: aps + health_check: + pprof: + endpoint: :1888 + zpages: + endpoint: :55679 + service: + extensions: [pprof, zpages, health_check, sigv4auth] + pipelines: + metrics: + receivers: [prometheus] + exporters: [logging, prometheusremotewrite] diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml new file mode 100644 index 000000000..3890415d5 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml @@ -0,0 +1,6 @@ +apiVersion: v1 +kind: ServiceAccount +metadata: + name: adot-collector + annotations: + eks.amazonaws.com/role-arn: ${ADOT_IAM_ROLE} diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/ingress-nginx-values.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/ingress-nginx-values.yaml new file mode 100644 index 000000000..9dc8c13e3 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/ingress-nginx-values.yaml @@ -0,0 +1,11 @@ +controller: + service: + externalTrafficPolicy: "Local" + annotations: + service.beta.kubernetes.io/aws-load-balancer-type: external + service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip + service.beta.kubernetes.io/aws-load-balancer-backend-protocol: http + service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing + targetPorts: + http: http + https: http \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice-ingress.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice-ingress.yaml new file mode 100644 index 000000000..a0159241a --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice-ingress.yaml @@ -0,0 +1,28 @@ +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: dogbooth + namespace: dogbooth + annotations: + nginx.ingress.kubernetes.io/rewrite-target: "/$1" +spec: + ingressClassName: nginx + rules: + - http: + paths: + # Ray Dashboard + - path: /dogbooth/(.*) + pathType: ImplementationSpecific + backend: + service: + name: dogbooth-head-svc + port: + number: 8265 + # Ray Serve + - path: /dogbooth/serve/(.*) + pathType: ImplementationSpecific + backend: + service: + name: dogbooth-head-svc + port: + number: 8000 \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml new file mode 100644 index 000000000..373e89cc6 --- /dev/null +++ b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml @@ -0,0 +1,99 @@ +--- +apiVersion: v1 +kind: Namespace +metadata: + name: dogbooth +--- +apiVersion: ray.io/v1alpha1 +kind: RayService +metadata: + name: dogbooth + namespace: dogbooth +spec: + serviceUnhealthySecondThreshold: 600 + deploymentUnhealthySecondThreshold: 600 + serveConfig: + importPath: dogbooth:entrypoint + runtimeEnv: | + env_vars: {"MODEL_ID": "askulkarni2/dogbooth"} + rayClusterConfig: + rayVersion: '2.6.0' + headGroupSpec: + rayStartParams: + dashboard-host: '0.0.0.0' + template: + spec: + containers: + - name: ray-head + image: public.ecr.aws/h3o5n2r0/dogbooth:0.0.1-gpu + resources: + limits: + cpu: 2 + memory: 8Gi + requests: + cpu: 2 + memory: 8Gi + ports: + - containerPort: 6379 + name: gcs-server + - containerPort: 8265 + name: dashboard + - containerPort: 10001 + name: client + - containerPort: 8000 + name: serve + workerGroupSpecs: + - replicas: 1 + minReplicas: 1 + maxReplicas: 5 + rayStartParams: {} + groupName: small-group + template: + spec: + containers: + - name: ray-worker + image: public.ecr.aws/h3o5n2r0/dogbooth:0.0.1-gpu + lifecycle: + preStop: + exec: + command: ["/bin/sh","-c","ray stop"] + resources: + limits: + cpu: "2" + memory: "12Gi" + nvidia.com/gpu: 1 + requests: + cpu: "2" + memory: "12Gi" + nvidia.com/gpu: 1 +--- + + +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: dogbooth + namespace: dogbooth + annotations: + nginx.ingress.kubernetes.io/rewrite-target: "/$1" +spec: + ingressClassName: nginx + rules: + - http: + paths: + # Ray Dashboard + - path: /dogbooth/(.*) + pathType: ImplementationSpecific + backend: + service: + name: dogbooth-head-svc + port: + number: 8265 + # Ray Serve + - path: /dogbooth/serve/(.*) + pathType: ImplementationSpecific + backend: + service: + name: dogbooth-head-svc + port: + number: 8000 diff --git a/policy.json b/policy.json new file mode 100644 index 000000000..f3be3e4f2 --- /dev/null +++ b/policy.json @@ -0,0 +1,10 @@ +{ + "Version": "2012-10-17", + "Statement": [ + { + "Effect": "Allow", + "Action": "sts:AssumeRole", + "Resource": "arn:aws:iam::981056732380:role/Admin" + } + ] +} diff --git a/website/docs/aiml/deploy-monitor-genai-model/_category_.json b/website/docs/aiml/deploy-monitor-genai-model/_category_.json new file mode 100644 index 000000000..9e3a55f45 --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/_category_.json @@ -0,0 +1,3 @@ +{ + "collapsed": true +} diff --git 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+sidebar_custom_props: { "module": true } +description: "Install Karpenter and Ray Operator" +--- + +{{% required-time %}} + +:::tip Before you start +Prepare your environment for this section: + +```bash timeout=1800 wait=30 +$ prepare-environment aiml/deploy-monitor-genai-model +``` + +This will make the following changes to your lab environment: + + - Creates EKS cluster + - Install EBS CSI Driver and AWS Load Balancer controller + - Installs Amazon Managed Prometheus, Grafana and AWS managed collector + - Prerequisite IAM roles for Karpenter +::: + + +# Install Karpenter + +The first thing we'll do is install Karpenter in our cluster. Various pre-requisites were created during the lab preparation stage, including: + +1. An IAM role for Karpenter to call AWS APIs +2. An IAM role and instance profile for the EC2 instances that Karpenter creates +3. An EKS cluster access entry for the node IAM role so the nodes can join the EKS cluster +4. An SQS queue for Karpenter to receive Spot interruption, instance re-balance and other events + +You can find the full installation documentation for Karpenter [here](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/). + +All that we have left to do is install Karpenter as a helm chart: + +```bash timeout=1800 wait=30 +$ helm upgrade --install karpenter oci://public.ecr.aws/karpenter/karpenter \ + --version "${KARPENTER_VERSION}" \ + --namespace "karpenter" --create-namespace \ + --set "settings.clusterName=${EKS_CLUSTER_NAME}" \ + --set "settings.interruptionQueue=${KARPENTER_SQS_QUEUE}" \ + --set controller.resources.requests.cpu=1 \ + --set controller.resources.requests.memory=1Gi \ + --set controller.resources.limits.cpu=1 \ + --set controller.resources.limits.memory=1Gi \ + --set replicas=1 \ + --wait +``` + +Karpenter will be running as a deployment in the `karpenter` namespace: + +```bash +$ kubectl get deployment -n karpenter +NAME READY UP-TO-DATE AVAILABLE AGE +karpenter 1/1 1 1 105s +``` + +For this lab, we will create two `NodePool` and `EC2NodeClass` with the following command, one to scale g5.2xlarge GPU EC2 instance and the second one to scale non-GPU instance-type. + +```bash timeout=180 +$ kubectl kustomize ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/karpenter \ + | envsubst | kubectl apply -f- +``` + +Throughout the workshop you can inspect the Karpenter logs with the following command to understand its behavior: + +```bash +$ kubectl logs -l app.kubernetes.io/instance=karpenter -n karpenter +``` + + +### Deploy NVIDIA GPU Operator + +The NVIDIA GPU Operator within Kubernetes to automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit), automatic node labelling using [GPU Feature Discovery(GFD)](https://github.com/NVIDIA/gpu-feature-discovery), [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) based monitoring and others. + + +Add the NVIDIA Helm repository and install the GPU Operator: + +```bash timeout=300 wait=60 + +$ helm repo add nvidia https://helm.ngc.nvidia.com/nvidia +$ helm repo update +$ helm install --wait --generate-name -n gpu-operator --create-namespace nvidia/gpu-operator +``` + +Once installed, we should see the node-feature-discovery running on each node, nvidia-container-toolkit, nvidia-cuda-validator, nvidia-dcgm-exporter, nvidia-device-plugin-daemonset, nvidia-driver-daemonset and nvidia-operator-validator running on GPU enabled nodes. + +```bash timeout=300 wait=60 +$ kubectl get all -n gpu-operator +NAME READY STATUS RESTARTS AGE +pod/gpu-feature-discovery-5z88x 1/1 Running 0 23m +pod/gpu-operator-1720906832-node-feature-discovery-gc-5769c875hsszf 1/1 Running 0 5h59m +pod/gpu-operator-1720906832-node-feature-discovery-master-7999vflt4 1/1 Running 0 5h59m +pod/gpu-operator-1720906832-node-feature-discovery-worker-4lv2d 1/1 Running 0 5h59m +pod/gpu-operator-1720906832-node-feature-discovery-worker-52tfr 1/1 Running 0 27m +pod/gpu-operator-1720906832-node-feature-discovery-worker-jw97f 1/1 Running 0 5h59m +pod/gpu-operator-1720906832-node-feature-discovery-worker-mnhtx 1/1 Running 0 25m +pod/gpu-operator-1720906832-node-feature-discovery-worker-sxs2s 1/1 Running 0 5h59m +pod/gpu-operator-7488b846b-z7vrb 1/1 Running 0 5h59m +pod/nvidia-container-toolkit-daemonset-jjgjr 1/1 Running 0 23m +pod/nvidia-cuda-validator-rk62c 0/1 Completed 0 20m +pod/nvidia-dcgm-exporter-cg7n5 1/1 Running 0 23m +pod/nvidia-device-plugin-daemonset-9jzkj 1/1 Running 0 23m +pod/nvidia-driver-daemonset-wt5tn 1/1 Running 0 25m +pod/nvidia-operator-validator-gmhjl 1/1 Running 0 23m + +``` + +### Deploy the Kuberay Operator + +Ray is a popular open-source distributed computing framework that is widely used for building and running scalable AI/ML applications. The Kuberay Operator integrates seamlessly with Ray, providing a declarative, Kubernetes-native interface for deploying and managing Ray-powered services. + +Install the Kuberay operator, and later in the lab we will create an Inference service using RayService. + +```bash timeout=300 wait=60 +$ helm repo add kuberay https://ray-project.github.io/kuberay-helm/ +$ helm repo update + + +# Install both CRDs and KubeRay operator 1.1.0 +$ helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.0 + +# Check the KubeRay operator Pod in `default` namespace +$ kubectl get pods +# NAME READY STATUS RESTARTS AGE +# kuberay-operator-6fcbb94f64-mbfnr 1/1 Running 0 17s +``` + diff --git a/website/docs/aiml/deploy-monitor-genai-model/index.md b/website/docs/aiml/deploy-monitor-genai-model/index.md new file mode 100644 index 000000000..b8559897d --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/index.md @@ -0,0 +1,23 @@ +--- +title: "Deploy and Monitor GenAI Model on EKS" +sidebar_position: 10 +chapter: true +sidebar_custom_props: { "module": true } +description: "Deploy and Monitor GenAI Model on EKS" +--- + + +# GenAI on EKS + +Welcome to the "GenAI on Amazon EKS" workshop! In this workshop, we'll cover aspects, from creating an EKS cluster with GPU nodes to model training, inference with Ray, and real-time monitoring using Amazon Managed Prometheus and Grafana. + +![Build Model](./assets/GenAI-on-EKS.png) + + +## High level overview + + 1. Create an EKS cluster, install EBS CSI driver & AWS Load Balancer controller, Amazon Managed Prometheus, Granfana and an AWS managed collector + 2. Install Karpenter, Nvidia device plugin and Ray cluster + 2. Install, configure and train the model using Jupyterhub notebook + 4. Create an Inference service using KubeRay service + 5. Monitor the GPU metrics using the Amazon Managed Prometheus and Grafana diff --git a/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md b/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md new file mode 100644 index 000000000..e38c26929 --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md @@ -0,0 +1,43 @@ +--- +title: "Install Jupyterhub" +sidebar_position: 30 +--- + +### Set Up JupyterHub for Model Training + +Let's go through the steps to get a JupyterHub instance running for model training and inference. +Add the Helm chart repo + +First we'll add the official JupyterHub Helm chart repository. This contains the pre-packaged JupyterHub that we can install on our Kubernetes cluster: + + +```bash timeout=300 wait=60 +$ helm repo add jupyterhub https://hub.jupyter.org/helm-chart/ +$ helm repo update +``` + +### Configure JupyterHub + +Next we need to customize JupyterHub by creating a values.yaml file. This is where we can configure things like users, authentication, resources, and more. + +```file +manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml +``` + +### Install JupyterHub + +Now we can install JupyterHub by running Helm, pointing it at the chart and our config: + + +```bash timeout=1800 wait=60 +$ kubectl create namespace jupyterhub +$ kubectl create -f ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-configmap.yaml +$ helm upgrade --cleanup-on-fail --install jupyterhub jupyterhub/jupyterhub --namespace jupyterhub--version=3.1.0 --values ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml +``` + + +### Login and Start Training + +Once Kubernetes finishes deploying everything, we can open the external IP of the JupyterHub service in our browser. + +Log in with the admin user and password set in values.yaml. Then launch notebooks and start training models on the GPUs configured diff --git a/website/docs/aiml/deploy-monitor-genai-model/modelinference.md b/website/docs/aiml/deploy-monitor-genai-model/modelinference.md new file mode 100644 index 000000000..2d8a3d20e --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/modelinference.md @@ -0,0 +1,93 @@ +--- +title: "Model Inference" +sidebar_position: 50 +--- + + +In this section we will create an inference service for Dogbooth using [Kuberay operator](https://ray-project.github.io/kuberay/components/operator/), [RayService](https://ray-project.github.io/kuberay/guidance/rayservice/) CRD + +### Deploy the Nginx Ingress Controller to expose the Inference service + +In this module, we will deploy the nginx ingress controller which can help create the Network Load balancer for the Inference service + +```bash timeout=300 wait=60 +$ helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx +$ helm repo update + +``` + +We will use the values.yaml file for deploying the ingress controller. + +```file +manifests/modules/aiml/deploy-monitor-genai-model/rayservice/ingress-nginx-values.yaml +``` + + +```bash timeout=300 wait=60 +$ helm install nginx-ingress ingress-nginx/ingress-nginx --set controller.admissionWebhooks.enabled=false -f ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/rayservice/ingress-nginx-values.yaml +``` + + + +### Deploy the Ray Service + +We have provided a ray-service.yaml to create the inference service, in this example we have used an existing model "askulkarni2/dogbooth" foro this inference, alternatively you can also use the model id uploaded to your hugging face from the previous training step. + +```text +apiVersion: ray.io/v1alpha1 +kind: RayService +metadata: + name: dogbooth + namespace: dogbooth +spec: + serviceUnhealthySecondThreshold: 600 + deploymentUnhealthySecondThreshold: 600 + serveConfig: + importPath: dogbooth:entrypoint + runtimeEnv: | + env_vars: {"MODEL_ID": "askulkarni2/dogbooth"} + +``` + + +Deploy the Ray services and the Ingress service to access it + +```bash timeout=1800 wait=60 +$ kubectl apply -f ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml +``` + +The above deployment will take roughly 10 mins to complete. Let's verify the deployments: + +```bash timeout=300 wait=60 +$ kubectl get all -n dogbooth +NAME READY STATUS RESTARTS AGE +pod/dogbooth-raycluster-9j8kw-head-87l8r 1/1 Running 0 30h +pod/dogbooth-raycluster-9j8kw-worker-small-group-9x4n8 1/1 Running 0 30h + +NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE +service/dogbooth-head-svc ClusterIP 10.100.202.221 10001/TCP,8265/TCP,52365/TCP,6379/TCP,8080/TCP,8000/TCP 30h +service/dogbooth-raycluster-9j8kw-head-svc NodePort 10.100.104.57 10001:31035/TCP,8265:30043/TCP,52365:32583/TCP,6379:32712/TCP,8080:32015/TCP,8000:31494/TCP 30h +service/dogbooth-serve-svc ClusterIP 10.100.125.143 8000/TCP +``` + + +Let's run the below command to get the NLB Endpoint + +```bash timeout=300 wait=60 +$ kubectl get ingress -n dogbooth +NAME CLASS HOSTS ADDRESS PORTS AGE +dogbooth alb * k8s-dogbooth-dogbooth-2757fb0219-1791700772.us-west-2.elb.amazonaws.com 80 7h14m + +$ kubectl get ingress -n dogbooth -o jsonpath='{.items[0].status.loadBalancer.ingress[0].hostname}' +k8s-dogbooth-dogbooth-2757fb0219-1791700772.us-west-2.elb.amazonaws.com + +``` + + +Let's now run a sample inference: Copy the NLB Endpoint in the browser and run a prompt as below + +http://NLBENDPOINT/dogbooth/serve/imagine?prompt=a photo of [v]happy pup + +This should generate an image like below : + +![Happy pup](./assets/happy-pup.jpeg) diff --git a/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md b/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md new file mode 100644 index 000000000..1b9b18e9a --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md @@ -0,0 +1,61 @@ +--- +title: "Model Training" +sidebar_position: 40 +--- + +Lets train a model to make efficient inference. We will be using huggingface's diffusers, which is a good pretrained diffusion model for generating images, audio and 3D structures. + +This particular lab will focus on text to image creation, upload the trained model to huggingface repository. Let's start. + +### Create a Huggingface token + +Create a huggingface token with `write` permission which would allow to upload the trained model. Save the token, we will use this token in the jupyterhub notebook while training the model + +- [Signup a huggingface account](https://huggingface.co/join) +- [Create huggingface user token](https://huggingface.co/docs/hub/en/security-tokens#user-access-tokens) + +![huggingface token](./assets/huggingface-token.png) + + +### Accessing the notebook + +We will be using the Jupyterhub's notebook created in the earlier step to train the model. Please use the below commands under section "Jupyter Notebook commands" + +We will be uploading the notebook to the jupyterhub console. The UI is exposed using proxy and can be retrieved using the below command: + + + +```bash timeout=300 wait=60 +$ kubectl get svc proxy-public -n jupyterhub + +NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE +proxy-public LoadBalancer 10.100.4.40 k8s-jupyterh-proxypub-f09eec1e26-6209b7ae072999d6.elb.us-west-2.amazonaws.com 80:30941/TCP 6d4h +``` + +### Train the model using the notebook + +Use your browser and paste the loadbalancer url obtained in the previous step to access the notebook + +Login using the username user1 and the password as specified in the jupyterhub-values.yaml. + +![jupyterhub login page](./assets/jupyterhub-login.png) + + +A jupyterhub-notebook.ipynb file is already created through a configmap and mounted as a volume inside the jupyterhub-user1 pod + +![upload nb](./assets/upload-nb.png) + + +Follow the commands and run each commands in the notebook from the top + +### Jupyter Notebook commands + + +```file +manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py +``` + + +This should take approximately 90 mins to run and the model will be uploaded to your huggingface account, after successful upload, you should see the uploaded model in your hugging face profile page. + +We will now proceed to inference stage and run a sample inference diff --git a/website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md b/website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md new file mode 100644 index 000000000..5905f7b3a --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md @@ -0,0 +1,106 @@ +--- +title: "Monitor GPU Workloads on EKS" +sidebar_position: 60 +--- + +### INTRODUCTION + +Monitoring GPUs is critical for infrastructure or site reliability engineering (SRE) teams who manage large-scale GPU clusters for AI or HPC workloads. GPU metrics allow teams to understand workload behavior and thus optimize resource allocation and utilization, diagnose anomalies, and increase overall data center efficiency. + +![Build Model](./assets/gpu-metrics-monitor.png) + + + +### NVIDIA GPU Operator with DCGM exporter + +The NVIDIA GPU Operator includes the [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) exporter that allows users to gather GPU metrics and understand workload behavior or monitor GPUs in clusters. DCGM Exporter is written in Go and exposes GPU metrics at an HTTP endpoint (/metrics) for monitoring solutions such as Prometheus. + +List the DCGM exporter which was installed with the NVIDIA GPU Operator + +```bash timeout=300 wait=60 +$ k get po -l app=nvidia-dcgm-exporter -n gpu-operator +NAME READY STATUS RESTARTS AGE +nvidia-dcgm-exporter-7kv5m 1/1 Running 0 7m17s +nvidia-dcgm-exporter-dzz7f 1/1 Running 0 7m15s +``` + + +### Amazon Managed Prometheus and AWS managed collectors + +Amazon Managed Service for Prometheus provides a fully managed, agentless scraper, or collector, that automatically discovers and pulls Prometheus-compatible metrics. An Amazon Managed Service for Prometheus collector provides reliable, stable, highly available, automatically scaled collection of metrics for your Amazon EKS cluster. + +Amazon Managed Service for Prometheus and Scraper config is installed as a part of `prepare-environment` + + +```bash timeout=300 wait=60 +$ aws amp list-workspaces +{ + "workspaces": [ + { + "alias": "eks-workshop", + "arn": "arn:aws:aps:us-west-2:xxxxxxx:workspace/ws-7fc19106-a435-4dfc-8eb5-85adab4fd8fb", + "createdAt": "2024-07-14T19:38:47.569000+00:00", + "status": { + "statusCode": "ACTIVE" + }, + "tags": { + "AMPAgentlessScraper": "" + }, + "workspaceId": "ws-7fc19106-a435-4dfc-8eb5-85adab4fd8fb" + } + ] +} +``` + + +```bash timeout=300 wait=60 +$ aws amp list-scrapers +{ + "scrapers": [ + { + "arn": "arn:aws:aps:us-west-2:xxxxxxxxxx :scraper/s-d68e97b6-91e7-4669-8430-ea7899d95861", + "createdAt": "2024-07-14T18:29:31.380000+00:00", + "destination": { + "ampConfiguration": { + "workspaceArn": "arn:aws:aps:us-west-2:xxxxxxxxx:workspace/ws-84ed8c27-64e8-460f-8fc0-2cf9b014e05e" + } + }, + "lastModifiedAt": "2024-07-14T18:48:22.262000+00:00", + "roleArn": "arn:aws:iam::xxxxxxxx:role/aws-service-role/scraper.aps.amazonaws.com/AWSServiceRoleForAmazonPrometheusScraper_94e9c92f-32c2-4", + "scraperId": "s-d68e97b6-91e7-4669-8430-ea7899d95861", + "source": { + "eksConfiguration": { + "clusterArn": "arn:aws:eks:us-west-2:xxxxxxxx:cluster/eks-workshop", +...... + +``` + + + +### Visualize the GPU metrics from Grafana + +An instance of Grafana has been pre-installed in your EKS cluster. To access it you first need to retrieve the URL: + +```bash hook=check-grafana +$ kubectl get ingress -n grafana grafana -o=jsonpath='{.status.loadBalancer.ingress[0].hostname}{"\n"}' +k8s-grafana-grafana-58ac8e1da0-1223202662.us-west-2.elb.amazonaws.com +``` + +Opening this URL in a browser will bring up a login screen. + +![Grafana dashboard](./assets/grafana-login.png) + +To retrieve the credentials for the user query the secret created by the Grafana helm chart: + +```bash +$ kubectl get -n grafana secrets/grafana -o=jsonpath='{.data.admin-user}' | base64 -d; printf "\n" +$ kubectl get -n grafana secrets/grafana -o=jsonpath='{.data.admin-password}' | base64 -d; printf "\n" +``` + +After logging into the Grafana console, let's take a look at the datasources section. You should see the Amazon Managed Service for Prometheus workspace configured as a datasource already. + +NVIDIA + +To now start a Grafana dashboard for GPU metrics, import the reference NVIDIA dashboard 12239 + +![Grafana dashboard](./assets/gpu-metrics-grafana.png) \ No newline at end of file diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-suite.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-suite.sh new file mode 100644 index 000000000..8b5a4baea --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-suite.sh @@ -0,0 +1,11 @@ +set -e + +before() { + echo "noop" +} + +after() { + prepare-environment +} + +"$@" From 2e1e6f7c992a6583098328d22aa849480d5111bc Mon Sep 17 00:00:00 2001 From: shivkumr Date: Tue, 16 Jul 2024 01:45:37 -0400 Subject: [PATCH 2/8] updated the syntax error in the kuberay install command --- .../.workshop/terraform/main.tf | 8 ++++---- .../aiml/deploy-monitor-genai-model/jupyterhubsetup.md | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf index 8a4ba41ea..4ffe960e1 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf @@ -306,17 +306,17 @@ dashboardProviders: editable: false options: path: /var/lib/grafana/dashboards/default - - name: nvidia-dcgm-exporter-dashboard + - name: gpu-metrics orgId: 1 - folder: "nvidia-dcgm-exporter-dashboard" + folder: gpu-metrics type: file disableDeletion: false editable: false options: - path: /var/lib/grafana/dashboards/nvidia-dcgm-exporter-dashboard + path: /var/lib/grafana/dashboards/gpu-metrics dashboardsConfigMaps: - nvidia-dcgm-exporter-dashboard: "nvidia-dcgm-exporter-dashboard" + gpu-metrics: "nvidia-dcgm-exporter-dashboard" dashboards: default: diff --git a/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md b/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md index e38c26929..04c91df87 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md +++ b/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md @@ -11,7 +11,7 @@ Add the Helm chart repo First we'll add the official JupyterHub Helm chart repository. This contains the pre-packaged JupyterHub that we can install on our Kubernetes cluster: -```bash timeout=300 wait=60 +```bash timeout=600 wait=60 $ helm repo add jupyterhub https://hub.jupyter.org/helm-chart/ $ helm repo update ``` @@ -32,7 +32,7 @@ Now we can install JupyterHub by running Helm, pointing it at the chart and our ```bash timeout=1800 wait=60 $ kubectl create namespace jupyterhub $ kubectl create -f ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-configmap.yaml -$ helm upgrade --cleanup-on-fail --install jupyterhub jupyterhub/jupyterhub --namespace jupyterhub--version=3.1.0 --values ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml +$ helm upgrade --cleanup-on-fail --install jupyterhub jupyterhub/jupyterhub --namespace jupyterhub --version=3.1.0 --values ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml ``` From 3b750b6722458997b45698afe4f94f74d44e2395 Mon Sep 17 00:00:00 2001 From: shivkumr Date: Sat, 27 Jul 2024 01:06:02 -0400 Subject: [PATCH 3/8] fixed all the module test errors --- .../.workshop/cleanup.sh | 7 +- .../jupyterhub/jupyter.ipynb | 220 ------------------ .../jupyterhub/jupyterhub-values.yaml | 3 + .../karpenter/g5-gpu-nodepool-nodeclass.yaml | 6 +- .../karpenter/x86-cpu-nodepool-nodeclass.yaml | 6 +- .../otel/clusterrole.yaml | 29 --- .../otel/clusterrolebinding.yaml | 12 - .../otel/kustomization.yaml | 7 - .../otel/namespace.yaml | 6 - .../otel/opentelemetrycollector.yaml | 114 --------- .../otel/serviceaccount.yaml | 6 - .../rayservice/rayservice.yaml | 8 +- .../assets/browse-grafana-dashboard.png | Bin 0 -> 85936 bytes .../assets/user1-session-launch.png | Bin 0 -> 56367 bytes .../depoly-prerequesite.md | 54 ++--- .../aiml/deploy-monitor-genai-model/index.md | 25 +- .../jupyterhubsetup.md | 5 +- .../modelinference.md | 12 +- .../modeltraining.md | 13 +- .../deploy-monitor-genai-model/monitorGPU.md | 16 +- .../tests/hook-check-grafana.sh | 25 ++ .../tests/hook-check-jupyterhub-install.sh | 35 +++ .../hook-check-karpenter-deployment-status.sh | 33 +++ .../tests/hook-gpu-operator-status-check.sh | 37 +++ ...-karpenter-nodepool-ec2nodeclass-status.sh | 31 +++ .../tests/hook-kuberay-operator-check.sh | 32 +++ .../tests/hook-rayservice-install.sh | 36 +++ 27 files changed, 319 insertions(+), 459 deletions(-) delete mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb delete mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml delete mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml delete mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml delete mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml delete mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml delete mode 100644 manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/browse-grafana-dashboard.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/assets/user1-session-launch.png create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-grafana.sh create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-jupyterhub-install.sh create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-karpenter-deployment-status.sh create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-gpu-operator-status-check.sh create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-karpenter-nodepool-ec2nodeclass-status.sh create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-kuberay-operator-check.sh create mode 100644 website/docs/aiml/deploy-monitor-genai-model/tests/hook-rayservice-install.sh diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh index 91d7d399e..e088168e1 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh @@ -7,10 +7,15 @@ kubectl delete ns dogbooth --ignore-not-found helm uninstall jupyterhub -n jupyterhub helm uninstall nginx-ingress helm uninstall kuberay-operator +kubectl delete ns karpenter +kubectl delete ns jupyterhub + # Uninstall gpu-operator GPU_OPERATOR_RELEASE_NAME=$(helm list -n gpu-operator -q) helm uninstall $GPU_OPERATOR_RELEASE_NAME -n gpu-operator +kubectl delete ns gpu-operator + delete-all-if-crd-exists nodepools.karpenter.sh delete-all-if-crd-exists ec2nodeclasses.karpenter.k8s.aws @@ -26,4 +31,4 @@ if [ $EXIT_CODE -ne 0 ]; then logmessage "Warning: Karpenter nodes did not clean up" fi -uninstall-helm-chart karpenter karpenter +helm uninstall karpenter -n karpenter \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb deleted file mode 100644 index 21b942b1e..000000000 --- a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "54217ee5-4e5a-418f-a850-7a096183f85c", - "metadata": {}, - "outputs": [], - "source": [ - "# Verify NVIDIA GPU is visible\n", - "!nvidia-smi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f2855eb-4cc0-42ca-b24e-79403344f82d", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir(\"/home/jovyan\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8526f6cf-d94e-4ef6-a870-b5cf2b7b3bbd", - "metadata": {}, - "outputs": [], - "source": [ - "# Clone the diffusers repo\n", - "!git clone https://github.com/huggingface/diffusers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "146c939b-9f67-4f9b-ab0a-d56f579ef574", - "metadata": {}, - "outputs": [], - "source": [ - "# Change the directory\n", - "os.chdir(\"diffusers\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3114141-0dfe-4521-8f74-10210e307ade", - "metadata": {}, - "outputs": [], - "source": [ - "# Install requirements\n", - "! pip install -e .\n", - "! pip install xformers==0.0.16 diffusers[torch]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40923ba1-5e9c-452a-9375-82e87e986c8a", - "metadata": {}, - "outputs": [], - "source": [ - "# Fix for bitsandbytes https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md\n", - "! wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh\n", - "! bash install_cuda.sh 117 ~/local 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b355fa8f-e2cb-4ef4-bce2-64739daef7db", - "metadata": {}, - "outputs": [], - "source": [ - "# Install bitsandbytes for optimizations\n", - "! pip install bitsandbytes==0.41.0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "871f97d8-ffeb-4b3d-8f69-ef8590ca3c16", - "metadata": {}, - "outputs": [], - "source": [ - "# Use the newly installed CUDA version for bitsandbytes\n", - "os.environ[\"BNB_CUDA_VERSION\"] = \"117\"\n", - "os.environ[\"LD_LIBRARY_PATH\"] = os.getenv(\"LD_LIBRARY_PATH\") + \":/home/jovyan/local/cuda-11.7\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0271afa3-1149-47f2-8269-3c28913659db", - "metadata": {}, - "outputs": [], - "source": [ - "# Validate successful install of bitsandbytes\n", - "! python -m bitsandbytes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "263054f2-aeae-4efb-8143-6e58a59581e8", - "metadata": {}, - "outputs": [], - "source": [ - "# Install requirements for dreambooth\n", - "os.chdir(\"examples/dreambooth\")\n", - "! pip install -r requirements.txt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "815e74fa-24a5-47c7-8b4f-63cba3fc1fc9", - "metadata": {}, - "outputs": [], - "source": [ - "# Setup default configuration for accelerate\n", - "! accelerate config default" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d67262c-8c11-4f32-951c-d4a05347b94a", - "metadata": {}, - "outputs": [], - "source": [ - "# Login to huggingface associated with your account (please create one if it doesn't exist)\n", - "! huggingface-cli login --token " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7aa7aecf-98c5-43b9-adce-5ffbe697dbfa", - "metadata": {}, - "outputs": [], - "source": [ - "# Download sample dataset of the subject. See the sample images here https://huggingface.co/datasets/diffusers/dog-example\n", - "from huggingface_hub import snapshot_download\n", - "local_dir = \"./dog\"\n", - "snapshot_download(\n", - " \"diffusers/dog-example\",\n", - " local_dir=local_dir, repo_type=\"dataset\",\n", - " ignore_patterns=\".gitattributes\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "712c8d7b-c316-411c-b7b8-e3d20db87a9b", - "metadata": {}, - "outputs": [], - "source": [ - "# Export environment variables to provide input model, dataset directory and output directory for the tuned model\n", - "os.environ[\"MODEL_NAME\"] = \"stabilityai/stable-diffusion-2-1\"\n", - "os.environ[\"INSTANCE_DIR\"] = \"dog\"\n", - "os.environ[\"OUTPUT_DIR\"] = \"dogbooth\"\n", - "os.environ[\"RESOLUTION\"] = \"768\"\n", - "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"garbage_collection_threshold:0.6,max_split_size_mb:128\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef9da088-63ba-4e0b-9537-abb1bdacd6c2", - "metadata": {}, - "outputs": [], - "source": [ - "# Launch the training and push the output model to huggingface\n", - "! accelerate launch train_dreambooth.py \\\n", - " --pretrained_model_name_or_path=$MODEL_NAME \\\n", - " --instance_data_dir=$INSTANCE_DIR \\\n", - " --output_dir=$OUTPUT_DIR \\\n", - " --instance_prompt=\"a photo of [v]dog\" \\\n", - " --resolution=768 \\\n", - " --train_batch_size=1 \\\n", - " --gradient_accumulation_steps=1 \\\n", - " --gradient_checkpointing \\\n", - " --learning_rate=1e-6 \\\n", - " --lr_scheduler=\"constant\" \\\n", - " --enable_xformers_memory_efficient_attention \\\n", - " --use_8bit_adam \\\n", - " --lr_warmup_steps=0 \\\n", - " --max_train_steps=800 \\\n", - " --push_to_hub\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml index b30032a62..0684afb58 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml +++ b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml @@ -21,8 +21,11 @@ proxy: singleuser: image: name: public.ecr.aws/h3o5n2r0/gpu-jupyter + # name: 981056732380.dkr.ecr.us-west-2.amazonaws.com/ptc/h3o5n2r0/gpu-jupyter tag: v1.5_cuda-11.6_ubuntu-20.04_python-only pullPolicy: Always + # imagePullSecrets: + # - name: ecr-credentials cmd: null startTimeout: 600 memory: diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml index d1bf39511..e8dabcf86 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml +++ b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml @@ -47,6 +47,7 @@ metadata: spec: amiFamily: Ubuntu role: "${KARPENTER_ROLE}" +# role: Karpenter-eks-workshop-20240724023441649900000009 subnetSelectorTerms: - tags: karpenter.sh/discovery: "eks-workshop" @@ -57,4 +58,7 @@ spec: - deviceName: /dev/sda1 ebs: volumeSize: 100Gi - volumeType: gp3 \ No newline at end of file + volumeType: gp3 + # userData: | + # #!/bin/bash + # docker pull public.ecr.aws/h3o5n2r0/dogbooth:0.0.1-gpu diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml index b80b9b1ac..203ba2bcb 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml +++ b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml @@ -42,6 +42,7 @@ metadata: spec: amiFamily: AL2 # Amazon Linux 2 role: ${KARPENTER_ROLE} +# role: Karpenter-eks-workshop-20240724023441649900000009 subnetSelectorTerms: - tags: karpenter.sh/discovery: "eks-workshop" @@ -52,4 +53,7 @@ spec: - deviceName: /dev/xvda ebs: volumeSize: 100Gi - volumeType: gp3 \ No newline at end of file + volumeType: gp3 + # userData: | + # #!/bin/bash + # docker pull public.ecr.aws/h3o5n2r0/dogbooth:0.0.1-gpu \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml deleted file mode 100644 index 4bb1fb762..000000000 --- a/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrole.yaml +++ /dev/null @@ -1,29 +0,0 @@ -apiVersion: rbac.authorization.k8s.io/v1 -kind: ClusterRole -metadata: - name: otel-prometheus-role -rules: - - apiGroups: - - "" - resources: - - nodes - - nodes/proxy - - services - - endpoints - - pods - verbs: - - get - - list - - watch - - apiGroups: - - extensions - resources: - - ingresses - verbs: - - get - - list - - watch - - nonResourceURLs: - - /metrics - verbs: - - get diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml deleted file mode 100644 index a5c187a1f..000000000 --- a/manifests/modules/aiml/deploy-monitor-genai-model/otel/clusterrolebinding.yaml +++ /dev/null @@ -1,12 +0,0 @@ -apiVersion: rbac.authorization.k8s.io/v1 -kind: ClusterRoleBinding -metadata: - name: otel-prometheus-role-binding -roleRef: - apiGroup: rbac.authorization.k8s.io - kind: ClusterRole - name: otel-prometheus-role -subjects: - - kind: ServiceAccount - name: adot-collector - namespace: other diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml deleted file mode 100644 index f3e3283c3..000000000 --- a/manifests/modules/aiml/deploy-monitor-genai-model/otel/kustomization.yaml +++ /dev/null @@ -1,7 +0,0 @@ -apiVersion: kustomize.config.k8s.io/v1beta1 -kind: Kustomization -namespace: checkout -resources: - - namespace.yaml - - service.yaml - - ingress.yaml \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml deleted file mode 100644 index 85c9f576c..000000000 --- a/manifests/modules/aiml/deploy-monitor-genai-model/otel/namespace.yaml +++ /dev/null @@ -1,6 +0,0 @@ -apiVersion: v1 -kind: Namespace -metadata: - name: dogbooth - labels: - app.kubernetes.io/created-by: eks-workshop diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml deleted file mode 100644 index 2e9822c73..000000000 --- a/manifests/modules/aiml/deploy-monitor-genai-model/otel/opentelemetrycollector.yaml +++ /dev/null @@ -1,114 +0,0 @@ -apiVersion: opentelemetry.io/v1alpha1 -kind: OpenTelemetryCollector -metadata: - name: adot - namespace: adot-col -spec: - image: public.ecr.aws/aws-observability/aws-otel-collector:v0.21.0 - mode: deployment - serviceAccount: adot-collector - config: | - receivers: - prometheus: - config: - global: - scrape_interval: 60s - scrape_timeout: 15s - external_labels: - cluster: ${EKS_CLUSTER_NAME} - account_id: ${AWS_ACCOUNT_ID} - region: ${AWS_REGION} - scrape_configs: - - job_name: 'kubernetes-kubelet' - scrape_interval: 60s - scrape_timeout: 15s - scheme: https - tls_config: - ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt - insecure_skip_verify: true - bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token - kubernetes_sd_configs: - - role: node - relabel_configs: - - action: labelmap - regex: __meta_kubernetes_node_label_(.+) - - target_label: __address__ - replacement: kubernetes.default.svc.cluster.local:443 - - source_labels: [__meta_kubernetes_node_name] - regex: (.+) - target_label: __metrics_path__ - replacement: /api/v1/nodes/$${1}/proxy/metrics - - job_name: 'kubelet' - scheme: https - tls_config: - ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt - insecure_skip_verify: true - bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token - kubernetes_sd_configs: - - role: node - relabel_configs: - - action: labelmap - regex: __meta_kubernetes_node_label_(.+) - - target_label: __address__ - replacement: kubernetes.default.svc.cluster.local:443 - - source_labels: [__meta_kubernetes_node_name] - regex: (.+) - target_label: __metrics_path__ - replacement: /api/v1/nodes/$${1}/proxy/metrics/cadvisor - - job_name: 'kubernetes-pods' - honor_labels: true - - kubernetes_sd_configs: - - role: pod - - relabel_configs: - - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape] - action: keep - regex: true - - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape_slow] - action: drop - regex: true - - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scheme] - action: replace - regex: (https?) - target_label: __scheme__ - - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path] - action: replace - target_label: __metrics_path__ - regex: (.+) - - action: labelmap - regex: __meta_kubernetes_pod_annotation_prometheus_io_param_(.+) - replacement: __param_$1 - - action: labelmap - regex: __meta_kubernetes_pod_label_(.+) - - source_labels: [__meta_kubernetes_namespace] - action: replace - target_label: namespace - - source_labels: [__meta_kubernetes_pod_name] - action: replace - target_label: pod - - source_labels: [__meta_kubernetes_pod_phase] - regex: Pending|Succeeded|Failed|Completed - action: drop - exporters: - prometheusremotewrite: - endpoint: ${AMP_ENDPOINT}api/v1/remote_write - auth: - authenticator: sigv4auth - logging: - loglevel: info - extensions: - sigv4auth: - region: ${AWS_REGION} - service: aps - health_check: - pprof: - endpoint: :1888 - zpages: - endpoint: :55679 - service: - extensions: [pprof, zpages, health_check, sigv4auth] - pipelines: - metrics: - receivers: [prometheus] - exporters: [logging, prometheusremotewrite] diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml deleted file mode 100644 index 3890415d5..000000000 --- a/manifests/modules/aiml/deploy-monitor-genai-model/otel/serviceaccount.yaml +++ /dev/null @@ -1,6 +0,0 @@ -apiVersion: v1 -kind: ServiceAccount -metadata: - name: adot-collector - annotations: - eks.amazonaws.com/role-arn: ${ADOT_IAM_ROLE} diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml index 373e89cc6..772f98e65 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml +++ b/manifests/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml @@ -29,10 +29,10 @@ spec: resources: limits: cpu: 2 - memory: 8Gi + memory: 16Gi requests: cpu: 2 - memory: 8Gi + memory: 16Gi ports: - containerPort: 6379 name: gcs-server @@ -60,11 +60,11 @@ spec: resources: limits: cpu: "2" - memory: "12Gi" + memory: "16Gi" nvidia.com/gpu: 1 requests: cpu: "2" - memory: "12Gi" + memory: "16Gi" nvidia.com/gpu: 1 --- diff --git a/website/docs/aiml/deploy-monitor-genai-model/assets/browse-grafana-dashboard.png 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Operators" --- -{{% required-time %}} - -:::tip Before you start -Prepare your environment for this section: - -```bash timeout=1800 wait=30 -$ prepare-environment aiml/deploy-monitor-genai-model -``` - -This will make the following changes to your lab environment: - - - Creates EKS cluster - - Install EBS CSI Driver and AWS Load Balancer controller - - Installs Amazon Managed Prometheus, Grafana and AWS managed collector - - Prerequisite IAM roles for Karpenter -::: - # Install Karpenter @@ -35,7 +16,7 @@ The first thing we'll do is install Karpenter in our cluster. Various pre-requis You can find the full installation documentation for Karpenter [here](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/). -All that we have left to do is install Karpenter as a helm chart: +All that we have left to do is install Karpenter: ```bash timeout=1800 wait=30 $ helm upgrade --install karpenter oci://public.ecr.aws/karpenter/karpenter \ @@ -53,7 +34,7 @@ $ helm upgrade --install karpenter oci://public.ecr.aws/karpenter/karpenter \ Karpenter will be running as a deployment in the `karpenter` namespace: -```bash +```bash hook=check-karpenter-deployment-status timeout=150 $ kubectl get deployment -n karpenter NAME READY UP-TO-DATE AVAILABLE AGE karpenter 1/1 1 1 105s @@ -61,9 +42,17 @@ karpenter 1/1 1 1 105s For this lab, we will create two `NodePool` and `EC2NodeClass` with the following command, one to scale g5.2xlarge GPU EC2 instance and the second one to scale non-GPU instance-type. -```bash timeout=180 +```bash timeout=180 hook=karpenter-nodepool-ec2nodeclass-status $ kubectl kustomize ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/karpenter \ | envsubst | kubectl apply -f- +$ kubectl get nodepool,ec2nodeclass +NAME NODECLASS +nodepool.karpenter.sh/g5-gpu-karpenter g5-gpu-karpenter +nodepool.karpenter.sh/x86-cpu-karpenter x86-cpu-karpenter + +NAME AGE +ec2nodeclass.karpenter.k8s.aws/g5-gpu-karpenter 32m +ec2nodeclass.karpenter.k8s.aws/x86-cpu-karpenter 32ms ``` Throughout the workshop you can inspect the Karpenter logs with the following command to understand its behavior: @@ -75,21 +64,20 @@ $ kubectl logs -l app.kubernetes.io/instance=karpenter -n karpenter ### Deploy NVIDIA GPU Operator -The NVIDIA GPU Operator within Kubernetes to automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit), automatic node labelling using [GPU Feature Discovery(GFD)](https://github.com/NVIDIA/gpu-feature-discovery), [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) based monitoring and others. +The NVIDIA GPU Operator within Kubernetes automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit), automatic node labelling using [GPU Feature Discovery(GFD)](https://github.com/NVIDIA/gpu-feature-discovery), [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) operator export the GPU metrics. Add the NVIDIA Helm repository and install the GPU Operator: -```bash timeout=300 wait=60 - +```bash timeout=300 wait=60 hook=gpu-operator-status-check $ helm repo add nvidia https://helm.ngc.nvidia.com/nvidia $ helm repo update $ helm install --wait --generate-name -n gpu-operator --create-namespace nvidia/gpu-operator ``` -Once installed, we should see the node-feature-discovery running on each node, nvidia-container-toolkit, nvidia-cuda-validator, nvidia-dcgm-exporter, nvidia-device-plugin-daemonset, nvidia-driver-daemonset and nvidia-operator-validator running on GPU enabled nodes. +Once installed, we should see the node-feature-discovery running on each node, nvidia-container-toolkit, nvidia-cuda-validator, nvidia-dcgm-exporter, nvidia-device-plugin-daemonset, nvidia-driver-daemonset and nvidia-operator-validator running on a GPU enabled nodes. -```bash timeout=300 wait=60 +```bash $ kubectl get all -n gpu-operator NAME READY STATUS RESTARTS AGE pod/gpu-feature-discovery-5z88x 1/1 Running 0 23m @@ -119,11 +107,13 @@ Install the Kuberay operator, and later in the lab we will create an Inference s ```bash timeout=300 wait=60 $ helm repo add kuberay https://ray-project.github.io/kuberay-helm/ $ helm repo update - - -# Install both CRDs and KubeRay operator 1.1.0 +# Install the KubeRay operator 1.1.0 $ helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.0 +``` + +Check the Ray operator status +```bash timeout=60 hook=kuberay-operator-check # Check the KubeRay operator Pod in `default` namespace $ kubectl get pods # NAME READY STATUS RESTARTS AGE diff --git a/website/docs/aiml/deploy-monitor-genai-model/index.md b/website/docs/aiml/deploy-monitor-genai-model/index.md index b8559897d..5c691899e 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/index.md +++ b/website/docs/aiml/deploy-monitor-genai-model/index.md @@ -1,12 +1,30 @@ --- title: "Deploy and Monitor GenAI Model on EKS" sidebar_position: 10 -chapter: true sidebar_custom_props: { "module": true } description: "Deploy and Monitor GenAI Model on EKS" --- +{{% required-time %}} + +:::tip Before you start +Prepare your environment for this section: + +```bash timeout=1800 wait=30 +$ prepare-environment aiml/deploy-monitor-genai-model +``` + +Its takes around 25 mins to complete the initial preparation, this will make the following changes to your lab environment: + + - Creates EKS cluster + - Install EBS CSI Driver and AWS Load Balancer controller + - Installs Amazon Managed Prometheus, Grafana and AWS managed collector + - Prerequisite IAM roles for Karpenter + +::: + + # GenAI on EKS Welcome to the "GenAI on Amazon EKS" workshop! In this workshop, we'll cover aspects, from creating an EKS cluster with GPU nodes to model training, inference with Ray, and real-time monitoring using Amazon Managed Prometheus and Grafana. @@ -17,7 +35,8 @@ Welcome to the "GenAI on Amazon EKS" workshop! In this workshop, we'll cover asp ## High level overview 1. Create an EKS cluster, install EBS CSI driver & AWS Load Balancer controller, Amazon Managed Prometheus, Granfana and an AWS managed collector - 2. Install Karpenter, Nvidia device plugin and Ray cluster - 2. Install, configure and train the model using Jupyterhub notebook + 2. Install Karpenter, Nvidia GPU Operator and Ray cluster + 2. Install Jupyterhub notebook and train the model 4. Create an Inference service using KubeRay service 5. Monitor the GPU metrics using the Amazon Managed Prometheus and Grafana + diff --git a/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md b/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md index 04c91df87..1f0aa5991 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md +++ b/website/docs/aiml/deploy-monitor-genai-model/jupyterhubsetup.md @@ -6,7 +6,6 @@ sidebar_position: 30 ### Set Up JupyterHub for Model Training Let's go through the steps to get a JupyterHub instance running for model training and inference. -Add the Helm chart repo First we'll add the official JupyterHub Helm chart repository. This contains the pre-packaged JupyterHub that we can install on our Kubernetes cluster: @@ -26,10 +25,10 @@ manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.y ### Install JupyterHub -Now we can install JupyterHub by running Helm, pointing it at the chart and our config: +Now, lets install JupyterHub, pointing it at the chart and our config: -```bash timeout=1800 wait=60 +```bash timeout=1800 wait=60 hook=check-jupyterhub-install $ kubectl create namespace jupyterhub $ kubectl create -f ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-configmap.yaml $ helm upgrade --cleanup-on-fail --install jupyterhub jupyterhub/jupyterhub --namespace jupyterhub --version=3.1.0 --values ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyterhub-values.yaml diff --git a/website/docs/aiml/deploy-monitor-genai-model/modelinference.md b/website/docs/aiml/deploy-monitor-genai-model/modelinference.md index 2d8a3d20e..770b14d88 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/modelinference.md +++ b/website/docs/aiml/deploy-monitor-genai-model/modelinference.md @@ -6,7 +6,7 @@ sidebar_position: 50 In this section we will create an inference service for Dogbooth using [Kuberay operator](https://ray-project.github.io/kuberay/components/operator/), [RayService](https://ray-project.github.io/kuberay/guidance/rayservice/) CRD -### Deploy the Nginx Ingress Controller to expose the Inference service +### Deploy the Nginx Controller to facilitate external access to the Inference service. In this module, we will deploy the nginx ingress controller which can help create the Network Load balancer for the Inference service @@ -31,7 +31,7 @@ $ helm install nginx-ingress ingress-nginx/ingress-nginx --set controller.admiss ### Deploy the Ray Service -We have provided a ray-service.yaml to create the inference service, in this example we have used an existing model "askulkarni2/dogbooth" foro this inference, alternatively you can also use the model id uploaded to your hugging face from the previous training step. +We have provided a ray-service.yaml to create the inference service, in this example we have used an existing model "askulkarni2/dogbooth" for this inference, alternatively you can also use the model id uploaded to your hugging face from the previous training step. ```text apiVersion: ray.io/v1alpha1 @@ -52,11 +52,11 @@ spec: Deploy the Ray services and the Ingress service to access it -```bash timeout=1800 wait=60 +```bash timeout=1800 wait=60 hook=rayservice-install $ kubectl apply -f ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml ``` -The above deployment will take roughly 10 mins to complete. Let's verify the deployments: +The above deployment will take roughly 12s mins to complete. Let's verify the deployments: ```bash timeout=300 wait=60 $ kubectl get all -n dogbooth @@ -71,14 +71,14 @@ service/dogbooth-serve-svc ClusterIP 10.100.125.143 ``` -Let's run the below command to get the NLB Endpoint +Run the below command to find the NLB Endpoint to access the service ```bash timeout=300 wait=60 $ kubectl get ingress -n dogbooth NAME CLASS HOSTS ADDRESS PORTS AGE dogbooth alb * k8s-dogbooth-dogbooth-2757fb0219-1791700772.us-west-2.elb.amazonaws.com 80 7h14m -$ kubectl get ingress -n dogbooth -o jsonpath='{.items[0].status.loadBalancer.ingress[0].hostname}' +$ kubectl get ingress -n dogbooth -o jsonpath='{.items[0].status.loadBalancer.ingress[0].hostname}{"\n"}' k8s-dogbooth-dogbooth-2757fb0219-1791700772.us-west-2.elb.amazonaws.com ``` diff --git a/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md b/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md index 1b9b18e9a..19af38fe2 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md +++ b/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md @@ -19,10 +19,9 @@ Create a huggingface token with `write` permission which would allow to upload t ### Accessing the notebook -We will be using the Jupyterhub's notebook created in the earlier step to train the model. Please use the below commands under section "Jupyter Notebook commands" - -We will be uploading the notebook to the jupyterhub console. The UI is exposed using proxy and can be retrieved using the below command: +We will be using the Jupyterhub's notebook created in the previous section to train the model. +The Jupyterhub UI is exposed using proxy and can be retrieved using the below command: ```bash timeout=300 wait=60 @@ -36,12 +35,16 @@ proxy-public LoadBalancer 10.100.4.40 k8s-jupyterh-proxypub-f09eec1e26-620 Use your browser and paste the loadbalancer url obtained in the previous step to access the notebook -Login using the username user1 and the password as specified in the jupyterhub-values.yaml. +Login using the username user1 and the password as specified in the jupyterhub-values.yaml. ![jupyterhub login page](./assets/jupyterhub-login.png) +Upon successful login, you would see a jupyterhub-user1 pod would have instantiated on a GPU node. If it the launch timesout after, please relaunch the server + +![upload nb](./assets/user1-session-launch.png) + -A jupyterhub-notebook.ipynb file is already created through a configmap and mounted as a volume inside the jupyterhub-user1 pod +After a successful jupyterhub-user1 server launch, you should see a jupyterhub-notebook.ipynb file is already created through a configmap and mounted as a volume inside the jupyterhub-user1 pod ![upload nb](./assets/upload-nb.png) diff --git a/website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md b/website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md index 5905f7b3a..3337090ab 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md +++ b/website/docs/aiml/deploy-monitor-genai-model/monitorGPU.md @@ -13,12 +13,12 @@ Monitoring GPUs is critical for infrastructure or site reliability engineering ( ### NVIDIA GPU Operator with DCGM exporter -The NVIDIA GPU Operator includes the [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) exporter that allows users to gather GPU metrics and understand workload behavior or monitor GPUs in clusters. DCGM Exporter is written in Go and exposes GPU metrics at an HTTP endpoint (/metrics) for monitoring solutions such as Prometheus. +The NVIDIA GPU Operator includes the [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) exporter that allows users to gather and monitor the GPU metrics in a clusters. DCGM Exporter is written in Go and exposes GPU metrics at an HTTP endpoint (/metrics) for monitoring solutions such as Prometheus. List the DCGM exporter which was installed with the NVIDIA GPU Operator -```bash timeout=300 wait=60 -$ k get po -l app=nvidia-dcgm-exporter -n gpu-operator +```bash +$ kubectl get po -l app=nvidia-dcgm-exporter -n gpu-operator NAME READY STATUS RESTARTS AGE nvidia-dcgm-exporter-7kv5m 1/1 Running 0 7m17s nvidia-dcgm-exporter-dzz7f 1/1 Running 0 7m15s @@ -32,7 +32,7 @@ Amazon Managed Service for Prometheus provides a fully managed, agentless scrape Amazon Managed Service for Prometheus and Scraper config is installed as a part of `prepare-environment` -```bash timeout=300 wait=60 +```bash $ aws amp list-workspaces { "workspaces": [ @@ -53,7 +53,7 @@ $ aws amp list-workspaces ``` -```bash timeout=300 wait=60 +```bash $ aws amp list-scrapers { "scrapers": [ @@ -97,10 +97,8 @@ $ kubectl get -n grafana secrets/grafana -o=jsonpath='{.data.admin-user}' | base $ kubectl get -n grafana secrets/grafana -o=jsonpath='{.data.admin-password}' | base64 -d; printf "\n" ``` -After logging into the Grafana console, let's take a look at the datasources section. You should see the Amazon Managed Service for Prometheus workspace configured as a datasource already. - -NVIDIA +NVIDIA-DCGM-exporter-dashboard is already setup as a part of prerequisite, so after logging into the Grafana console, access the Grafana NVIDIA-DCGM-exporter-dashboard dashboard for GPU metrics, this is the dashboard 12239 imported from the the public grafana dashboard, alternatively you can also create a custom dashboard based on your requirement. -To now start a Grafana dashboard for GPU metrics, import the reference NVIDIA dashboard 12239 +![Browse dashboard](./assets/browse-grafana-dashboard.png) ![Grafana dashboard](./assets/gpu-metrics-grafana.png) \ No newline at end of file diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-grafana.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-grafana.sh new file mode 100644 index 000000000..487cf450d --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-grafana.sh @@ -0,0 +1,25 @@ +set -Eeuo pipefail + +before() { + echo "noop" +} + +after() { + grafana_endpoint=$(kubectl get ingress -n grafana grafana -o=jsonpath='{.status.loadBalancer.ingress[0].hostname}') + export grafana_url="${grafana_endpoint}/healthz" + + EXIT_CODE=0 + + timeout -s TERM 120 bash -c \ + 'while [[ "$(curl -s -o /dev/null -L -w ''%{http_code}'' ${grafana_url})" != "200" ]];\ + do sleep 5;\ + done' || EXIT_CODE=$? + + if [ $EXIT_CODE -ne 0 ]; then + echo "Grafana did not become available or return HTTP 200 for 600 seconds" + kubectl get ingress -n grafana + exit 1 + fi +} + +"$@" diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-jupyterhub-install.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-jupyterhub-install.sh new file mode 100644 index 000000000..5489735bc --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-jupyterhub-install.sh @@ -0,0 +1,35 @@ +set -e + +before() { + echo "noop" +} + +after() { + +NAMESPACE="jupyterhub" +RETRY_INTERVAL=60 # Wait time in seconds between retries +RETRY_LIMIT=2 # Number of retries (5 minutes) + +# Check pod status +for i in $(seq 1 $RETRY_LIMIT); do + PODS=$(kubectl get pods -n "$NAMESPACE" -o jsonpath='{.items[*].metadata.name}') + + for pod in $PODS; do + STATUS=$(kubectl get pod -n "$NAMESPACE" "$pod" -o jsonpath='{.status.phase}') + if [ "$STATUS" != "Running" ]; then + echo "Pod '$pod' in namespace '$NAMESPACE' is not in the 'Running' phase (current phase: $STATUS). Retrying in $RETRY_INTERVAL seconds..." + sleep $RETRY_INTERVAL +# break 2 + fi + done + + echo "All pods in namespace '$NAMESPACE' are in the 'Running' phase." + exit 0 +done + +echo "Not all pods in namespace '$NAMESPACE' became 'Running' after $RETRY_LIMIT retries ($(($RETRY_LIMIT * $RETRY_INTERVAL)) seconds)." +exit 1 + +} + +"$@" \ No newline at end of file diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-karpenter-deployment-status.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-karpenter-deployment-status.sh new file mode 100644 index 000000000..cd263a0fe --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-check-karpenter-deployment-status.sh @@ -0,0 +1,33 @@ +set -e + +before() { + +NAMESPACE="karpenter" +DEPLOYMENT_NAME="karpenter" +RETRY_INTERVAL=60 # Wait time in seconds between retries +RETRY_LIMIT=2 # Number of retries (2 minutes) + +# Check the deployment status +for i in $(seq 1 $RETRY_LIMIT); do + READY_REPLICAS=$(kubectl get deployment -n "$NAMESPACE" "$DEPLOYMENT_NAME" -o jsonpath='{.status.readyReplicas}') + DESIRED_REPLICAS=$(kubectl get deployment -n "$NAMESPACE" "$DEPLOYMENT_NAME" -o jsonpath='{.spec.replicas}') + + if [ "$READY_REPLICAS" -eq "$DESIRED_REPLICAS" ]; then + echo "Deployment '$DEPLOYMENT_NAME' in namespace '$NAMESPACE' is successfully deployed and running." + exit 0 + else + echo "Deployment '$DEPLOYMENT_NAME' in namespace '$NAMESPACE' is not fully ready. Expected $DESIRED_REPLICAS replicas, but only $READY_REPLICAS are ready. Retrying in $RETRY_INTERVAL seconds..." + sleep $RETRY_INTERVAL + fi +done + +echo "Deployment '$DEPLOYMENT_NAME' in namespace '$NAMESPACE' did not become fully ready after $RETRY_LIMIT retries ($(($RETRY_LIMIT * $RETRY_INTERVAL)) seconds)." +exit 1 +} + + +after() { + echo "noop" +} + +"$@" diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-gpu-operator-status-check.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-gpu-operator-status-check.sh new file mode 100644 index 000000000..0711a8d9d --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-gpu-operator-status-check.sh @@ -0,0 +1,37 @@ +#!/bin/bash -x +set -e + +before() { + echo "noop" +} + + +after() { + +NAMESPACE="gpu-operator" +RETRY_INTERVAL=60 # Wait time in seconds between retries +RETRY_LIMIT=5 # Number of retries (5 minutes) + +# Check pod status +for i in $(seq 1 $RETRY_LIMIT); do + PODS=$(kubectl get pods -n "$NAMESPACE" -o jsonpath='{.items[*].metadata.name}') + + for pod in $PODS; do + STATUS=$(kubectl get pod -n "$NAMESPACE" "$pod" -o jsonpath='{.status.phase}') + if [ "$STATUS" != "Running" ] && [ "$STATUS" != "Completed" ]; then + echo "Pod '$pod' in namespace '$NAMESPACE' is not in the 'Running' or 'Completed' phase (current phase: $STATUS). Retrying in $RETRY_INTERVAL seconds..." + sleep $RETRY_INTERVAL +# break 2 + fi + done + + echo "All pods in namespace '$NAMESPACE' are in the 'Running' or 'Completed' phase." + exit 0 +done + +echo "Not all pods in namespace '$NAMESPACE' became 'Running' or 'Completed' after $RETRY_LIMIT retries ($(($RETRY_LIMIT * $RETRY_INTERVAL)) seconds)." +exit 1 + +} + +"$@" diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-karpenter-nodepool-ec2nodeclass-status.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-karpenter-nodepool-ec2nodeclass-status.sh new file mode 100644 index 000000000..84d7e05dc --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-karpenter-nodepool-ec2nodeclass-status.sh @@ -0,0 +1,31 @@ +set -e + +before() { + echo "noop" +} + + + +after() { + +# Set the Karpenter namespace +NAMESPACE="karpenter" + +# Check if the Karpenter nodepool exists +if ! kubectl get nodepool -n "$NAMESPACE" &> /dev/null; then + echo "Karpenter nodepool not found in namespace '$NAMESPACE'" + exit 1 +fi + +# Check if the Karpenter EC2 nodeclass exists +if ! kubectl get ec2nodeclass -n "$NAMESPACE" &> /dev/null; then + echo "Karpenter EC2 nodeclass not found in namespace '$NAMESPACE'" + exit 1 +fi + +echo "Karpenter nodepool and EC2 nodeclass found in namespace '$NAMESPACE'" +exit 0 + +} + +"$@" diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-kuberay-operator-check.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-kuberay-operator-check.sh new file mode 100644 index 000000000..7f3da6ed2 --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-kuberay-operator-check.sh @@ -0,0 +1,32 @@ +set -e + +before() { + echo "noop" +} + + +after() { +NAMESPACE=default +DEPLOYMENT_NAME="kuberay-operator" +RETRY_INTERVAL=60 # Wait time in seconds between retries +RETRY_LIMIT=2 # Number of retries (2 minutes) + +# Check the deployment status +for i in $(seq 1 $RETRY_LIMIT); do + READY_REPLICAS=$(kubectl get deployment -n "$NAMESPACE" "$DEPLOYMENT_NAME" -o jsonpath='{.status.readyReplicas}') + DESIRED_REPLICAS=$(kubectl get deployment -n "$NAMESPACE" "$DEPLOYMENT_NAME" -o jsonpath='{.spec.replicas}') + + if [ "$READY_REPLICAS" -eq "$DESIRED_REPLICAS" ]; then + echo "Deployment '$DEPLOYMENT_NAME' in namespace '$NAMESPACE' is successfully deployed and running." + exit 0 + else + echo "Deployment '$DEPLOYMENT_NAME' in namespace '$NAMESPACE' is not fully ready. Expected $DESIRED_REPLICAS replicas, but only $READY_REPLICAS are ready. Retrying in $RETRY_INTERVAL seconds..." + sleep $RETRY_INTERVAL + fi +done + +echo "Deployment '$DEPLOYMENT_NAME' in namespace '$NAMESPACE' did not become fully ready after $RETRY_LIMIT retries ($(($RETRY_LIMIT * $RETRY_INTERVAL)) seconds)." +exit 1 +} + +"$@" diff --git a/website/docs/aiml/deploy-monitor-genai-model/tests/hook-rayservice-install.sh b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-rayservice-install.sh new file mode 100644 index 000000000..6c847cd83 --- /dev/null +++ b/website/docs/aiml/deploy-monitor-genai-model/tests/hook-rayservice-install.sh @@ -0,0 +1,36 @@ +#!/bin/bash -x +set -e + +before() { + echo "noop" +} + +after() { + +NAMESPACE="dogbooth" +RETRY_INTERVAL=60 # Wait time in seconds between retries +RETRY_LIMIT=20 # Number of retries (20 minutes) + +# Check pod status +for i in $(seq 1 $RETRY_LIMIT); do + PODS=$(kubectl get pods -n "$NAMESPACE" -o jsonpath='{.items[*].metadata.name}') + + for pod in $PODS; do + STATUS=$(kubectl get pod -n "$NAMESPACE" "$pod" -o jsonpath='{.status.phase}') + if [ "$STATUS" != "Running" ]; then + echo "Pod '$pod' in namespace '$NAMESPACE' is not in the 'Running' phase (current phase: $STATUS). Retrying in $RETRY_INTERVAL seconds..." + sleep $RETRY_INTERVAL +# break 2 + fi + done + + echo "All pods in namespace '$NAMESPACE' are in the 'Running' phase." + exit 0 +done + +echo "Not all pods in namespace '$NAMESPACE' became 'Running' after $RETRY_LIMIT retries ($(($RETRY_LIMIT * $RETRY_INTERVAL)) seconds)." +exit 1 + +} + +"$@" \ No newline at end of file From 96ca496d6b63a2e7bec1080e4bf637efc5ac6d23 Mon Sep 17 00:00:00 2001 From: shivkumr Date: Wed, 11 Sep 2024 00:38:55 -0400 Subject: [PATCH 4/8] Moved Karpenter installation into the preinstall script --- cluster/eksctl/cluster.yaml | 2 +- .../.workshop/cleanup.sh | 21 +- .../.workshop/terraform/main.tf | 227 +++++++++++++++--- .../.workshop/terraform/output.tf | 4 +- .../jupyterhub/jupyter.py | 14 +- ...aml => g5-x86-gpu-nodepool-nodeclass.yaml} | 61 +++++ .../karpenter/x86-cpu-nodepool-nodeclass.yaml | 1 - .../depoly-prerequesite.md | 62 +---- .../aiml/deploy-monitor-genai-model/index.md | 8 +- .../modelinference.md | 2 +- .../modeltraining.md | 10 +- 11 files changed, 279 insertions(+), 133 deletions(-) rename manifests/modules/aiml/deploy-monitor-genai-model/karpenter/{g5-gpu-nodepool-nodeclass.yaml => g5-x86-gpu-nodepool-nodeclass.yaml} (52%) diff --git a/cluster/eksctl/cluster.yaml b/cluster/eksctl/cluster.yaml index d6275bd52..83d0f6066 100644 --- a/cluster/eksctl/cluster.yaml +++ b/cluster/eksctl/cluster.yaml @@ -21,7 +21,7 @@ vpc: publicAccess: true addons: - name: vpc-cni - version: 1.16.0 + version: 1.18.3 configurationValues: '{"env":{"ENABLE_PREFIX_DELEGATION":"true", "ENABLE_POD_ENI":"true", "POD_SECURITY_GROUP_ENFORCING_MODE":"standard"},"enableNetworkPolicy": "true"}' resolveConflicts: overwrite managedNodeGroups: diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh index e088168e1..d7c845f77 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh @@ -7,28 +7,9 @@ kubectl delete ns dogbooth --ignore-not-found helm uninstall jupyterhub -n jupyterhub helm uninstall nginx-ingress helm uninstall kuberay-operator -kubectl delete ns karpenter kubectl delete ns jupyterhub - # Uninstall gpu-operator GPU_OPERATOR_RELEASE_NAME=$(helm list -n gpu-operator -q) helm uninstall $GPU_OPERATOR_RELEASE_NAME -n gpu-operator -kubectl delete ns gpu-operator - - -delete-all-if-crd-exists nodepools.karpenter.sh -delete-all-if-crd-exists ec2nodeclasses.karpenter.k8s.aws - -EXIT_CODE=0 - -timeout --foreground -s TERM 30 bash -c \ - 'while [[ $(kubectl get nodes --selector=type=karpenter -o json | jq -r ".items | length") -gt 0 ]];\ - do sleep 5;\ - done' || EXIT_CODE=$? - -if [ $EXIT_CODE -ne 0 ]; then - logmessage "Warning: Karpenter nodes did not clean up" -fi - -helm uninstall karpenter -n karpenter \ No newline at end of file +kubectl delete ns gpu-operator \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf index 4ffe960e1..e9b4fc71f 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf @@ -1,3 +1,11 @@ +terraform { + required_providers { + kubectl = { + source = "alekc/kubectl" + } + } +} + provider "aws" { region = "us-east-1" alias = "virginia" @@ -48,27 +56,6 @@ module "eks_blueprints_addons" { } } -resource "aws_eks_addon" "pod_identity" { - cluster_name = var.addon_context.eks_cluster_id - addon_name = "eks-pod-identity-agent" - resolve_conflicts_on_create = "OVERWRITE" - preserve = false -} - -module "karpenter" { - source = "terraform-aws-modules/eks/aws//modules/karpenter" - version = "20.13.1" - - cluster_name = var.addon_context.eks_cluster_id - enable_pod_identity = true - create_pod_identity_association = true - namespace = "karpenter" - - node_iam_role_additional_policies = { - AmazonSSMManagedInstanceCore = "arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" - } -} - resource "kubernetes_annotations" "disable_gp2" { annotations = { "storageclass.kubernetes.io/is-default-class" : "false" @@ -79,7 +66,6 @@ resource "kubernetes_annotations" "disable_gp2" { name = "gp2" } force = true - } resource "kubernetes_storage_class" "default_gp3" { @@ -89,7 +75,6 @@ resource "kubernetes_storage_class" "default_gp3" { "storageclass.kubernetes.io/is-default-class" : "true" } } - storage_provisioner = "ebs.csi.aws.com" reclaim_policy = "Delete" allow_volume_expansion = true @@ -99,15 +84,205 @@ resource "kubernetes_storage_class" "default_gp3" { encrypted = true type = "gp3" } - depends_on = [kubernetes_annotations.disable_gp2] } +module "eks_blueprints_addons_karpenter" { + source = "aws-ia/eks-blueprints-addons/aws" + version = "1.16.3" + + enable_karpenter = true + + karpenter_enable_spot_termination = true + karpenter_enable_instance_profile_creation = true + karpenter = { + repository_username = data.aws_ecrpublic_authorization_token.token.user_name + repository_password = data.aws_ecrpublic_authorization_token.token.password + } + + cluster_name = var.addon_context.eks_cluster_id + cluster_endpoint = var.addon_context.aws_eks_cluster_endpoint + cluster_version = var.eks_cluster_version + oidc_provider_arn = var.addon_context.eks_oidc_provider_arn + + depends_on = [ + module.eks_blueprints_addons + ] +} + + +resource "aws_eks_access_entry" "node" { + + cluster_name = var.addon_context.eks_cluster_id + principal_arn = module.eks_blueprints_addons_karpenter.karpenter.node_iam_role_arn + type = "EC2_LINUX" + + depends_on = [ + module.eks_blueprints_addons_karpenter + ] +} + +resource "kubectl_manifest" "g5-gpu-karpenter-nodepool" { + yaml_body = < id }) diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py index 1ea36237c..39c9ffb8f 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py +++ b/manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py @@ -1,5 +1,5 @@ # Verify NVIDIA GPU is visible -!nvidia-smi +! nvidia-smi import os @@ -7,22 +7,16 @@ # Clone the diffusers repo -!git clone https://github.com/huggingface/diffusers +! git clone https://github.com/huggingface/diffusers # Change the directory os.chdir("diffusers") -# Install requirements +# Installs the necessary Python packages, including the Diffusers library, xformers, and bitsandbytes (for memory-efficient attention optimization). ! pip install -e . ! pip install xformers==0.0.16 diffusers[torch] - - -# Fix for bitsandbytes https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md ! wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/cuda_install.sh ! bash cuda_install.sh 117 ~/local 1 - - -# Install bitsandbytes for optimizations ! pip install bitsandbytes==0.41.0 # Use the newly installed CUDA version for bitsandbytes @@ -36,8 +30,6 @@ os.chdir("examples/dreambooth") ! pip install -r requirements.txt - - # Setup default configuration for accelerate ! accelerate config default diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-x86-gpu-nodepool-nodeclass.yaml similarity index 52% rename from manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml rename to manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-x86-gpu-nodepool-nodeclass.yaml index e8dabcf86..2b3ba5f6d 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-gpu-nodepool-nodeclass.yaml +++ b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/g5-x86-gpu-nodepool-nodeclass.yaml @@ -62,3 +62,64 @@ spec: # userData: | # #!/bin/bash # docker pull public.ecr.aws/h3o5n2r0/dogbooth:0.0.1-gpu + +--- + +apiVersion: karpenter.sh/v1beta1 +kind: NodePool +metadata: + name: x86-cpu-karpenter + labels: + type: karpenter + NodeGroupType: x86-cpu-karpenter +spec: + template: + spec: + nodeClassRef: + apiVersion: karpenter.k8s.aws/v1beta1 + kind: EC2NodeClass + name: x86-cpu-karpenter + requirements: + - key: "karpenter.k8s.aws/instance-family" + operator: In + values: ["m5"] + - key: "karpenter.k8s.aws/instance-size" + operator: In + values: [ "xlarge", "2xlarge", "4xlarge", "8xlarge"] + - key: "kubernetes.io/arch" + operator: In + values: ["amd64"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["on-demand"] + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmpty + consolidateAfter: 300s + expireAfter: 720h + weight: 100 + +--- + +apiVersion: karpenter.k8s.aws/v1beta1 +kind: EC2NodeClass +metadata: + name: x86-cpu-karpenter +spec: + amiFamily: AL2 # Amazon Linux 2 + role: ${KARPENTER_ROLE} + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: "eks-workshop" + securityGroupSelectorTerms: + - tags: + karpenter.sh/discovery: "eks-workshop" + blockDeviceMappings: + - deviceName: /dev/xvda + ebs: + volumeSize: 100Gi + volumeType: gp3 + # userData: | + # #!/bin/bash + # docker pull public.ecr.aws/h3o5n2r0/dogbooth:0.0.1-gpu diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml index 203ba2bcb..69a3a875c 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml +++ b/manifests/modules/aiml/deploy-monitor-genai-model/karpenter/x86-cpu-nodepool-nodeclass.yaml @@ -42,7 +42,6 @@ metadata: spec: amiFamily: AL2 # Amazon Linux 2 role: ${KARPENTER_ROLE} -# role: Karpenter-eks-workshop-20240724023441649900000009 subnetSelectorTerms: - tags: karpenter.sh/discovery: "eks-workshop" diff --git a/website/docs/aiml/deploy-monitor-genai-model/depoly-prerequesite.md b/website/docs/aiml/deploy-monitor-genai-model/depoly-prerequesite.md index 4be137f1f..d80f8a927 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/depoly-prerequesite.md +++ b/website/docs/aiml/deploy-monitor-genai-model/depoly-prerequesite.md @@ -1,67 +1,9 @@ --- -title: "Install Karpenter, Nvidia GPU and Ray Operators" +title: "Install Nvidia GPU and Ray Operators" sidebar_position: 20 -description: "Install Karpenter, Nvidia GPU and Ray Operators" +description: "Install Nvidia GPU and Ray Operators" --- - -# Install Karpenter - -The first thing we'll do is install Karpenter in our cluster. Various pre-requisites were created during the lab preparation stage, including: - -1. An IAM role for Karpenter to call AWS APIs -2. An IAM role and instance profile for the EC2 instances that Karpenter creates -3. An EKS cluster access entry for the node IAM role so the nodes can join the EKS cluster -4. An SQS queue for Karpenter to receive Spot interruption, instance re-balance and other events - -You can find the full installation documentation for Karpenter [here](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/). - -All that we have left to do is install Karpenter: - -```bash timeout=1800 wait=30 -$ helm upgrade --install karpenter oci://public.ecr.aws/karpenter/karpenter \ - --version "${KARPENTER_VERSION}" \ - --namespace "karpenter" --create-namespace \ - --set "settings.clusterName=${EKS_CLUSTER_NAME}" \ - --set "settings.interruptionQueue=${KARPENTER_SQS_QUEUE}" \ - --set controller.resources.requests.cpu=1 \ - --set controller.resources.requests.memory=1Gi \ - --set controller.resources.limits.cpu=1 \ - --set controller.resources.limits.memory=1Gi \ - --set replicas=1 \ - --wait -``` - -Karpenter will be running as a deployment in the `karpenter` namespace: - -```bash hook=check-karpenter-deployment-status timeout=150 -$ kubectl get deployment -n karpenter -NAME READY UP-TO-DATE AVAILABLE AGE -karpenter 1/1 1 1 105s -``` - -For this lab, we will create two `NodePool` and `EC2NodeClass` with the following command, one to scale g5.2xlarge GPU EC2 instance and the second one to scale non-GPU instance-type. - -```bash timeout=180 hook=karpenter-nodepool-ec2nodeclass-status -$ kubectl kustomize ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/karpenter \ - | envsubst | kubectl apply -f- -$ kubectl get nodepool,ec2nodeclass -NAME NODECLASS -nodepool.karpenter.sh/g5-gpu-karpenter g5-gpu-karpenter -nodepool.karpenter.sh/x86-cpu-karpenter x86-cpu-karpenter - -NAME AGE -ec2nodeclass.karpenter.k8s.aws/g5-gpu-karpenter 32m -ec2nodeclass.karpenter.k8s.aws/x86-cpu-karpenter 32ms -``` - -Throughout the workshop you can inspect the Karpenter logs with the following command to understand its behavior: - -```bash -$ kubectl logs -l app.kubernetes.io/instance=karpenter -n karpenter -``` - - ### Deploy NVIDIA GPU Operator The NVIDIA GPU Operator within Kubernetes automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit), automatic node labelling using [GPU Feature Discovery(GFD)](https://github.com/NVIDIA/gpu-feature-discovery), [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) operator export the GPU metrics. diff --git a/website/docs/aiml/deploy-monitor-genai-model/index.md b/website/docs/aiml/deploy-monitor-genai-model/index.md index 5c691899e..f3f55e276 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/index.md +++ b/website/docs/aiml/deploy-monitor-genai-model/index.md @@ -18,10 +18,8 @@ $ prepare-environment aiml/deploy-monitor-genai-model Its takes around 25 mins to complete the initial preparation, this will make the following changes to your lab environment: - Creates EKS cluster - - Install EBS CSI Driver and AWS Load Balancer controller + - Install EBS CSI Driver, AWS Load Balancer controller and Karpenter controller - Installs Amazon Managed Prometheus, Grafana and AWS managed collector - - Prerequisite IAM roles for Karpenter - ::: @@ -34,8 +32,8 @@ Welcome to the "GenAI on Amazon EKS" workshop! In this workshop, we'll cover asp ## High level overview - 1. Create an EKS cluster, install EBS CSI driver & AWS Load Balancer controller, Amazon Managed Prometheus, Granfana and an AWS managed collector - 2. Install Karpenter, Nvidia GPU Operator and Ray cluster + 1. Create an EKS cluster, install EBS CSI driver, AWS Load Balancer controller, Karpenter, Amazon Managed Prometheus, Granfana and AWS managed collector + 2. Install Nvidia GPU Operator and Ray cluster 2. Install Jupyterhub notebook and train the model 4. Create an Inference service using KubeRay service 5. Monitor the GPU metrics using the Amazon Managed Prometheus and Grafana diff --git a/website/docs/aiml/deploy-monitor-genai-model/modelinference.md b/website/docs/aiml/deploy-monitor-genai-model/modelinference.md index 770b14d88..721efee33 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/modelinference.md +++ b/website/docs/aiml/deploy-monitor-genai-model/modelinference.md @@ -56,7 +56,7 @@ Deploy the Ray services and the Ingress service to access it $ kubectl apply -f ~/environment/eks-workshop/modules/aiml/deploy-monitor-genai-model/rayservice/rayservice.yaml ``` -The above deployment will take roughly 12s mins to complete. Let's verify the deployments: +The above deployment will take roughly 12 mins to complete. Let's verify the deployments: ```bash timeout=300 wait=60 $ kubectl get all -n dogbooth diff --git a/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md b/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md index 19af38fe2..47f776a63 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md +++ b/website/docs/aiml/deploy-monitor-genai-model/modeltraining.md @@ -35,11 +35,11 @@ proxy-public LoadBalancer 10.100.4.40 k8s-jupyterh-proxypub-f09eec1e26-620 Use your browser and paste the loadbalancer url obtained in the previous step to access the notebook -Login using the username user1 and the password as specified in the jupyterhub-values.yaml. +Login using the username "user1" and the password as specified in the jupyterhub-values.yaml. ![jupyterhub login page](./assets/jupyterhub-login.png) -Upon successful login, you would see a jupyterhub-user1 pod would have instantiated on a GPU node. If it the launch timesout after, please relaunch the server +Upon successful login, a jupyterhub-user1 pod will have to launch on a GPU instance-type, so karpenter would launch a new GPU instance-type. If the request timesout after, please relaunch the server. Note: This step could take about 10 mins to download the image and to launch the pod. ![upload nb](./assets/user1-session-launch.png) @@ -49,10 +49,12 @@ After a successful jupyterhub-user1 server launch, you should see a jupyterhub-n ![upload nb](./assets/upload-nb.png) -Follow the commands and run each commands in the notebook from the top - ### Jupyter Notebook commands +This code is a set of instructions for setting up and running a Dreambooth training script using the Hugging Face Diffusers library. Dreambooth is a technique for fine-tuning a pre-trained text-to-image model, like Stable Diffusion, to generate images of a specific subject or object. Here's a breakdown of what the code is doing: + +Follow the commands and run each commands in the notebook from the top + ```file manifests/modules/aiml/deploy-monitor-genai-model/jupyterhub/jupyter.py From dca61f4c36c180ee1655274f9bebe0c1bfaa76e1 Mon Sep 17 00:00:00 2001 From: Niall Thomson Date: Thu, 26 Sep 2024 16:12:29 -0700 Subject: [PATCH 5/8] Update index.md --- .../aiml/deploy-monitor-genai-model/index.md | 22 +++++-------------- 1 file changed, 5 insertions(+), 17 deletions(-) diff --git a/website/docs/aiml/deploy-monitor-genai-model/index.md b/website/docs/aiml/deploy-monitor-genai-model/index.md index f3f55e276..8d1655cef 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/index.md +++ b/website/docs/aiml/deploy-monitor-genai-model/index.md @@ -1,12 +1,13 @@ --- title: "Deploy and Monitor GenAI Model on EKS" sidebar_position: 10 -sidebar_custom_props: { "module": true } +sidebar_custom_props: { "beta": true } description: "Deploy and Monitor GenAI Model on EKS" --- - -{{% required-time %}} +:::danger +This module is not supported at AWS events or in AWS-vended accounts through Workshop Studio. This module is only supported for clusters created through the "[In your AWS account](/docs/introduction/setup/your-account)" steps. +::: :::tip Before you start Prepare your environment for this section: @@ -22,19 +23,6 @@ Its takes around 25 mins to complete the initial preparation, this will make the - Installs Amazon Managed Prometheus, Grafana and AWS managed collector ::: - -# GenAI on EKS - -Welcome to the "GenAI on Amazon EKS" workshop! In this workshop, we'll cover aspects, from creating an EKS cluster with GPU nodes to model training, inference with Ray, and real-time monitoring using Amazon Managed Prometheus and Grafana. +This lab will cover we'll cover various aspects from provisioning GPU nodes, model training, inference with Ray, and real-time monitoring using Amazon Managed Prometheus and Grafana. ![Build Model](./assets/GenAI-on-EKS.png) - - -## High level overview - - 1. Create an EKS cluster, install EBS CSI driver, AWS Load Balancer controller, Karpenter, Amazon Managed Prometheus, Granfana and AWS managed collector - 2. Install Nvidia GPU Operator and Ray cluster - 2. Install Jupyterhub notebook and train the model - 4. Create an Inference service using KubeRay service - 5. Monitor the GPU metrics using the Amazon Managed Prometheus and Grafana - From a69cba76d28539d868527e261de295b4b6369f37 Mon Sep 17 00:00:00 2001 From: Niall Thomson Date: Fri, 27 Sep 2024 09:10:32 -0600 Subject: [PATCH 6/8] Require intro update for consistency, fixed spelling in file name --- ...{depoly-prerequesite.md => deploy-prerequisite.md} | 0 website/docs/aiml/deploy-monitor-genai-model/index.md | 11 ++++++----- 2 files changed, 6 insertions(+), 5 deletions(-) rename website/docs/aiml/deploy-monitor-genai-model/{depoly-prerequesite.md => deploy-prerequisite.md} (100%) diff --git a/website/docs/aiml/deploy-monitor-genai-model/depoly-prerequesite.md b/website/docs/aiml/deploy-monitor-genai-model/deploy-prerequisite.md similarity index 100% rename from website/docs/aiml/deploy-monitor-genai-model/depoly-prerequesite.md rename to website/docs/aiml/deploy-monitor-genai-model/deploy-prerequisite.md diff --git a/website/docs/aiml/deploy-monitor-genai-model/index.md b/website/docs/aiml/deploy-monitor-genai-model/index.md index 8d1655cef..4119d04fd 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/index.md +++ b/website/docs/aiml/deploy-monitor-genai-model/index.md @@ -1,8 +1,8 @@ --- title: "Deploy and Monitor GenAI Model on EKS" -sidebar_position: 10 +sidebar_position: 40 sidebar_custom_props: { "beta": true } -description: "Deploy and Monitor GenAI Model on EKS" +description: "Deploy and monitor GenAI models on EKS" --- :::danger @@ -18,9 +18,10 @@ $ prepare-environment aiml/deploy-monitor-genai-model Its takes around 25 mins to complete the initial preparation, this will make the following changes to your lab environment: - - Creates EKS cluster - - Install EBS CSI Driver, AWS Load Balancer controller and Karpenter controller - - Installs Amazon Managed Prometheus, Grafana and AWS managed collector +- Creates EKS cluster +- Install EBS CSI Driver, AWS Load Balancer controller and Karpenter controller +- Installs Amazon Managed Prometheus, Grafana and AWS managed collector + ::: This lab will cover we'll cover various aspects from provisioning GPU nodes, model training, inference with Ray, and real-time monitoring using Amazon Managed Prometheus and Grafana. From b9995fe3b9409ae62f6e59af540525595cd23987 Mon Sep 17 00:00:00 2001 From: Niall Thomson Date: Mon, 28 Oct 2024 20:29:50 -0600 Subject: [PATCH 7/8] Various fixes to bring lab in line with rest of the workshop --- lab/iam/policies/iam.yaml | 1 + lab/iam/policies/labs1.yaml | 3 + manifests/.workshop/terraform/base.tf | 4 +- manifests/manifests | 1 - .../.workshop/cleanup.sh | 17 +- .../.workshop/terraform/main.tf | 215 +++++++++++------- .../nvidia-dcgm-exporter-dashboard.json | 0 .../.workshop/terraform/output.tf | 4 +- .../.workshop/terraform/vars.tf | 2 +- .../deploy-prerequisite.md | 55 ++--- 10 files changed, 175 insertions(+), 127 deletions(-) delete mode 120000 manifests/manifests rename manifests/modules/aiml/deploy-monitor-genai-model/{grafana => .workshop/terraform}/nvidia-dcgm-exporter-dashboard.json (100%) diff --git a/lab/iam/policies/iam.yaml b/lab/iam/policies/iam.yaml index 15641fc6c..56ef8280f 100644 --- a/lab/iam/policies/iam.yaml +++ b/lab/iam/policies/iam.yaml @@ -73,3 +73,4 @@ Statement: - eks.amazonaws.com - eks-nodegroup.amazonaws.com - eks-fargate.amazonaws.com + - scraper.aps.amazonaws.com diff --git a/lab/iam/policies/labs1.yaml b/lab/iam/policies/labs1.yaml index 20217d34f..bdd601bc7 100644 --- a/lab/iam/policies/labs1.yaml +++ b/lab/iam/policies/labs1.yaml @@ -12,9 +12,12 @@ Statement: - Effect: Allow Action: - aps:DeleteWorkspace + - aps:DeleteScraper - aps:Describe* - aps:List* - aps:QueryMetrics + - aps:CreateScraper + - aps:TagResource Resource: ["*"] Condition: StringLike: diff --git a/manifests/.workshop/terraform/base.tf b/manifests/.workshop/terraform/base.tf index 6e0be63d4..5bb071bbd 100644 --- a/manifests/.workshop/terraform/base.tf +++ b/manifests/.workshop/terraform/base.tf @@ -15,8 +15,8 @@ terraform { version = "2.15.0" } kubectl = { - source = "gavinbunney/kubectl" - version = "1.14.0" + source = "alekc/kubectl" + version = "2.0.4" } local = { version = "2.5.1" diff --git a/manifests/manifests b/manifests/manifests deleted file mode 120000 index c112814e8..000000000 --- a/manifests/manifests +++ /dev/null @@ -1 +0,0 @@ -/manifests \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh index d7c845f77..ee9f7631c 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/cleanup.sh @@ -1,15 +1,14 @@ #!/bin/bash -kubectl delete ingress dogbooth -n dogbooth --ignore-not-found -kubectl delete rayservice dogbooth -n dogbooth --ignore-not-found kubectl delete ns dogbooth --ignore-not-found -helm uninstall jupyterhub -n jupyterhub -helm uninstall nginx-ingress -helm uninstall kuberay-operator -kubectl delete ns jupyterhub +uninstall-helm-chart jupyterhub jupyterhub +uninstall-helm-chart nginx-ingress default +uninstall-helm-chart kuberay-operator kuberay + +kubectl delete ns jupyterhub --ignore-not-found # Uninstall gpu-operator -GPU_OPERATOR_RELEASE_NAME=$(helm list -n gpu-operator -q) -helm uninstall $GPU_OPERATOR_RELEASE_NAME -n gpu-operator -kubectl delete ns gpu-operator \ No newline at end of file +uninstall-helm-chart gpu-operator gpu-operator + +kubectl delete ns gpu-operator --ignore-not-found \ No newline at end of file diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf index e9b4fc71f..33625e1db 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/main.tf @@ -1,3 +1,8 @@ +provider "aws" { + region = "us-east-1" + alias = "virginia" +} + terraform { required_providers { kubectl = { @@ -6,9 +11,8 @@ terraform { } } -provider "aws" { - region = "us-east-1" - alias = "virginia" +locals { + namespace = "kube-system" } data "aws_caller_identity" "current" {} @@ -17,9 +21,10 @@ data "aws_ecrpublic_authorization_token" "token" { provider = aws.virginia } module "ebs_csi_driver_irsa" { source = "terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks" - version = "~> 5.20" + version = "5.44.0" - role_name_prefix = "${var.addon_context.eks_cluster_id}-ebs-csi-" + role_name_prefix = "${var.addon_context.eks_cluster_id}-ebs-csi-" + policy_name_prefix = "${var.addon_context.eks_cluster_id}-ebs-csi-" attach_ebs_csi_policy = true @@ -47,15 +52,26 @@ module "eks_blueprints_addons" { most_recent = true service_account_role_arn = module.ebs_csi_driver_irsa.iam_role_arn preserve = false + configuration_values = jsonencode({ defaultStorageClass = { enabled = true } }) } } enable_aws_load_balancer_controller = true aws_load_balancer_controller = { - wait = true + wait = true + role_name = "${var.addon_context.eks_cluster_id}-alb-controller" + policy_name = "${var.addon_context.eks_cluster_id}-alb-controller" } } +resource "time_sleep" "wait" { + depends_on = [ + module.eks_blueprints_addons + ] + + create_duration = "10s" +} + resource "kubernetes_annotations" "disable_gp2" { annotations = { "storageclass.kubernetes.io/is-default-class" : "false" @@ -75,68 +91,101 @@ resource "kubernetes_storage_class" "default_gp3" { "storageclass.kubernetes.io/is-default-class" : "true" } } + storage_provisioner = "ebs.csi.aws.com" reclaim_policy = "Delete" allow_volume_expansion = true volume_binding_mode = "WaitForFirstConsumer" + parameters = { fsType = "ext4" encrypted = true type = "gp3" } + depends_on = [kubernetes_annotations.disable_gp2] } +resource "aws_eks_addon" "pod_identity" { + cluster_name = var.addon_context.eks_cluster_id + addon_name = "eks-pod-identity-agent" + resolve_conflicts_on_create = "OVERWRITE" + preserve = false +} -module "eks_blueprints_addons_karpenter" { - source = "aws-ia/eks-blueprints-addons/aws" - version = "1.16.3" +# Karpenter controller & Node IAM roles, SQS Queue, Eventbridge Rules - enable_karpenter = true +module "karpenter" { + source = "terraform-aws-modules/eks/aws//modules/karpenter" + version = "20.24.0" - karpenter_enable_spot_termination = true - karpenter_enable_instance_profile_creation = true - karpenter = { - repository_username = data.aws_ecrpublic_authorization_token.token.user_name - repository_password = data.aws_ecrpublic_authorization_token.token.password - } + cluster_name = var.addon_context.eks_cluster_id + enable_v1_permissions = true + namespace = local.namespace - cluster_name = var.addon_context.eks_cluster_id - cluster_endpoint = var.addon_context.aws_eks_cluster_endpoint - cluster_version = var.eks_cluster_version - oidc_provider_arn = var.addon_context.eks_oidc_provider_arn + iam_role_name = "${var.addon_context.eks_cluster_id}-karpenter-controller" + iam_role_use_name_prefix = false + iam_policy_name = "${var.addon_context.eks_cluster_id}-karpenter-controller" + iam_policy_use_name_prefix = false + node_iam_role_name = "${var.addon_context.eks_cluster_id}-karpenter-node" + node_iam_role_use_name_prefix = false + queue_name = "${var.addon_context.eks_cluster_id}-karpenter" + rule_name_prefix = "eks-workshop" + create_pod_identity_association = true - depends_on = [ - module.eks_blueprints_addons - ] + tags = { + created-by = "eks-workshop-v2" + env = var.addon_context.eks_cluster_id + } } - -resource "aws_eks_access_entry" "node" { - - cluster_name = var.addon_context.eks_cluster_id - principal_arn = module.eks_blueprints_addons_karpenter.karpenter.node_iam_role_arn - type = "EC2_LINUX" - - depends_on = [ - module.eks_blueprints_addons_karpenter +# Helm chart + +resource "helm_release" "karpenter" { + name = "karpenter" + namespace = local.namespace + create_namespace = true + repository = "oci://public.ecr.aws/karpenter" + repository_username = data.aws_ecrpublic_authorization_token.token.user_name + repository_password = data.aws_ecrpublic_authorization_token.token.password + chart = "karpenter" + version = var.karpenter_version + wait = true + + values = [ + <<-EOT + settings: + clusterName: ${var.addon_context.eks_cluster_id} + clusterEndpoint: ${var.addon_context.aws_eks_cluster_endpoint} + interruptionQueue: ${module.karpenter.queue_name} + tolerations: + - key: CriticalAddonsOnly + operator: Exists + EOT ] + + lifecycle { + ignore_changes = [ + repository_password + ] + } } -resource "kubectl_manifest" "g5-gpu-karpenter-nodepool" { - yaml_body = < id }) diff --git a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf index 2d0a20b31..121d3ced4 100644 --- a/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf +++ b/manifests/modules/aiml/deploy-monitor-genai-model/.workshop/terraform/vars.tf @@ -38,5 +38,5 @@ variable "karpenter_version" { description = "The version of Karpenter to use" type = string # renovate: datasource=github-releases depName=aws/karpenter-provider-aws - default = "0.35.4" + default = "1.0.3" } \ No newline at end of file diff --git a/website/docs/aiml/deploy-monitor-genai-model/deploy-prerequisite.md b/website/docs/aiml/deploy-monitor-genai-model/deploy-prerequisite.md index d80f8a927..12ca51baa 100644 --- a/website/docs/aiml/deploy-monitor-genai-model/deploy-prerequisite.md +++ b/website/docs/aiml/deploy-monitor-genai-model/deploy-prerequisite.md @@ -1,35 +1,36 @@ --- -title: "Install Nvidia GPU and Ray Operators" +title: "Install Nvidia GPU and Ray operators" sidebar_position: 20 -description: "Install Nvidia GPU and Ray Operators" --- -### Deploy NVIDIA GPU Operator +### Deploy NVIDIA GPU Operator The NVIDIA GPU Operator within Kubernetes automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit), automatic node labelling using [GPU Feature Discovery(GFD)](https://github.com/NVIDIA/gpu-feature-discovery), [Data Center GPU Manager(DCGM)](https://developer.nvidia.com/dcgm) operator export the GPU metrics. - -Add the NVIDIA Helm repository and install the GPU Operator: +Add the NVIDIA Helm repository and install the GPU operator: ```bash timeout=300 wait=60 hook=gpu-operator-status-check $ helm repo add nvidia https://helm.ngc.nvidia.com/nvidia $ helm repo update -$ helm install --wait --generate-name -n gpu-operator --create-namespace nvidia/gpu-operator +$ helm upgrade --install gpu-operator -n gpu-operator \ + --create-namespace nvidia/gpu-operator --wait +$ kubectl wait pod --all \ + --for=condition=Ready -n gpu-operator ``` -Once installed, we should see the node-feature-discovery running on each node, nvidia-container-toolkit, nvidia-cuda-validator, nvidia-dcgm-exporter, nvidia-device-plugin-daemonset, nvidia-driver-daemonset and nvidia-operator-validator running on a GPU enabled nodes. +Once installed, we should see the node-feature-discovery running on each node, nvidia-container-toolkit, nvidia-cuda-validator, nvidia-dcgm-exporter, nvidia-device-plugin-daemonset, nvidia-driver-daemonset and nvidia-operator-validator running on a GPU enabled nodes. -```bash +```bash $ kubectl get all -n gpu-operator NAME READY STATUS RESTARTS AGE pod/gpu-feature-discovery-5z88x 1/1 Running 0 23m -pod/gpu-operator-1720906832-node-feature-discovery-gc-5769c875hsszf 1/1 Running 0 5h59m -pod/gpu-operator-1720906832-node-feature-discovery-master-7999vflt4 1/1 Running 0 5h59m -pod/gpu-operator-1720906832-node-feature-discovery-worker-4lv2d 1/1 Running 0 5h59m -pod/gpu-operator-1720906832-node-feature-discovery-worker-52tfr 1/1 Running 0 27m -pod/gpu-operator-1720906832-node-feature-discovery-worker-jw97f 1/1 Running 0 5h59m -pod/gpu-operator-1720906832-node-feature-discovery-worker-mnhtx 1/1 Running 0 25m -pod/gpu-operator-1720906832-node-feature-discovery-worker-sxs2s 1/1 Running 0 5h59m +pod/gpu-operator-node-feature-discovery-gc-5769c875hsszf 1/1 Running 0 5h59m +pod/gpu-operator-node-feature-discovery-master-7999vflt4 1/1 Running 0 5h59m +pod/gpu-operator-node-feature-discovery-worker-4lv2d 1/1 Running 0 5h59m +pod/gpu-operator-node-feature-discovery-worker-52tfr 1/1 Running 0 27m +pod/gpu-operator-node-feature-discovery-worker-jw97f 1/1 Running 0 5h59m +pod/gpu-operator-node-feature-discovery-worker-mnhtx 1/1 Running 0 25m +pod/gpu-operator-node-feature-discovery-worker-sxs2s 1/1 Running 0 5h59m pod/gpu-operator-7488b846b-z7vrb 1/1 Running 0 5h59m pod/nvidia-container-toolkit-daemonset-jjgjr 1/1 Running 0 23m pod/nvidia-cuda-validator-rk62c 0/1 Completed 0 20m @@ -37,28 +38,28 @@ pod/nvidia-dcgm-exporter-cg7n5 1/1 Ru pod/nvidia-device-plugin-daemonset-9jzkj 1/1 Running 0 23m pod/nvidia-driver-daemonset-wt5tn 1/1 Running 0 25m pod/nvidia-operator-validator-gmhjl 1/1 Running 0 23m - + ``` ### Deploy the Kuberay Operator Ray is a popular open-source distributed computing framework that is widely used for building and running scalable AI/ML applications. The Kuberay Operator integrates seamlessly with Ray, providing a declarative, Kubernetes-native interface for deploying and managing Ray-powered services. -Install the Kuberay operator, and later in the lab we will create an Inference service using RayService. +Install the Kuberay operator, and later in the lab we will create an Inference service using RayService. -```bash timeout=300 wait=60 +```bash timeout=300 $ helm repo add kuberay https://ray-project.github.io/kuberay-helm/ $ helm repo update -# Install the KubeRay operator 1.1.0 -$ helm install kuberay-operator kuberay/kuberay-operator --version 1.0.0-rc.0 +$ helm install kuberay-operator kuberay/kuberay-operator -n kuberay \ + --create-namespace --version 1.0.0 --wait +$ kubectl wait pod --all \ + --for=condition=Ready -n kuberay ``` -Check the Ray operator status +Check the Ray operator status -```bash timeout=60 hook=kuberay-operator-check -# Check the KubeRay operator Pod in `default` namespace -$ kubectl get pods -# NAME READY STATUS RESTARTS AGE -# kuberay-operator-6fcbb94f64-mbfnr 1/1 Running 0 17s +```bash timeout=60 +$ kubectl get pods -n kuberay +NAME READY STATUS RESTARTS AGE +kuberay-operator-6fcbb94f64-mbfnr 1/1 Running 0 17s ``` - From 3021994ce5daada7b302f5bff54630943d5e7f2e Mon Sep 17 00:00:00 2001 From: Niall Thomson Date: Mon, 28 Oct 2024 20:35:57 -0600 Subject: [PATCH 8/8] Fix describe permissions --- lab/iam/policies/labs1.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/lab/iam/policies/labs1.yaml b/lab/iam/policies/labs1.yaml index bdd601bc7..6f9f1e56b 100644 --- a/lab/iam/policies/labs1.yaml +++ b/lab/iam/policies/labs1.yaml @@ -23,6 +23,10 @@ Statement: StringLike: aws:ResourceTag/env: - ${Env}* + - Effect: Allow + Action: + - aps:DescribeScraper + Resource: ["*"] - Effect: Allow Action: - dynamodb:ListTables