From 8228f0f1c1c35e8dcf95dcde40ebae6de5cc4ed9 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Thu, 1 Aug 2024 11:23:45 +0800 Subject: [PATCH 01/53] feat: run GPU node with BR and EBS snapshot with container image cache --- ai-ml/jark-stack/terraform/addons.tf | 40 +++++++++++++++++++++++++--- 1 file changed, 36 insertions(+), 4 deletions(-) diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index 4a079b748..f56eb700d 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -145,9 +145,10 @@ module "eks_blueprints_addons" { #--------------------------------------------------------------- # Data on EKS Kubernetes Addons #--------------------------------------------------------------- + module "data_addons" { source = "aws-ia/eks-data-addons/aws" - version = "~> 1.31.4" # ensure to update this to the latest/desired version + version = "~> 1.40" # source = "aws-ia/eks-data-addons/aws" oidc_provider_arn = module.eks.oidc_provider_arn @@ -182,7 +183,7 @@ module "data_addons" { #--------------------------------------------------------------- enable_nvidia_device_plugin = true nvidia_device_plugin_helm_config = { - version = "v0.15.1" + version = "v0.16.1" name = "nvidia-device-plugin" values = [ <<-EOT @@ -223,14 +224,16 @@ module "data_addons" { #--------------------------------------------------------------- # Karpenter Resources Add-on #--------------------------------------------------------------- - enable_karpenter_resources = false + enable_karpenter_resources = true karpenter_resources_helm_config = { + g5-gpu-karpenter = { values = [ <<-EOT name: g5-gpu-karpenter clusterName: ${module.eks.cluster_name} ec2NodeClass: + amiFamily: Bottlerocket karpenterRole: ${split("/", module.eks_blueprints_addons.karpenter.node_iam_role_arn)[1]} subnetSelectorTerms: id: ${module.vpc.private_subnets[2]} @@ -238,6 +241,20 @@ module "data_addons" { tags: Name: ${module.eks.cluster_name}-node instanceStorePolicy: RAID0 + blockDeviceMappings: + # Root device + - deviceName: /dev/xvda + ebs: + volumeSize: 50Gi + volumeType: gp3 + encrypted: true + # Data device: Container resources such as images and logs + - deviceName: /dev/xvdb + ebs: + volumeSize: 300Gi + volumeType: gp3 + encrypted: true + ${var.bottlerocket_data_disk_snpashot_id != null ? "snapshotID: ${var.bottlerocket_data_disk_snpashot_id}" : ""} nodePool: labels: @@ -276,13 +293,28 @@ module "data_addons" { name: x86-cpu-karpenter clusterName: ${module.eks.cluster_name} ec2NodeClass: + amiFamily: Bottlerocket karpenterRole: ${split("/", module.eks_blueprints_addons.karpenter.node_iam_role_arn)[1]} subnetSelectorTerms: id: ${module.vpc.private_subnets[3]} securityGroupSelectorTerms: tags: Name: ${module.eks.cluster_name}-node - instanceStorePolicy: RAID0 + # instanceStorePolicy: RAID0 + blockDeviceMappings: + # Root device + - deviceName: /dev/xvda + ebs: + volumeSize: 100Gi + volumeType: gp3 + encrypted: true + # Data device: Container resources such as images and logs + - deviceName: /dev/xvdb + ebs: + volumeSize: 300Gi + volumeType: gp3 + encrypted: true + ${var.bottlerocket_data_disk_snpashot_id != null ? "snapshotID: ${var.bottlerocket_data_disk_snpashot_id}" : ""} nodePool: labels: From efaabb99b86fa768e9d5b31cb227149dd0536bf6 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Thu, 1 Aug 2024 11:27:52 +0800 Subject: [PATCH 02/53] refactor: remove kubectl_manifest of karpenter custom resources --- ai-ml/jark-stack/terraform/karpenter.tf | 141 ------------------------ 1 file changed, 141 deletions(-) diff --git a/ai-ml/jark-stack/terraform/karpenter.tf b/ai-ml/jark-stack/terraform/karpenter.tf index f63d9d5a9..87bcded99 100644 --- a/ai-ml/jark-stack/terraform/karpenter.tf +++ b/ai-ml/jark-stack/terraform/karpenter.tf @@ -1,144 +1,3 @@ - -resource "kubectl_manifest" "karpenter_gpu_node_class" { - yaml_body = <<-YAML - apiVersion: karpenter.k8s.aws/v1beta1 - kind: EC2NodeClass - metadata: - name: default - spec: - amiFamily: Bottlerocket - role: ${module.eks_blueprints_addons.karpenter.node_iam_role_name} - securityGroupSelectorTerms: - - tags: - Name: ${module.eks.cluster_name}-node - subnetSelectorTerms: - - tags: - karpenter.sh/discovery: ${module.eks.cluster_name} - tags: - karpenter.sh/discovery: ${module.eks.cluster_name} - blockDeviceMappings: - # Root device - - deviceName: /dev/xvda - ebs: - volumeSize: 50Gi - volumeType: gp3 - encrypted: true - # Data device: Container resources such as images and logs - - deviceName: /dev/xvdb - ebs: - volumeSize: 300Gi - volumeType: gp3 - encrypted: true - ${var.bottlerocket_data_disk_snpashot_id != null ? "snapshotID: ${var.bottlerocket_data_disk_snpashot_id}" : ""} - YAML - depends_on = [module.eks_blueprints_addons] -} - -resource "kubectl_manifest" "karpenter_gpu_node_pool" { - yaml_body = <<-YAML - apiVersion: karpenter.sh/v1beta1 - kind: NodePool - metadata: - name: gpu - spec: - disruption: - consolidateAfter: 600s - consolidationPolicy: WhenEmpty - expireAfter: 720h - limits: - cpu: 1k - memory: 1000Gi - nvidia.com/gpu: 50 - template: - metadata: - labels: - NodeGroupType: g5-gpu-karpenter - type: karpenter - spec: - nodeClassRef: - name: default - requirements: - - key: kubernetes.io/arch - operator: In - values: ["amd64"] - - key: karpenter.k8s.aws/instance-category - operator: In - values: ["g"] - - key: karpenter.k8s.aws/instance-generation - operator: Gt - values: ["4"] - - key: "karpenter.k8s.aws/instance-cpu" - operator: In - values: ["4", "8", "16"] - - key: kubernetes.io/os - operator: In - values: ["linux"] - - key: "karpenter.k8s.aws/instance-hypervisor" - operator: In - values: ["nitro"] - - key: "topology.kubernetes.io/zone" - operator: In - values: ${jsonencode(local.azs)} - - key: karpenter.sh/capacity-type - operator: In - values: ["on-demand"] - taints: - - key: nvidia.com/gpu - value: "Exists" - effect: "NoSchedule" - - YAML - depends_on = [module.eks_blueprints_addons] -} - -resource "kubectl_manifest" "karpenter_node_pool" { - yaml_body = <<-YAML - apiVersion: karpenter.sh/v1beta1 - kind: NodePool - metadata: - name: default - - spec: - disruption: - consolidateAfter: 600s - consolidationPolicy: WhenEmpty - expireAfter: 720h - limits: - cpu: 1k - template: - metadata: - labels: - NodeGroupType: x86-cpu-karpenter - type: karpenter - spec: - kubelet: - maxPods: 110 - nodeClassRef: - name: default - requirements: - - key: "karpenter.k8s.aws/instance-category" - operator: In - values: ["c", "m", "r"] - - key: "karpenter.k8s.aws/instance-cpu" - operator: In - values: ["4", "8", "16"] - - key: "karpenter.k8s.aws/instance-hypervisor" - operator: In - values: ["nitro"] - - key: "topology.kubernetes.io/zone" - operator: In - values: ${jsonencode(local.azs)} - - key: "kubernetes.io/arch" - operator: In - values: ["amd64"] - - key: "karpenter.sh/capacity-type" - operator: In - values: ["on-demand"] - YAML - depends_on = [module.eks_blueprints_addons] -} - - resource "aws_iam_policy" "karpenter_controlloer_policy" { description = "Additional IAM policy for Karpenter controller" policy = data.aws_iam_policy_document.karpenter_controller_policy.json From a3dcd3255a0b6f55dd04cfeb0370dd6bc27dbc6c Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Thu, 1 Aug 2024 11:28:45 +0800 Subject: [PATCH 03/53] feat: locust file fo load testing --- .../locust/locustfile.py | 30 +++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/locust/locustfile.py diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/locust/locustfile.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/locust/locustfile.py new file mode 100644 index 000000000..e441c1420 --- /dev/null +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/locust/locustfile.py @@ -0,0 +1,30 @@ +import json +from locust import HttpUser, task, between + +class StableDiffusionUser(HttpUser): + wait_time = between(1, 2) # Seconds between requests + + @task + def generate_image(self): + prompt = "A beautiful sunset over the ocean" + payload = { + "prompt": prompt + } + + headers = { + "Content-Type": "application/json" + } + + response = self.client.get( + "/imagine", + params=payload, + data=json.dumps(payload), + headers=headers + ) + + if response.status_code == 200: + print(f"Generated image for prompt: {prompt}") + else: + print(f"Error generating image: {response.text}") + + # You can add more tasks here if needed From 67e29bb38ab4361cbaaf7179ebeb946be10579c8 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Thu, 1 Aug 2024 12:45:39 +0800 Subject: [PATCH 04/53] feat: End-to-end deployment of Bottlerocket nodes with container image cache --- ai-ml/jark-stack/terraform/README.md | 22 ++- ai-ml/jark-stack/terraform/install.sh | 4 + .../img/bottlerocket-image-cache.png | Bin 0 -> 111517 bytes .../bestpractices/preload-container-images.md | 173 ++++++++++++++++++ 4 files changed, 189 insertions(+), 10 deletions(-) create mode 100644 website/docs/bestpractices/img/bottlerocket-image-cache.png create mode 100644 website/docs/bestpractices/preload-container-images.md diff --git a/ai-ml/jark-stack/terraform/README.md b/ai-ml/jark-stack/terraform/README.md index f2f5c96cd..40c8b892b 100644 --- a/ai-ml/jark-stack/terraform/README.md +++ b/ai-ml/jark-stack/terraform/README.md @@ -8,16 +8,6 @@ Before we begin, ensure you have all the prerequisites in place to make the depl - kubectl - terraform -## Reduce Cold Start Time by Preloading Container Images in Bottlerocket OS - -Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to provision Bottlerocket worker nodes with EBS Snapshots, to reduce cold start for container startup. This will likely to save 10 mins for downloading and extracting container images from Amazon ECR. - -To build snapshots with preloaded container images, refer to [this page](../preload-container-image-ami/README.md) for details. - -``` -# Get the snapshot ID with ./snapshot.sh - -export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0c6d965cf431785ed ./install.sh ... @@ -44,6 +34,8 @@ Events: # Deploy + + Clone the repository ``` @@ -54,6 +46,16 @@ Navigate into one of the example directories and run install.sh script Important Note: Ensure that you update the region in the variables.tf file before deploying the blueprint. Additionally, confirm that your local region setting matches the specified region to prevent any discrepancies. For example, set your `export AWS_DEFAULT_REGION=""` to the desired region: +## Reduce Cold Start Time by Preloading Container Images in Bottlerocket OS (o) + +Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to provision Bottlerocket worker nodes with EBS Snapshots, to reduce cold start for container startup. This will likely to save 10 mins for downloading and extracting container images from Amazon ECR. + +To build snapshots with preloaded container images, refer to [this page](../preload-container-image-ami/README.md) for details. + +# Get the snapshot ID with ./snapshot.sh + +export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0c6d965cf431785ed + ``` cd data-on-eks/ai-ml/jark-stack/ && chmod +x install.sh ./install.sh diff --git a/ai-ml/jark-stack/terraform/install.sh b/ai-ml/jark-stack/terraform/install.sh index 1814a9044..c1b95e6c0 100755 --- a/ai-ml/jark-stack/terraform/install.sh +++ b/ai-ml/jark-stack/terraform/install.sh @@ -6,6 +6,10 @@ targets=( "module.eks" ) +# Get the snapshot ID with ./snapshot.sh +# Uncomment the command line below and replace the value with the snapshot ID +# export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0ffe58efdd845d938 + # Initialize Terraform terraform init -upgrade diff --git a/website/docs/bestpractices/img/bottlerocket-image-cache.png 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b/website/docs/bestpractices/preload-container-images.md new file mode 100644 index 000000000..e94d3662b --- /dev/null +++ b/website/docs/bestpractices/preload-container-images.md @@ -0,0 +1,173 @@ +--- +title: Preload container images into Bottlerocket data volumes with Karpenter +sidebar_position: 2 +--- +import CollapsibleContent from '../../../src/components/CollapsibleContent'; + +# Preload container images into Bottlerocket data volumes with Karpenter with EBS Snapshots + +The purpose of this pattern is to reduce the cold start time of containers with large images by caching the images in the data volume of Bottlerocket OS. + +Data analytics and machine learning workloads often require large container images (usually measured by Gigabytes), which can take several minutes to pull and extract from Amazon ECR or other image registry. Reduce image pulling time is the key of improving speed of launching these containers. + +Bottlerocket OS is a Linux-based open-source operating system built by AWS specifically for running containers. It has two volumes, an OS volume and a data volume, with the latter used for storing artifacts and container images. This sample will leverage the data volume to pull images and take snapshots for later usage. + +To demonstrate the process of caching images in EBS snapshots and launching them in an EKS cluster, this sample will use Amazon EKS optimized Bottlerocket AMIs. + +For details, refer to the GitHub sample and blog post: +https://github.com/aws-samples/bottlerocket-images-cache/tree/main +https://aws.amazon.com/blogs/containers/reduce-container-startup-time-on-amazon-eks-with-bottlerocket-data-volume/ + + +## Overview of this script + +![](img/bottlerocket-image-cache.png) + +1. Launch an EC2 instance with Bottlerocket for EKS AMI, +2. Access to instance via Amazon System Manager +3. Pull images to be cached in this EC2 using Amazon System Manager Run Command. +4. Shut down the instance, build the EBS snapshot for the data volume. +5. Terminate the instance. + +## Usage Example + +``` +git clone https://github.com/aws-samples/bottlerocket-images-cache/ +cd bottlerocket-images-cache/ + +# Using nohup in terminals to avoid disconnections +❯ nohup ./snapshot.sh --snapshot-size 150 -r us-west-2 \ + docker.io/rayproject/ray-ml:2.10.0-py310-gpu,public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest & + +❯ tail -f nohup.out + +2024-07-15 17:18:53 I - [1/8] Deploying EC2 CFN stack ... +2024-07-15 17:22:07 I - [2/8] Launching SSM . +2024-07-15 17:22:08 I - SSM launched in instance i-07d10182abc8a86e1. +2024-07-15 17:22:08 I - [3/8] Stopping kubelet.service .. +2024-07-15 17:22:10 I - Kubelet service stopped. +2024-07-15 17:22:10 I - [4/8] Cleanup existing images .. +2024-07-15 17:22:12 I - Existing images cleaned +2024-07-15 17:22:12 I - [5/8] Pulling images: +2024-07-15 17:22:12 I - Pulling docker.io/rayproject/ray-ml:2.10.0-py310-gpu - amd64 ... +2024-07-15 17:27:50 I - docker.io/rayproject/ray-ml:2.10.0-py310-gpu - amd64 pulled. +2024-07-15 17:27:50 I - Pulling docker.io/rayproject/ray-ml:2.10.0-py310-gpu - arm64 ... +2024-07-15 17:27:58 I - docker.io/rayproject/ray-ml:2.10.0-py310-gpu - arm64 pulled. +2024-07-15 17:27:58 I - Pulling public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest - amd64 ... +2024-07-15 17:31:34 I - public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest - amd64 pulled. +2024-07-15 17:31:34 I - Pulling public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest - arm64 ... +2024-07-15 17:31:36 I - public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest - arm64 pulled. +2024-07-15 17:31:36 I - [6/8] Stopping instance ... +2024-07-15 17:32:25 I - Instance i-07d10182abc8a86e1 stopped +2024-07-15 17:32:25 I - [7/8] Creating snapshot ... +2024-07-15 17:38:36 I - Snapshot snap-0c6d965cf431785ed generated. +2024-07-15 17:38:36 I - [8/8] Cleanup. +2024-07-15 17:38:37 I - Stack deleted. +2024-07-15 17:38:37 I - -------------------------------------------------- +2024-07-15 17:38:37 I - All done! Created snapshot in us-west-2: snap-0c6d965cf431785ed +``` + +You can copy the snapshot ID `snap-0c6d965cf431785ed` and configure it as a snapshot for worker nodes. + +# Using Snapshot with Amazon EKS and Karpenter + +You can specify `snapshotID` in a Karpenter node class. Add the content on EC2NodeClass: + +``` +apiVersion: karpenter.k8s.aws/v1beta1 +kind: EC2NodeClass +metadata: + name: default +spec: + amiFamily: Bottlerocket # Ensure OS is BottleRocket + blockDeviceMappings: + - deviceName: /dev/xvdb + ebs: + volumeSize: 150Gi + volumeType: gp3 + kmsKeyID: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" # Specify KMS ID if you use custom KMS key + snapshotID: snap-0123456789 # Specify your snapshot ID here +``` + +# End-to-End deployment example + +An end-to-end deployment example can be found in [Stable Diffusion on GPU](../terraform/README.md) + +Clone the repository + +``` +git clone https://github.com/awslabs/data-on-eks.git +``` + +Navigate into one of the example directories and run install.sh script + +Important Note: Ensure that you update the region in the variables.tf file before deploying the blueprint. Additionally, confirm that your local region setting matches the specified region to prevent any discrepancies. For example, set your `export AWS_DEFAULT_REGION=""` to the desired region: + +## Reduce Cold Start Time by Preloading Container Images in Bottlerocket OS + +Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to provision Bottlerocket worker nodes with EBS Snapshots, to reduce cold start for container startup. This will likely to save 10 mins for downloading and extracting container images from Amazon ECR. + +``` +export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0c6d965cf431785ed + +cd data-on-eks/ai-ml/jark-stack/ && chmod +x install.sh +./install.sh +``` + +# Verify the resources +Verify the Amazon EKS Cluster + +``` +aws eks --region us-west-2 describe-cluster --name jark-stack + +# Creates k8s config file to authenticate with EKS +aws eks --region us-west-2 update-kubeconfig --name jark-stack + +# Output shows the EKS Managed Node group nodes +kubectl get nodes + +## Deploying the Ray Cluster with Stable Diffusion Model +Ensure the cluster is configured locally + +```bash +aws eks --region us-west-2 update-kubeconfig --name jark-stack +``` + +**Deploy RayServe Cluster** + +```bash +cd ./../gen-ai/inference/stable-diffusion-rayserve-gpu +kubectl apply -f ray-service-stablediffusion.yaml +``` + +This deployment establishes a Ray head pod running on an x86 instance and a worker pod on a GPU G5 instance as shown below. + +```bash +kubectl get pods -n stablediffusion + +NAME READY STATUS +rservice-raycluster-hb4l4-worker-gpu-worker-group-z8gdw 1/1 Running +stablediffusion-service-raycluster-hb4l4-head-4kfzz 2/2 Running +``` +You can find the container image is preloaded into the disk disk. + + +``` +kubectl describe pod + +... +Events: + Type Reason Age From Message + ---- ------ ---- ---- ------- + Warning FailedScheduling 41m default-scheduler 0/8 nodes are available: 1 Insufficient cpu, 3 Insufficient memory, 8 Insufficient nvidia.com/gpu. preemption: 0/8 nodes are available: 8 No preemption victims found for incoming pod. + Normal Nominated 41m karpenter Pod should schedule on: nodeclaim/gpu-ljvhl + Normal Scheduled 40m default-scheduler Successfully assigned stablediffusion/stablediffusion-raycluster-ms6pl-worker-gpu-85d22 to ip-100-64-136-72.us-west-2.compute.internal + Normal Pulled 40m kubelet Container image "public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest" already present on machine + Normal Created 40m kubelet Created container wait-gcs-ready + Normal Started 40m kubelet Started container wait-gcs-ready + Normal Pulled 39m kubelet Container image "public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest" already present on machine + Normal Created 39m kubelet Created container worker + Normal Started 38m kubelet Started container worker + ``` + +TODO: add benchmark results and the time save for downloading container images \ No newline at end of file From 1ec10cefd604a08ce2a689bbc45d23e924b3a965 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 13 Aug 2024 11:16:23 +0800 Subject: [PATCH 05/53] feat: bump ray version to 2.24 --- gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile index 943dabcbb..acfe1f1d2 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile @@ -1,8 +1,8 @@ # https://hub.docker.com/layers/rayproject/ray-ml/2.10.0-py310-gpu/images/sha256-4181ed53b0b25a758b155312ca6ab29a65cb78cd57296d42cfbe4806a2b77df4?context=explore -# docker buildx build --platform=linux/amd64 -t ray2.10.0-py310-gpu-stablediffusion:v1.0 -f Dockerfile . +# docker buildx build --platform=linux/amd64 -t ray2.24.0-py310-gpu-stablediffusion:v1.0 -f Dockerfile . # Use Ray base image -FROM rayproject/ray-ml:2.10.0-py310-gpu +FROM docker.io/rayproject/ray-ml:2.24.0-py311-gpu # Maintainer label LABEL maintainer="DoEKS" From 521553f2bd95f21aa1a8b8288043fd2d07aac9d7 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 13 Aug 2024 11:20:49 +0800 Subject: [PATCH 06/53] feat: add s3 mountpoint csi driver --- ai-ml/jark-stack/terraform/addons.tf | 30 ++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index f56eb700d..23c4d7f60 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -82,6 +82,10 @@ module "eks_blueprints_addons" { vpc-cni = { preserve = true } + + aws-mountpoint-s3-csi-driver = { + service_account_role_arn = module.s3_csi_driver_irsa.iam_role_arn + } } #--------------------------------------- @@ -351,6 +355,32 @@ module "data_addons" { ] } +#--------------------------------------------------------------- +# S3 Mountpoint +#--------------------------------------------------------------- + +#--------------------------------------------------------------- +# IRSA for Mountpoint for Amazon S3 CSI Driver +#--------------------------------------------------------------- +module "s3_csi_driver_irsa" { + source = "terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks" + version = "~> 5.34" + role_name_prefix = format("%s-%s-", local.name, "s3-csi-driver") + role_policy_arns = { + # WARNING: Demo purpose only. Bring your own IAM policy with least privileges + s3_csi_driver = "arn:aws:iam::aws:policy/AmazonS3FullAccess" + } + oidc_providers = { + main = { + provider_arn = module.eks.oidc_provider_arn + namespace_service_accounts = ["kube-system:s3-csi-driver-sa"] + } + } + tags = local.tags +} + + + #--------------------------------------------------------------- # Additional Resources From ecc8ba6a136ff8585e3d8ae8ca00bcbc48f2507f Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 13 Aug 2024 11:21:42 +0800 Subject: [PATCH 07/53] feat: add loading SD models from s3 bucket --- ai-ml/jark-stack/terraform/s3.tf | 4 ++ .../ray_serve_sd.py | 70 +++++++++++++++++++ 2 files changed, 74 insertions(+) create mode 100644 ai-ml/jark-stack/terraform/s3.tf create mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py diff --git a/ai-ml/jark-stack/terraform/s3.tf b/ai-ml/jark-stack/terraform/s3.tf new file mode 100644 index 000000000..309697c5a --- /dev/null +++ b/ai-ml/jark-stack/terraform/s3.tf @@ -0,0 +1,4 @@ + +resource "aws_s3_bucket" "model_storage" { + bucket_prefix = "model-storage" +} diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py new file mode 100644 index 000000000..ab87df30c --- /dev/null +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py @@ -0,0 +1,70 @@ +from io import BytesIO +from fastapi import FastAPI +from fastapi.responses import Response +import torch +import os +import ray +from ray import serve + + + +app = FastAPI() + + +@serve.deployment(num_replicas=1) +@serve.ingress(app) +class APIIngress: + def __init__(self, diffusion_model_handle) -> None: + self.handle = diffusion_model_handle + + @app.get( + "/imagine", + responses={200: {"content": {"image/png": {}}}}, + response_class=Response, + ) + async def generate(self, prompt: str, img_size: int = 768): + assert len(prompt), "prompt parameter cannot be empty" + + image = await self.handle.generate.remote(prompt, img_size=img_size) + file_stream = BytesIO() + image.save(file_stream, "PNG") + return Response(content=file_stream.getvalue(), media_type="image/png") + + +@serve.deployment(name="stable-diffusion-v2", + ray_actor_options={"num_gpus": 1}, + autoscaling_config={"min_replicas": 1, "max_replicas": 2}, +) +class StableDiffusionV2: + def __init__(self): + from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline + + model_name = "stabilityai/stable-diffusion-2" + + model_path = os.getenv('MODEL_PATH') + if model_path is None: + model_path = "./stable-diffusion-2" + + # Load the model + # pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory) + + model_id = os.getenv('MODEL_ID') + # model_id = "stabilityai/stable-diffusion-2" + # model_path = os.getenv('MODEL_PATH') + self.pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) + + # scheduler = EulerDiscreteScheduler.from_pretrained( + # model_id, subfolder="scheduler" + # ) + # self.pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, cache_dir=model_path) + + self.pipe = self.pipe.to("cuda") + + def generate(self, prompt: str, img_size: int = 768): + assert len(prompt), "prompt parameter cannot be empty" + + image = self.pipe(prompt, height=img_size, width=img_size, negative_prompt="",num_inference_steps=50,guidance_scale=7.0).images[0] + return image + + +entrypoint = APIIngress.bind(StableDiffusionV2.bind()) From e75f6bf7e99d06c2aeeb4fccd4b3ce5c15262277 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 13 Aug 2024 11:22:55 +0800 Subject: [PATCH 08/53] feat: download stable diffusion from huggingface and upload to s3 --- .../download_models.ipynb | 81 +++++++++++++++++++ .../download_models.py | 15 ++++ 2 files changed, 96 insertions(+) create mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb create mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb new file mode 100644 index 000000000..59acc7a01 --- /dev/null +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb @@ -0,0 +1,81 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U diffusers accelerate --quiet \n", + "!pip install accelerate --quiet " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from diffusers import StableDiffusionPipeline\n", + "import torch\n", + "\n", + "# Set the model you want to download\n", + "model_name = \"stabilityai/stable-diffusion-2\"\n", + "model_directory = \"./stable-diffusion-2\"\n", + "\n", + "# Load the model\n", + "# pipe = StableDiffusionPipeline.from_pretrained(model_directory, torch_dtype=torch.float16)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the model to the local directory\n", + "pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory)\n", + "pipe.save_pretrained(model_directory)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload the files to Amazon S3 bucket from a local directory using AWS CLI\n", + "\n", + "bucket_name = \"ml-model-storage-091550601287\"\n", + "prefix = \"stable-diffusion-2\"\n", + "\n", + "! rm -rf ./stable-diffusion-2/.locks\n", + "! rm -rf ./stable-diffusion-2/models--stabilityai--stable-diffusion-2\n", + "\n", + "! aws s3 cp --recursive {model_directory} s3://{bucket_name}/{prefix}/ \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py new file mode 100644 index 000000000..8dd431900 --- /dev/null +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py @@ -0,0 +1,15 @@ +from diffusers import StableDiffusionPipeline +import torch + +# Set the model you want to download +model_name = "stabilityai/stable-diffusion-2" +model_directory = "./stable-diffusion-2" + +# Load the model +pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory) +# pipe = StableDiffusionPipeline.from_pretrained(model_directory, torch_dtype=torch.float16) + +# Save the model to the local directory +pipe.save_pretrained(model_directory) + +# print(f"Model saved to {model_directory}") \ No newline at end of file From d43d962f09e108aa79dba63b561153c28a37e464 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 13 Aug 2024 11:31:05 +0800 Subject: [PATCH 09/53] feat: update the preloaded images to bottlerocket node ami and snapshot --- ai-ml/jark-stack/preload-container-image-ami/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ai-ml/jark-stack/preload-container-image-ami/README.md b/ai-ml/jark-stack/preload-container-image-ami/README.md index 9a480f64e..8e9eca9f0 100644 --- a/ai-ml/jark-stack/preload-container-image-ami/README.md +++ b/ai-ml/jark-stack/preload-container-image-ami/README.md @@ -10,7 +10,7 @@ Usage Example: ``` # Using nohup in terminals to avoid disconnections -❯ nohup ./snapshot.sh --snapshot-size 150 -r us-west-2 docker.io/rayproject/ray-ml:2.10.0-py310-gpu,public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest & +❯ nohup ./snapshot.sh --snapshot-size 200 -r us-west-2 docker.io/rayproject/ray-ml:2.10.0-py310-gpu,public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest,docker.io/rayproject/ray:2.33.0-py311-gpu,public.ecr.aws/lindarr/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu & ❯ tail -f nohup.out 2024-07-15 17:18:53 I - [1/8] Deploying EC2 CFN stack ... 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10:19:23 +0800 Subject: [PATCH 11/53] fix: fix docs path --- .../bestpractices/cache-models-to-s3-bucket.md | 5 +++++ .../bestpractices/preload-container-images.md | 15 ++++++--------- 2 files changed, 11 insertions(+), 9 deletions(-) create mode 100644 website/docs/bestpractices/cache-models-to-s3-bucket.md diff --git a/website/docs/bestpractices/cache-models-to-s3-bucket.md b/website/docs/bestpractices/cache-models-to-s3-bucket.md new file mode 100644 index 000000000..f4ec725bd --- /dev/null +++ b/website/docs/bestpractices/cache-models-to-s3-bucket.md @@ -0,0 +1,5 @@ +--- +title: Store ML models in S3 bucket and use Amazon S3 CSI Driver +sidebar_position: 3 +--- + diff --git a/website/docs/bestpractices/preload-container-images.md b/website/docs/bestpractices/preload-container-images.md index e94d3662b..e8842886e 100644 --- a/website/docs/bestpractices/preload-container-images.md +++ b/website/docs/bestpractices/preload-container-images.md @@ -2,7 +2,7 @@ title: Preload container images into Bottlerocket data volumes with Karpenter sidebar_position: 2 --- -import CollapsibleContent from '../../../src/components/CollapsibleContent'; +import CollapsibleContent from '../../src/components/CollapsibleContent'; # Preload container images into Bottlerocket data volumes with Karpenter with EBS Snapshots @@ -18,12 +18,11 @@ For details, refer to the GitHub sample and blog post: https://github.com/aws-samples/bottlerocket-images-cache/tree/main https://aws.amazon.com/blogs/containers/reduce-container-startup-time-on-amazon-eks-with-bottlerocket-data-volume/ - ## Overview of this script ![](img/bottlerocket-image-cache.png) -1. Launch an EC2 instance with Bottlerocket for EKS AMI, +1. Launch an EC2 instance with Bottlerocket for EKS AMI. 2. Access to instance via Amazon System Manager 3. Pull images to be cached in this EC2 using Amazon System Manager Run Command. 4. Shut down the instance, build the EBS snapshot for the data volume. @@ -91,21 +90,19 @@ spec: # End-to-End deployment example -An end-to-end deployment example can be found in [Stable Diffusion on GPU](../terraform/README.md) +An end-to-end deployment example can be found in [Stable Diffusion on GPU](../gen-ai/inference/stablediffusion-gpus). -Clone the repository +### Clone the repository ``` git clone https://github.com/awslabs/data-on-eks.git ``` -Navigate into one of the example directories and run install.sh script - Important Note: Ensure that you update the region in the variables.tf file before deploying the blueprint. Additionally, confirm that your local region setting matches the specified region to prevent any discrepancies. For example, set your `export AWS_DEFAULT_REGION=""` to the desired region: -## Reduce Cold Start Time by Preloading Container Images in Bottlerocket OS +### Reduce Cold Start Time by Preloading Container Images in Bottlerocket OS -Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to provision Bottlerocket worker nodes with EBS Snapshots, to reduce cold start for container startup. This will likely to save 10 mins for downloading and extracting container images from Amazon ECR. +Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to provision Bottlerocket worker nodes with EBS Snapshots, to reduce cold start for container startup. This will likely to save 10 mins (depending on the image size) for downloading and extracting container images from Amazon ECR. ``` export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0c6d965cf431785ed From e2ccf5522366ab986a1451375083b571933eee04 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 19 Aug 2024 10:20:08 +0800 Subject: [PATCH 12/53] feat: add notebook of model downloading --- .gitignore | 1 + .../download_models.ipynb | 44 +++++++++++++------ 2 files changed, 31 insertions(+), 14 deletions(-) diff --git a/.gitignore b/.gitignore index ac76e6158..069380ea3 100755 --- a/.gitignore +++ b/.gitignore @@ -60,3 +60,4 @@ site # node modules node_modules gen-ai/inference/stable-diffusion-rayserve-gpu/locust/__pycache__/* +gen-ai/inference/stable-diffusion-rayserve-gpu/stable-diffusion-2/* diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb index 59acc7a01..4688634e4 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb @@ -2,17 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "!pip install -U diffusers accelerate --quiet \n", - "!pip install accelerate --quiet " + "!pip install -U diffusers accelerate accelerate awscliv2 --quiet " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -21,10 +20,7 @@ "\n", "# Set the model you want to download\n", "model_name = \"stabilityai/stable-diffusion-2\"\n", - "model_directory = \"./stable-diffusion-2\"\n", - "\n", - "# Load the model\n", - "# pipe = StableDiffusionPipeline.from_pretrained(model_directory, torch_dtype=torch.float16)" + "model_directory = \"./stable-diffusion-2\"" ] }, { @@ -40,19 +36,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "091550601287\n" + ] + } + ], "source": [ "# Upload the files to Amazon S3 bucket from a local directory using AWS CLI\n", "\n", - "bucket_name = \"ml-model-storage-091550601287\"\n", + "# Get Caller Identity\n", + "import boto3, json\n", + "account = boto3.client('sts').get_caller_identity()['Account']\n", + "print(account)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bucket_name = \"ml-model-storage-{account}\"\n", "prefix = \"stable-diffusion-2\"\n", "\n", - "! rm -rf ./stable-diffusion-2/.locks\n", - "! rm -rf ./stable-diffusion-2/models--stabilityai--stable-diffusion-2\n", + "! rm -rf {model_directory}/.locks\n", + "! rm -rf {model_directory}/models--stabilityai--stable-diffusion-2\n", "\n", - "! aws s3 cp --recursive {model_directory} s3://{bucket_name}/{prefix}/ \n", + "! aws s3 sync {model_directory} s3://{bucket_name}/{prefix}/ \n", "\n" ] } From 2fa6bc9ad6f1b98c78de21e1f9f620eedeebf4aa Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 19 Aug 2024 15:18:25 +0800 Subject: [PATCH 13/53] fix: add additional IAM policy statements for karpenter to launch from snapshot --- ai-ml/jark-stack/terraform/addons.tf | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index 23c4d7f60..685e27aa0 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -123,6 +123,9 @@ module "eks_blueprints_addons" { chart_version = "0.37.0" repository_username = data.aws_ecrpublic_authorization_token.token.user_name repository_password = data.aws_ecrpublic_authorization_token.token.password + source_policy_documents = [ + data.aws_iam_policy_document.karpenter_controller_policy.json + ] } #--------------------------------------- @@ -152,7 +155,7 @@ module "eks_blueprints_addons" { module "data_addons" { source = "aws-ia/eks-data-addons/aws" - version = "~> 1.40" # source = "aws-ia/eks-data-addons/aws" + version = "~> 1.40" oidc_provider_arn = module.eks.oidc_provider_arn @@ -414,3 +417,15 @@ resource "kubernetes_config_map_v1" "notebook" { "dogbooth.ipynb" = file("${path.module}/src/notebook/dogbooth.ipynb") } } + +data "aws_iam_policy_document" "karpenter_controller_policy" { + statement { + actions = [ + "ec2:RunInstances", + "ec2:CreateLaunchTemplate", + ] + resources = ["*"] + effect = "Allow" + sid = "KarpenterControllerAdditionalPolicy" + } +} From 0c5cab07d656bf522b1ff352a59d1818281380e5 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 19 Aug 2024 15:14:27 +0800 Subject: [PATCH 14/53] feat: add s3 as model cache stroage --- .../ray-serve-stablediffusion-from-s3.yaml | 179 ++++++++++++++++++ .../s3-pv-model-storage.yaml | 33 ++++ 2 files changed, 212 insertions(+) create mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml create mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml new file mode 100644 index 000000000..7629b0060 --- /dev/null +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -0,0 +1,179 @@ +#---------------------------------------------------------------------- +# NOTE: For deployment instructions, refer to the DoEKS website. +#---------------------------------------------------------------------- +--- +apiVersion: v1 +kind: Namespace +metadata: + name: stablediffusion +--- +apiVersion: ray.io/v1 +kind: RayService +metadata: + name: stablediffusion + namespace: stablediffusion +spec: + serviceUnhealthySecondThreshold: 900 + deploymentUnhealthySecondThreshold: 300 +# Ray Serve can automatically scale deployment replicas up to 3 based on incoming traffic. +# Each replica in this example requires one GPU. For GPU types g5.xlarge to g5.16xlarge, a single node with one GPU can only run one replica. +# However, g5.12xlarge comes with 4 GPUs, allowing you to run all 4 replicas on a single node. + serveConfigV2: | + applications: + - name: stable-diffusion-deployment + import_path: "ray_serve_sd:entrypoint" + route_prefix: "/" + runtime_env: + env_vars: + MODEL_PATH: "./stable-diffusion-2" + MODEL_ID: "stabilityai/stable-diffusion-2-1" + pip: + - transformers==4.25.1 + - accelerate==0.30.1 + deployments: + - name: stable-diffusion-v2 + autoscaling_config: + metrics_interval_s: 0.2 + min_replicas: 1 + max_replicas: 4 + look_back_period_s: 2 + downscale_delay_s: 600 + upscale_delay_s: 30 + target_ongoing_requests: 1 + graceful_shutdown_timeout_s: 5 + max_concurrent_queries: 100 + ray_actor_options: + num_cpus: 3 + num_gpus: 1 + rayClusterConfig: + rayVersion: '2.24.0' + enableInTreeAutoscaling: true + headGroupSpec: + headService: + metadata: + name: stablediffusion + namespace: stablediffusion + rayStartParams: + dashboard-host: '0.0.0.0' + template: + spec: + containers: + # Important Performance Note: + # This image is large (20GB uncompressed, 10GB compressed) and may take up to 5 minutes to pull. + # For faster inference scaling, consider building a custom image with only your workload's essential dependencies. + # Smaller images lead to faster scaling, especially across multiple nodes. + # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. + # Preload Container Image into data volumes for faster new ray worker nodes + - name: head + image: public.ecr.aws/lindarr/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu + imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated + lifecycle: + preStop: + exec: + command: ["/bin/sh", "-c", "ray stop"] + ports: + - containerPort: 6379 + name: gcs-server + - containerPort: 8265 + name: dashboard + - containerPort: 10001 + name: client + - containerPort: 8000 + name: serve + volumeMounts: + - mountPath: /tmp/ray + name: ray-logs + - mountPath: /serve_app/stable-diffusion-2 + name: cache-dir + resources: + limits: + cpu: 2 + memory: 16Gi + requests: + cpu: 2 + memory: 16Gi + nodeSelector: + NodeGroupType: x86-cpu-karpenter + type: karpenter + volumes: + - name: ray-logs + emptyDir: {} + - name: cache-dir + persistentVolumeClaim: + claimName: s3-claim + workerGroupSpecs: + - groupName: gpu + # With g5.2xlarge instance, Ray can scale up to 4 nodes, with one pod per node and 1 GPU per pod. + minReplicas: 1 + maxReplicas: 4 + rayStartParams: {} + template: + spec: + containers: + # Important Performance Note: + # This image is large (20GB uncompressed, 10GB compressed) and may take up to 5 minutes to pull. + # For faster inference scaling, consider building a custom image with only your workload's essential dependencies. + # Smaller images lead to faster scaling, especially across multiple nodes. + # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. + - name: worker + image: public.ecr.aws/lindarr/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu + imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated + lifecycle: + preStop: + exec: + command: ["/bin/sh", "-c", "ray stop"] + resources: + limits: + cpu: "3" + memory: "14Gi" + nvidia.com/gpu: 1 + requests: + cpu: "3" + memory: "14Gi" + nvidia.com/gpu: 1 + volumeMounts: + - mountPath: /serve_app/stable-diffusion-2 + name: cache-dir + nodeSelector: + NodeGroupType: g5-gpu-karpenter + type: karpenter + tolerations: + - key: "nvidia.com/gpu" + operator: "Exists" + effect: "NoSchedule" + volumes: + - name: cache-dir + persistentVolumeClaim: + claimName: s3-claim +--- +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: stablediffusion + namespace: stablediffusion + annotations: + nginx.ingress.kubernetes.io/rewrite-target: "/$1" +spec: + ingressClassName: nginx + rules: + - http: + paths: + # Ray Dashboard: you can access the dashboard using the NLB DNS name with port 8265 and the path "/dashboard". However, the NLB is currently configured as private, preventing access from outside the VPC. + # To access the dashboard: + # Option 1: Use the `kubectl port-forward` command. + # Option 2: Deploy a public-facing NLB by modifying the configuration file "ai-ml/jark-stack/terraform/helm-values/ingress-nginx-values.yaml". + - path: /dashboard/(.*) + pathType: ImplementationSpecific + backend: + service: + name: stablediffusion + port: + number: 8265 + # Ray Serve + - path: /serve/(.*) + pathType: ImplementationSpecific + backend: + service: + name: stablediffusion + port: + number: 8000 diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml new file mode 100644 index 000000000..ecfcd99c7 --- /dev/null +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml @@ -0,0 +1,33 @@ +apiVersion: v1 +kind: PersistentVolume +metadata: + name: s3-pv-model +spec: + capacity: + storage: 1200Gi # ignored, required + accessModes: + - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany + mountOptions: + - allow-overwrite + - allow-delete + - region us-west-2 + - prefix stable-diffusion-2/ + csi: + driver: s3.csi.aws.com # required + volumeHandle: s3-csi-driver-volume + volumeAttributes: + bucketName: ml-model-storage-091550601287 # Replace bucketName with the actual S3 bucket name +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: s3-claim + namespace: stablediffusion +spec: + accessModes: + - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany + storageClassName: "" # required for static provisioning + resources: + requests: + storage: 1200Gi # ignored, required + volumeName: s3-pv-model \ No newline at end of file From eda4c689c3224fa5985f84fe59b9ce145599db14 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 19 Aug 2024 16:25:09 +0800 Subject: [PATCH 15/53] feat: model download script from hugging face --- .../stable-diffusion-rayserve-gpu/download_models.ipynb | 3 +-- .../inference/stable-diffusion-rayserve-gpu/download_models.py | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb index 4688634e4..2bc8f3c7f 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb @@ -68,8 +68,7 @@ "! rm -rf {model_directory}/.locks\n", "! rm -rf {model_directory}/models--stabilityai--stable-diffusion-2\n", "\n", - "! aws s3 sync {model_directory} s3://{bucket_name}/{prefix}/ \n", - "\n" + "! aws s3 sync {model_directory} s3://{bucket_name}/{prefix}/ " ] } ], diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py index 8dd431900..b0a7a9973 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py @@ -7,9 +7,8 @@ # Load the model pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory) -# pipe = StableDiffusionPipeline.from_pretrained(model_directory, torch_dtype=torch.float16) # Save the model to the local directory pipe.save_pretrained(model_directory) -# print(f"Model saved to {model_directory}") \ No newline at end of file +print(f"Model saved to {model_directory}") \ No newline at end of file From 252fee831e14dac5dd3d4d7240e3ba9a311eb6b0 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 19 Aug 2024 16:33:54 +0800 Subject: [PATCH 16/53] chore: bump ray version to 2.33 --- .../stable-diffusion-rayserve-gpu/Dockerfile | 6 +- .../ray_serve_stablediffusion.py | 59 ------------------- 2 files changed, 3 insertions(+), 62 deletions(-) delete mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_stablediffusion.py diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile index acfe1f1d2..6ded5624f 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile @@ -2,7 +2,7 @@ # docker buildx build --platform=linux/amd64 -t ray2.24.0-py310-gpu-stablediffusion:v1.0 -f Dockerfile . # Use Ray base image -FROM docker.io/rayproject/ray-ml:2.24.0-py311-gpu +FROM docker.io/rayproject/ray-ml:2.33.0-py311-gpu # Maintainer label LABEL maintainer="DoEKS" @@ -14,13 +14,13 @@ ENV DEBIAN_FRONTEND=non-interactive USER $USER # Install Ray Serve and other Python packages with specific versions -RUN pip install --no-cache-dir requests torch "diffusers==0.12.1" "transformers==4.25.1" +RUN pip install --no-cache-dir requests torch "diffusers==0.29.2" "transformers==4.42.4" "accelerate==0.30.1" # Set a working directory WORKDIR /serve_app # Copy your Ray Serve script into the container -COPY ray_serve_stablediffusion.py /serve_app/ray_serve_stablediffusion.py +COPY ray_serve_sd.py /serve_app/ray_serve_sd.py # Set the PYTHONPATH environment variable ENV PYTHONPATH=/serve_app:$PYTHONPATH diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_stablediffusion.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_stablediffusion.py deleted file mode 100644 index c4a7c59a4..000000000 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_stablediffusion.py +++ /dev/null @@ -1,59 +0,0 @@ -from io import BytesIO -from fastapi import FastAPI -from fastapi.responses import Response -import torch -import os -import ray -from ray import serve - - - -app = FastAPI() - - -@serve.deployment(num_replicas=1) -@serve.ingress(app) -class APIIngress: - def __init__(self, diffusion_model_handle) -> None: - self.handle = diffusion_model_handle - - @app.get( - "/imagine", - responses={200: {"content": {"image/png": {}}}}, - response_class=Response, - ) - async def generate(self, prompt: str, img_size: int = 768): - assert len(prompt), "prompt parameter cannot be empty" - - image = await self.handle.generate.remote(prompt, img_size=img_size) - file_stream = BytesIO() - image.save(file_stream, "PNG") - return Response(content=file_stream.getvalue(), media_type="image/png") - - -@serve.deployment(name="stable-diffusion-v2", - ray_actor_options={"num_gpus": 1}, - autoscaling_config={"min_replicas": 1, "max_replicas": 2}, -) -class StableDiffusionV2: - def __init__(self): - from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline - - model_id = os.getenv('MODEL_ID') - - scheduler = EulerDiscreteScheduler.from_pretrained( - model_id, subfolder="scheduler" - ) - self.pipe = StableDiffusionPipeline.from_pretrained( - model_id, scheduler=scheduler - ) - self.pipe = self.pipe.to("cuda") - - def generate(self, prompt: str, img_size: int = 768): - assert len(prompt), "prompt parameter cannot be empty" - - image = self.pipe(prompt, height=img_size, width=img_size).images[0] - return image - - -entrypoint = APIIngress.bind(StableDiffusionV2.bind()) From 71008365b49b290691cc9f18f3a2bf1b3658aee4 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 20 Aug 2024 10:01:36 +0800 Subject: [PATCH 17/53] fix: update required ver of data_addons module to 1.33 --- ai-ml/jark-stack/terraform/addons.tf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index 685e27aa0..f88ab937a 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -155,7 +155,7 @@ module "eks_blueprints_addons" { module "data_addons" { source = "aws-ia/eks-data-addons/aws" - version = "~> 1.40" + version = "~> 1.33" oidc_provider_arn = module.eks.oidc_provider_arn From 097f858ef4b81e9c04a179c802762d07ae404860 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 20 Aug 2024 13:26:05 +0800 Subject: [PATCH 18/53] feat: add s3 bucket for model storage --- ai-ml/jark-stack/terraform/outputs.tf | 4 ++++ ai-ml/jark-stack/terraform/s3.tf | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/ai-ml/jark-stack/terraform/outputs.tf b/ai-ml/jark-stack/terraform/outputs.tf index f6444daab..b7dd37326 100644 --- a/ai-ml/jark-stack/terraform/outputs.tf +++ b/ai-ml/jark-stack/terraform/outputs.tf @@ -2,3 +2,7 @@ output "configure_kubectl" { description = "Configure kubectl: make sure you're logged in with the correct AWS profile and run the following command to update your kubeconfig" value = "aws eks --region ${var.region} update-kubeconfig --name ${var.name}" } + +output "model_s3_bucket" { + value = aws_s3_bucket.model_storage.bucket +} \ No newline at end of file diff --git a/ai-ml/jark-stack/terraform/s3.tf b/ai-ml/jark-stack/terraform/s3.tf index 309697c5a..8fd7687a9 100644 --- a/ai-ml/jark-stack/terraform/s3.tf +++ b/ai-ml/jark-stack/terraform/s3.tf @@ -1,4 +1,4 @@ resource "aws_s3_bucket" "model_storage" { - bucket_prefix = "model-storage" + bucket_prefix = "model-storage-" } From f55ce334473389fb501ef5d4ebdad941f645cf96 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 20 Aug 2024 13:27:24 +0800 Subject: [PATCH 19/53] feat: using self-managed s3 csi driver to tolerate all taints --- ai-ml/jark-stack/terraform/addons.tf | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index f88ab937a..adb380b21 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -82,10 +82,6 @@ module "eks_blueprints_addons" { vpc-cni = { preserve = true } - - aws-mountpoint-s3-csi-driver = { - service_account_role_arn = module.s3_csi_driver_irsa.iam_role_arn - } } #--------------------------------------- @@ -359,9 +355,27 @@ module "data_addons" { } #--------------------------------------------------------------- -# S3 Mountpoint +# S3 CSI Mountpoint Driver #--------------------------------------------------------------- +resource "helm_release" "aws_mountpoint-s3-csi-driver" { + repository = "https://github.com/awslabs/mountpoint-s3-csi-driver" + name = "aws-mountpoint-s3-csi-driver" + namespace = "kube-system" + chart = "aws-mountpoint-s3-csi-driver" + + values = [ + <<-EOT + node: + serviceAccount: + create: false + tolerateAllTaints: false + EOT + ] + + depends_on = [ module.s3_csi_driver_irsa ] +} + #--------------------------------------------------------------- # IRSA for Mountpoint for Amazon S3 CSI Driver #--------------------------------------------------------------- From b9b03b8f05df5c05a98bde16625565c08648d66b Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 20 Aug 2024 13:27:56 +0800 Subject: [PATCH 20/53] fix: spilt notebook code blocks --- .../download_models.ipynb | 256 +++++++++++++++++- 1 file changed, 248 insertions(+), 8 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb index 2bc8f3c7f..55d09775b 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -25,9 +25,220 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "44675e78b75141f6b0a3b4d3f0ff01e9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model_index.json: 0%| | 0.00/537 [00:00 Date: Tue, 20 Aug 2024 15:53:09 +0800 Subject: [PATCH 21/53] feat: add a variable to make creating s3 bucket optional --- ai-ml/jark-stack/terraform/outputs.tf | 4 ++-- ai-ml/jark-stack/terraform/s3.tf | 2 ++ ai-ml/jark-stack/terraform/variables.tf | 6 ++++++ 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/ai-ml/jark-stack/terraform/outputs.tf b/ai-ml/jark-stack/terraform/outputs.tf index b7dd37326..9a59c367a 100644 --- a/ai-ml/jark-stack/terraform/outputs.tf +++ b/ai-ml/jark-stack/terraform/outputs.tf @@ -4,5 +4,5 @@ output "configure_kubectl" { } output "model_s3_bucket" { - value = aws_s3_bucket.model_storage.bucket -} \ No newline at end of file + value = length(aws_s3_bucket.model_storage) > 0 ? aws_s3_bucket.model_storage[0].bucket : "" +} diff --git a/ai-ml/jark-stack/terraform/s3.tf b/ai-ml/jark-stack/terraform/s3.tf index 8fd7687a9..7bfb79450 100644 --- a/ai-ml/jark-stack/terraform/s3.tf +++ b/ai-ml/jark-stack/terraform/s3.tf @@ -1,4 +1,6 @@ + resource "aws_s3_bucket" "model_storage" { + count = var.create_s3_bucket ? 1 : 0 bucket_prefix = "model-storage-" } diff --git a/ai-ml/jark-stack/terraform/variables.tf b/ai-ml/jark-stack/terraform/variables.tf index efd9697db..99cec2d71 100644 --- a/ai-ml/jark-stack/terraform/variables.tf +++ b/ai-ml/jark-stack/terraform/variables.tf @@ -57,4 +57,10 @@ variable "bottlerocket_data_disk_snpashot_id" { type = string default = "" +} + +variable "create_s3_bucket" { + description = "Create S3 Bucket for Model Storage" + default = false + type = bool } \ No newline at end of file From 50ecb5d215a4822995300462059dd2011722262a Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 20 Aug 2024 15:53:29 +0800 Subject: [PATCH 22/53] chore: cleanup comments --- .../ray_serve_sd.py | 19 ++++--------------- 1 file changed, 4 insertions(+), 15 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py index ab87df30c..393eb9dc8 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py @@ -41,23 +41,12 @@ def __init__(self): model_name = "stabilityai/stable-diffusion-2" - model_path = os.getenv('MODEL_PATH') - if model_path is None: - model_path = "./stable-diffusion-2" - - # Load the model - # pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory) - model_id = os.getenv('MODEL_ID') - # model_id = "stabilityai/stable-diffusion-2" - # model_path = os.getenv('MODEL_PATH') - self.pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) + model_path = os.getenv('MODEL_PATH') + if model_path is not None: + model_id = model_path - # scheduler = EulerDiscreteScheduler.from_pretrained( - # model_id, subfolder="scheduler" - # ) - # self.pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, cache_dir=model_path) - + self.pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) self.pipe = self.pipe.to("cuda") def generate(self, prompt: str, img_size: int = 768): From 4549a71fd3c39f874232de1d46a8795ed0b14b29 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 20 Aug 2024 16:07:29 +0800 Subject: [PATCH 23/53] fix: Dockerfile base image --- gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile | 2 +- website/docs/bestpractices/cache-models-to-s3-bucket.md | 7 +++++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile index 6ded5624f..801bb091d 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile @@ -2,7 +2,7 @@ # docker buildx build --platform=linux/amd64 -t ray2.24.0-py310-gpu-stablediffusion:v1.0 -f Dockerfile . # Use Ray base image -FROM docker.io/rayproject/ray-ml:2.33.0-py311-gpu +FROM docker.io/rayproject/ray:2.33.0-py311-gpu # Maintainer label LABEL maintainer="DoEKS" diff --git a/website/docs/bestpractices/cache-models-to-s3-bucket.md b/website/docs/bestpractices/cache-models-to-s3-bucket.md index f4ec725bd..aae708d77 100644 --- a/website/docs/bestpractices/cache-models-to-s3-bucket.md +++ b/website/docs/bestpractices/cache-models-to-s3-bucket.md @@ -3,3 +3,10 @@ title: Store ML models in S3 bucket and use Amazon S3 CSI Driver sidebar_position: 3 --- + + +[Mounpoint S3: Enhancing Amazon S3 File Access for Data & AI Workloads on Amazon EKS](../resources/mountpoint-s3) +TODO: To write the docs: + + +gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml \ No newline at end of file From 342fea9b65624d667ac67a56771fade4375b95f5 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 20 Aug 2024 16:13:21 +0800 Subject: [PATCH 24/53] docs: add cache model to s3 draft --- .../bestpractices/cache-models-to-s3-bucket.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/website/docs/bestpractices/cache-models-to-s3-bucket.md b/website/docs/bestpractices/cache-models-to-s3-bucket.md index aae708d77..ac10a6e20 100644 --- a/website/docs/bestpractices/cache-models-to-s3-bucket.md +++ b/website/docs/bestpractices/cache-models-to-s3-bucket.md @@ -3,10 +3,20 @@ title: Store ML models in S3 bucket and use Amazon S3 CSI Driver sidebar_position: 3 --- +Storing a machine learning model in Amazon S3, instead of downloading it from Huggingface, offers several advantages: +1. Reduced Download Latency: Storing the model in S3 can reduce the network latency that may occur when downloading the model from Huggingface, especially when you need to quickly deploy the model or use it across multiple instances simultaneously. -[Mounpoint S3: Enhancing Amazon S3 File Access for Data & AI Workloads on Amazon EKS](../resources/mountpoint-s3) -TODO: To write the docs: +2. Improved Access Speed and Reliability: S3's high availability and global reach mean that the model can be accessed faster and more reliably, particularly when used in conjunction with AWS infrastructure (such as EC2 or Lambda) within the same region. +Refer to the page [Mounpoint S3: Enhancing Amazon S3 File Access for Data & AI Workloads on Amazon EKS](../resources/mountpoint-s3) for learn more about Amazon S3 Mountpoint CSI Driver. -gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml \ No newline at end of file +End-to-end Example + +TODO: +- Create a S3 bucket +- Download ML models to the local computer +- Upload the ML models to S3 +- Specify the Kubernetes PV and PVC +- Mount the volume in the Ray Pods + \ No newline at end of file From dac490ad14bbb76234639094e8460f70ab5b188e Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 4 Sep 2024 16:07:35 +0800 Subject: [PATCH 25/53] Use EKS addon for S3 Mountpoint CSI --- ai-ml/jark-stack/terraform/addons.tf | 37 +++++++++++----------------- 1 file changed, 15 insertions(+), 22 deletions(-) diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index adb380b21..0add387dc 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -82,8 +82,18 @@ module "eks_blueprints_addons" { vpc-cni = { preserve = true } + + aws-mountpoint-s3-csi-driver = { + service_account_role_arn = module.s3_csi_driver_irsa.iam_role_arn + configuration_values = <<-EOF + node: + tolerateAllTaints: true + EOF + } } + + #--------------------------------------- # AWS Load Balancer Controller Add-on #--------------------------------------- @@ -354,28 +364,6 @@ module "data_addons" { ] } -#--------------------------------------------------------------- -# S3 CSI Mountpoint Driver -#--------------------------------------------------------------- - -resource "helm_release" "aws_mountpoint-s3-csi-driver" { - repository = "https://github.com/awslabs/mountpoint-s3-csi-driver" - name = "aws-mountpoint-s3-csi-driver" - namespace = "kube-system" - chart = "aws-mountpoint-s3-csi-driver" - - values = [ - <<-EOT - node: - serviceAccount: - create: false - tolerateAllTaints: false - EOT - ] - - depends_on = [ module.s3_csi_driver_irsa ] -} - #--------------------------------------------------------------- # IRSA for Mountpoint for Amazon S3 CSI Driver #--------------------------------------------------------------- @@ -443,3 +431,8 @@ data "aws_iam_policy_document" "karpenter_controller_policy" { sid = "KarpenterControllerAdditionalPolicy" } } + +resource "aws_s3_bucket" "model_storage" { + count = var.create_s3_bucket ? 1 : 0 + bucket_prefix = "model-storage-" +} \ No newline at end of file From 8876cc3c85254380abde343d123611c02052f725 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 4 Sep 2024 16:08:41 +0800 Subject: [PATCH 26/53] Merge S3 Bucket from s3.tf to addon.tf Remove empty line --- ai-ml/jark-stack/terraform/s3.tf | 6 ------ ai-ml/jark-stack/terraform/variables.tf | 3 +-- 2 files changed, 1 insertion(+), 8 deletions(-) delete mode 100644 ai-ml/jark-stack/terraform/s3.tf diff --git a/ai-ml/jark-stack/terraform/s3.tf b/ai-ml/jark-stack/terraform/s3.tf deleted file mode 100644 index 7bfb79450..000000000 --- a/ai-ml/jark-stack/terraform/s3.tf +++ /dev/null @@ -1,6 +0,0 @@ - - -resource "aws_s3_bucket" "model_storage" { - count = var.create_s3_bucket ? 1 : 0 - bucket_prefix = "model-storage-" -} diff --git a/ai-ml/jark-stack/terraform/variables.tf b/ai-ml/jark-stack/terraform/variables.tf index 99cec2d71..19956d849 100644 --- a/ai-ml/jark-stack/terraform/variables.tf +++ b/ai-ml/jark-stack/terraform/variables.tf @@ -55,8 +55,7 @@ variable "enable_kubecost" { variable "bottlerocket_data_disk_snpashot_id" { description = "Bottlerocket Data Disk Snapshot ID" type = string - default = "" - + default = "" } variable "create_s3_bucket" { From c6574bb024e1defa5de8142af8011fe426b30ce1 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 4 Sep 2024 16:09:49 +0800 Subject: [PATCH 27/53] Clear output of download_models.ipynb --- .../download_models.ipynb | 267 +----------------- 1 file changed, 13 insertions(+), 254 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb index 55d09775b..720f5eb09 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -25,220 +25,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "44675e78b75141f6b0a3b4d3f0ff01e9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "model_index.json: 0%| | 0.00/537 [00:00 Date: Wed, 4 Sep 2024 16:10:13 +0800 Subject: [PATCH 28/53] Use data-on-eks ECR public repo --- .../ray-serve-stablediffusion-from-s3.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index 7629b0060..fcf23607a 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -65,7 +65,7 @@ spec: # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. # Preload Container Image into data volumes for faster new ray worker nodes - name: head - image: public.ecr.aws/lindarr/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu + image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated lifecycle: preStop: @@ -116,7 +116,7 @@ spec: # Smaller images lead to faster scaling, especially across multiple nodes. # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. - name: worker - image: public.ecr.aws/lindarr/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu + image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated lifecycle: preStop: From 361d0776714f8a04177c5ffdfea3c5e04e3989fb Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 4 Sep 2024 16:10:33 +0800 Subject: [PATCH 29/53] Add placeholder for S3 Bucket Name --- .../stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml index ecfcd99c7..edd592e1f 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml @@ -16,7 +16,7 @@ spec: driver: s3.csi.aws.com # required volumeHandle: s3-csi-driver-volume volumeAttributes: - bucketName: ml-model-storage-091550601287 # Replace bucketName with the actual S3 bucket name + bucketName: # Replace bucketName with S3 bucket name created from `terraform output` --- apiVersion: v1 kind: PersistentVolumeClaim From 2f3a131277890e1b8926307e72a543ec3834a128 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 4 Sep 2024 16:11:50 +0800 Subject: [PATCH 30/53] Update docs to download SD models and upload to S3 --- .../bestpractices/cache-models-to-s3-bucket.md | 14 ++++---------- .../gen-ai/inference/stablediffusion-gpus.md | 16 +++++++++++++++- 2 files changed, 19 insertions(+), 11 deletions(-) diff --git a/website/docs/bestpractices/cache-models-to-s3-bucket.md b/website/docs/bestpractices/cache-models-to-s3-bucket.md index ac10a6e20..98745a9da 100644 --- a/website/docs/bestpractices/cache-models-to-s3-bucket.md +++ b/website/docs/bestpractices/cache-models-to-s3-bucket.md @@ -7,16 +7,10 @@ Storing a machine learning model in Amazon S3, instead of downloading it from Hu 1. Reduced Download Latency: Storing the model in S3 can reduce the network latency that may occur when downloading the model from Huggingface, especially when you need to quickly deploy the model or use it across multiple instances simultaneously. -2. Improved Access Speed and Reliability: S3's high availability and global reach mean that the model can be accessed faster and more reliably, particularly when used in conjunction with AWS infrastructure (such as EC2 or Lambda) within the same region. +2. Improved Access Speed and Reliability: S3's high availability and global reach mean that the model can be accessed faster and more reliably. -Refer to the page [Mounpoint S3: Enhancing Amazon S3 File Access for Data & AI Workloads on Amazon EKS](../resources/mountpoint-s3) for learn more about Amazon S3 Mountpoint CSI Driver. +Refer to the page [Mounpoint S3: Enhancing Amazon S3 File Access for Data & AI Workloads on Amazon EKS](../resources/mountpoint-s3) to learn more about Amazon S3 Mountpoint CSI Driver. -End-to-end Example +## End-to-end Example -TODO: -- Create a S3 bucket -- Download ML models to the local computer -- Upload the ML models to S3 -- Specify the Kubernetes PV and PVC -- Mount the volume in the Ray Pods - \ No newline at end of file +An end-to-end deployment example can be found in [Stable Diffusion on GPU](../gen-ai/inference/GPUs/stablediffusion-gpus). \ No newline at end of file diff --git a/website/docs/gen-ai/inference/stablediffusion-gpus.md b/website/docs/gen-ai/inference/stablediffusion-gpus.md index 1202af61f..0265bd358 100644 --- a/website/docs/gen-ai/inference/stablediffusion-gpus.md +++ b/website/docs/gen-ai/inference/stablediffusion-gpus.md @@ -43,11 +43,16 @@ Ensure that you have installed the following tools on your machine. To accelerate the deployment of image retrieval on Ray workers, refer to [Preload container images into Bottlerocket data volumes with Karpenter with EBS Snapshots](../../bestpractices/preload-container-images) -Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to provision Bottlerocket worker nodes with EBS Snapshots, to reduce cold start for container startup. This will likely to save 10 mins (depending on the image size) for downloading and extracting container images from Amazon ECR. +Define the variable `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to provision Bottlerocket worker nodes with EBS Snapshots, to reduce cold start for container startup. This will likely to save 10 mins (depending on the image size) for downloading and extracting container images from Amazon ECR. ``` export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0c6d965cf431785ed ``` + +### (Optional) Create S3 Bucket for Model Storage + +Define the variable `TF_VAR_create_s3_bucket=true` to create s3 bucket for model storage. + ### Deploy Clone the repository @@ -184,6 +189,15 @@ The screenshots provided will show the Serve deployment and the Ray Cluster depl ![RayServe Cluster](img/ray-serve-gpu-sd-cluster.png) +### (Optional) Save and Load Stable Diffusion Model in S3 Bucket + +1. Open `download_models.ipynb` Python notebook and run the cells to download the model from Huggingface and upload model files to Amazon S3. +2. Open `s3-pv-model-storage.yaml` and replace with the actual bucket name in `terraform output` +3. Deploy the PVC and RayService: +``` +kubectl apply -f s3-pv-model-storage.yaml -f ray-serve-stablediffusion-from-s3.yaml +``` + ## Deploying the Gradio WebUI App Discover how to create a user-friendly chat interface using [Gradio](https://www.gradio.app/) that integrates seamlessly with deployed models. From f705fc79005ff290b92bd83dfc698ae9531ddbab Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 4 Sep 2024 17:46:37 +0800 Subject: [PATCH 31/53] Merge PVC and RayService into a single YAML --- .../ray-serve-stablediffusion-from-s3.yaml | 34 +++++++++++++++++++ .../s3-pv-model-storage.yaml | 33 ------------------ 2 files changed, 34 insertions(+), 33 deletions(-) delete mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index fcf23607a..a425d4d65 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -177,3 +177,37 @@ spec: name: stablediffusion port: number: 8000 +--- +apiVersion: v1 +kind: PersistentVolume +metadata: + name: s3-pv-model +spec: + capacity: + storage: 1200Gi # ignored, required + accessModes: + - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany + mountOptions: + - allow-overwrite + - allow-delete + - region us-west-2 + - prefix stable-diffusion-2/ + csi: + driver: s3.csi.aws.com # required + volumeHandle: s3-csi-driver-volume + volumeAttributes: + bucketName: # Replace bucketName with S3 bucket name created from `terraform output` +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: s3-claim + namespace: stablediffusion +spec: + accessModes: + - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany + storageClassName: "" # required for static provisioning + resources: + requests: + storage: 1200Gi # ignored, required + volumeName: s3-pv-model \ No newline at end of file diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml deleted file mode 100644 index edd592e1f..000000000 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/s3-pv-model-storage.yaml +++ /dev/null @@ -1,33 +0,0 @@ -apiVersion: v1 -kind: PersistentVolume -metadata: - name: s3-pv-model -spec: - capacity: - storage: 1200Gi # ignored, required - accessModes: - - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany - mountOptions: - - allow-overwrite - - allow-delete - - region us-west-2 - - prefix stable-diffusion-2/ - csi: - driver: s3.csi.aws.com # required - volumeHandle: s3-csi-driver-volume - volumeAttributes: - bucketName: # Replace bucketName with S3 bucket name created from `terraform output` ---- -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: s3-claim - namespace: stablediffusion -spec: - accessModes: - - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany - storageClassName: "" # required for static provisioning - resources: - requests: - storage: 1200Gi # ignored, required - volumeName: s3-pv-model \ No newline at end of file From 6e45d624dbc28847cf23dd6e87eeb66950352eeb Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 4 Sep 2024 17:47:05 +0800 Subject: [PATCH 32/53] Merge PVC and RayService into a single YAML --- website/docs/gen-ai/inference/stablediffusion-gpus.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/website/docs/gen-ai/inference/stablediffusion-gpus.md b/website/docs/gen-ai/inference/stablediffusion-gpus.md index 0265bd358..3cb05a87a 100644 --- a/website/docs/gen-ai/inference/stablediffusion-gpus.md +++ b/website/docs/gen-ai/inference/stablediffusion-gpus.md @@ -195,9 +195,10 @@ The screenshots provided will show the Serve deployment and the Ray Cluster depl 2. Open `s3-pv-model-storage.yaml` and replace with the actual bucket name in `terraform output` 3. Deploy the PVC and RayService: ``` -kubectl apply -f s3-pv-model-storage.yaml -f ray-serve-stablediffusion-from-s3.yaml +kubectl apply -f ray-serve-stablediffusion-from-s3.yaml ``` + ## Deploying the Gradio WebUI App Discover how to create a user-friendly chat interface using [Gradio](https://www.gradio.app/) that integrates seamlessly with deployed models. From c70394fb39e92f94f588cfd2a0f75d6abb69acd0 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 6 Sep 2024 10:43:57 +0800 Subject: [PATCH 33/53] Revert "Merge branch 'cache-model-from-s3' of https://github.com/lindarr915/data-on-eks into cache-model-from-s3" This reverts commit 1bfb117dc196a1814a510bf4922442b1c997a061, reversing changes made to 4549a71fd3c39f874232de1d46a8795ed0b14b29. --- .../gen-ai/inference/img/nim-architecture.png | Bin 0 -> 160731 bytes .../gen-ai/inference/img/nim-on-eks-arch.png | Bin 466386 -> 0 bytes .../docs/gen-ai/inference/nvidia-nim-llama3.md | 6 ++++-- 3 files changed, 4 insertions(+), 2 deletions(-) create mode 100644 website/docs/gen-ai/inference/img/nim-architecture.png delete mode 100644 website/docs/gen-ai/inference/img/nim-on-eks-arch.png diff --git a/website/docs/gen-ai/inference/img/nim-architecture.png b/website/docs/gen-ai/inference/img/nim-architecture.png new file mode 100644 index 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zH1$Iv(6ndQ-u!OF0(2p8i1wUo^7O=o|H*H1fTk%JUij^q0(9X&gY%!k`H$rU{>O6u dSGzST?bvC1B>Q8N))T--ML|=({PFXk{{!?Dd};sy diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index 79d66335b..34b8b9eca 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -28,6 +28,10 @@ NIM abstracts away model inference internals such as execution engine and runtim NIMs are packaged as container images on a per model/model family basis. Each NIM container is with a model, such as `meta/llama3-8b-instruct`. These containers include a runtime that runs on any NVIDIA GPU with sufficient GPU memory, but some model/GPU combinations are optimized. NIM automatically downloads the model from NVIDIA NGC Catalog, leveraging a local filesystem cache if available. +![NIM Architecture](img/nim-architecture.png) + +Source: https://docs.nvidia.com/nim/large-language-models/latest/introduction.html#architecture + ## Overview of this deployment pattern on Amazon EKS This pattern combines the capabilities of NVIDIA NIM, Amazon Elastic Kubernetes Service (EKS), and various AWS services to deliver a high-performance and cost-optimized model serving infrastructure. @@ -44,8 +48,6 @@ This pattern combines the capabilities of NVIDIA NIM, Amazon Elastic Kubernetes By combining these components, our proposed solution delivers a powerful and cost-effective model serving infrastructure tailored for large language models. With NVIDIA NIM's seamless integration, Amazon EKS's scalability with Karpenter, customers can achieve high performance while minimizing infrastructure costs. -![NIM on EKS Architecture](img/nim-on-eks-arch.png) - ## Deploying the Solution ### Prerequisites From 770d69a39b0262025954255e71e92a719a4457dc Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 6 Sep 2024 10:44:53 +0800 Subject: [PATCH 34/53] Remove Comments --- ai-ml/jark-stack/terraform/install.sh | 4 ---- 1 file changed, 4 deletions(-) diff --git a/ai-ml/jark-stack/terraform/install.sh b/ai-ml/jark-stack/terraform/install.sh index c1b95e6c0..1814a9044 100755 --- a/ai-ml/jark-stack/terraform/install.sh +++ b/ai-ml/jark-stack/terraform/install.sh @@ -6,10 +6,6 @@ targets=( "module.eks" ) -# Get the snapshot ID with ./snapshot.sh -# Uncomment the command line below and replace the value with the snapshot ID -# export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0ffe58efdd845d938 - # Initialize Terraform terraform init -upgrade From 12a1f83f3722e25a28dcfb758a49f4e09ae3f275 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 6 Sep 2024 10:48:21 +0800 Subject: [PATCH 35/53] neat: remove used code --- .../ray_serve_sd.py | 23 ++++++++++++------- .../gen-ai/inference/stablediffusion-gpus.md | 2 +- 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py index 393eb9dc8..ae3ff164a 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray_serve_sd.py @@ -7,7 +7,6 @@ from ray import serve - app = FastAPI() @@ -31,7 +30,8 @@ async def generate(self, prompt: str, img_size: int = 768): return Response(content=file_stream.getvalue(), media_type="image/png") -@serve.deployment(name="stable-diffusion-v2", +@serve.deployment( + name="stable-diffusion-v2", ray_actor_options={"num_gpus": 1}, autoscaling_config={"min_replicas": 1, "max_replicas": 2}, ) @@ -39,20 +39,27 @@ class StableDiffusionV2: def __init__(self): from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline - model_name = "stabilityai/stable-diffusion-2" - - model_id = os.getenv('MODEL_ID') - model_path = os.getenv('MODEL_PATH') + model_id = os.getenv("MODEL_ID") + model_path = os.getenv("MODEL_PATH") if model_path is not None: model_id = model_path - self.pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) + self.pipe = StableDiffusionPipeline.from_pretrained( + model_id, torch_dtype=torch.float16 + ) self.pipe = self.pipe.to("cuda") def generate(self, prompt: str, img_size: int = 768): assert len(prompt), "prompt parameter cannot be empty" - image = self.pipe(prompt, height=img_size, width=img_size, negative_prompt="",num_inference_steps=50,guidance_scale=7.0).images[0] + image = self.pipe( + prompt, + height=img_size, + width=img_size, + negative_prompt="", + num_inference_steps=50, + guidance_scale=7.0, + ).images[0] return image diff --git a/website/docs/gen-ai/inference/stablediffusion-gpus.md b/website/docs/gen-ai/inference/stablediffusion-gpus.md index 3cb05a87a..bacec82f4 100644 --- a/website/docs/gen-ai/inference/stablediffusion-gpus.md +++ b/website/docs/gen-ai/inference/stablediffusion-gpus.md @@ -7,7 +7,7 @@ import CollapsibleContent from '../../../src/components/CollapsibleContent'; :::info - We are actively enhancing this blueprint to incorporate improvements in observability and logging. - +git pull origin cache-model-from-s3 ::: From eba5e58e171393fe58572062a1954d54aea5cf1c Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 6 Sep 2024 11:16:50 +0800 Subject: [PATCH 36/53] fix docs --- ai-ml/jark-stack/terraform/variables.tf | 2 +- website/docs/gen-ai/inference/stablediffusion-gpus.md | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/ai-ml/jark-stack/terraform/variables.tf b/ai-ml/jark-stack/terraform/variables.tf index 19956d849..75378b43c 100644 --- a/ai-ml/jark-stack/terraform/variables.tf +++ b/ai-ml/jark-stack/terraform/variables.tf @@ -55,7 +55,7 @@ variable "enable_kubecost" { variable "bottlerocket_data_disk_snpashot_id" { description = "Bottlerocket Data Disk Snapshot ID" type = string - default = "" + default = "" } variable "create_s3_bucket" { diff --git a/website/docs/gen-ai/inference/stablediffusion-gpus.md b/website/docs/gen-ai/inference/stablediffusion-gpus.md index bacec82f4..3469819df 100644 --- a/website/docs/gen-ai/inference/stablediffusion-gpus.md +++ b/website/docs/gen-ai/inference/stablediffusion-gpus.md @@ -7,7 +7,6 @@ import CollapsibleContent from '../../../src/components/CollapsibleContent'; :::info - We are actively enhancing this blueprint to incorporate improvements in observability and logging. -git pull origin cache-model-from-s3 ::: From a0352f93802596c69e1b596ddbb25355df6fb8c3 Mon Sep 17 00:00:00 2001 From: Darren Lin 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zH1$Iv(6ndQ-u!OF0(2p8i1wUo^7O=o|H*H1fTk%JUij^q0(9X&gY%!k`H$rU{>O6u dSGzST?bvC1B>Q8N))T--ML|=({PFXk{{!?Dd};sy literal 0 HcmV?d00001 From 922bfe5bc19d8762b86559671c7d57a49396bf52 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 6 Sep 2024 11:31:08 +0800 Subject: [PATCH 38/53] fix: remove duplicated lines --- .gitignore | 1 - 1 file changed, 1 deletion(-) diff --git a/.gitignore b/.gitignore index 408f7e745..35f44345c 100755 --- a/.gitignore +++ b/.gitignore @@ -61,6 +61,5 @@ site node_modules gen-ai/inference/stable-diffusion-rayserve-gpu/locust/__pycache__/* gen-ai/inference/stable-diffusion-rayserve-gpu/stable-diffusion-2/* -gen-ai/inference/stable-diffusion-rayserve-gpu/locust/__pycache__/* website/package-lock.json website/package.json From 213d3dc95cda3cf1f57f8b8d04db35018001bb7f Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 6 Sep 2024 15:12:29 +0800 Subject: [PATCH 39/53] fix: remove whitespaces --- ai-ml/jark-stack/terraform/addons.tf | 10 +++++----- ai-ml/jark-stack/terraform/variables.tf | 4 ++-- .../ray-serve-stablediffusion-from-s3.yaml | 8 ++++---- .../docs/bestpractices/cache-models-to-s3-bucket.md | 2 +- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index 0add387dc..28392b804 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -82,10 +82,10 @@ module "eks_blueprints_addons" { vpc-cni = { preserve = true } - + aws-mountpoint-s3-csi-driver = { service_account_role_arn = module.s3_csi_driver_irsa.iam_role_arn - configuration_values = <<-EOF + configuration_values = <<-EOF node: tolerateAllTaints: true EOF @@ -368,9 +368,9 @@ module "data_addons" { # IRSA for Mountpoint for Amazon S3 CSI Driver #--------------------------------------------------------------- module "s3_csi_driver_irsa" { - source = "terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks" - version = "~> 5.34" - role_name_prefix = format("%s-%s-", local.name, "s3-csi-driver") + source = "terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks" + version = "~> 5.34" + role_name_prefix = format("%s-%s-", local.name, "s3-csi-driver") role_policy_arns = { # WARNING: Demo purpose only. Bring your own IAM policy with least privileges s3_csi_driver = "arn:aws:iam::aws:policy/AmazonS3FullAccess" diff --git a/ai-ml/jark-stack/terraform/variables.tf b/ai-ml/jark-stack/terraform/variables.tf index a747a189f..60d87f2bf 100644 --- a/ai-ml/jark-stack/terraform/variables.tf +++ b/ai-ml/jark-stack/terraform/variables.tf @@ -60,6 +60,6 @@ variable "bottlerocket_data_disk_snpashot_id" { variable "create_s3_bucket" { description = "Create S3 Bucket for Model Storage" - default = false - type = bool + default = false + type = bool } diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index a425d4d65..de5f0f00a 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -21,7 +21,7 @@ spec: serveConfigV2: | applications: - name: stable-diffusion-deployment - import_path: "ray_serve_sd:entrypoint" + import_path: "ray_serve_sd:entrypoint" route_prefix: "/" runtime_env: env_vars: @@ -63,7 +63,7 @@ spec: # For faster inference scaling, consider building a custom image with only your workload's essential dependencies. # Smaller images lead to faster scaling, especially across multiple nodes. # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. - # Preload Container Image into data volumes for faster new ray worker nodes + # Preload Container Image into data volumes for faster new ray worker nodes - name: head image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated @@ -84,7 +84,7 @@ spec: - mountPath: /tmp/ray name: ray-logs - mountPath: /serve_app/stable-diffusion-2 - name: cache-dir + name: cache-dir resources: limits: cpu: 2 @@ -210,4 +210,4 @@ spec: resources: requests: storage: 1200Gi # ignored, required - volumeName: s3-pv-model \ No newline at end of file + volumeName: s3-pv-model diff --git a/website/docs/bestpractices/cache-models-to-s3-bucket.md b/website/docs/bestpractices/cache-models-to-s3-bucket.md index 98745a9da..135bac680 100644 --- a/website/docs/bestpractices/cache-models-to-s3-bucket.md +++ b/website/docs/bestpractices/cache-models-to-s3-bucket.md @@ -13,4 +13,4 @@ Refer to the page [Mounpoint S3: Enhancing Amazon S3 File Access for Data & AI W ## End-to-end Example -An end-to-end deployment example can be found in [Stable Diffusion on GPU](../gen-ai/inference/GPUs/stablediffusion-gpus). \ No newline at end of file +An end-to-end deployment example can be found in [Stable Diffusion on GPU](../gen-ai/inference/GPUs/stablediffusion-gpus). From 2ae7fb597d1dfd30eef13c43715a2e2b447b49d6 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 6 Sep 2024 18:15:27 +0800 Subject: [PATCH 40/53] fix: Change imagePullPolicy to Always --- .../ray-serve-stablediffusion-from-s3.yaml | 4 ++-- .../ray-serve-stablediffusion.yaml | 11 ++++++----- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index de5f0f00a..e448f33cd 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -66,7 +66,7 @@ spec: # Preload Container Image into data volumes for faster new ray worker nodes - name: head image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu - imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated + imagePullPolicy: Always # Ensure the image is always pulled when updated lifecycle: preStop: exec: @@ -117,7 +117,7 @@ spec: # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. - name: worker image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu - imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated + imagePullPolicy: Always # Ensure the image is always pulled when updated lifecycle: preStop: exec: diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml index 7bb996f72..335f83044 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml @@ -28,6 +28,7 @@ spec: MODEL_ID: "stabilityai/stable-diffusion-2-1" pip: - transformers==4.25.1 + - accelerate==0.30.1 deployments: - name: stable-diffusion-v2 autoscaling_config: @@ -44,7 +45,7 @@ spec: num_cpus: 3 num_gpus: 1 rayClusterConfig: - rayVersion: '2.11.0' + rayVersion: '2.24.0' enableInTreeAutoscaling: true headGroupSpec: headService: @@ -63,8 +64,8 @@ spec: # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. # Preload Container Image into data volumes for faster new ray worker nodes - name: head - image: public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest - imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated + image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu + imagePullPolicy: Always # Ensure the image is always pulled when updated lifecycle: preStop: exec: @@ -109,8 +110,8 @@ spec: # Smaller images lead to faster scaling, especially across multiple nodes. # Notice that we are using the same image for both the head and worker nodes. You might hit ModuleNotFoundError if you use a different image for head and worker nodes. - name: worker - image: public.ecr.aws/data-on-eks/ray2.11.0-py310-gpu-stablediffusion:latest - imagePullPolicy: IfNotPresent # Ensure the image is always pulled when updated + image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu + imagePullPolicy: Always # Ensure the image is always pulled when updated lifecycle: preStop: exec: From c307539bf950a1bd35b16d7ffd79b8f8d07d0ee6 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 20 Sep 2024 14:46:33 +0800 Subject: [PATCH 41/53] Create a Job to save models to Amazon S3 --- .../download_models.yaml | 103 ++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.yaml diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.yaml new file mode 100644 index 000000000..3ce139c6b --- /dev/null +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.yaml @@ -0,0 +1,103 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: stablediffusion +--- +apiVersion: v1 +kind: PersistentVolume +metadata: + name: s3-pv-model-storage +spec: + capacity: + storage: 1200Gi # ignored, required + accessModes: + - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany + mountOptions: + - allow-overwrite + - allow-delete + - region us-west-2 + - prefix stable-diffusion-2/ + csi: + driver: s3.csi.aws.com # required + volumeHandle: s3-csi-driver-volume + volumeAttributes: + bucketName: model-storage-091550601287 # Replace bucketName with S3 bucket name created from `terraform output` +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: s3-model-storage-claim + namespace: stablediffusion +spec: + accessModes: + - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany + storageClassName: "" # required for static provisioning + resources: + requests: + storage: 1200Gi # ignored, required + volumeName: s3-pv-model-storage +--- +apiVersion: v1 +kind: ConfigMap +metadata: + name: shell-script-configmap +data: + download_models.py: | + from diffusers import StableDiffusionPipeline + import torch + + # Set the model you want to download + model_name = "stabilityai/stable-diffusion-2" + model_directory = "/serve_app/temp-stable-diffusion-2" + + # Load the model + pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory) + + # Save the model to the local directory + pipe.save_pretrained(model_directory) + + print(f"Model saved to {model_directory}") + + import shutil + + # Source directory (the one you want to copy) + src_dir = model_directory + + # Destination directory (where you want to copy to) + dst_dir = "/serve_app/stable-diffusion-2/" + + shutil.rmtree(f"{model_directory}/.locks") + shutil.rmtree(f"{model_directory}/models--stabilityai--stable-diffusion-2") + + # Copy the directory recursively + shutil.copytree(src_dir, dst_dir, dirs_exist_ok=True) + + # NOTICE: since shutil.copytree will call copystat(src, dst), it will generate errors as Amazon S3 does not support modifying metadata. + # Anyway, the files will be copied and the error message can be ignored +--- +apiVersion: batch/v1 +kind: Job +metadata: + name: shell-script-job +spec: + template: + spec: + containers: + - name: shell-script-container + image: public.ecr.aws/data-on-eks/ray-serve-gpu-stablediffusion:2.33.0-py311-gpu + command: ["python"] + args: ["/scripts/download_models.py"] + volumeMounts: + - name: script-volume + mountPath: /scripts + - mountPath: /serve_app/stable-diffusion-2 + name: cache-dir + volumes: + - name: cache-dir + persistentVolumeClaim: + claimName: s3-model-storage-claim + - name: script-volume + configMap: + name: shell-script-configmap + restartPolicy: Never + backoffLimit: 4 From 9de005d6f43a50bbadcbf904522ea7d88c7e82ea Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 20 Sep 2024 15:51:38 +0800 Subject: [PATCH 42/53] feat: Add job to download and save models to Amazon S3 - Added a new job to download and save models to Amazon S3. - Created a YAML file for the job configuration. - Defined a persistent volume and persistent volume claim for model storage. - Created a config map for the shell script used in the job. - Configured the job container with the necessary image and command. - Mounted the script and model storage directories in the container. --- ...models.yaml => download-models-to-s3.yaml} | 14 +- .../ray-serve-stablediffusion-from-s3.yaml | 126 ++++++++++++------ 2 files changed, 94 insertions(+), 46 deletions(-) rename gen-ai/inference/stable-diffusion-rayserve-gpu/{download_models.yaml => download-models-to-s3.yaml} (84%) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml similarity index 84% rename from gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.yaml rename to gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml index 3ce139c6b..052921b26 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml @@ -21,7 +21,7 @@ spec: driver: s3.csi.aws.com # required volumeHandle: s3-csi-driver-volume volumeAttributes: - bucketName: model-storage-091550601287 # Replace bucketName with S3 bucket name created from `terraform output` + bucketName: # Replace bucketName with S3 bucket name created from `terraform output` --- apiVersion: v1 kind: PersistentVolumeClaim @@ -58,7 +58,7 @@ data: print(f"Model saved to {model_directory}") - import shutil + import shutil, logging # Source directory (the one you want to copy) src_dir = model_directory @@ -70,7 +70,15 @@ data: shutil.rmtree(f"{model_directory}/models--stabilityai--stable-diffusion-2") # Copy the directory recursively - shutil.copytree(src_dir, dst_dir, dirs_exist_ok=True) + try: + shutil.copytree(src_dir, dst_dir, dirs_exist_ok=True) + logging.info("Directory copied successfully") + except shutil.Error as e: + logging.error(f"Error copying directory: {e}") + except OSError as e: + logging.error(f"OS error occurred: {e}") + except Exception as e: + logging.error(f"Unexpected error occurred: {e}") # NOTICE: since shutil.copytree will call copystat(src, dst), it will generate errors as Amazon S3 does not support modifying metadata. # Anyway, the files will be copied and the error message can be ignored diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index e448f33cd..6202ed388 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -1,12 +1,3 @@ -#---------------------------------------------------------------------- -# NOTE: For deployment instructions, refer to the DoEKS website. -#---------------------------------------------------------------------- ---- -apiVersion: v1 -kind: Namespace -metadata: - name: stablediffusion ---- apiVersion: ray.io/v1 kind: RayService metadata: @@ -85,6 +76,8 @@ spec: name: ray-logs - mountPath: /serve_app/stable-diffusion-2 name: cache-dir + - mountPath: /serve_app + name: serve-script resources: limits: cpu: 2 @@ -96,11 +89,15 @@ spec: NodeGroupType: x86-cpu-karpenter type: karpenter volumes: + - name: serve-script + configMap: + name: stable-diffusion-script + defaultMode: 0777 - name: ray-logs emptyDir: {} - name: cache-dir persistentVolumeClaim: - claimName: s3-claim + claimName: s3-model-storage-claim workerGroupSpecs: - groupName: gpu # With g5.2xlarge instance, Ray can scale up to 4 nodes, with one pod per node and 1 GPU per pod. @@ -134,6 +131,8 @@ spec: volumeMounts: - mountPath: /serve_app/stable-diffusion-2 name: cache-dir + - mountPath: /serve_app + name: serve-script nodeSelector: NodeGroupType: g5-gpu-karpenter type: karpenter @@ -142,9 +141,13 @@ spec: operator: "Exists" effect: "NoSchedule" volumes: - - name: cache-dir - persistentVolumeClaim: - claimName: s3-claim + - name: serve-script + configMap: + name: stable-diffusion-script + defaultMode: 0777 + - name: cache-dir + persistentVolumeClaim: + claimName: s3-model-storage-claim --- apiVersion: networking.k8s.io/v1 kind: Ingress @@ -179,35 +182,72 @@ spec: number: 8000 --- apiVersion: v1 -kind: PersistentVolume -metadata: - name: s3-pv-model -spec: - capacity: - storage: 1200Gi # ignored, required - accessModes: - - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany - mountOptions: - - allow-overwrite - - allow-delete - - region us-west-2 - - prefix stable-diffusion-2/ - csi: - driver: s3.csi.aws.com # required - volumeHandle: s3-csi-driver-volume - volumeAttributes: - bucketName: # Replace bucketName with S3 bucket name created from `terraform output` ---- -apiVersion: v1 -kind: PersistentVolumeClaim +kind: ConfigMap metadata: - name: s3-claim + name: stable-diffusion-script namespace: stablediffusion -spec: - accessModes: - - ReadWriteMany # supported options: ReadWriteMany / ReadOnlyMany - storageClassName: "" # required for static provisioning - resources: - requests: - storage: 1200Gi # ignored, required - volumeName: s3-pv-model +data: + ray_serve_sd.py: | + from io import BytesIO + from fastapi import FastAPI + from fastapi.responses import Response + import torch + import os + import ray + from ray import serve + + app = FastAPI() + + @serve.deployment(num_replicas=1) + @serve.ingress(app) + class APIIngress: + def __init__(self, diffusion_model_handle) -> None: + self.handle = diffusion_model_handle + + @app.get( + "/imagine", + responses={200: {"content": {"image/png": {}}}}, + response_class=Response, + ) + async def generate(self, prompt: str, img_size: int = 768): + assert len(prompt), "prompt parameter cannot be empty" + + image = await self.handle.generate.remote(prompt, img_size=img_size) + file_stream = BytesIO() + image.save(file_stream, "PNG") + return Response(content=file_stream.getvalue(), media_type="image/png") + + + @serve.deployment( + name="stable-diffusion-v2", + ray_actor_options={"num_gpus": 1}, + autoscaling_config={"min_replicas": 1, "max_replicas": 2}, + ) + class StableDiffusionV2: + def __init__(self): + from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline + + model_id = os.getenv("MODEL_ID") + model_path = os.getenv("MODEL_PATH") + if model_path is not None: + model_id = model_path + + self.pipe = StableDiffusionPipeline.from_pretrained( + model_id, torch_dtype=torch.float16 + ) + self.pipe = self.pipe.to("cuda") + + def generate(self, prompt: str, img_size: int = 768): + assert len(prompt), "prompt parameter cannot be empty" + + image = self.pipe( + prompt, + height=img_size, + width=img_size, + negative_prompt="", + num_inference_steps=50, + guidance_scale=7.0, + ).images[0] + return image + + entrypoint = APIIngress.bind(StableDiffusionV2.bind()) From 4488eca266d681667e93ec3bb2b0fe8a1c7e16d3 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 20 Sep 2024 16:50:20 +0800 Subject: [PATCH 43/53] feat: support save ML models to S3 --- .../cache-models-to-s3-bucket.md | 1 + .../inference/GPUs/stablediffusion-gpus.md | 25 +++++++++++++++---- 2 files changed, 21 insertions(+), 5 deletions(-) diff --git a/website/docs/bestpractices/cache-models-to-s3-bucket.md b/website/docs/bestpractices/cache-models-to-s3-bucket.md index 135bac680..d65dbc6b7 100644 --- a/website/docs/bestpractices/cache-models-to-s3-bucket.md +++ b/website/docs/bestpractices/cache-models-to-s3-bucket.md @@ -8,6 +8,7 @@ Storing a machine learning model in Amazon S3, instead of downloading it from Hu 1. Reduced Download Latency: Storing the model in S3 can reduce the network latency that may occur when downloading the model from Huggingface, especially when you need to quickly deploy the model or use it across multiple instances simultaneously. 2. Improved Access Speed and Reliability: S3's high availability and global reach mean that the model can be accessed faster and more reliably. +3. Reduced Data Transfer Costs with VPC Endpoints: In many cases, using VPC endpoints can help reduce data transfer costs. For data transfer between your VPC and S3 through 5h3 gateway endpoints is free. Refer to the page [Mounpoint S3: Enhancing Amazon S3 File Access for Data & AI Workloads on Amazon EKS](../resources/mountpoint-s3) to learn more about Amazon S3 Mountpoint CSI Driver. diff --git a/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md b/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md index 99f759bab..354dc707d 100644 --- a/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md +++ b/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md @@ -52,6 +52,13 @@ Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to pr ``` export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0c6d965cf431785ed ``` + +### (Optional) Save Machine Learning Models in Amazon S3 +Define the TF_VAR_create_s3_bucket to be `true` so that running Terraform module will provision a S3 bucket. +``` +export TF_VAR_create_s3_bucket=true +``` + ### Deploy Clone the repository @@ -75,16 +82,16 @@ For example, set your `export AWS_DEFAULT_REGION=""` to the desired regi ```bash cd data-on-eks/ai-ml/jark-stack/ && chmod +x install.sh ./install.sh + +terraform output +configure_kubectl = "aws eks --region us-west-2 update-kubeconfig --name jark-stack" +model_s3_bucket = "model-storage-20240820044102407400000001" ``` ### Verify the resources Verify the Amazon EKS Cluster -```bash -aws eks --region us-west-2 describe-cluster --name jark-stack -``` - ```bash # Creates k8s config file to authenticate with EKS aws eks --region us-west-2 update-kubeconfig --name jark-stack @@ -118,7 +125,7 @@ Ensure the cluster is configured locally aws eks --region us-west-2 update-kubeconfig --name jark-stack ``` -**Deploy RayServe Cluster** +**(Approach 1) Deploy RayServe Cluster (Downloading Model from Huggingface)** ```bash cd ./../gen-ai/inference/stable-diffusion-rayserve-gpu @@ -133,6 +140,14 @@ If you did not preload container images into the data volume, the deployment pro ::: +**(Approach 2) Deploy RayServe Cluster (Downloading Model from Amazon S3)** +```bash +cd ./../gen-ai/inference/stable-diffusion-rayserve-gpu +# edit the file download-models-to-s3 and fill the actual bucket name by running terraform output +kubectl apply -f download-models-to-s3.yaml +kubectl apply -f ray-service-stablediffusion-from-s3.yaml +``` + This deployment establishes a Ray head pod running on an x86 instance and a worker pod on a GPU G5 instance as shown below. ```bash From 06c678d60e1a2698b69b7a2d6b9fb2c55cb6bb50 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 20 Sep 2024 17:10:41 +0800 Subject: [PATCH 44/53] feat: add S3 Gateway VPCe --- ai-ml/jark-stack/terraform/vpc.tf | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/ai-ml/jark-stack/terraform/vpc.tf b/ai-ml/jark-stack/terraform/vpc.tf index 59c3da89c..5bb1b9621 100644 --- a/ai-ml/jark-stack/terraform/vpc.tf +++ b/ai-ml/jark-stack/terraform/vpc.tf @@ -51,3 +51,30 @@ module "vpc" { tags = local.tags } + + +module "vpc_endpoints" { + source = "terraform-aws-modules/vpc/aws//modules/vpc-endpoints" + version = "~> 5.0" + + # create = var.enable_vpc_endpoints + + create = true + + vpc_id = module.vpc.vpc_id + security_group_ids = [module.vpc_endpoints_sg.security_group_id] + + endpoints = merge({ + s3 = { + service = "s3" + service_type = "Gateway" + route_table_ids = module.vpc.private_route_table_ids + tags = { + Name = "${local.name}-s3" + } + } + } + ) + + tags = local.tags +} From f19986d8db4b809e606e3ac12fcd99091550d1c0 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 20 Sep 2024 17:11:05 +0800 Subject: [PATCH 45/53] chore: improve logging --- .../download-models-to-s3.yaml | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml index 052921b26..a7bcbd340 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml @@ -80,8 +80,16 @@ data: except Exception as e: logging.error(f"Unexpected error occurred: {e}") - # NOTICE: since shutil.copytree will call copystat(src, dst), it will generate errors as Amazon S3 does not support modifying metadata. - # Anyway, the files will be copied and the error message can be ignored + import logging + + notice_message = """ + NOTICE: shutil.copytree() may generate errors when used with Amazon S3. + This is because it calls copystat(src, dst) at: + /home/ray/anaconda3/lib/python3.11/shutil.py, line 527, in _copytree + Amazon S3 does not support modifying metadata, which causes these errors. + """ + + logging.warning(notice_message.strip()) --- apiVersion: batch/v1 kind: Job From 22f4d3deae9288ff0a434b8ccec14fa9b1efe273 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 20 Sep 2024 17:26:08 +0800 Subject: [PATCH 46/53] fix: remove whitespaces --- ai-ml/jark-stack/terraform/addons.tf | 2 +- ai-ml/jark-stack/terraform/outputs.tf | 3 +- .../stable-diffusion-rayserve-gpu/Dockerfile | 3 +- .../download-models-to-s3.yaml | 2 +- .../download_models.ipynb | 95 ------------------- .../download_models.py | 14 --- .../ray-serve-stablediffusion-from-s3.yaml | 4 +- .../inference/GPUs/stablediffusion-gpus.md | 10 +- 8 files changed, 13 insertions(+), 120 deletions(-) delete mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb delete mode 100644 gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py diff --git a/ai-ml/jark-stack/terraform/addons.tf b/ai-ml/jark-stack/terraform/addons.tf index 28392b804..e8d2a2b91 100644 --- a/ai-ml/jark-stack/terraform/addons.tf +++ b/ai-ml/jark-stack/terraform/addons.tf @@ -435,4 +435,4 @@ data "aws_iam_policy_document" "karpenter_controller_policy" { resource "aws_s3_bucket" "model_storage" { count = var.create_s3_bucket ? 1 : 0 bucket_prefix = "model-storage-" -} \ No newline at end of file +} diff --git a/ai-ml/jark-stack/terraform/outputs.tf b/ai-ml/jark-stack/terraform/outputs.tf index 9a59c367a..531e8924e 100644 --- a/ai-ml/jark-stack/terraform/outputs.tf +++ b/ai-ml/jark-stack/terraform/outputs.tf @@ -4,5 +4,6 @@ output "configure_kubectl" { } output "model_s3_bucket" { - value = length(aws_s3_bucket.model_storage) > 0 ? aws_s3_bucket.model_storage[0].bucket : "" + description = "The S3 bucket name for storing ML models" + value = length(aws_s3_bucket.model_storage) > 0 ? aws_s3_bucket.model_storage[0].bucket : "" } diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile index 801bb091d..464881c85 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile @@ -20,7 +20,8 @@ RUN pip install --no-cache-dir requests torch "diffusers==0.29.2" "transformers= WORKDIR /serve_app # Copy your Ray Serve script into the container -COPY ray_serve_sd.py /serve_app/ray_serve_sd.py +# The serving script can moved to the ConfigMap, or copied in the container image +# COPY ray_serve_sd.py /serve_app/ray_serve_sd.py # Set the PYTHONPATH environment variable ENV PYTHONPATH=/serve_app:$PYTHONPATH diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml index a7bcbd340..a34c77774 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml @@ -42,7 +42,7 @@ kind: ConfigMap metadata: name: shell-script-configmap data: - download_models.py: | + download_models.py: | from diffusers import StableDiffusionPipeline import torch diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb deleted file mode 100644 index 720f5eb09..000000000 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.ipynb +++ /dev/null @@ -1,95 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -U diffusers accelerate accelerate awscliv2 --quiet " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from diffusers import StableDiffusionPipeline\n", - "import torch\n", - "\n", - "# Set the model you want to download\n", - "model_name = \"stabilityai/stable-diffusion-2\"\n", - "model_directory = \"./stable-diffusion-2\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the model to the local directory\n", - "pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory)\n", - "pipe.save_pretrained(model_directory)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload the files to Amazon S3 bucket from a local directory using AWS CLI\n", - "\n", - "# Get Caller Identity\n", - "import boto3, json\n", - "account = boto3.client('sts').get_caller_identity()['Account']\n", - "print(account)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bucket_name = \"model-storage-20240820044102407400000001\" # Replace the bucket_name with the output in `terraform output` \n", - "prefix = \"stable-diffusion-2\"\n", - "\n", - "! rm -rf {model_directory}/.locks\n", - "! rm -rf {model_directory}/models--stabilityai--stable-diffusion-2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! aws s3 sync {model_directory} s3://{bucket_name}/{prefix}/ " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py b/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py deleted file mode 100644 index b0a7a9973..000000000 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download_models.py +++ /dev/null @@ -1,14 +0,0 @@ -from diffusers import StableDiffusionPipeline -import torch - -# Set the model you want to download -model_name = "stabilityai/stable-diffusion-2" -model_directory = "./stable-diffusion-2" - -# Load the model -pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=model_directory) - -# Save the model to the local directory -pipe.save_pretrained(model_directory) - -print(f"Model saved to {model_directory}") \ No newline at end of file diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index 6202ed388..08a142afa 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -187,7 +187,7 @@ metadata: name: stable-diffusion-script namespace: stablediffusion data: - ray_serve_sd.py: | + ray_serve_sd.py: | from io import BytesIO from fastapi import FastAPI from fastapi.responses import Response @@ -249,5 +249,5 @@ data: guidance_scale=7.0, ).images[0] return image - + entrypoint = APIIngress.bind(StableDiffusionV2.bind()) diff --git a/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md b/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md index 354dc707d..f33c48efe 100644 --- a/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md +++ b/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md @@ -53,8 +53,8 @@ Define the `TF_VAR_bottlerocket_data_disk_snpashot_id` to enable Karpenter to pr export TF_VAR_bottlerocket_data_disk_snpashot_id=snap-0c6d965cf431785ed ``` -### (Optional) Save Machine Learning Models in Amazon S3 -Define the TF_VAR_create_s3_bucket to be `true` so that running Terraform module will provision a S3 bucket. +### (Optional) Save Machine Learning Models in Amazon S3 +Define the TF_VAR_create_s3_bucket to be `true` so that running Terraform module will provision a S3 bucket. ``` export TF_VAR_create_s3_bucket=true ``` @@ -83,9 +83,9 @@ For example, set your `export AWS_DEFAULT_REGION=""` to the desired regi cd data-on-eks/ai-ml/jark-stack/ && chmod +x install.sh ./install.sh -terraform output +terraform output configure_kubectl = "aws eks --region us-west-2 update-kubeconfig --name jark-stack" -model_s3_bucket = "model-storage-20240820044102407400000001" +model_s3_bucket = "model-storage-20240820044102407400000001" ``` ### Verify the resources @@ -143,7 +143,7 @@ If you did not preload container images into the data volume, the deployment pro **(Approach 2) Deploy RayServe Cluster (Downloading Model from Amazon S3)** ```bash cd ./../gen-ai/inference/stable-diffusion-rayserve-gpu -# edit the file download-models-to-s3 and fill the actual bucket name by running terraform output +# edit the file download-models-to-s3 and fill the actual bucket name by running terraform output kubectl apply -f download-models-to-s3.yaml kubectl apply -f ray-service-stablediffusion-from-s3.yaml ``` From 81c707591cab2616a6b2478de651680192dc6223 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Fri, 20 Sep 2024 17:29:34 +0800 Subject: [PATCH 47/53] fix: resolve conflict --- ai-ml/jark-stack/terraform/outputs.tf | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ai-ml/jark-stack/terraform/outputs.tf b/ai-ml/jark-stack/terraform/outputs.tf index 531e8924e..5ba927551 100644 --- a/ai-ml/jark-stack/terraform/outputs.tf +++ b/ai-ml/jark-stack/terraform/outputs.tf @@ -2,7 +2,10 @@ output "configure_kubectl" { description = "Configure kubectl: make sure you're logged in with the correct AWS profile and run the following command to update your kubeconfig" value = "aws eks --region ${var.region} update-kubeconfig --name ${var.name}" } - +output "grafana_secret_name" { + description = "The name of the secret containing the Grafana admin password." + value = aws_secretsmanager_secret.grafana.name +} output "model_s3_bucket" { description = "The S3 bucket name for storing ML models" value = length(aws_s3_bucket.model_storage) > 0 ? aws_s3_bucket.model_storage[0].bucket : "" From b29458ce81e3b2ca54e79598a14fa826a0a66f19 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Wed, 25 Sep 2024 16:12:34 +0800 Subject: [PATCH 48/53] docs: add comments for volume mounts --- .../ray-serve-stablediffusion-from-s3.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index 08a142afa..b7dc76a23 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -141,10 +141,12 @@ spec: operator: "Exists" effect: "NoSchedule" volumes: + # mount the serving script - name: serve-script configMap: name: stable-diffusion-script defaultMode: 0777 + # mount the model storage on Amazon S3s - name: cache-dir persistentVolumeClaim: claimName: s3-model-storage-claim From 2c1e05325b6a09a4e6152fb3f7552354fa942e0b Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 11 Nov 2024 17:40:45 +0800 Subject: [PATCH 49/53] docs: Update page title --- website/docs/bestpractices/cache-models-to-s3-bucket.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/bestpractices/cache-models-to-s3-bucket.md b/website/docs/bestpractices/cache-models-to-s3-bucket.md index d65dbc6b7..0a69dec9c 100644 --- a/website/docs/bestpractices/cache-models-to-s3-bucket.md +++ b/website/docs/bestpractices/cache-models-to-s3-bucket.md @@ -1,5 +1,5 @@ --- -title: Store ML models in S3 bucket and use Amazon S3 CSI Driver +title: Best Practices for ML Model Storage and Retrieval with S3 and Amazon S3 CSI Driver sidebar_position: 3 --- From 3b6d6833eba181f39ecc5be874f09bdcaada0335 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 11 Nov 2024 17:41:04 +0800 Subject: [PATCH 50/53] fix: remove unnecessary code --- ai-ml/jark-stack/terraform/vpc.tf | 2 -- 1 file changed, 2 deletions(-) diff --git a/ai-ml/jark-stack/terraform/vpc.tf b/ai-ml/jark-stack/terraform/vpc.tf index 5bb1b9621..728dd53ad 100644 --- a/ai-ml/jark-stack/terraform/vpc.tf +++ b/ai-ml/jark-stack/terraform/vpc.tf @@ -57,8 +57,6 @@ module "vpc_endpoints" { source = "terraform-aws-modules/vpc/aws//modules/vpc-endpoints" version = "~> 5.0" - # create = var.enable_vpc_endpoints - create = true vpc_id = module.vpc.vpc_id From 63a9a347aacb237e804cf4dfba1e0ef78b7f388e Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 11 Nov 2024 17:45:43 +0800 Subject: [PATCH 51/53] feat: bump diffuser and transformers versions in Dockerfile --- gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile index 464881c85..de767ff88 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/Dockerfile @@ -14,7 +14,7 @@ ENV DEBIAN_FRONTEND=non-interactive USER $USER # Install Ray Serve and other Python packages with specific versions -RUN pip install --no-cache-dir requests torch "diffusers==0.29.2" "transformers==4.42.4" "accelerate==0.30.1" +RUN pip install --no-cache-dir requests torch "diffusers==0.29.2" "transformers==4.25.1" "accelerate==0.30.1" # Set a working directory WORKDIR /serve_app From 11c87e3f63a6b9b7fbb9bc55e0dfc75e1ae7ae74 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Mon, 11 Nov 2024 17:47:57 +0800 Subject: [PATCH 52/53] fix: remove pip section --- .../ray-serve-stablediffusion-from-s3.yaml | 3 --- .../ray-serve-stablediffusion.yaml | 3 --- 2 files changed, 6 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml index b7dc76a23..b5e5fb02c 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion-from-s3.yaml @@ -18,9 +18,6 @@ spec: env_vars: MODEL_PATH: "./stable-diffusion-2" MODEL_ID: "stabilityai/stable-diffusion-2-1" - pip: - - transformers==4.25.1 - - accelerate==0.30.1 deployments: - name: stable-diffusion-v2 autoscaling_config: diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml index 335f83044..c60ab1776 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml @@ -26,9 +26,6 @@ spec: runtime_env: env_vars: MODEL_ID: "stabilityai/stable-diffusion-2-1" - pip: - - transformers==4.25.1 - - accelerate==0.30.1 deployments: - name: stable-diffusion-v2 autoscaling_config: From ba6c45cb3f82373396ed1e8c13fd7a143b7a8676 Mon Sep 17 00:00:00 2001 From: Darren Lin Date: Tue, 12 Nov 2024 11:07:30 +0800 Subject: [PATCH 53/53] fix: add namespace for downloader job fix: ray service yaml filename --- .../stable-diffusion-rayserve-gpu/download-models-to-s3.yaml | 2 ++ website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml index a34c77774..1d23ff78a 100644 --- a/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml +++ b/gen-ai/inference/stable-diffusion-rayserve-gpu/download-models-to-s3.yaml @@ -41,6 +41,7 @@ apiVersion: v1 kind: ConfigMap metadata: name: shell-script-configmap + namespace: stablediffusion data: download_models.py: | from diffusers import StableDiffusionPipeline @@ -95,6 +96,7 @@ apiVersion: batch/v1 kind: Job metadata: name: shell-script-job + namespace: stablediffusion spec: template: spec: diff --git a/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md b/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md index f33c48efe..227d54cca 100644 --- a/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md +++ b/website/docs/gen-ai/inference/GPUs/stablediffusion-gpus.md @@ -129,7 +129,7 @@ aws eks --region us-west-2 update-kubeconfig --name jark-stack ```bash cd ./../gen-ai/inference/stable-diffusion-rayserve-gpu -kubectl apply -f ray-service-stablediffusion.yaml +kubectl apply -f ray-serve-stablediffusion.yaml ``` Verify the deployment by running the following commands @@ -145,7 +145,7 @@ If you did not preload container images into the data volume, the deployment pro cd ./../gen-ai/inference/stable-diffusion-rayserve-gpu # edit the file download-models-to-s3 and fill the actual bucket name by running terraform output kubectl apply -f download-models-to-s3.yaml -kubectl apply -f ray-service-stablediffusion-from-s3.yaml +kubectl apply -f ray-serve-stablediffusion-from-s3.yaml ``` This deployment establishes a Ray head pod running on an x86 instance and a worker pod on a GPU G5 instance as shown below.

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