From fed91efefd373948870b0a0a01167c493a8ecf18 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Sun, 30 Jun 2024 15:34:27 +0800 Subject: [PATCH 01/16] feat: added terraform code for nim-llm pattern --- gen-ai/nvidia-nims/.gitignore | 2 + gen-ai/nvidia-nims/README.md | 154 +++++++++ gen-ai/nvidia-nims/addons.tf | 314 ++++++++++++++++++ gen-ai/nvidia-nims/cleanup.sh | 74 +++++ gen-ai/nvidia-nims/efs.tf | 72 ++++ gen-ai/nvidia-nims/eks.tf | 118 +++++++ .../helm-values/ingress-nginx-values.yaml | 12 + .../helm-values/kube-prometheus.yaml | 48 +++ gen-ai/nvidia-nims/helm-values/nim-llm.yaml | 24 ++ gen-ai/nvidia-nims/images/all-pod-svc.png | Bin 0 -> 438473 bytes gen-ai/nvidia-nims/install.sh | 44 +++ gen-ai/nvidia-nims/main.tf | 51 +++ gen-ai/nvidia-nims/outputs.tf | 19 ++ gen-ai/nvidia-nims/variables.tf | 49 +++ gen-ai/nvidia-nims/versions.tf | 37 +++ gen-ai/nvidia-nims/vpc.tf | 53 +++ 16 files changed, 1071 insertions(+) create mode 100644 gen-ai/nvidia-nims/.gitignore create mode 100644 gen-ai/nvidia-nims/README.md create mode 100644 gen-ai/nvidia-nims/addons.tf create mode 100755 gen-ai/nvidia-nims/cleanup.sh create mode 100644 gen-ai/nvidia-nims/efs.tf create mode 100644 gen-ai/nvidia-nims/eks.tf create mode 100644 gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml create mode 100644 gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml create mode 100644 gen-ai/nvidia-nims/helm-values/nim-llm.yaml create mode 100644 gen-ai/nvidia-nims/images/all-pod-svc.png create mode 100755 gen-ai/nvidia-nims/install.sh create mode 100644 gen-ai/nvidia-nims/main.tf create mode 100644 gen-ai/nvidia-nims/outputs.tf create mode 100644 gen-ai/nvidia-nims/variables.tf create mode 100644 gen-ai/nvidia-nims/versions.tf create mode 100644 gen-ai/nvidia-nims/vpc.tf diff --git a/gen-ai/nvidia-nims/.gitignore b/gen-ai/nvidia-nims/.gitignore new file mode 100644 index 000000000..d6f8a9ea3 --- /dev/null +++ b/gen-ai/nvidia-nims/.gitignore @@ -0,0 +1,2 @@ +nim-llm/ +planfile \ No newline at end of file diff --git a/gen-ai/nvidia-nims/README.md b/gen-ai/nvidia-nims/README.md new file mode 100644 index 000000000..fe77a03f6 --- /dev/null +++ b/gen-ai/nvidia-nims/README.md @@ -0,0 +1,154 @@ +# NVIDIA NIM LLM on Amazon EKS + +This guide provides instructions for deploying NVIDIA NIM (NVIDIA Inference Microservices) Large Language Models (LLMs) on Amazon Elastic Kubernetes Service (EKS). + +## Prerequisites + +- An active AWS account with appropriate permissions +- AWS CLI installed and configured +- kubectl installed and configured to work with your EKS cluster +- Helm installed +- NVIDIA NGC account and API key + +## Setup + +1. Configure the NGC API Key + +Retrieve your NGC API key from [NVIDIA](https://docs.nvidia.com/ai-enterprise/deployment-guide-spark-rapids-accelerator/0.1.0/appendix-ngc.html) and set it as an environment variable: + +```bash +export TF_VAR_ngc_api_key= +``` + +2. Installation + +Run the installation script: + +```bash +./install.sh +``` + +This process will take approximately 20 minutes to complete. + +3. Verify the Installation + +Check the status of your pods deployed + +```bash +kubectl get po -A +``` + +You should see output similar to the following: +![alt text](images/all-pod-svc.png) + +4. Verify the deployed model + +Once all pods in `nim` namespace is ready with `1/1` status, use below command to verify it's ready to serve the traffic. + +``` +export INGRESS_URL=$(kubectl get ingress -n nim -o jsonpath='{.items[0].status.loadBalancer.ingress[0].hostname}') + +curl -X 'POST' \ +"http://$INGRESS_URL/v1/completions" \ +-H 'accept: application/json' \ +-H 'Content-Type: application/json' \ +-d '{ +"model": "meta/llama3-8b-instruct", +"prompt": "Once upon a time", +"max_tokens": 64 +}' +``` + +## Scaling + +You can scale up and down to number of replicas with following: + +```bash +kubectl scale sts nim-llm -n --replicas=2 +``` + +## Clean up + +To remove all resources created by this deployment, run: + +``` +./cleanup.sh +``` + + +## Additional Resources + +- [NVIDIA NIM Documentation](https://docs.nvidia.com/nim/) +- [Amazon EKS Documentation](https://docs.aws.amazon.com/eks/) +- [NVIDIA NGC Catalog](https://catalog.ngc.nvidia.com/) + +--- + + +## Requirements + +| Name | Version | +|------|---------| +| [terraform](#requirement\_terraform) | >= 1.0.0 | +| [aws](#requirement\_aws) | >= 3.72 | +| [helm](#requirement\_helm) | >= 2.4.1 | +| [kubectl](#requirement\_kubectl) | >= 1.14 | +| [kubernetes](#requirement\_kubernetes) | >= 2.10 | +| [null](#requirement\_null) | >= 3.1 | +| [random](#requirement\_random) | >= 3.1 | + +## Providers + +| Name | Version | +|------|---------| +| [aws](#provider\_aws) | 5.56.1 | +| [aws.ecr](#provider\_aws.ecr) | 5.56.1 | +| [kubernetes](#provider\_kubernetes) | 2.31.0 | +| [random](#provider\_random) | 3.6.2 | + +## Modules + +| Name | Source | Version | +|------|--------|---------| +| [data\_addons](#module\_data\_addons) | aws-ia/eks-data-addons/aws | ~> 1.32.0 | +| [ebs\_csi\_driver\_irsa](#module\_ebs\_csi\_driver\_irsa) | terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks | ~> 5.20 | +| [efs](#module\_efs) | terraform-aws-modules/efs/aws | ~> 1.6 | +| [eks](#module\_eks) | terraform-aws-modules/eks/aws | ~> 19.15 | +| [eks\_blueprints\_addons](#module\_eks\_blueprints\_addons) | aws-ia/eks-blueprints-addons/aws | ~> 1.2 | +| [vpc](#module\_vpc) | terraform-aws-modules/vpc/aws | ~> 5.0 | + +## Resources + +| Name | Type | +|------|------| +| [aws_secretsmanager_secret.grafana](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/secretsmanager_secret) | resource | +| [aws_secretsmanager_secret_version.grafana](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/secretsmanager_secret_version) | resource | +| [kubernetes_annotations.disable_gp2](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/annotations) | resource | +| [kubernetes_namespace.nim](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/namespace) | resource | +| [kubernetes_persistent_volume_claim_v1.efs_pvc](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/persistent_volume_claim_v1) | resource | +| [kubernetes_storage_class.default_gp3](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/storage_class) | resource | +| [kubernetes_storage_class_v1.efs](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/storage_class_v1) | resource | +| [random_password.grafana](https://registry.terraform.io/providers/hashicorp/random/latest/docs/resources/password) | resource | +| [aws_availability_zones.available](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/availability_zones) | data source | +| [aws_ecrpublic_authorization_token.token](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/ecrpublic_authorization_token) | data source | +| [aws_eks_cluster_auth.this](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/eks_cluster_auth) | data source | +| [aws_secretsmanager_secret_version.admin_password_version](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/secretsmanager_secret_version) | data source | + +## Inputs + +| Name | Description | Type | Default | Required | +|------|-------------|------|---------|:--------:| +| [eks\_cluster\_version](#input\_eks\_cluster\_version) | EKS Cluster version | `string` | `"1.29"` | no | +| [name](#input\_name) | Name of the VPC and EKS Cluster | `string` | `"nvidia-nims"` | no | +| [ngc\_api\_key](#input\_ngc\_api\_key) | NGC API Key | `string` | n/a | yes | +| [region](#input\_region) | region | `string` | `"us-west-2"` | no | +| [secondary\_cidr\_blocks](#input\_secondary\_cidr\_blocks) | Secondary CIDR blocks to be attached to VPC | `list(string)` |
[
"100.64.0.0/16"
]
| no | +| [vpc\_cidr](#input\_vpc\_cidr) | VPC CIDR. This should be a valid private (RFC 1918) CIDR range | `string` | `"10.1.0.0/21"` | no | + +## Outputs + +| Name | Description | +|------|-------------| +| [configure\_kubectl](#output\_configure\_kubectl) | Configure kubectl: make sure you're logged in with the correct AWS profile and run the following command to update your kubeconfig | +| [grafana\_secret\_name](#output\_grafana\_secret\_name) | The name of the secret containing the Grafana admin password. | + \ No newline at end of file diff --git a/gen-ai/nvidia-nims/addons.tf b/gen-ai/nvidia-nims/addons.tf new file mode 100644 index 000000000..2ad774caf --- /dev/null +++ b/gen-ai/nvidia-nims/addons.tf @@ -0,0 +1,314 @@ +#--------------------------------------------------------------- +# GP3 Encrypted Storage Class +#--------------------------------------------------------------- +resource "kubernetes_annotations" "disable_gp2" { + annotations = { + "storageclass.kubernetes.io/is-default-class" : "false" + } + api_version = "storage.k8s.io/v1" + kind = "StorageClass" + metadata { + name = "gp2" + } + force = true + + depends_on = [module.eks.eks_cluster_id] +} + +resource "kubernetes_storage_class" "default_gp3" { + metadata { + name = "gp3" + annotations = { + "storageclass.kubernetes.io/is-default-class" : "true" + } + } + + storage_provisioner = "ebs.csi.aws.com" + reclaim_policy = "Delete" + allow_volume_expansion = true + volume_binding_mode = "WaitForFirstConsumer" + parameters = { + fsType = "ext4" + encrypted = true + type = "gp3" + } + + depends_on = [kubernetes_annotations.disable_gp2] +} + +#--------------------------------------------------------------- +# IRSA for EBS CSI Driver +#--------------------------------------------------------------- +module "ebs_csi_driver_irsa" { + source = "terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks" + version = "~> 5.20" + role_name_prefix = format("%s-%s-", local.name, "ebs-csi-driver") + attach_ebs_csi_policy = true + oidc_providers = { + main = { + provider_arn = module.eks.oidc_provider_arn + namespace_service_accounts = ["kube-system:ebs-csi-controller-sa"] + } + } + tags = local.tags +} + +#--------------------------------------------------------------- +# EKS Blueprints Addons +#--------------------------------------------------------------- +module "eks_blueprints_addons" { + source = "aws-ia/eks-blueprints-addons/aws" + version = "~> 1.2" + + cluster_name = module.eks.cluster_name + cluster_endpoint = module.eks.cluster_endpoint + cluster_version = module.eks.cluster_version + oidc_provider_arn = module.eks.oidc_provider_arn + + #--------------------------------------- + # Amazon EKS Managed Add-ons + #--------------------------------------- + eks_addons = { + aws-ebs-csi-driver = { + service_account_role_arn = module.ebs_csi_driver_irsa.iam_role_arn + } + coredns = {} + kube-proxy = {} + # VPC CNI uses worker node IAM role policies + vpc-cni = {} + } + + enable_aws_efs_csi_driver = true + + #--------------------------------------- + # AWS Load Balancer Controller Add-on + #--------------------------------------- + enable_aws_load_balancer_controller = true + # turn off the mutating webhook for services because we are using + # service.beta.kubernetes.io/aws-load-balancer-type: external + aws_load_balancer_controller = { + set = [{ + name = "enableServiceMutatorWebhook" + value = "false" + }] + } + + #--------------------------------------- + # Ingress Nginx Add-on + #--------------------------------------- + enable_ingress_nginx = true + ingress_nginx = { + values = [templatefile("${path.module}/helm-values/ingress-nginx-values.yaml", {})] + } + + #--------------------------------------- + # Karpenter Autoscaler for EKS Cluster + #--------------------------------------- + enable_karpenter = true + karpenter_enable_spot_termination = true + karpenter_node = { + iam_role_additional_policies = { + AmazonSSMManagedInstanceCore = "arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" + } + } + karpenter = { + chart_version = "0.35.5" + repository_username = data.aws_ecrpublic_authorization_token.token.user_name + repository_password = data.aws_ecrpublic_authorization_token.token.password + } + + #--------------------------------------- + # Prommetheus and Grafana stack + #--------------------------------------- + #--------------------------------------------------------------- + # 1- Grafana port-forward `kubectl port-forward svc/kube-prometheus-stack-grafana 8080:80 -n kube-prometheus-stack` + # 2- Grafana Admin user: admin + # 3- Get sexret name from Terrafrom output: `terraform output grafana_secret_name` + # 3- Get admin user password: `aws secretsmanager get-secret-value --secret-id --region $AWS_REGION --query "SecretString" --output text` + #--------------------------------------------------------------- + enable_kube_prometheus_stack = true + kube_prometheus_stack = { + values = [ + templatefile("${path.module}/helm-values/kube-prometheus.yaml", { + storage_class_type = kubernetes_storage_class.default_gp3.id + }) + ] + chart_version = "48.1.1" + set_sensitive = [ + { + name = "grafana.adminPassword" + value = data.aws_secretsmanager_secret_version.admin_password_version.secret_string + } + ], + } + #--------------------------------------- + # Install the nim-llm, and you can customize it from the + # helm chart values under ./helm-values/nim-llm.yaml + #--------------------------------------- + helm_releases = { + "nim-llm" = { + chart = "${path.module}/nim-llm" + create_namespace = true + namespace = "nim" + values = [ + templatefile( + "${path.module}/helm-values/nim-llm.yaml", { + ngc_api_key = var.ngc_api_key + pvc_name = kubernetes_persistent_volume_claim_v1.efs_pvc.metadata[0].name + }) + ] + } + } + +} + +#--------------------------------------------------------------- +# Data on EKS Kubernetes Addons +#--------------------------------------------------------------- +module "data_addons" { + source = "aws-ia/eks-data-addons/aws" + version = "~> 1.32.0" # ensure to update this to the latest/desired version + + oidc_provider_arn = module.eks.oidc_provider_arn + + #--------------------------------------------------------------- + # NVIDIA Device Plugin Add-on + #--------------------------------------------------------------- + enable_nvidia_device_plugin = true + nvidia_device_plugin_helm_config = { + version = "v0.14.5" + name = "nvidia-device-plugin" + values = [ + <<-EOT + gfd: + enabled: true + nfd: + worker: + tolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule + - operator: "Exists" + EOT + ] + } + + #--------------------------------------------------------------- + # Karpenter Resources Add-on + #--------------------------------------------------------------- + enable_karpenter_resources = true + karpenter_resources_helm_config = { + g5-gpu-karpenter = { + values = [ + <<-EOT + name: g5-gpu-karpenter + clusterName: ${module.eks.cluster_name} + ec2NodeClass: + karpenterRole: ${split("/", module.eks_blueprints_addons.karpenter.node_iam_role_arn)[1]} + subnetSelectorTerms: + id: ${module.vpc.private_subnets[2]} + securityGroupSelectorTerms: + tags: + Name: ${module.eks.cluster_name}-node + instanceStorePolicy: RAID0 + + nodePool: + labels: + - type: karpenter + - NodeGroupType: g5-gpu-karpenter + taints: + - key: nvidia.com/gpu + value: "Exists" + effect: "NoSchedule" + requirements: + - key: "karpenter.k8s.aws/instance-family" + operator: In + values: ["g5"] + - key: "karpenter.k8s.aws/instance-size" + operator: In + values: [ "2xlarge", "4xlarge", "8xlarge", "12xlarge", "24xlarge"] + - key: "kubernetes.io/arch" + operator: In + values: ["amd64"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["spot", "on-demand"] + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmpty + consolidateAfter: 180s + expireAfter: 720h + weight: 100 + EOT + ] + } + x86-cpu-karpenter = { + values = [ + <<-EOT + name: x86-cpu-karpenter + clusterName: ${module.eks.cluster_name} + ec2NodeClass: + karpenterRole: ${split("/", module.eks_blueprints_addons.karpenter.node_iam_role_arn)[1]} + subnetSelectorTerms: + id: ${module.vpc.private_subnets[3]} + securityGroupSelectorTerms: + tags: + Name: ${module.eks.cluster_name}-node + instanceStorePolicy: RAID0 + + nodePool: + labels: + - type: karpenter + - NodeGroupType: x86-cpu-karpenter + requirements: + - key: "karpenter.k8s.aws/instance-family" + operator: In + values: ["m5"] + - key: "karpenter.k8s.aws/instance-size" + operator: In + values: [ "xlarge", "2xlarge", "4xlarge", "8xlarge"] + - key: "kubernetes.io/arch" + operator: In + values: ["amd64"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["spot", "on-demand"] + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmpty + consolidateAfter: 180s + expireAfter: 720h + weight: 100 + EOT + ] + } + } +} + +#--------------------------------------------------------------- +# Grafana Admin credentials resources +# Login to AWS secrets manager with the same role as Terraform to extract the Grafana admin password with the secret name as "grafana" +#--------------------------------------------------------------- +data "aws_secretsmanager_secret_version" "admin_password_version" { + secret_id = aws_secretsmanager_secret.grafana.id + depends_on = [aws_secretsmanager_secret_version.grafana] +} + +resource "random_password" "grafana" { + length = 16 + special = true + override_special = "@_" +} + +#tfsec:ignore:aws-ssm-secret-use-customer-key +resource "aws_secretsmanager_secret" "grafana" { + name_prefix = "${local.name}-oss-grafana" + recovery_window_in_days = 0 # Set to zero for this example to force delete during Terraform destroy +} + +resource "aws_secretsmanager_secret_version" "grafana" { + secret_id = aws_secretsmanager_secret.grafana.id + secret_string = random_password.grafana.result +} diff --git a/gen-ai/nvidia-nims/cleanup.sh b/gen-ai/nvidia-nims/cleanup.sh new file mode 100755 index 000000000..f9c5daca6 --- /dev/null +++ b/gen-ai/nvidia-nims/cleanup.sh @@ -0,0 +1,74 @@ +#!/bin/bash + +read -p "Enter the region: " region +export AWS_DEFAULT_REGION=$region + +echo "Destroying RayService..." + +# Delete the Ingress/SVC before removing the addons +TMPFILE=$(mktemp) +terraform output -raw configure_kubectl > "$TMPFILE" +# check if TMPFILE contains the string "No outputs found" +if [[ ! $(cat $TMPFILE) == *"No outputs found"* ]]; then + echo "No outputs found, skipping kubectl delete" + source "$TMPFILE" + kubectl delete -f src/service/ray-service.yaml +fi + + +# List of Terraform modules to apply in sequence +targets=( + "module.data_addons" + "module.eks_blueprints_addons" + "module.eks" + "module.vpc" +) + +# Destroy modules in sequence +for target in "${targets[@]}" +do + echo "Destroying module $target..." + destroy_output=$(terraform destroy -target="$target" -var="region=$region" -auto-approve 2>&1 | tee /dev/tty) + if [[ ${PIPESTATUS[0]} -eq 0 && $destroy_output == *"Destroy complete"* ]]; then + echo "SUCCESS: Terraform destroy of $target completed successfully" + else + echo "FAILED: Terraform destroy of $target failed" + exit 1 + fi +done + +echo "Destroying Load Balancers..." + +for arn in $(aws resourcegroupstaggingapi get-resources \ + --resource-type-filters elasticloadbalancing:loadbalancer \ + --tag-filters "Key=elbv2.k8s.aws/cluster,Values=jark-stack" \ + --query 'ResourceTagMappingList[].ResourceARN' \ + --output text); do \ + aws elbv2 delete-load-balancer --load-balancer-arn "$arn"; \ + done + +echo "Destroying Target Groups..." +for arn in $(aws resourcegroupstaggingapi get-resources \ + --resource-type-filters elasticloadbalancing:targetgroup \ + --tag-filters "Key=elbv2.k8s.aws/cluster,Values=jark-stack" \ + --query 'ResourceTagMappingList[].ResourceARN' \ + --output text); do \ + aws elbv2 delete-target-group --target-group-arn "$arn"; \ + done + +echo "Destroying Security Groups..." +for sg in $(aws ec2 describe-security-groups \ + --filters "Name=tag:elbv2.k8s.aws/cluster,Values=jark-stack" \ + --query 'SecurityGroups[].GroupId' --output text); do \ + aws ec2 delete-security-group --group-id "$sg"; \ + done + +## Final destroy to catch any remaining resources +echo "Destroying remaining resources..." +destroy_output=$(terraform destroy -var="region=$region" -auto-approve 2>&1 | tee /dev/tty) +if [[ ${PIPESTATUS[0]} -eq 0 && $destroy_output == *"Destroy complete"* ]]; then + echo "SUCCESS: Terraform destroy of all modules completed successfully" +else + echo "FAILED: Terraform destroy of all modules failed" + exit 1 +fi diff --git a/gen-ai/nvidia-nims/efs.tf b/gen-ai/nvidia-nims/efs.tf new file mode 100644 index 000000000..f49fafcca --- /dev/null +++ b/gen-ai/nvidia-nims/efs.tf @@ -0,0 +1,72 @@ +#--------------------------------------------------------------- +# EFS +#--------------------------------------------------------------- +module "efs" { + source = "terraform-aws-modules/efs/aws" + version = "~> 1.6" + + creation_token = local.name + name = local.name + + + # Mount targets / security group + mount_targets = { + for k, v in zipmap(local.azs, slice(module.vpc.private_subnets, length(module.vpc.private_subnets) - 2, length(module.vpc.private_subnets))) : k => { subnet_id = v } + } + security_group_description = "${local.name} EFS security group" + security_group_vpc_id = module.vpc.vpc_id + security_group_rules = { + vpc = { + # relying on the defaults provided for EFS/NFS (2049/TCP + ingress) + description = "NFS ingress from VPC private subnets" + cidr_blocks = module.vpc.private_subnets_cidr_blocks + } + } + + tags = local.tags +} + +resource "kubernetes_storage_class_v1" "efs" { + metadata { + name = "efs" + } + + storage_provisioner = "efs.csi.aws.com" + parameters = { + provisioningMode = "efs-ap" # Dynamic provisioning + fileSystemId = module.efs.id + directoryPerms = "777" + } + + mount_options = [ + "iam" + ] + + depends_on = [ + module.eks_blueprints_addons.aws_efs_csi_driver + ] +} + +resource "kubernetes_namespace" "nim" { + metadata { + name = "nim" + } + + depends_on = [module.eks] +} + +resource "kubernetes_persistent_volume_claim_v1" "efs_pvc" { + metadata { + name = "nim-llm-pvc" + namespace = "nim" + } + spec { + access_modes = ["ReadWriteMany"] + storage_class_name = kubernetes_storage_class_v1.efs.id + resources { + requests = { + storage = "50Gi" + } + } + } +} diff --git a/gen-ai/nvidia-nims/eks.tf b/gen-ai/nvidia-nims/eks.tf new file mode 100644 index 000000000..469947520 --- /dev/null +++ b/gen-ai/nvidia-nims/eks.tf @@ -0,0 +1,118 @@ +#--------------------------------------------------------------- +# EKS Cluster +#--------------------------------------------------------------- +module "eks" { + source = "terraform-aws-modules/eks/aws" + version = "~> 19.15" + + cluster_name = local.name + cluster_version = var.eks_cluster_version + + # if true, Your cluster API server is accessible from the internet. + # You can, optionally, limit the CIDR blocks that can access the public endpoint. + #WARNING: Avoid using this option (cluster_endpoint_public_access = true) in preprod or prod accounts. This feature is designed for sandbox accounts, simplifying cluster deployment and testing. + cluster_endpoint_public_access = true + + vpc_id = module.vpc.vpc_id + # Filtering only Secondary CIDR private subnets starting with "100.". + # Subnet IDs where the EKS Control Plane ENIs will be created + subnet_ids = compact([ + for subnet_id, cidr_block in zipmap(module.vpc.private_subnets, module.vpc.private_subnets_cidr_blocks) : + substr(cidr_block, 0, 4) == "100." ? subnet_id : null]) + + manage_aws_auth_configmap = true + aws_auth_roles = [ + # We need to add in the Karpenter node IAM role for nodes launched by Karpenter + { + rolearn = module.eks_blueprints_addons.karpenter.node_iam_role_arn + username = "system:node:{{EC2PrivateDNSName}}" + groups = [ + "system:bootstrappers", + "system:nodes", + ] + } + ] + #--------------------------------------- + # Note: This can further restricted to specific required for each Add-on and your application + #--------------------------------------- + # Extend cluster security group rules + cluster_security_group_additional_rules = { + ingress_nodes_ephemeral_ports_tcp = { + description = "Nodes on ephemeral ports" + protocol = "tcp" + from_port = 0 + to_port = 65535 + type = "ingress" + source_node_security_group = true + } + } + + node_security_group_additional_rules = { + # Allows Control Plane Nodes to talk to Worker nodes on all ports. + # Added this to simplify the example and further avoid issues with Add-ons communication with Control plane. + # This can be restricted further to specific port based on the requirement for each Add-on + # e.g., coreDNS 53, metrics-server 4443. + # Update this according to your security requirements if needed + ingress_cluster_to_node_all_traffic = { + description = "Cluster API to Nodegroup all traffic" + protocol = "-1" + from_port = 0 + to_port = 0 + type = "ingress" + source_cluster_security_group = true + } + } + + eks_managed_node_group_defaults = { + iam_role_additional_policies = { + # Not required, but used in the example to access the nodes to inspect mounted volumes + AmazonSSMManagedInstanceCore = "arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" + } + + ebs_optimized = true + # This block device is used only for root volume. Adjust volume according to your size. + # NOTE: Don't use this volume for ML workloads + block_device_mappings = { + xvda = { + device_name = "/dev/xvda" + ebs = { + volume_size = 100 + volume_type = "gp3" + } + } + } + } + + eks_managed_node_groups = { + # It's recommended to have a Managed Node group for hosting critical add-ons + # It's recommended to use Karpenter to place your workloads instead of using Managed Node groups + # You can leverage nodeSelector and Taints/tolerations to distribute workloads across Managed Node group or Karpenter nodes. + core_node_group = { + name = "core-node-group" + description = "EKS Core node group for hosting system add-ons" + # Filtering only Secondary CIDR private subnets starting with "100.". + # Subnet IDs where the nodes/node groups will be provisioned + subnet_ids = compact([ + for subnet_id, cidr_block in zipmap(module.vpc.private_subnets, module.vpc.private_subnets_cidr_blocks) : + substr(cidr_block, 0, 4) == "100." ? subnet_id : null] + ) + + # aws ssm get-parameters --names /aws/service/eks/optimized-ami/1.27/amazon-linux-2/recommended/image_id --region us-west-2 + ami_type = "AL2_x86_64" # Use this for Graviton AL2_ARM_64 + min_size = 2 + max_size = 8 + desired_size = 2 + + instance_types = ["m5.xlarge"] + + labels = { + WorkerType = "ON_DEMAND" + NodeGroupType = "core" + } + + tags = merge(local.tags, { + Name = "core-node-grp" + }) + } + } +} diff --git a/gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml b/gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml new file mode 100644 index 000000000..0b7eee5e4 --- /dev/null +++ b/gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml @@ -0,0 +1,12 @@ +controller: + service: + externalTrafficPolicy: "Local" + annotations: + service.beta.kubernetes.io/aws-load-balancer-type: external + service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip + service.beta.kubernetes.io/aws-load-balancer-backend-protocol: http + service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing + # service.beta.kubernetes.io/aws-load-balancer-scheme: internal # Private Load Balancer can only be accessed within the VPC + targetPorts: + http: http + https: http diff --git a/gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml b/gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml new file mode 100644 index 000000000..47e090743 --- /dev/null +++ b/gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml @@ -0,0 +1,48 @@ +prometheus: + prometheusSpec: + retention: 5h + scrapeInterval: 30s + evaluationInterval: 30s + scrapeTimeout: 10s + serviceMonitorSelectorNilUsesHelmValues: false # This is required to use the serviceMonitorSelector + storageSpec: + volumeClaimTemplate: + metadata: + name: data + spec: + storageClassName: ${storage_class_type} + accessModes: + - ReadWriteOnce + resources: + requests: + storage: 50Gi +alertmanager: + enabled: false + +grafana: + enabled: true + defaultDashboardsEnabled: true +prometheus: + prometheusSpec: + retention: 5h + scrapeInterval: 30s + evaluationInterval: 30s + scrapeTimeout: 10s + serviceMonitorSelectorNilUsesHelmValues: false # This is required to use the serviceMonitorSelector + storageSpec: + volumeClaimTemplate: + metadata: + name: data + spec: + storageClassName: ${storage_class_type} + accessModes: + - ReadWriteOnce + resources: + requests: + storage: 50Gi +alertmanager: + enabled: false + +grafana: + enabled: true + defaultDashboardsEnabled: true diff --git a/gen-ai/nvidia-nims/helm-values/nim-llm.yaml b/gen-ai/nvidia-nims/helm-values/nim-llm.yaml new file mode 100644 index 000000000..a2dbd6d52 --- /dev/null +++ b/gen-ai/nvidia-nims/helm-values/nim-llm.yaml @@ -0,0 +1,24 @@ +image: + repository: nvcr.io/nim/meta/llama3-8b-instruct + tag: latest +model: + ngcAPIKey: ${ngc_api_key} +resources: + limits: + nvidia.com/gpu: 1 + requests: + nvidia.com/gpu: 1 +statefulSet: + enabled: true +persistence: + enabled: true + existingClaim: ${pvc_name} 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"${targets[@]}" -do - echo "Applying module $target..." - apply_output=$(terraform apply -target="$target" -auto-approve 2>&1 | tee /dev/tty) - if [[ ${PIPESTATUS[0]} -eq 0 && $apply_output == *"Apply complete"* ]]; then - echo "SUCCESS: Terraform apply of $target completed successfully" - else - echo "FAILED: Terraform apply of $target failed" - exit 1 - fi -done - -# Final apply to catch any remaining resources -echo "Applying remaining resources..." -apply_output=$(terraform apply -auto-approve 2>&1 | tee /dev/tty) -if [[ ${PIPESTATUS[0]} -eq 0 && $apply_output == *"Apply complete"* ]]; then - echo "SUCCESS: Terraform apply of all modules completed successfully" -else - echo "FAILED: Terraform apply of all modules failed" - exit 1 -fi diff --git a/gen-ai/nvidia-nims/main.tf b/gen-ai/nvidia-nims/main.tf deleted file mode 100644 index f93511951..000000000 --- a/gen-ai/nvidia-nims/main.tf +++ /dev/null @@ -1,51 +0,0 @@ -provider "aws" { - region = local.region -} - -# ECR always authenticates with `us-east-1` region -# Docs -> https://docs.aws.amazon.com/AmazonECR/latest/public/public-registries.html -provider "aws" { - alias = "ecr" - region = "us-east-1" -} - -provider "kubernetes" { - host = module.eks.cluster_endpoint - cluster_ca_certificate = base64decode(module.eks.cluster_certificate_authority_data) - token = data.aws_eks_cluster_auth.this.token -} - -provider "helm" { - kubernetes { - host = module.eks.cluster_endpoint - cluster_ca_certificate = base64decode(module.eks.cluster_certificate_authority_data) - token = data.aws_eks_cluster_auth.this.token - } -} -provider "kubectl" { - apply_retry_count = 30 - host = module.eks.cluster_endpoint - cluster_ca_certificate = base64decode(module.eks.cluster_certificate_authority_data) - token = data.aws_eks_cluster_auth.this.token - load_config_file = false -} - -data "aws_eks_cluster_auth" "this" { - name = module.eks.cluster_name -} - -data "aws_availability_zones" "available" {} - -data "aws_ecrpublic_authorization_token" "token" { - provider = aws.ecr -} - -locals { - name = var.name - region = var.region - azs = slice(data.aws_availability_zones.available.names, 0, 2) - tags = { - Blueprint = local.name - GithubRepo = "github.com/awslabs/data-on-eks" - } -} diff --git a/gen-ai/nvidia-nims/outputs.tf b/gen-ai/nvidia-nims/outputs.tf deleted file mode 100644 index 08cf6f38d..000000000 --- a/gen-ai/nvidia-nims/outputs.tf +++ /dev/null @@ -1,19 +0,0 @@ -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 "s3_bucket_name" { -# description = "The name of the S3 bucket." -# value = module.s3_bucket.s3_bucket_id -# } - -# output "s3_bucket_region" { -# description = "The AWS region this bucket resides in." -# value = module.s3_bucket.s3_bucket_region -# } - -output "grafana_secret_name" { - description = "The name of the secret containing the Grafana admin password." - value = aws_secretsmanager_secret.grafana.name -} diff --git a/gen-ai/nvidia-nims/variables.tf b/gen-ai/nvidia-nims/variables.tf deleted file mode 100644 index 0189d2253..000000000 --- a/gen-ai/nvidia-nims/variables.tf +++ /dev/null @@ -1,48 +0,0 @@ -variable "name" { - description = "Name of the VPC and EKS Cluster" - default = "nvidia-nims" - type = string -} - -# NOTE: Trainium and Inferentia are only available in us-west-2 and us-east-1 regions -variable "region" { - description = "region" - default = "us-west-2" - type = string -} - -variable "eks_cluster_version" { - description = "EKS Cluster version" - default = "1.29" - type = string -} - -# VPC with 2046 IPs (10.1.0.0/21) and 2 AZs -variable "vpc_cidr" { - description = "VPC CIDR. This should be a valid private (RFC 1918) CIDR range" - default = "10.1.0.0/21" - type = string -} - -# RFC6598 range 100.64.0.0/10 -# Note you can only /16 range to VPC. You can add multiples of /16 if required -variable "secondary_cidr_blocks" { - description = "Secondary CIDR blocks to be attached to VPC" - default = ["100.64.0.0/16"] - type = list(string) -} - -#------------------------------------------------------------------- -# Instructions for Securely Setting the NVIDIA NGC API key -# ------------------------------------------------------------------- -# 1. Obtain your NVIDIA NGC API key from https://docs.nvidia.com/ai-enterprise/deployment-guide-spark-rapids-accelerator/0.1.0/appendix-ngc.html -# 2. Before running 'terraform apply', set the environment variable: -# * Linux/macOS: -# export TF_VAR_ngc_api_key= -# 3. Now you can safely run 'terraform apply' -#------------------------------------------------------------------- -variable "ngc_api_key" { - description = "NGC API Key" - type = string - sensitive = true -} diff --git a/gen-ai/nvidia-nims/versions.tf b/gen-ai/nvidia-nims/versions.tf deleted file mode 100644 index 97b9a9e87..000000000 --- a/gen-ai/nvidia-nims/versions.tf +++ /dev/null @@ -1,37 +0,0 @@ -terraform { - required_version = ">= 1.0.0" - - required_providers { - aws = { - source = "hashicorp/aws" - version = ">= 3.72" - } - kubernetes = { - source = "hashicorp/kubernetes" - version = ">= 2.10" - } - helm = { - source = "hashicorp/helm" - version = ">= 2.4.1" - } - kubectl = { - source = "gavinbunney/kubectl" - version = ">= 1.14" - } - random = { - source = "hashicorp/random" - version = ">= 3.1" - } - null = { - source = "hashicorp/null" - version = ">= 3.1" - } - } - - # ## Used for end-to-end testing on project; update to suit your needs - # backend "s3" { - # bucket = "doeks-github-actions-e2e-test-state" - # region = "us-west-2" - # key = "e2e/nvidia-triton-server/terraform.tfstate" - # } -} diff --git a/gen-ai/nvidia-nims/vpc.tf b/gen-ai/nvidia-nims/vpc.tf deleted file mode 100644 index 59c3da89c..000000000 --- a/gen-ai/nvidia-nims/vpc.tf +++ /dev/null @@ -1,53 +0,0 @@ -locals { - # Routable Private subnets only for Private NAT Gateway -> Transit Gateway -> Second VPC for overlapping CIDRs - # e.g., var.vpc_cidr = "10.1.0.0/21" => output: ["10.1.0.0/24", "10.1.1.0/24"] => 256-2 = 254 usable IPs per subnet/AZ - private_subnets = [for k, v in local.azs : cidrsubnet(var.vpc_cidr, 3, k)] - # Routable Public subnets with NAT Gateway and Internet Gateway - # e.g., var.vpc_cidr = "10.1.0.0/21" => output: ["10.1.2.0/26", "10.1.2.64/26"] => 64-2 = 62 usable IPs per subnet/AZ - public_subnets = [for k, v in local.azs : cidrsubnet(var.vpc_cidr, 5, k + 8)] - # RFC6598 range 100.64.0.0/16 for EKS Data Plane for two subnets(32768 IPs per Subnet) across two AZs for EKS Control Plane ENI + Nodes + Pods - # e.g., var.secondary_cidr_blocks = "100.64.0.0/16" => output: ["100.64.0.0/17", "100.64.128.0/17"] => 32768-2 = 32766 usable IPs per subnet/AZ - secondary_ip_range_private_subnets = [for k, v in local.azs : cidrsubnet(element(var.secondary_cidr_blocks, 0), 1, k)] -} - -#--------------------------------------------------------------- -# VPC -#--------------------------------------------------------------- -# WARNING: This VPC module includes the creation of an Internet Gateway and NAT Gateway, which simplifies cluster deployment and testing, primarily intended for sandbox accounts. -# IMPORTANT: For preprod and prod use cases, it is crucial to consult with your security team and AWS architects to design a private infrastructure solution that aligns with your security requirements - -module "vpc" { - source = "terraform-aws-modules/vpc/aws" - version = "~> 5.0" - - name = local.name - cidr = var.vpc_cidr - azs = local.azs - - # Secondary CIDR block attached to VPC for EKS Control Plane ENI + Nodes + Pods - secondary_cidr_blocks = var.secondary_cidr_blocks - - # 1/ EKS Data Plane secondary CIDR blocks for two subnets across two AZs for EKS Control Plane ENI + Nodes + Pods - # 2/ Two private Subnets with RFC1918 private IPv4 address range for Private NAT + NLB + Airflow + EC2 Jumphost etc. - private_subnets = concat(local.private_subnets, local.secondary_ip_range_private_subnets) - - # ------------------------------ - # Optional Public Subnets for NAT and IGW for PoC/Dev/Test environments - # Public Subnets can be disabled while deploying to Production and use Private NAT + TGW - public_subnets = local.public_subnets - enable_nat_gateway = true - single_nat_gateway = true - #------------------------------- - - public_subnet_tags = { - "kubernetes.io/role/elb" = 1 - } - - private_subnet_tags = { - "kubernetes.io/role/internal-elb" = 1 - # Tags subnets for Karpenter auto-discovery - "karpenter.sh/discovery" = local.name - } - - tags = local.tags -} diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index 231ba08de..6b345f934 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -79,7 +79,8 @@ Important Note: Ensure that you update the region in the variables.tf file befor Run the installation script: ```bash -cd gen-ai/nvidia-nims +cd data-on-eks/ai-ml/nvidia-triton-server +export TF_VAR_enable_nvidia_triton_server=false ./install.sh ``` @@ -87,6 +88,13 @@ This process will take approximately 20 minutes to complete. **3. Verify the Installation** +Once the installation finishes, verify the Amazon EKS Cluster + +```bash +# Creates k8s config file to authenticate with EKS +aws eks --region us-west-2 update-kubeconfig --name nvidia-triton-server +``` + Check the status of your pods deployed ```bash @@ -94,10 +102,54 @@ kubectl get po -A ``` You should see output similar to the following: -![alt text](../../../../gen-ai/nvidia-nims/images/all-pod-svc.png) +
    +Click to expand the deployment details + +```text +NAMESPACE NAME READY STATUS RESTARTS AGE +ingress-nginx ingress-nginx-controller-55474d95c5-994fc 1/1 Running 0 29h +karpenter karpenter-57f7f6bc4f-c6cts 1/1 Running 0 29h +karpenter karpenter-57f7f6bc4f-cfwwt 1/1 Running 0 29h +kube-prometheus-stack kube-prometheus-stack-grafana-558586c645-hv7hm 3/3 Running 0 29h +kube-prometheus-stack kube-prometheus-stack-kube-state-metrics-6669bff85f-fsmfz 1/1 Running 0 29h +kube-prometheus-stack kube-prometheus-stack-operator-67b968589d-k6ndp 1/1 Running 0 29h +kube-prometheus-stack kube-prometheus-stack-prometheus-node-exporter-58pfp 1/1 Running 0 19h +kube-prometheus-stack kube-prometheus-stack-prometheus-node-exporter-95xzb 1/1 Running 0 29h +kube-prometheus-stack kube-prometheus-stack-prometheus-node-exporter-wtpgc 1/1 Running 0 29h +kube-prometheus-stack prometheus-adapter-6f4ff878bc-64ntq 1/1 Running 0 24h +kube-prometheus-stack prometheus-kube-prometheus-stack-prometheus-0 2/2 Running 0 29h +kube-system aws-load-balancer-controller-55cb4579f6-9bp8d 1/1 Running 0 29h +kube-system aws-load-balancer-controller-55cb4579f6-n2trc 1/1 Running 0 29h +kube-system aws-node-rlxwv 2/2 Running 0 29h +kube-system aws-node-tz56x 2/2 Running 0 19h +kube-system aws-node-v29s9 2/2 Running 0 29h +kube-system coredns-848555ff5-kkngd 1/1 Running 0 29h +kube-system coredns-848555ff5-n6dnv 1/1 Running 0 29h +kube-system ebs-csi-controller-657544c77c-hl4z5 6/6 Running 0 29h +kube-system ebs-csi-controller-657544c77c-sncv6 6/6 Running 0 29h +kube-system ebs-csi-node-9xjnt 3/3 Running 0 19h +kube-system ebs-csi-node-fhphc 3/3 Running 0 29h +kube-system ebs-csi-node-hjg9v 3/3 Running 0 29h +kube-system efs-csi-controller-77c44b5fc7-pqwv9 3/3 Running 0 25h +kube-system efs-csi-controller-77c44b5fc7-vxpng 3/3 Running 0 25h +kube-system efs-csi-node-5k7k8 3/3 Running 0 25h +kube-system efs-csi-node-l4n5t 3/3 Running 0 25h +kube-system efs-csi-node-wxl97 3/3 Running 0 19h +kube-system kube-proxy-5qg9q 1/1 Running 0 29h +kube-system kube-proxy-7fzdh 1/1 Running 0 29h +kube-system kube-proxy-vm56n 1/1 Running 0 19h +nim nim-llm-0 1/1 Running 0 15m +nvidia-device-plugin nvidia-device-plugin-gpu-feature-discovery-64c9v 1/1 Running 0 19h +nvidia-device-plugin nvidia-device-plugin-node-feature-discovery-master-568b497ddvx9 1/1 Running 0 29h +nvidia-device-plugin nvidia-device-plugin-node-feature-discovery-worker-28wvj 1/1 Running 1 (29h ago) 29h +nvidia-device-plugin nvidia-device-plugin-node-feature-discovery-worker-5nplt 1/1 Running 0 29h +nvidia-device-plugin nvidia-device-plugin-node-feature-discovery-worker-hztcq 1/1 Running 0 19h +nvidia-device-plugin nvidia-device-plugin-vn5dn 1/1 Running 0 19h +``` +
    :::info -The `Llama3` model deployed is specified in `gen-ai/nvidia-nims/helm-values/nim-llm.yaml` with below config. Please visit [this page](https://build.nvidia.com/explore/discover) to explore more. You may simply update this image configuration if you want to change to deploy another model. +The `Llama3` model deployed is specified in `ai-ml/nvidia-triton-server/helm-values/nim-llm.yaml` with below config. Please visit [this page](https://build.nvidia.com/explore/discover) to explore more. You may simply update this image configuration if you want to change to deploy another model. ::: ```yaml @@ -130,6 +182,86 @@ you will see similar output like the following {"id":"cmpl-63a0b66aeda1440c8b6ca1ce3583b173","object":"text_completion","created":1719742336,"model":"meta/llama3-8b-instruct","choices":[{"index":0,"text":", there was a young man named Jack who lived in a small village at the foot of a vast and ancient forest. Jack was a curious and adventurous soul, always eager to explore the world beyond his village. One day, he decided to venture into the forest, hoping to discover its secrets.\nAs he wandered deeper into","logprobs":null,"finish_reason":"length","stop_reason":null}],"usage":{"prompt_tokens":5,"total_tokens":69,"completion_tokens":64}} ``` +### Testing the Llama3 model deployed with NIM +It's time to test the Llama3 just deployed. We will run the following commands with the same prompts to verify the generated outputs. + +First, expose the model serving service with port-forward using kubectl + +```bash +kubectl port-forward -n nim svc/nim-llm 8000 +``` + +Next, open another Terminal window, run the client using the existing prompts: + +```bash +cd gen-ai/inference/nvidia-nim/nim-client +python3 client.py --input-prompts prompts.txt --results-file results.txt +``` + +You will see an output something like below: + +```text +Loading inputs from `prompts.txt`... +Model meta/llama3-8b-instruct - Request 14: 4655.84 ms +Model meta/llama3-8b-instruct - Request 10: 6463.45 ms +Model meta/llama3-8b-instruct - Request 3: 7787.67 ms +Model meta/llama3-8b-instruct - Request 15: 7794.37 ms +Model meta/llama3-8b-instruct - Request 1: 8320.75 ms +Model meta/llama3-8b-instruct - Request 12: 8925.80 ms +Model meta/llama3-8b-instruct - Request 5: 9079.14 ms +Model meta/llama3-8b-instruct - Request 16: 9501.56 ms +Model meta/llama3-8b-instruct - Request 18: 9790.65 ms +Model meta/llama3-8b-instruct - Request 0: 10248.90 ms +Model meta/llama3-8b-instruct - Request 6: 10243.54 ms +Model meta/llama3-8b-instruct - Request 4: 10596.04 ms +Model meta/llama3-8b-instruct - Request 11: 10798.81 ms +Model meta/llama3-8b-instruct - Request 17: 10793.39 ms +Model meta/llama3-8b-instruct - Request 8: 10977.41 ms +Model meta/llama3-8b-instruct - Request 2: 12085.08 ms +Model meta/llama3-8b-instruct - Request 19: 12645.12 ms +Model meta/llama3-8b-instruct - Request 9: 13346.84 ms +Model meta/llama3-8b-instruct - Request 13: 13581.21 ms +Model meta/llama3-8b-instruct - Request 7: 14917.04 ms +Storing results into `results.txt`... +Total time for all requests: 202.55 seconds (202552.60 milliseconds) +PASS: NVIDIA NIM example +``` + +Output for `results.txt` should look like the follwoing + +
    +Click to expand the partial output +```text +The key differences between traditional machine learning models and very large language models (vLLM) are: + +1. **Scale**: vLLMs are massive, with billions of parameters, whereas traditional models typically have millions. +2. **Training data**: vLLMs are trained on vast amounts of text data, often sourced from the internet, whereas traditional models are trained on smaller, curated datasets. +3. **Architecture**: vLLMs often use transformer architectures, which are designed for sequential data like text, whereas traditional models may use feedforward networks or recurrent neural networks. +4. **Training objectives**: vLLMs are often trained using masked language modeling or next sentence prediction tasks, whereas traditional models may use classification, regression, or clustering objectives. +5. **Evaluation metrics**: vLLMs are typically evaluated using metrics like perplexity, accuracy, or fluency, whereas traditional models may use metrics like accuracy, precision, or recall. +6. **Interpretability**: vLLMs are often less interpretable due to their massive size and complex architecture, whereas traditional models may be more interpretable due to their smaller size and simpler architecture. + +These differences enable vLLMs to excel in tasks like language translation, text generation, and conversational AI, whereas traditional models are better suited for tasks like image classification or regression. + +========= + +TensorRT (Triton Runtime) optimizes LLM (Large Language Model) inference on NVIDIA hardware by: + +1. **Model Pruning**: Removing unnecessary weights and connections to reduce model size and computational requirements. +2. **Quantization**: Converting floating-point models to lower-precision integer formats (e.g., INT8) to reduce memory bandwidth and improve performance. +3. **Kernel Fusion**: Combining multiple kernel launches into a single launch to reduce overhead and improve parallelism. +4. **Optimized Tensor Cores**: Utilizing NVIDIA's Tensor Cores for matrix multiplication, which provides significant performance boosts. +5. **Batching**: Processing multiple input batches concurrently to improve throughput. +6. **Mixed Precision**: Using a combination of floating-point and integer precision to balance accuracy and performance. +7. **Graph Optimization**: Reordering and reorganizing the computation graph to minimize memory access and optimize data transfer. + +By applying these optimizations, TensorRT can significantly accelerate LLM inference on NVIDIA hardware, achieving faster inference times and improved performance. + +========= +``` +
    + + ## Cleanup To remove all resources created by this deployment, run: From 7844cdfc6421272a2f689d3e21910db1f55e0a6e Mon Sep 17 00:00:00 2001 From: hustshawn Date: Thu, 4 Jul 2024 16:22:17 +0800 Subject: [PATCH 07/16] fix: fixed the typo in website/docs/gen-ai/inference/nvidia-nim-llama3.md and terraform doc --- ai-ml/nvidia-triton-server/README.md | 10 ++++++++++ website/docs/gen-ai/inference/nvidia-nim-llama3.md | 2 +- 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/ai-ml/nvidia-triton-server/README.md b/ai-ml/nvidia-triton-server/README.md index bbbe8f998..21defd296 100644 --- a/ai-ml/nvidia-triton-server/README.md +++ b/ai-ml/nvidia-triton-server/README.md @@ -19,6 +19,7 @@ |------|---------| |
    [aws](#provider\_aws) | >= 3.72 | | [aws.ecr](#provider\_aws.ecr) | >= 3.72 | +| [helm](#provider\_helm) | >= 2.4.1 | | [kubernetes](#provider\_kubernetes) | >= 2.10 | | [null](#provider\_null) | >= 3.1 | | [random](#provider\_random) | >= 3.1 | @@ -29,6 +30,7 @@ |------|--------|---------| | [data\_addons](#module\_data\_addons) | aws-ia/eks-data-addons/aws | ~> 1.32.0 | | [ebs\_csi\_driver\_irsa](#module\_ebs\_csi\_driver\_irsa) | terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks | ~> 5.20 | +| [efs](#module\_efs) | terraform-aws-modules/efs/aws | ~> 1.6 | | [eks](#module\_eks) | terraform-aws-modules/eks/aws | ~> 19.15 | | [eks\_blueprints\_addons](#module\_eks\_blueprints\_addons) | aws-ia/eks-blueprints-addons/aws | ~> 1.2 | | [s3\_bucket](#module\_s3\_bucket) | terraform-aws-modules/s3-bucket/aws | 4.1.2 | @@ -43,12 +45,17 @@ | [aws_iam_policy.triton](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/iam_policy) | resource | | [aws_secretsmanager_secret.grafana](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/secretsmanager_secret) | resource | | [aws_secretsmanager_secret_version.grafana](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/secretsmanager_secret_version) | resource | +| [helm_release.nim_llm](https://registry.terraform.io/providers/hashicorp/helm/latest/docs/resources/release) | resource | | [kubernetes_annotations.disable_gp2](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/annotations) | resource | +| [kubernetes_namespace.nim](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/namespace) | resource | | [kubernetes_namespace_v1.triton](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/namespace_v1) | resource | +| [kubernetes_persistent_volume_claim_v1.efs_pvc](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/persistent_volume_claim_v1) | resource | | [kubernetes_secret_v1.huggingface_token](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/secret_v1) | resource | | [kubernetes_secret_v1.triton](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/secret_v1) | resource | | [kubernetes_service_account_v1.triton](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/service_account_v1) | resource | | [kubernetes_storage_class.default_gp3](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/storage_class) | resource | +| [kubernetes_storage_class_v1.efs](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/storage_class_v1) | resource | +| [null_resource.download_nim_deploy](https://registry.terraform.io/providers/hashicorp/null/latest/docs/resources/resource) | resource | | [null_resource.sync_local_to_s3](https://registry.terraform.io/providers/hashicorp/null/latest/docs/resources/resource) | resource | | [random_password.grafana](https://registry.terraform.io/providers/hashicorp/random/latest/docs/resources/password) | resource | | [aws_availability_zones.available](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/availability_zones) | data source | @@ -65,8 +72,11 @@ | Name | Description | Type | Default | Required | |------|-------------|------|---------|:--------:| | [eks\_cluster\_version](#input\_eks\_cluster\_version) | EKS Cluster version | `string` | `"1.29"` | no | +| [enable\_nvidia\_nim](#input\_enable\_nvidia\_nim) | Toggle to enable or disable NVIDIA NIM pattern resource creation | `bool` | `true` | no | +| [enable\_nvidia\_triton\_server](#input\_enable\_nvidia\_triton\_server) | Toggle to enable or disable NVIDIA Triton server resource creation | `bool` | `true` | no | | [huggingface\_token](#input\_huggingface\_token) | Hugging Face Secret Token | `string` | `"DUMMY_TOKEN_REPLACE_ME"` | no | | [name](#input\_name) | Name of the VPC and EKS Cluster | `string` | `"nvidia-triton-server"` | no | +| [ngc\_api\_key](#input\_ngc\_api\_key) | NGC API Key | `string` | `"DUMMY_NGC_API_KEY_REPLACE_ME"` | no | | [region](#input\_region) | region | `string` | `"us-west-2"` | no | | [secondary\_cidr\_blocks](#input\_secondary\_cidr\_blocks) | Secondary CIDR blocks to be attached to VPC | `list(string)` |
    [
    "100.64.0.0/16"
    ]
    | no | | [vpc\_cidr](#input\_vpc\_cidr) | VPC CIDR. This should be a valid private (RFC 1918) CIDR range | `string` | `"10.1.0.0/21"` | no | diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index 6b345f934..370611592 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -227,7 +227,7 @@ Total time for all requests: 202.55 seconds (202552.60 milliseconds) PASS: NVIDIA NIM example ``` -Output for `results.txt` should look like the follwoing +Output for `results.txt` should look like the following
    Click to expand the partial output From 658107bfcb2282610f5247c9d4bc1e6dbb94f61c Mon Sep 17 00:00:00 2001 From: hustshawn Date: Thu, 4 Jul 2024 16:54:17 +0800 Subject: [PATCH 08/16] chore: removed unecessary commented code and updated the testing client output for actual execution time --- ai-ml/nvidia-triton-server/nvidia-nim.tf | 10 +++++----- gen-ai/inference/nvidia-nim/nim-client/client.py | 8 +++++++- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/ai-ml/nvidia-triton-server/nvidia-nim.tf b/ai-ml/nvidia-triton-server/nvidia-nim.tf index 5bf7d3937..5fd23a7e9 100644 --- a/ai-ml/nvidia-triton-server/nvidia-nim.tf +++ b/ai-ml/nvidia-triton-server/nvidia-nim.tf @@ -60,13 +60,11 @@ resource "kubernetes_namespace" "nim" { resource "kubernetes_persistent_volume_claim_v1" "efs_pvc" { count = var.enable_nvidia_nim ? 1 : 0 metadata { - # name = "nim-llm-pvc" name = kubernetes_namespace.nim[count.index].metadata[0].name namespace = "nim" } spec { - access_modes = ["ReadWriteMany"] - # storage_class_name = kubernetes_storage_class_v1.efs.id + access_modes = ["ReadWriteMany"] storage_class_name = kubernetes_storage_class_v1.efs[count.index].metadata[0].name resources { requests = { @@ -76,6 +74,9 @@ resource "kubernetes_persistent_volume_claim_v1" "efs_pvc" { } } +#--------------------------------------------------------------- +# NIM LLM Helm Chart +#--------------------------------------------------------------- resource "null_resource" "download_nim_deploy" { count = var.enable_nvidia_nim ? 1 : 0 @@ -114,8 +115,7 @@ resource "helm_release" "nim_llm" { "${path.module}/helm-values/nim-llm.yaml", { ngc_api_key = var.ngc_api_key - # pvc_name = kubernetes_persistent_volume_claim_v1.efs_pvc.metadata[0].name - pvc_name = kubernetes_persistent_volume_claim_v1.efs_pvc[count.index].metadata[0].name + pvc_name = kubernetes_persistent_volume_claim_v1.efs_pvc[count.index].metadata[0].name } ) ] diff --git a/gen-ai/inference/nvidia-nim/nim-client/client.py b/gen-ai/inference/nvidia-nim/nim-client/client.py index d4cb0719d..95478c65b 100644 --- a/gen-ai/inference/nvidia-nim/nim-client/client.py +++ b/gen-ai/inference/nvidia-nim/nim-client/client.py @@ -45,6 +45,7 @@ async def process_prompt(client, prompt, prompt_id, sampling_parameters, results async def main(FLAGS): + start = time.time() sampling_parameters = { "temperature": "0.01", "top_p": "1.0", @@ -86,9 +87,14 @@ async def main(FLAGS): total_time_ms = total_time_sec * 1000 print( - f"Total time for all requests: {total_time_sec:.2f} seconds ({total_time_ms:.2f} milliseconds)" + f"Accumulated time for all requests: {total_time_sec:.2f} seconds ({total_time_ms:.2f} milliseconds)" ) print("PASS: NVIDIA NIM example") + end = time.time() + actual_duration = end - start + print( + f"Actual execution time used with concurrency {len(tasks)} is: {actual_duration:.2f} seconds ..." + ) if __name__ == "__main__": From ac1d01ac08f8611b05eb2b3994a3470ed24d19ca Mon Sep 17 00:00:00 2001 From: hustshawn Date: Sun, 7 Jul 2024 20:55:17 +0800 Subject: [PATCH 09/16] feat: added monitoring for NIM --- .../helm-values/kube-prometheus.yaml | 40 ++++------ .../gen-ai/inference/img/nim-dashboard.png | Bin 0 -> 512203 bytes .../gen-ai/inference/nvidia-nim-llama3.md | 71 ++++++++++++++++++ 3 files changed, 87 insertions(+), 24 deletions(-) create mode 100644 website/docs/gen-ai/inference/img/nim-dashboard.png diff --git a/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml b/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml index 47e090743..034eec443 100644 --- a/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml +++ b/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml @@ -21,28 +21,20 @@ alertmanager: grafana: enabled: true + dashboardProviders: + dashboardproviders.yaml: + apiVersion: 1 + providers: + - name: 'default' + orgId: 1 + folder: '' + type: file + disableDeletion: false + editable: true + options: + path: /var/lib/grafana/dashboards/default defaultDashboardsEnabled: true -prometheus: - prometheusSpec: - retention: 5h - scrapeInterval: 30s - evaluationInterval: 30s - scrapeTimeout: 10s - serviceMonitorSelectorNilUsesHelmValues: false # This is required to use the serviceMonitorSelector - storageSpec: - volumeClaimTemplate: - metadata: - name: data - spec: - storageClassName: ${storage_class_type} - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 50Gi -alertmanager: - enabled: false - -grafana: - enabled: true - defaultDashboardsEnabled: true + dashboards: + default: + nim-llm-monitoring: + url: https://raw.githubusercontent.com/hustshawn/nim-llm-eks/main/monitoring/nim-llm-monitoring.json diff --git a/website/docs/gen-ai/inference/img/nim-dashboard.png b/website/docs/gen-ai/inference/img/nim-dashboard.png new file mode 100644 index 0000000000000000000000000000000000000000..2d09115b81984bcb5c1e4afb3420b6e089682e8d GIT binary patch literal 512203 zcmeFabyQS)+c&Nlh^VL_NLzrkNJ*mzNOz}nr}V&x97I}T=oE$y=?<0dZW!ri92$lh zew+Jw?&rCkbC!7Ci|N z8=7a%;N)Do2>fy-9uj@#%vlFhAt70FAyFY~3u`+Co0s~A&rR)ZEMB_EvYt8fFvQ_~ zjl>sOf<~Ea`5x|f*T~5p`w9*8U3RoJp(hZ2qO!zg# z7h>s;Get$gVnREU^|iXa`At7lB2NOuAZ|ZGwePao+j236be?SxcBG$SZi&~(RN|mY zWFy&%T*pg%>f3&@a-QceOoNa5g9L&Y?NoGSnS!wx+MbKuz%Ojr>Pdci^Rexvrz`Kv z?N>eUa*?f;mhf9qmNs3?V`InjH6GK%oBPppf zbinsZXU_VVo;eSEI}3bBfX|sTSV>s_{^f>d(z$(~mmy4Uf2m;O@KcI`GDv(? 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Verify it by running +```bash +kubectl get svc -n nim +kubectl port-forward -n nim svc/nim-llm 8000 + +curl localhost:8000/metrics # run this in another terminal +``` + +We also provided a pre-configured Grafana dashboard. + +In the Grafana dashboard below, you can see several important metrics: + +- **Running Count**: This gauge shows number of requests currently running on GPU. +- **Latency (p95 percentile)**: It includes metrics `Time to First Token`, `Time per Output Token` and `End to End` latency. These metrics illustrate the time taken to compute inference requests, helping identify any latency issues. +- **Token Count**: It includes metrics `Prompt Token Count` and `Generation Token Count`, illustrate the input and output consumption. + +You can find more metrics description from this [document](https://docs.nvidia.com/nim/large-language-models/latest/observability.html). + +![NVIDIA LLM Server](img/nim-dashboard.png) + +You can visualize these metrics using the Grafana. To view the Grafana dashboard to monitor these metrics, follow the steps below: + +```bash +- Port-forward Grafana service: +kubectl port-forward svc/kube-prometheus-stack-grafana 3000:80 -n kube-prometheus-stack + +- Grafana Admin user +admin + +- Get secret name from Terraform output +terraform output grafana_secret_name + +- Get admin user password +aws secretsmanager get-secret-value --secret-id --region $AWS_REGION --query "SecretString" --output text +``` + +**Login to Grafana:** + +- Open your web browser and navigate to [http://localhost:3000](http://localhost:3000). +- Login with the username `admin` and the password retrieved from AWS Secrets Manager. + +**Open the NIM Monitoring Dashboard:** + +- Once logged in, click "Dashboards" on the left sidebar and search "nim" +- You can find the Dashboard `NVIDIA NIM Monitoring` from the list +- Click and entering to the dashboard. + +You should now see the metrics displayed on the Grafana dashboard, allowing you to monitor the performance your NVIDIA NIM service deployment. ## Cleanup From 5da02c33bbe301e0701b7dc4da0536e04bfa3a59 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Tue, 9 Jul 2024 10:13:46 +0800 Subject: [PATCH 10/16] chore: change enable_nvidia_nim default to false and updated install script and docs; updated the nim-client as well --- ai-ml/nvidia-triton-server/install.sh | 11 +++- ai-ml/nvidia-triton-server/nvidia-nim.tf | 3 + ai-ml/nvidia-triton-server/variables.tf | 2 +- .../inference/nvidia-nim/nim-client/client.py | 10 +++- .../gen-ai/inference/nvidia-nim-llama3.md | 55 ++++++++++--------- 5 files changed, 51 insertions(+), 30 deletions(-) diff --git a/ai-ml/nvidia-triton-server/install.sh b/ai-ml/nvidia-triton-server/install.sh index f56e5fbab..8c9da9b1d 100755 --- a/ai-ml/nvidia-triton-server/install.sh +++ b/ai-ml/nvidia-triton-server/install.sh @@ -17,9 +17,18 @@ if [[ "${TF_VAR_huggingface_token}" = "DUMMY_TOKEN_REPLACE_ME" ]] ; then exit 1 fi +if [ "$TF_VAR_enable_nvidia_nim" = true ]; then + # Check if server_token does not start with "nvapi-" + # Obtain your NVIDIA NGC API key from https://docs.nvidia.com/ai-enterprise/deployment-guide-spark-rapids-accelerator/0.1.0/appendix-ngc.html + if [[ ! "$TF_VAR_ngc_api_key" == nvapi-* ]]; then + echo "FAILED: TF_VAR_ngc_api_key must start with 'nvapi-'" + exit 1 + fi +fi + echo "Proceed with deployment of targets..." -List of Terraform modules to apply in sequence +# List of Terraform modules to apply in sequence targets=( "module.vpc" "module.eks" diff --git a/ai-ml/nvidia-triton-server/nvidia-nim.tf b/ai-ml/nvidia-triton-server/nvidia-nim.tf index 5fd23a7e9..af4bcfbbb 100644 --- a/ai-ml/nvidia-triton-server/nvidia-nim.tf +++ b/ai-ml/nvidia-triton-server/nvidia-nim.tf @@ -78,6 +78,9 @@ resource "kubernetes_persistent_volume_claim_v1" "efs_pvc" { # NIM LLM Helm Chart #--------------------------------------------------------------- +# As of now, this helm chart from NVIDIA is not hosted in a Helm registry, +# so we will download it from the github repo. +# https://github.com/NVIDIA/nim-deploy.git resource "null_resource" "download_nim_deploy" { count = var.enable_nvidia_nim ? 1 : 0 # This trigger ensures the script runs only when the file doesn't exist diff --git a/ai-ml/nvidia-triton-server/variables.tf b/ai-ml/nvidia-triton-server/variables.tf index 2e216021f..23db2d079 100644 --- a/ai-ml/nvidia-triton-server/variables.tf +++ b/ai-ml/nvidia-triton-server/variables.tf @@ -56,7 +56,7 @@ variable "huggingface_token" { variable "enable_nvidia_nim" { description = "Toggle to enable or disable NVIDIA NIM pattern resource creation" - default = true + default = false type = bool } diff --git a/gen-ai/inference/nvidia-nim/nim-client/client.py b/gen-ai/inference/nvidia-nim/nim-client/client.py index 95478c65b..84df2ff0a 100644 --- a/gen-ai/inference/nvidia-nim/nim-client/client.py +++ b/gen-ai/inference/nvidia-nim/nim-client/client.py @@ -36,8 +36,11 @@ async def process_prompt(client, prompt, prompt_id, sampling_parameters, results if response: results_dict[str(prompt_id)].append(response.encode("utf-8")) - duration = (end_time - start_time) * 1000 - print(f"Model {FLAGS.model_name} - Request {prompt_id}: {duration:.2f} ms") + duration = end_time - start_time + duration_ms = duration * 1000 + print( + f"Model {FLAGS.model_name} - Request {prompt_id}: {duration:.2f}s ({duration_ms:.2f}ms)" + ) return end_time - start_time else: print(f"Error processing prompt {prompt_id}") @@ -92,8 +95,9 @@ async def main(FLAGS): print("PASS: NVIDIA NIM example") end = time.time() actual_duration = end - start + actual_duration_ms = end - start print( - f"Actual execution time used with concurrency {len(tasks)} is: {actual_duration:.2f} seconds ..." + f"Actual execution time used with concurrency {len(tasks)} is: {actual_duration:.2f} seconds ({actual_duration_ms:.2f} milliseconds)" ) diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index 17f5fdba9..ce89509d3 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -80,6 +80,7 @@ Run the installation script: ```bash cd data-on-eks/ai-ml/nvidia-triton-server +export TF_VAR_enable_nvidia_nim=true export TF_VAR_enable_nvidia_triton_server=false ./install.sh ``` @@ -195,6 +196,9 @@ Next, open another Terminal window, run the client using the existing prompts: ```bash cd gen-ai/inference/nvidia-nim/nim-client +python3 -m venv .venv +source .venv/bin/activate +pip install openai python3 client.py --input-prompts prompts.txt --results-file results.txt ``` @@ -202,29 +206,30 @@ You will see an output something like below: ```text Loading inputs from `prompts.txt`... -Model meta/llama3-8b-instruct - Request 14: 4655.84 ms -Model meta/llama3-8b-instruct - Request 10: 6463.45 ms -Model meta/llama3-8b-instruct - Request 3: 7787.67 ms -Model meta/llama3-8b-instruct - Request 15: 7794.37 ms -Model meta/llama3-8b-instruct - Request 1: 8320.75 ms -Model meta/llama3-8b-instruct - Request 12: 8925.80 ms -Model meta/llama3-8b-instruct - Request 5: 9079.14 ms -Model meta/llama3-8b-instruct - Request 16: 9501.56 ms -Model meta/llama3-8b-instruct - Request 18: 9790.65 ms -Model meta/llama3-8b-instruct - Request 0: 10248.90 ms -Model meta/llama3-8b-instruct - Request 6: 10243.54 ms -Model meta/llama3-8b-instruct - Request 4: 10596.04 ms -Model meta/llama3-8b-instruct - Request 11: 10798.81 ms -Model meta/llama3-8b-instruct - Request 17: 10793.39 ms -Model meta/llama3-8b-instruct - Request 8: 10977.41 ms -Model meta/llama3-8b-instruct - Request 2: 12085.08 ms -Model meta/llama3-8b-instruct - Request 19: 12645.12 ms -Model meta/llama3-8b-instruct - Request 9: 13346.84 ms -Model meta/llama3-8b-instruct - Request 13: 13581.21 ms -Model meta/llama3-8b-instruct - Request 7: 14917.04 ms +Model meta/llama3-8b-instruct - Request 14: 4.68s (4678.46ms) +Model meta/llama3-8b-instruct - Request 10: 6.43s (6434.32ms) +Model meta/llama3-8b-instruct - Request 3: 7.82s (7824.33ms) +Model meta/llama3-8b-instruct - Request 1: 8.54s (8540.69ms) +Model meta/llama3-8b-instruct - Request 5: 8.81s (8807.52ms) +Model meta/llama3-8b-instruct - Request 12: 8.95s (8945.85ms) +Model meta/llama3-8b-instruct - Request 18: 9.77s (9774.75ms) +Model meta/llama3-8b-instruct - Request 16: 9.99s (9994.51ms) +Model meta/llama3-8b-instruct - Request 6: 10.26s (10263.60ms) +Model meta/llama3-8b-instruct - Request 0: 10.27s (10274.35ms) +Model meta/llama3-8b-instruct - Request 4: 10.65s (10654.39ms) +Model meta/llama3-8b-instruct - Request 17: 10.75s (10746.08ms) +Model meta/llama3-8b-instruct - Request 11: 10.86s (10859.91ms) +Model meta/llama3-8b-instruct - Request 15: 10.86s (10857.15ms) +Model meta/llama3-8b-instruct - Request 8: 11.07s (11068.78ms) +Model meta/llama3-8b-instruct - Request 2: 12.11s (12105.07ms) +Model meta/llama3-8b-instruct - Request 19: 12.64s (12636.42ms) +Model meta/llama3-8b-instruct - Request 9: 13.37s (13370.75ms) +Model meta/llama3-8b-instruct - Request 13: 13.57s (13571.28ms) +Model meta/llama3-8b-instruct - Request 7: 14.90s (14901.51ms) Storing results into `results.txt`... -Total time for all requests: 202.55 seconds (202552.60 milliseconds) +Accumulated time for all requests: 206.31 seconds (206309.73 milliseconds) PASS: NVIDIA NIM example +Actual execution time used with concurrency 20 is: 14.92 seconds (14.92 milliseconds) ``` Output for `results.txt` should look like the following @@ -292,11 +297,11 @@ kubectl port-forward -n nim svc/nim-llm 8000 curl localhost:8000/metrics # run this in another terminal ``` -We also provided a pre-configured Grafana dashboard. In the Grafana dashboard below, you can see several important metrics: +We also provided a pre-configured Grafana dashboard. In the Grafana dashboard below, it contains several important metrics: -- **Running Count**: This gauge shows number of requests currently running on GPU. -- **Latency (p95 percentile)**: It includes metrics `Time to First Token`, `Time per Output Token` and `End to End` latency. These metrics illustrate the time taken to compute inference requests, helping identify any latency issues. -- **Token Count**: It includes metrics `Prompt Token Count` and `Generation Token Count`, illustrate the input and output consumption. +- **Time to First Token (TTFT)**: The latency between the initial inference request to the model and the return of the first token. +- **Inter-Token Latency (ITL)**: The latency between each token after the first. +- **Total Throughput**: The total number of tokens generated per second by the NIM. You can find more metrics description from this [document](https://docs.nvidia.com/nim/large-language-models/latest/observability.html). From b6fd53f6ebbfce30a6206ca792b38257892002b6 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Tue, 9 Jul 2024 10:17:49 +0800 Subject: [PATCH 11/16] fix: fixed terraform doc according to pre-commit suggestion --- ai-ml/nvidia-triton-server/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ai-ml/nvidia-triton-server/README.md b/ai-ml/nvidia-triton-server/README.md index 323a39749..ea4aadd66 100644 --- a/ai-ml/nvidia-triton-server/README.md +++ b/ai-ml/nvidia-triton-server/README.md @@ -72,7 +72,7 @@ | Name | Description | Type | Default | Required | |------|-------------|------|---------|:--------:| | [eks\_cluster\_version](#input\_eks\_cluster\_version) | EKS Cluster version | `string` | `"1.30"` | no | -| [enable\_nvidia\_nim](#input\_enable\_nvidia\_nim) | Toggle to enable or disable NVIDIA NIM pattern resource creation | `bool` | `true` | no | +| [enable\_nvidia\_nim](#input\_enable\_nvidia\_nim) | Toggle to enable or disable NVIDIA NIM pattern resource creation | `bool` | `false` | no | | [enable\_nvidia\_triton\_server](#input\_enable\_nvidia\_triton\_server) | Toggle to enable or disable NVIDIA Triton server resource creation | `bool` | `true` | no | | [huggingface\_token](#input\_huggingface\_token) | Hugging Face Secret Token | `string` | `"DUMMY_TOKEN_REPLACE_ME"` | no | | [name](#input\_name) | Name of the VPC and EKS Cluster | `string` | `"nvidia-triton-server"` | no | From e503da6d95c26bff3a3ce292a305871077d13001 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Tue, 9 Jul 2024 11:01:07 +0800 Subject: [PATCH 12/16] refactoring: created nim-llm dashboard json locally --- ai-ml/nvidia-triton-server/addons.tf | 3 +- .../helm-values/kube-prometheus.yaml | 3 +- .../monitoring/nim-llm-dashboard.json | 654 ++++++++++++++++++ 3 files changed, 658 insertions(+), 2 deletions(-) create mode 100644 ai-ml/nvidia-triton-server/monitoring/nim-llm-dashboard.json diff --git a/ai-ml/nvidia-triton-server/addons.tf b/ai-ml/nvidia-triton-server/addons.tf index 8fdb8d327..414be87f2 100644 --- a/ai-ml/nvidia-triton-server/addons.tf +++ b/ai-ml/nvidia-triton-server/addons.tf @@ -133,7 +133,8 @@ module "eks_blueprints_addons" { kube_prometheus_stack = { values = [ templatefile("${path.module}/helm-values/kube-prometheus.yaml", { - storage_class_type = kubernetes_storage_class.default_gp3.id + storage_class_type = kubernetes_storage_class.default_gp3.id + nim_llm_dashbaord_json = indent(10, file("${path.module}/monitoring/nim-llm-dashboard.json")) }) ] chart_version = "48.1.1" diff --git a/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml b/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml index 034eec443..61aa9fee4 100644 --- a/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml +++ b/ai-ml/nvidia-triton-server/helm-values/kube-prometheus.yaml @@ -37,4 +37,5 @@ grafana: dashboards: default: nim-llm-monitoring: - url: https://raw.githubusercontent.com/hustshawn/nim-llm-eks/main/monitoring/nim-llm-monitoring.json + json: | + ${nim_llm_dashbaord_json} diff --git a/ai-ml/nvidia-triton-server/monitoring/nim-llm-dashboard.json b/ai-ml/nvidia-triton-server/monitoring/nim-llm-dashboard.json new file mode 100644 index 000000000..960ab476f --- /dev/null +++ b/ai-ml/nvidia-triton-server/monitoring/nim-llm-dashboard.json @@ -0,0 +1,654 @@ +{ + "annotations": { + "list": [ + { + "builtIn": 1, + "datasource": { + "type": "grafana", + "uid": "-- Grafana --" + }, + "enable": true, + "hide": true, + "iconColor": "rgba(0, 211, 255, 1)", + "name": "Annotations & Alerts", + "type": "dashboard" + } + ] + }, + "editable": true, + "fiscalYearStartMonth": 0, + "graphTooltip": 0, + "links": [], + "liveNow": false, + "panels": [ + { + "datasource": { + "type": "prometheus", + "uid": "prometheus" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "thresholds" + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + }, + { + "color": "red", + "value": 80 + } + ] + } + }, + "overrides": [] + }, + "gridPos": { + "h": 8, + "w": 12, + "x": 0, + "y": 0 + }, + "options": { + "colorMode": "value", + "graphMode": "area", + "justifyMode": "auto", + "orientation": "auto", + "reduceOptions": { + "calcs": [ + "lastNotNull" + ], + "fields": "", + "values": false + }, + "textMode": "auto" + }, + "pluginVersion": "10.0.1", + "targets": [ + { + "datasource": { + "type": "prometheus", + "uid": "prometheus" + }, + "editorMode": "code", + "expr": "sum(kube_pod_status_phase{phase=\"Running\", namespace=\"nim\"})", + "instant": false, + "range": true, + "refId": "A" + } + ], + "title": "Running Pods ", + "type": "stat" + }, + { + "datasource": { + "type": "prometheus", + "uid": "prometheus" + }, + "description": "", + "fieldConfig": { + "defaults": { + "color": { + "mode": "thresholds" + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + 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Usage", + "type": "timeseries" + }, + { + "datasource": { + "type": "prometheus", + "uid": "prometheus" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "thresholds" + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + }, + { + "color": "red", + "value": 80 + } + ] + } + }, + "overrides": [] + }, + "gridPos": { + "h": 8, + "w": 12, + "x": 12, + "y": 27 + }, + "options": { + "colorMode": "value", + "graphMode": "area", + "justifyMode": "auto", + "orientation": "auto", + "reduceOptions": { + "calcs": [ + "lastNotNull" + ], + "fields": "", + "values": false + }, + "textMode": "auto" + }, + "pluginVersion": "10.0.1", + "targets": [ + { + "datasource": { + "type": "prometheus", + "uid": "prometheus" + }, + "editorMode": "builder", + "expr": "max(num_request_max{service=\"nim-llm\"})", + "instant": false, + "legendFormat": "{{pod}}", + "range": true, + "refId": "A" + } + ], + "title": "Max of Requests", + "type": "stat" + } + ], + "refresh": "5s", + "schemaVersion": 38, + "style": "dark", + "tags": [], + "templating": { + "list": [] + }, + "time": { + "from": "now-15m", + "to": "now" + }, + "timepicker": {}, + "timezone": "", + "title": "NVIDIA NIM Monitoring", + "version": 1, + "weekStart": "" +} From c1510d25853e3e4617b8971f6ce44e6c9bb28607 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Tue, 9 Jul 2024 14:12:15 +0800 Subject: [PATCH 13/16] chore: dyanmically inject prometheus namespace to prometheus-adapter addon, and updated nim-llm doc for NVIDIA potential license warning --- ai-ml/nvidia-triton-server/addons.tf | 4 +++- .../helm-values/prometheus-adapter.yaml | 2 +- website/docs/gen-ai/inference/nvidia-nim-llama3.md | 8 ++++---- 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/ai-ml/nvidia-triton-server/addons.tf b/ai-ml/nvidia-triton-server/addons.tf index 414be87f2..dd2b5167c 100644 --- a/ai-ml/nvidia-triton-server/addons.tf +++ b/ai-ml/nvidia-triton-server/addons.tf @@ -154,7 +154,9 @@ module "eks_blueprints_addons" { version = "4.10.0" values = [ templatefile( - "${path.module}/helm-values/prometheus-adapter.yaml", {} + "${path.module}/helm-values/prometheus-adapter.yaml", { + prometheus_namespace = module.eks_blueprints_addons.kube_prometheus_stack.namespace + } ) ] } diff --git a/ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml b/ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml index 9311c37db..b3f88b348 100644 --- a/ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml +++ b/ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml @@ -1,6 +1,6 @@ # ref: https://github.com/prometheus-community/helm-charts/blob/main/charts/prometheus-adapter/values.yaml prometheus: - url: http://kube-prometheus-stack-prometheus.kube-prometheus-stack + url: http://kube-prometheus-stack-prometheus.${prometheus_namespace} port: 9090 rules: default: false diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index ce89509d3..b8d7abaeb 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -1,14 +1,14 @@ --- -title: NVIDIA NIM Llama-3 Deployment on Amazon EKS +title: NVIDIA NIM LLM on Amazon EKS sidebar_position: 8 --- import CollapsibleContent from '../../../src/components/CollapsibleContent'; -:::danger +:::warning -Note: Use of this Llama-3 Instruct model is governed by the Meta license. -In order to download the model weights and tokenizer, please visit the [website](https://huggingface.co/meta-llama/Meta-Llama-3-8B) and accept the license before requesting access. +Note: Before implementing NVIDIA NIM, please be aware it is part of [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/), which may introduce potential cost and licensing for production use. +For evaluation, NVIDIA also offers a free evaluation license to try NVIDIA AI Enterprise for 90 days, and you can [register](https://enterpriseproductregistration.nvidia.com/?LicType=EVAL&ProductFamily=NVAIEnterprise) it with your corporate email. ::: :::info From e3b5cbe5e1c9562b1c6c7b5ca5f45173ba3edb12 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Tue, 9 Jul 2024 22:47:25 +0800 Subject: [PATCH 14/16] chore: updated the nim-client script to accept cli passed model name --- gen-ai/inference/nvidia-nim/nim-client/client.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gen-ai/inference/nvidia-nim/nim-client/client.py b/gen-ai/inference/nvidia-nim/nim-client/client.py index 84df2ff0a..6c88bf5d4 100644 --- a/gen-ai/inference/nvidia-nim/nim-client/client.py +++ b/gen-ai/inference/nvidia-nim/nim-client/client.py @@ -9,7 +9,7 @@ async def generate(client, prompt, sampling_parameters): try: response = await client.chat.completions.create( - model="meta/llama3-8b-instruct", # You can change this to the appropriate model + model=FLAGS.model_name, messages=[ { "role": "system", From 9d6bb92dc1de7a0cb5e3b0b6075b1ff8dabc835b Mon Sep 17 00:00:00 2001 From: hustshawn Date: Wed, 10 Jul 2024 17:42:23 +0800 Subject: [PATCH 15/16] fix: updated the cleanup for resources smooth destroy --- ai-ml/nvidia-triton-server/cleanup.sh | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/ai-ml/nvidia-triton-server/cleanup.sh b/ai-ml/nvidia-triton-server/cleanup.sh index f9c5daca6..0ff1dc9b5 100755 --- a/ai-ml/nvidia-triton-server/cleanup.sh +++ b/ai-ml/nvidia-triton-server/cleanup.sh @@ -2,8 +2,9 @@ read -p "Enter the region: " region export AWS_DEFAULT_REGION=$region +export STACK_NAME="nvidia-triton-server" -echo "Destroying RayService..." +echo "Destroying LoadBalancer type service from Nginx ingress controller..." # Delete the Ingress/SVC before removing the addons TMPFILE=$(mktemp) @@ -12,7 +13,7 @@ terraform output -raw configure_kubectl > "$TMPFILE" if [[ ! $(cat $TMPFILE) == *"No outputs found"* ]]; then echo "No outputs found, skipping kubectl delete" source "$TMPFILE" - kubectl delete -f src/service/ray-service.yaml + kubectl delete svc -n ingress-nginx ingress-nginx-controller fi @@ -21,7 +22,6 @@ targets=( "module.data_addons" "module.eks_blueprints_addons" "module.eks" - "module.vpc" ) # Destroy modules in sequence @@ -41,7 +41,7 @@ echo "Destroying Load Balancers..." for arn in $(aws resourcegroupstaggingapi get-resources \ --resource-type-filters elasticloadbalancing:loadbalancer \ - --tag-filters "Key=elbv2.k8s.aws/cluster,Values=jark-stack" \ + --tag-filters "Key=elbv2.k8s.aws/cluster,Values=${STACK_NAME}" \ --query 'ResourceTagMappingList[].ResourceARN' \ --output text); do \ aws elbv2 delete-load-balancer --load-balancer-arn "$arn"; \ @@ -50,7 +50,7 @@ for arn in $(aws resourcegroupstaggingapi get-resources \ echo "Destroying Target Groups..." for arn in $(aws resourcegroupstaggingapi get-resources \ --resource-type-filters elasticloadbalancing:targetgroup \ - --tag-filters "Key=elbv2.k8s.aws/cluster,Values=jark-stack" \ + --tag-filters "Key=elbv2.k8s.aws/cluster,Values=${STACK_NAME}" \ --query 'ResourceTagMappingList[].ResourceARN' \ --output text); do \ aws elbv2 delete-target-group --target-group-arn "$arn"; \ @@ -58,11 +58,22 @@ for arn in $(aws resourcegroupstaggingapi get-resources \ echo "Destroying Security Groups..." for sg in $(aws ec2 describe-security-groups \ - --filters "Name=tag:elbv2.k8s.aws/cluster,Values=jark-stack" \ + --filters "Name=tag:elbv2.k8s.aws/cluster,Values=${STACK_NAME}" \ --query 'SecurityGroups[].GroupId' --output text); do \ aws ec2 delete-security-group --group-id "$sg"; \ done +## Destroy to VPC module +echo "Destroying VPC related resources..." +target="module.vpc" +destroy_output=$(terraform destroy -target="$target" -var="region=$region" -auto-approve 2>&1 | tee /dev/tty) +if [[ ${PIPESTATUS[0]} -eq 0 && $destroy_output == *"Destroy complete"* ]]; then + echo "SUCCESS: Terraform destroy of $target completed successfully" +else + echo "FAILED: Terraform destroy of $target failed" + exit 1 +fi + ## Final destroy to catch any remaining resources echo "Destroying remaining resources..." destroy_output=$(terraform destroy -var="region=$region" -auto-approve 2>&1 | tee /dev/tty) From 49a1d5623f3fd7e7496b3311a536277f17b3ae26 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Thu, 11 Jul 2024 14:27:40 +0800 Subject: [PATCH 16/16] feat: added genai-perf tool with instructions --- .../nvidia-nim/genaiperf-deploy.yaml | 24 ++++++++ .../gen-ai/inference/nvidia-nim-llama3.md | 56 +++++++++++++++++++ 2 files changed, 80 insertions(+) create mode 100644 gen-ai/inference/nvidia-nim/genaiperf-deploy.yaml diff --git a/gen-ai/inference/nvidia-nim/genaiperf-deploy.yaml b/gen-ai/inference/nvidia-nim/genaiperf-deploy.yaml new file mode 100644 index 000000000..3151c4a97 --- /dev/null +++ b/gen-ai/inference/nvidia-nim/genaiperf-deploy.yaml @@ -0,0 +1,24 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: tritonserver-deployment +spec: + selector: + matchLabels: + app: tritonserver + template: + metadata: + labels: + app: tritonserver + spec: + containers: + - name: tritonserver + image: nvcr.io/nvidia/tritonserver:24.06-py3-sdk + command: ["/bin/bash", "-c", "--"] + args: ["while true; do sleep 30; done;"] + env: + - name: NVIDIA_VISIBLE_DEVICES + value: all + resources: + limits: + nvidia.com/gpu: 1 diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index b8d7abaeb..e4cf9dfa4 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -266,6 +266,62 @@ By applying these optimizations, TensorRT can significantly accelerate LLM infer ```

    +## Performance Testing with NVIDIA GenAI-Perf Tool + +[GenAI-Perf](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/client/src/c%2B%2B/perf_analyzer/genai-perf/README.html) is a command line tool for measuring the throughput and latency of generative AI models as served through an inference server. + +GenAI-Perf can be used as standard tool to benchmark with other models deployed with inference server. But this tool requires a GPU. To make it easier, we provide you a pre-configured manifest `genaiperf-deploy.yaml` to run the tool. + +```bash +cd gen-ai/inference/nvidia-nim +kubectl apply -f genaiperf-deploy.yaml +``` + +Once the pod is ready with running status `1/1`, can execute into the pod. +```bash +export POD_NAME=$(kubectl get po -l app=tritonserver -ojsonpath='{.items[0].metadata.name}') +kubectl exec -it $POD_NAME -- bash +``` +Run the testing to the deployed NIM Llama3 model +```bash +genai-perf \ + -m meta/llama3-8b-instruct \ + --service-kind openai \ + --endpoint v1/completions \ + --endpoint-type completions \ + --num-prompts 100 \ + --random-seed 123 \ + --synthetic-input-tokens-mean 200 \ + --synthetic-input-tokens-stddev 0 \ + --output-tokens-mean 100 \ + --output-tokens-stddev 0 \ + --tokenizer hf-internal-testing/llama-tokenizer \ + --concurrency 10 \ + --measurement-interval 4000 \ + --profile-export-file my_profile_export.json \ + --url nim-llm.nim:8000 +``` +You should see similar output like the following + +```bash +2024-07-11 03:32 [INFO] genai_perf.parser:166 - Model name 'meta/llama3-8b-instruct' cannot be used to create artifact directory. Instead, 'meta_llama3-8b-instruct' will be used. +2024-07-11 03:32 [INFO] genai_perf.wrapper:137 - Running Perf Analyzer : 'perf_analyzer -m meta/llama3-8b-instruct --async --input-data artifacts/meta_llama3-8b-instruct-openai-completions-concurrency10/llm_inputs.json --endpoint v1/completions --service-kind openai -u nim-llm.nim:8000 --measurement-interval 4000 --stability-percentage 999 --profile-export-file artifacts/meta_llama3-8b-instruct-openai-completions-concurrency10/my_profile_export.json -i http --concurrency-range 10' + LLM Metrics +┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Statistic ┃ avg ┃ min ┃ max ┃ p99 ┃ p90 ┃ p75 ┃ +┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ Request latency (ns) │ 3,934,624,446 │ 3,897,758,114 │ 3,936,987,882 │ 3,936,860,185 │ 3,936,429,317 │ 3,936,333,682 │ +│ Num output token │ 112 │ 105 │ 119 │ 119 │ 117 │ 115 │ +│ Num input token │ 200 │ 200 │ 200 │ 200 │ 200 │ 200 │ +└──────────────────────┴───────────────┴───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘ +Output token throughput (per sec): 284.64 +Request throughput (per sec): 2.54 +``` +You should be able to see the [metrics](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/client/src/c%2B%2B/perf_analyzer/genai-perf/README.html#metrics) that genai-perf collects, including Request latency, Out token throughput, Request throughput. + +To understand the command line options, please refer to [this documentation](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/client/src/c%2B%2B/perf_analyzer/genai-perf/README.html#command-line-options). + + ## Observability As part of this blueprint, we have also deployed the Kube Prometheus stack, which provides Prometheus server and Grafana deployments for monitoring and observability.
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sequence +for target in "${targets[@]}" +do + echo "Applying module $target..." + apply_output=$(terraform apply -target="$target" -auto-approve 2>&1 | tee /dev/tty) + if [[ ${PIPESTATUS[0]} -eq 0 && $apply_output == *"Apply complete"* ]]; then + echo "SUCCESS: Terraform apply of $target completed successfully" + else + echo "FAILED: Terraform apply of $target failed" + exit 1 + fi +done + +# Final apply to catch any remaining resources +echo "Applying remaining resources..." +apply_output=$(terraform apply -auto-approve 2>&1 | tee /dev/tty) +if [[ ${PIPESTATUS[0]} -eq 0 && $apply_output == *"Apply complete"* ]]; then + echo "SUCCESS: Terraform apply of all modules completed successfully" +else + echo "FAILED: Terraform apply of all modules failed" + exit 1 +fi diff --git a/gen-ai/nvidia-nims/main.tf b/gen-ai/nvidia-nims/main.tf new file mode 100644 index 000000000..f93511951 --- /dev/null +++ b/gen-ai/nvidia-nims/main.tf @@ -0,0 +1,51 @@ +provider "aws" { + region = local.region +} + +# ECR always authenticates with `us-east-1` region +# Docs -> https://docs.aws.amazon.com/AmazonECR/latest/public/public-registries.html +provider "aws" { + alias = "ecr" + region = "us-east-1" +} + +provider "kubernetes" { + host = module.eks.cluster_endpoint + cluster_ca_certificate = base64decode(module.eks.cluster_certificate_authority_data) + token = data.aws_eks_cluster_auth.this.token +} + +provider "helm" { + kubernetes { + host = module.eks.cluster_endpoint + cluster_ca_certificate = base64decode(module.eks.cluster_certificate_authority_data) + token = data.aws_eks_cluster_auth.this.token + } +} +provider "kubectl" { + apply_retry_count = 30 + host = module.eks.cluster_endpoint + cluster_ca_certificate = base64decode(module.eks.cluster_certificate_authority_data) + token = data.aws_eks_cluster_auth.this.token + load_config_file = false +} + +data "aws_eks_cluster_auth" "this" { + name = module.eks.cluster_name +} + +data "aws_availability_zones" "available" {} + +data "aws_ecrpublic_authorization_token" "token" { + provider = aws.ecr +} + +locals { + name = var.name + region = var.region + azs = slice(data.aws_availability_zones.available.names, 0, 2) + tags = { + Blueprint = local.name + GithubRepo = "github.com/awslabs/data-on-eks" + } +} diff --git a/gen-ai/nvidia-nims/outputs.tf b/gen-ai/nvidia-nims/outputs.tf new file mode 100644 index 000000000..08cf6f38d --- /dev/null +++ b/gen-ai/nvidia-nims/outputs.tf @@ -0,0 +1,19 @@ +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 "s3_bucket_name" { +# description = "The name of the S3 bucket." +# value = module.s3_bucket.s3_bucket_id +# } + +# output "s3_bucket_region" { +# description = "The AWS region this bucket resides in." +# value = module.s3_bucket.s3_bucket_region +# } + +output "grafana_secret_name" { + description = "The name of the secret containing the Grafana admin password." + value = aws_secretsmanager_secret.grafana.name +} diff --git a/gen-ai/nvidia-nims/variables.tf b/gen-ai/nvidia-nims/variables.tf new file mode 100644 index 000000000..157edff13 --- /dev/null +++ b/gen-ai/nvidia-nims/variables.tf @@ -0,0 +1,49 @@ +variable "name" { + description = "Name of the VPC and EKS Cluster" + default = "nvidia-nims" + type = string +} + +# NOTE: Trainium and Inferentia are only available in us-west-2 and us-east-1 regions +variable "region" { + description = "region" + default = "us-west-2" + type = string +} + +variable "eks_cluster_version" { + description = "EKS Cluster version" + default = "1.29" + type = string +} + +# VPC with 2046 IPs (10.1.0.0/21) and 2 AZs +variable "vpc_cidr" { + description = "VPC CIDR. This should be a valid private (RFC 1918) CIDR range" + default = "10.1.0.0/21" + type = string +} + +# RFC6598 range 100.64.0.0/10 +# Note you can only /16 range to VPC. You can add multiples of /16 if required +variable "secondary_cidr_blocks" { + description = "Secondary CIDR blocks to be attached to VPC" + default = ["100.64.0.0/16"] + type = list(string) +} + +#------------------------------------------------------------------- +# Instructions for Securely Setting the NVIDIA NGC API key +# ------------------------------------------------------------------- +# 1. Obtain your NVIDIA NGC API key from https://docs.nvidia.com/ai-enterprise/deployment-guide-spark-rapids-accelerator/0.1.0/appendix-ngc.html +# 2. Before running 'terraform apply', set the environment variable: +# * Linux/macOS: +# export TF_VAR_ngc_api_key= +# 3. Now you can safely run 'terraform apply' +#------------------------------------------------------------------- +variable "ngc_api_key" { + description = "NGC API Key" + type = string + sensitive = true +} + diff --git a/gen-ai/nvidia-nims/versions.tf b/gen-ai/nvidia-nims/versions.tf new file mode 100644 index 000000000..97b9a9e87 --- /dev/null +++ b/gen-ai/nvidia-nims/versions.tf @@ -0,0 +1,37 @@ +terraform { + required_version = ">= 1.0.0" + + required_providers { + aws = { + source = "hashicorp/aws" + version = ">= 3.72" + } + kubernetes = { + source = "hashicorp/kubernetes" + version = ">= 2.10" + } + helm = { + source = "hashicorp/helm" + version = ">= 2.4.1" + } + kubectl = { + source = "gavinbunney/kubectl" + version = ">= 1.14" + } + random = { + source = "hashicorp/random" + version = ">= 3.1" + } + null = { + source = "hashicorp/null" + version = ">= 3.1" + } + } + + # ## Used for end-to-end testing on project; update to suit your needs + # backend "s3" { + # bucket = "doeks-github-actions-e2e-test-state" + # region = "us-west-2" + # key = "e2e/nvidia-triton-server/terraform.tfstate" + # } +} diff --git a/gen-ai/nvidia-nims/vpc.tf b/gen-ai/nvidia-nims/vpc.tf new file mode 100644 index 000000000..59c3da89c --- /dev/null +++ b/gen-ai/nvidia-nims/vpc.tf @@ -0,0 +1,53 @@ +locals { + # Routable Private subnets only for Private NAT Gateway -> Transit Gateway -> Second VPC for overlapping CIDRs + # e.g., var.vpc_cidr = "10.1.0.0/21" => output: ["10.1.0.0/24", "10.1.1.0/24"] => 256-2 = 254 usable IPs per subnet/AZ + private_subnets = [for k, v in local.azs : cidrsubnet(var.vpc_cidr, 3, k)] + # Routable Public subnets with NAT Gateway and Internet Gateway + # e.g., var.vpc_cidr = "10.1.0.0/21" => output: ["10.1.2.0/26", "10.1.2.64/26"] => 64-2 = 62 usable IPs per subnet/AZ + public_subnets = [for k, v in local.azs : cidrsubnet(var.vpc_cidr, 5, k + 8)] + # RFC6598 range 100.64.0.0/16 for EKS Data Plane for two subnets(32768 IPs per Subnet) across two AZs for EKS Control Plane ENI + Nodes + Pods + # e.g., var.secondary_cidr_blocks = "100.64.0.0/16" => output: ["100.64.0.0/17", "100.64.128.0/17"] => 32768-2 = 32766 usable IPs per subnet/AZ + secondary_ip_range_private_subnets = [for k, v in local.azs : cidrsubnet(element(var.secondary_cidr_blocks, 0), 1, k)] +} + +#--------------------------------------------------------------- +# VPC +#--------------------------------------------------------------- +# WARNING: This VPC module includes the creation of an Internet Gateway and NAT Gateway, which simplifies cluster deployment and testing, primarily intended for sandbox accounts. +# IMPORTANT: For preprod and prod use cases, it is crucial to consult with your security team and AWS architects to design a private infrastructure solution that aligns with your security requirements + +module "vpc" { + source = "terraform-aws-modules/vpc/aws" + version = "~> 5.0" + + name = local.name + cidr = var.vpc_cidr + azs = local.azs + + # Secondary CIDR block attached to VPC for EKS Control Plane ENI + Nodes + Pods + secondary_cidr_blocks = var.secondary_cidr_blocks + + # 1/ EKS Data Plane secondary CIDR blocks for two subnets across two AZs for EKS Control Plane ENI + Nodes + Pods + # 2/ Two private Subnets with RFC1918 private IPv4 address range for Private NAT + NLB + Airflow + EC2 Jumphost etc. + private_subnets = concat(local.private_subnets, local.secondary_ip_range_private_subnets) + + # ------------------------------ + # Optional Public Subnets for NAT and IGW for PoC/Dev/Test environments + # Public Subnets can be disabled while deploying to Production and use Private NAT + TGW + public_subnets = local.public_subnets + enable_nat_gateway = true + single_nat_gateway = true + #------------------------------- + + public_subnet_tags = { + "kubernetes.io/role/elb" = 1 + } + + private_subnet_tags = { + "kubernetes.io/role/internal-elb" = 1 + # Tags subnets for Karpenter auto-discovery + "karpenter.sh/discovery" = local.name + } + + tags = local.tags +} From d9ce21dfc6bd8b98227a32d3cb7de0fed0a3aa0a Mon Sep 17 00:00:00 2001 From: hustshawn Date: Sun, 30 Jun 2024 16:47:07 +0800 Subject: [PATCH 02/16] feat: added website docs for nvidia-nims pattern --- gen-ai/nvidia-nims/README.md | 14 +- .../gen-ai/inference/img/nim-architecture.png | Bin 0 -> 160731 bytes .../gen-ai/inference/nvidia-nim-llama3.md | 133 ++++++++++++++++++ 3 files changed, 140 insertions(+), 7 deletions(-) create mode 100644 website/docs/gen-ai/inference/img/nim-architecture.png create mode 100644 website/docs/gen-ai/inference/nvidia-nim-llama3.md diff --git a/gen-ai/nvidia-nims/README.md b/gen-ai/nvidia-nims/README.md index fe77a03f6..094a7f7f8 100644 --- a/gen-ai/nvidia-nims/README.md +++ b/gen-ai/nvidia-nims/README.md @@ -4,10 +4,10 @@ This guide provides instructions for deploying NVIDIA NIM (NVIDIA Inference Micr ## Prerequisites -- An active AWS account with appropriate permissions -- AWS CLI installed and configured -- kubectl installed and configured to work with your EKS cluster -- Helm installed +- An active AWS account with admin equivalent permissions +- [aws cli](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html) +- [kubectl](https://Kubernetes.io/docs/tasks/tools/) +- [Terraform](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli) installed - NVIDIA NGC account and API key ## Setup @@ -25,6 +25,7 @@ export TF_VAR_ngc_api_key= Run the installation script: ```bash +cd ai-ml/trainimum-inferentia ./install.sh ``` @@ -45,7 +46,7 @@ You should see output similar to the following: Once all pods in `nim` namespace is ready with `1/1` status, use below command to verify it's ready to serve the traffic. -``` +```bash export INGRESS_URL=$(kubectl get ingress -n nim -o jsonpath='{.items[0].status.loadBalancer.ingress[0].hostname}') curl -X 'POST' \ @@ -71,11 +72,10 @@ kubectl scale sts nim-llm -n --replicas=2 To remove all resources created by this deployment, run: -``` +```bash ./cleanup.sh ``` - ## Additional Resources - [NVIDIA NIM Documentation](https://docs.nvidia.com/nim/) 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 0000000000000000000000000000000000000000..4b65a235b919511c608b30df449203453d856148 GIT binary patch literal 160731 zcmce;Wmp~CvIU9;0)*fM5AN>n?z(US!Cit|aCdiimjx_bgS)%CyTfCjyU)32-}m=@ z??->#t9y2L^{i22)~u?zLKNi0zrbO`fq{X2k(3Zo0t16o``CzJAwJG*Mf4qi?7$tA z#D%~r$8Zlneux;UOB&0{f>C`O!-9banSnw5)#PKt{@6Zl%?1a9{Mdv4buJs?-&Y~k zvLXL-O!QYn*O`41FfaiyNfAL67x0r+lX_!uSMn$J42P#*DvkDP@TAev3Sb%{AwOiQ z8Ogp``kACB@@g|%RGz2Wv|smGjCy>lay-|p+Z%HUKYu{{frIeplMGWg(|2DYB3Ma^ zA~j3N3blJqo8}G^{{53R&sZ+%Usk}QnM${#lAN zXi#tv&_2GMoBjg(w&s>*7XLgi@Lhr82Nujn3xWS!K;(vkgXTGDZe?lu??(va{5C@P z`*Hu%nBxSA0_~#u_-bn7zix+v2!Kdr{I7Ql2yRphXDf8}R1Qn~!T%-Af8GVB{^vi( z{}QwZ(MJgUYYQFy7rK9o{;vifLPGxcjZpvp33+pe_0s{-MzTD zIB8mMK1-#q{B`rGe${&Z5{2>~0wXPWLuI<|rC4uw_6GLk*cur4UZtd9Z5k!%1jgdI 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    [
    "100.64.0.0/16"
    ]
    | no | -| [vpc\_cidr](#input\_vpc\_cidr) | VPC CIDR. This should be a valid private (RFC 1918) CIDR range | `string` | `"10.1.0.0/21"` | no | - -## Outputs - -| Name | Description | -|------|-------------| -| [configure\_kubectl](#output\_configure\_kubectl) | Configure kubectl: make sure you're logged in with the correct AWS profile and run the following command to update your kubeconfig | -| [grafana\_secret\_name](#output\_grafana\_secret\_name) | The name of the secret containing the Grafana admin password. | - diff --git a/gen-ai/nvidia-nims/addons.tf b/gen-ai/nvidia-nims/addons.tf deleted file mode 100644 index 2ad774caf..000000000 --- a/gen-ai/nvidia-nims/addons.tf +++ /dev/null @@ -1,314 +0,0 @@ -#--------------------------------------------------------------- -# GP3 Encrypted Storage Class -#--------------------------------------------------------------- -resource "kubernetes_annotations" "disable_gp2" { - annotations = { - "storageclass.kubernetes.io/is-default-class" : "false" - } - api_version = "storage.k8s.io/v1" - kind = "StorageClass" - metadata { - name = "gp2" - } - force = true - - depends_on = [module.eks.eks_cluster_id] -} - -resource "kubernetes_storage_class" "default_gp3" { - metadata { - name = "gp3" - annotations = { - "storageclass.kubernetes.io/is-default-class" : "true" - } - } - - storage_provisioner = "ebs.csi.aws.com" - reclaim_policy = "Delete" - allow_volume_expansion = true - volume_binding_mode = "WaitForFirstConsumer" - parameters = { - fsType = "ext4" - encrypted = true - type = "gp3" - } - - depends_on = [kubernetes_annotations.disable_gp2] -} - -#--------------------------------------------------------------- -# IRSA for EBS CSI Driver -#--------------------------------------------------------------- -module "ebs_csi_driver_irsa" { - source = "terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks" - version = "~> 5.20" - role_name_prefix = format("%s-%s-", local.name, "ebs-csi-driver") - attach_ebs_csi_policy = true - oidc_providers = { - main = { - provider_arn = module.eks.oidc_provider_arn - namespace_service_accounts = ["kube-system:ebs-csi-controller-sa"] - } - } - tags = local.tags -} - -#--------------------------------------------------------------- -# EKS Blueprints Addons -#--------------------------------------------------------------- -module "eks_blueprints_addons" { - source = "aws-ia/eks-blueprints-addons/aws" - version = "~> 1.2" - - cluster_name = module.eks.cluster_name - cluster_endpoint = module.eks.cluster_endpoint - cluster_version = module.eks.cluster_version - oidc_provider_arn = module.eks.oidc_provider_arn - - #--------------------------------------- - # Amazon EKS Managed Add-ons - #--------------------------------------- - eks_addons = { - aws-ebs-csi-driver = { - service_account_role_arn = module.ebs_csi_driver_irsa.iam_role_arn - } - coredns = {} - kube-proxy = {} - # VPC CNI uses worker node IAM role policies - vpc-cni = {} - } - - enable_aws_efs_csi_driver = true - - #--------------------------------------- - # AWS Load Balancer Controller Add-on - #--------------------------------------- - enable_aws_load_balancer_controller = true - # turn off the mutating webhook for services because we are using - # service.beta.kubernetes.io/aws-load-balancer-type: external - aws_load_balancer_controller = { - set = [{ - name = "enableServiceMutatorWebhook" - value = "false" - }] - } - - #--------------------------------------- - # Ingress Nginx Add-on - #--------------------------------------- - enable_ingress_nginx = true - ingress_nginx = { - values = [templatefile("${path.module}/helm-values/ingress-nginx-values.yaml", {})] - } - - #--------------------------------------- - # Karpenter Autoscaler for EKS Cluster - #--------------------------------------- - enable_karpenter = true - karpenter_enable_spot_termination = true - karpenter_node = { - iam_role_additional_policies = { - AmazonSSMManagedInstanceCore = "arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" - } - } - karpenter = { - chart_version = "0.35.5" - repository_username = data.aws_ecrpublic_authorization_token.token.user_name - repository_password = data.aws_ecrpublic_authorization_token.token.password - } - - #--------------------------------------- - # Prommetheus and Grafana stack - #--------------------------------------- - #--------------------------------------------------------------- - # 1- Grafana port-forward `kubectl port-forward svc/kube-prometheus-stack-grafana 8080:80 -n kube-prometheus-stack` - # 2- Grafana Admin user: admin - # 3- Get sexret name from Terrafrom output: `terraform output grafana_secret_name` - # 3- Get admin user password: `aws secretsmanager get-secret-value --secret-id --region $AWS_REGION --query "SecretString" --output text` - #--------------------------------------------------------------- - enable_kube_prometheus_stack = true - kube_prometheus_stack = { - values = [ - templatefile("${path.module}/helm-values/kube-prometheus.yaml", { - storage_class_type = kubernetes_storage_class.default_gp3.id - }) - ] - chart_version = "48.1.1" - set_sensitive = [ - { - name = "grafana.adminPassword" - value = data.aws_secretsmanager_secret_version.admin_password_version.secret_string - } - ], - } - #--------------------------------------- - # Install the nim-llm, and you can customize it from the - # helm chart values under ./helm-values/nim-llm.yaml - #--------------------------------------- - helm_releases = { - "nim-llm" = { - chart = "${path.module}/nim-llm" - create_namespace = true - namespace = "nim" - values = [ - templatefile( - "${path.module}/helm-values/nim-llm.yaml", { - ngc_api_key = var.ngc_api_key - pvc_name = kubernetes_persistent_volume_claim_v1.efs_pvc.metadata[0].name - }) - ] - } - } - -} - -#--------------------------------------------------------------- -# Data on EKS Kubernetes Addons -#--------------------------------------------------------------- -module "data_addons" { - source = "aws-ia/eks-data-addons/aws" - version = "~> 1.32.0" # ensure to update this to the latest/desired version - - oidc_provider_arn = module.eks.oidc_provider_arn - - #--------------------------------------------------------------- - # NVIDIA Device Plugin Add-on - #--------------------------------------------------------------- - enable_nvidia_device_plugin = true - nvidia_device_plugin_helm_config = { - version = "v0.14.5" - name = "nvidia-device-plugin" - values = [ - <<-EOT - gfd: - enabled: true - nfd: - worker: - tolerations: - - key: nvidia.com/gpu - operator: Exists - effect: NoSchedule - - operator: "Exists" - EOT - ] - } - - #--------------------------------------------------------------- - # Karpenter Resources Add-on - #--------------------------------------------------------------- - enable_karpenter_resources = true - karpenter_resources_helm_config = { - g5-gpu-karpenter = { - values = [ - <<-EOT - name: g5-gpu-karpenter - clusterName: ${module.eks.cluster_name} - ec2NodeClass: - karpenterRole: ${split("/", module.eks_blueprints_addons.karpenter.node_iam_role_arn)[1]} - subnetSelectorTerms: - id: ${module.vpc.private_subnets[2]} - securityGroupSelectorTerms: - tags: - Name: ${module.eks.cluster_name}-node - instanceStorePolicy: RAID0 - - nodePool: - labels: - - type: karpenter - - NodeGroupType: g5-gpu-karpenter - taints: - - key: nvidia.com/gpu - value: "Exists" - effect: "NoSchedule" - requirements: - - key: "karpenter.k8s.aws/instance-family" - operator: In - values: ["g5"] - - key: "karpenter.k8s.aws/instance-size" - operator: In - values: [ "2xlarge", "4xlarge", "8xlarge", "12xlarge", "24xlarge"] - - key: "kubernetes.io/arch" - operator: In - values: ["amd64"] - - key: "karpenter.sh/capacity-type" - operator: In - values: ["spot", "on-demand"] - limits: - cpu: 1000 - disruption: - consolidationPolicy: WhenEmpty - consolidateAfter: 180s - expireAfter: 720h - weight: 100 - EOT - ] - } - x86-cpu-karpenter = { - values = [ - <<-EOT - name: x86-cpu-karpenter - clusterName: ${module.eks.cluster_name} - ec2NodeClass: - karpenterRole: ${split("/", module.eks_blueprints_addons.karpenter.node_iam_role_arn)[1]} - subnetSelectorTerms: - id: ${module.vpc.private_subnets[3]} - securityGroupSelectorTerms: - tags: - Name: ${module.eks.cluster_name}-node - instanceStorePolicy: RAID0 - - nodePool: - labels: - - type: karpenter - - NodeGroupType: x86-cpu-karpenter - requirements: - - key: "karpenter.k8s.aws/instance-family" - operator: In - values: ["m5"] - - key: "karpenter.k8s.aws/instance-size" - operator: In - values: [ "xlarge", "2xlarge", "4xlarge", "8xlarge"] - - key: "kubernetes.io/arch" - operator: In - values: ["amd64"] - - key: "karpenter.sh/capacity-type" - operator: In - values: ["spot", "on-demand"] - limits: - cpu: 1000 - disruption: - consolidationPolicy: WhenEmpty - consolidateAfter: 180s - expireAfter: 720h - weight: 100 - EOT - ] - } - } -} - -#--------------------------------------------------------------- -# Grafana Admin credentials resources -# Login to AWS secrets manager with the same role as Terraform to extract the Grafana admin password with the secret name as "grafana" -#--------------------------------------------------------------- -data "aws_secretsmanager_secret_version" "admin_password_version" { - secret_id = aws_secretsmanager_secret.grafana.id - depends_on = [aws_secretsmanager_secret_version.grafana] -} - -resource "random_password" "grafana" { - length = 16 - special = true - override_special = "@_" -} - -#tfsec:ignore:aws-ssm-secret-use-customer-key -resource "aws_secretsmanager_secret" "grafana" { - name_prefix = "${local.name}-oss-grafana" - recovery_window_in_days = 0 # Set to zero for this example to force delete during Terraform destroy -} - -resource "aws_secretsmanager_secret_version" "grafana" { - secret_id = aws_secretsmanager_secret.grafana.id - secret_string = random_password.grafana.result -} diff --git a/gen-ai/nvidia-nims/cleanup.sh b/gen-ai/nvidia-nims/cleanup.sh deleted file mode 100755 index 96ea3fc3c..000000000 --- a/gen-ai/nvidia-nims/cleanup.sh +++ /dev/null @@ -1,75 +0,0 @@ -#!/bin/bash - -read -p "Enter the region: " region -export AWS_DEFAULT_REGION=$region - -echo "Destroying Ingress and LoadBalancer type service..." - -# Delete the Ingress/SVC before removing the addons -TMPFILE=$(mktemp) -terraform output -raw configure_kubectl > "$TMPFILE" -# check if TMPFILE contains the string "No outputs found" -if [[ ! $(cat $TMPFILE) == *"No outputs found"* ]]; then - echo "No outputs found, skipping kubectl delete" - source "$TMPFILE" - kubectl delete ingress -n nim --all - kubectl delete svc -n ingress-nginx --all -fi - - -# List of Terraform modules to apply in sequence -targets=( - "module.data_addons" - "module.eks_blueprints_addons" - "module.eks" - "module.vpc" -) - -# Destroy modules in sequence -for target in "${targets[@]}" -do - echo "Destroying module $target..." - destroy_output=$(terraform destroy -target="$target" -var="region=$region" -auto-approve 2>&1 | tee /dev/tty) - if [[ ${PIPESTATUS[0]} -eq 0 && $destroy_output == *"Destroy complete"* ]]; then - echo "SUCCESS: Terraform destroy of $target completed successfully" - else - echo "FAILED: Terraform destroy of $target failed" - exit 1 - fi -done - -echo "Destroying Load Balancers..." - -for arn in $(aws resourcegroupstaggingapi get-resources \ - --resource-type-filters elasticloadbalancing:loadbalancer \ - --tag-filters "Key=elbv2.k8s.aws/cluster,Values=jark-stack" \ - --query 'ResourceTagMappingList[].ResourceARN' \ - --output text); do \ - aws elbv2 delete-load-balancer --load-balancer-arn "$arn"; \ - done - -echo "Destroying Target Groups..." -for arn in $(aws resourcegroupstaggingapi get-resources \ - --resource-type-filters elasticloadbalancing:targetgroup \ - --tag-filters "Key=elbv2.k8s.aws/cluster,Values=jark-stack" \ - --query 'ResourceTagMappingList[].ResourceARN' \ - --output text); do \ - aws elbv2 delete-target-group --target-group-arn "$arn"; \ - done - -echo "Destroying Security Groups..." -for sg in $(aws ec2 describe-security-groups \ - --filters "Name=tag:elbv2.k8s.aws/cluster,Values=jark-stack" \ - --query 'SecurityGroups[].GroupId' --output text); do \ - aws ec2 delete-security-group --group-id "$sg"; \ - done - -## Final destroy to catch any remaining resources -echo "Destroying remaining resources..." -destroy_output=$(terraform destroy -var="region=$region" -auto-approve 2>&1 | tee /dev/tty) -if [[ ${PIPESTATUS[0]} -eq 0 && $destroy_output == *"Destroy complete"* ]]; then - echo "SUCCESS: Terraform destroy of all modules completed successfully" -else - echo "FAILED: Terraform destroy of all modules failed" - exit 1 -fi diff --git a/gen-ai/nvidia-nims/efs.tf b/gen-ai/nvidia-nims/efs.tf deleted file mode 100644 index f49fafcca..000000000 --- a/gen-ai/nvidia-nims/efs.tf +++ /dev/null @@ -1,72 +0,0 @@ -#--------------------------------------------------------------- -# EFS -#--------------------------------------------------------------- -module "efs" { - source = "terraform-aws-modules/efs/aws" - version = "~> 1.6" - - creation_token = local.name - name = local.name - - - # Mount targets / security group - mount_targets = { - for k, v in zipmap(local.azs, slice(module.vpc.private_subnets, length(module.vpc.private_subnets) - 2, length(module.vpc.private_subnets))) : k => { subnet_id = v } - } - security_group_description = "${local.name} EFS security group" - security_group_vpc_id = module.vpc.vpc_id - security_group_rules = { - vpc = { - # relying on the defaults provided for EFS/NFS (2049/TCP + ingress) - description = "NFS ingress from VPC private subnets" - cidr_blocks = module.vpc.private_subnets_cidr_blocks - } - } - - tags = local.tags -} - -resource "kubernetes_storage_class_v1" "efs" { - metadata { - name = "efs" - } - - storage_provisioner = "efs.csi.aws.com" - parameters = { - provisioningMode = "efs-ap" # Dynamic provisioning - fileSystemId = module.efs.id - directoryPerms = "777" - } - - mount_options = [ - "iam" - ] - - depends_on = [ - module.eks_blueprints_addons.aws_efs_csi_driver - ] -} - -resource "kubernetes_namespace" "nim" { - metadata { - name = "nim" - } - - depends_on = [module.eks] -} - -resource "kubernetes_persistent_volume_claim_v1" "efs_pvc" { - metadata { - name = "nim-llm-pvc" - namespace = "nim" - } - spec { - access_modes = ["ReadWriteMany"] - storage_class_name = kubernetes_storage_class_v1.efs.id - resources { - requests = { - storage = "50Gi" - } - } - } -} diff --git a/gen-ai/nvidia-nims/eks.tf b/gen-ai/nvidia-nims/eks.tf deleted file mode 100644 index 469947520..000000000 --- a/gen-ai/nvidia-nims/eks.tf +++ /dev/null @@ -1,118 +0,0 @@ -#--------------------------------------------------------------- -# EKS Cluster -#--------------------------------------------------------------- -module "eks" { - source = "terraform-aws-modules/eks/aws" - version = "~> 19.15" - - cluster_name = local.name - cluster_version = var.eks_cluster_version - - # if true, Your cluster API server is accessible from the internet. - # You can, optionally, limit the CIDR blocks that can access the public endpoint. - #WARNING: Avoid using this option (cluster_endpoint_public_access = true) in preprod or prod accounts. This feature is designed for sandbox accounts, simplifying cluster deployment and testing. - cluster_endpoint_public_access = true - - vpc_id = module.vpc.vpc_id - # Filtering only Secondary CIDR private subnets starting with "100.". - # Subnet IDs where the EKS Control Plane ENIs will be created - subnet_ids = compact([ - for subnet_id, cidr_block in zipmap(module.vpc.private_subnets, module.vpc.private_subnets_cidr_blocks) : - substr(cidr_block, 0, 4) == "100." ? subnet_id : null]) - - manage_aws_auth_configmap = true - aws_auth_roles = [ - # We need to add in the Karpenter node IAM role for nodes launched by Karpenter - { - rolearn = module.eks_blueprints_addons.karpenter.node_iam_role_arn - username = "system:node:{{EC2PrivateDNSName}}" - groups = [ - "system:bootstrappers", - "system:nodes", - ] - } - ] - #--------------------------------------- - # Note: This can further restricted to specific required for each Add-on and your application - #--------------------------------------- - # Extend cluster security group rules - cluster_security_group_additional_rules = { - ingress_nodes_ephemeral_ports_tcp = { - description = "Nodes on ephemeral ports" - protocol = "tcp" - from_port = 0 - to_port = 65535 - type = "ingress" - source_node_security_group = true - } - } - - node_security_group_additional_rules = { - # Allows Control Plane Nodes to talk to Worker nodes on all ports. - # Added this to simplify the example and further avoid issues with Add-ons communication with Control plane. - # This can be restricted further to specific port based on the requirement for each Add-on - # e.g., coreDNS 53, metrics-server 4443. - # Update this according to your security requirements if needed - ingress_cluster_to_node_all_traffic = { - description = "Cluster API to Nodegroup all traffic" - protocol = "-1" - from_port = 0 - to_port = 0 - type = "ingress" - source_cluster_security_group = true - } - } - - eks_managed_node_group_defaults = { - iam_role_additional_policies = { - # Not required, but used in the example to access the nodes to inspect mounted volumes - AmazonSSMManagedInstanceCore = "arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" - } - - ebs_optimized = true - # This block device is used only for root volume. Adjust volume according to your size. - # NOTE: Don't use this volume for ML workloads - block_device_mappings = { - xvda = { - device_name = "/dev/xvda" - ebs = { - volume_size = 100 - volume_type = "gp3" - } - } - } - } - - eks_managed_node_groups = { - # It's recommended to have a Managed Node group for hosting critical add-ons - # It's recommended to use Karpenter to place your workloads instead of using Managed Node groups - # You can leverage nodeSelector and Taints/tolerations to distribute workloads across Managed Node group or Karpenter nodes. - core_node_group = { - name = "core-node-group" - description = "EKS Core node group for hosting system add-ons" - # Filtering only Secondary CIDR private subnets starting with "100.". - # Subnet IDs where the nodes/node groups will be provisioned - subnet_ids = compact([ - for subnet_id, cidr_block in zipmap(module.vpc.private_subnets, module.vpc.private_subnets_cidr_blocks) : - substr(cidr_block, 0, 4) == "100." ? subnet_id : null] - ) - - # aws ssm get-parameters --names /aws/service/eks/optimized-ami/1.27/amazon-linux-2/recommended/image_id --region us-west-2 - ami_type = "AL2_x86_64" # Use this for Graviton AL2_ARM_64 - min_size = 2 - max_size = 8 - desired_size = 2 - - instance_types = ["m5.xlarge"] - - labels = { - WorkerType = "ON_DEMAND" - NodeGroupType = "core" - } - - tags = merge(local.tags, { - Name = "core-node-grp" - }) - } - } -} diff --git a/gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml b/gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml deleted file mode 100644 index 0b7eee5e4..000000000 --- a/gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml +++ /dev/null @@ -1,12 +0,0 @@ -controller: - service: - externalTrafficPolicy: "Local" - annotations: - service.beta.kubernetes.io/aws-load-balancer-type: external - service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip - service.beta.kubernetes.io/aws-load-balancer-backend-protocol: http - service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing - # service.beta.kubernetes.io/aws-load-balancer-scheme: internal # Private Load Balancer can only be accessed within the VPC - targetPorts: - http: http - https: http diff --git a/gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml b/gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml deleted file mode 100644 index 47e090743..000000000 --- a/gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml +++ /dev/null @@ -1,48 +0,0 @@ -prometheus: - prometheusSpec: - retention: 5h - scrapeInterval: 30s - evaluationInterval: 30s - scrapeTimeout: 10s - serviceMonitorSelectorNilUsesHelmValues: false # This is required to use the serviceMonitorSelector - storageSpec: - volumeClaimTemplate: - metadata: - name: data - spec: - storageClassName: ${storage_class_type} - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 50Gi -alertmanager: - enabled: false - -grafana: - enabled: true - defaultDashboardsEnabled: true -prometheus: - prometheusSpec: - retention: 5h - scrapeInterval: 30s - evaluationInterval: 30s - scrapeTimeout: 10s - serviceMonitorSelectorNilUsesHelmValues: false # This is required to use the serviceMonitorSelector - storageSpec: - volumeClaimTemplate: - metadata: - name: data - spec: - storageClassName: ${storage_class_type} - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 50Gi -alertmanager: - enabled: false - -grafana: - enabled: true - defaultDashboardsEnabled: true diff --git a/gen-ai/nvidia-nims/helm-values/nim-llm.yaml b/gen-ai/nvidia-nims/helm-values/nim-llm.yaml deleted file mode 100644 index a2dbd6d52..000000000 --- a/gen-ai/nvidia-nims/helm-values/nim-llm.yaml +++ /dev/null @@ -1,24 +0,0 @@ -image: - repository: nvcr.io/nim/meta/llama3-8b-instruct - tag: latest -model: - ngcAPIKey: ${ngc_api_key} -resources: - limits: - nvidia.com/gpu: 1 - requests: - nvidia.com/gpu: 1 -statefulSet: - enabled: true -persistence: - enabled: true - existingClaim: 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b/website/docs/gen-ai/inference/nvidia-nim-llama3.md new file mode 100644 index 000000000..c48d3e63e --- /dev/null +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -0,0 +1,133 @@ +--- +title: NVIDIA NIM Llama-3 Deployment on Amazon EKS +sidebar_position: 8 +--- +import CollapsibleContent from '../../../src/components/CollapsibleContent'; + +:::danger + +Note: Use of this Llama-3 Instruct model is governed by the Meta license. +In order to download the model weights and tokenizer, please visit the [website](https://huggingface.co/meta-llama/Meta-Llama-3-8B) and accept the license before requesting access. + +::: + +:::info + +We are actively enhancing this blueprint to incorporate improvements in observability, logging, and scalability aspects. +::: + +# NVIDIA NIM LLM Deployment on Amazon EKS + +## What is NVIDIA NIM? + +NVIDIA NIM makes it easy for IT and DevOps teams to self-host large language models (LLMs) in their own managed environments while still providing developers with industry standard APIs that allow them to build powerful copilots, chatbots, and AI assistants that can transform their business. Leveraging NVIDIA’s cutting-edge GPU acceleration and scalable deployment, NIM offers the fastest path to inference with unparalleled performance. + +## Why NIM? + +NIM abstracts away model inference internals such as execution engine and runtime operations. They are also the most performant option available whether it be with TRT-LLM, vLLM or others. + +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) + +## 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. + +1. NVIDIA NIM Container Images: NVIDIA NIM provides a streamlined approach to hosting LLM models like Llama3 within containerized environments. This allows customers to leverage their private models while ensuring seamless integration with existing infrastructure. We will bring detailed setup steps to NIM deployments. + +2. Karpenter for Instance-Level Scaling: Karpenter, an open-source node provisioning project, enables rapid and efficient scaling of Amazon EKS clusters at the instance level. This ensures that the model serving infrastructure can adapt to dynamic workload demands, optimizing resource utilization and cost-effectiveness. + +3. Spot instances: Considering LLMs are stateless, customers can leverage spot instances to significantly reduce costs. + +4. Amazon Elastic File System (EFS): Amazon EFS provides scalable, elastic file storage for use with Amazon EKS. It allows multiple pods to access the same file system concurrently, making it ideal for storing and sharing model artifacts, datasets, and other persistent data across the cluster. EFS automatically grows and shrinks as you add and remove files, eliminating the need for capacity planning and management. + +5. Terraform with EKS Blueprints: To streamline the deployment and management of this solution, we leverage Terraform and EKS Blueprints. This infrastructure-as-code approach enables automated provisioning of the entire stack, ensuring consistency, reproducibility, and efficient resource management. + +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. + +## Deploying the Solution + +### Prerequisites + +- An active AWS account with admin equivalent permissions +- [aws cli](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html) +- [kubectl](https://Kubernetes.io/docs/tasks/tools/) +- [Terraform](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli) installed +- NVIDIA NGC account and API key + +### Deploy + +Clone the repository + +```bash +git clone https://github.com/awslabs/data-on-eks.git +``` + +**1. Configure the NGC API Key** + +Retrieve your NGC API key from [NVIDIA](https://docs.nvidia.com/ai-enterprise/deployment-guide-spark-rapids-accelerator/0.1.0/appendix-ngc.html) and set it as an environment variable: + +```bash +export TF_VAR_ngc_api_key= +``` + +**2. Installation** + +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: + +Run the installation script: + +```bash +cd gen-ai/nvidia-nims +./install.sh +``` + +This process will take approximately 20 minutes to complete. + +**3. Verify the Installation** + +Check the status of your pods deployed + +```bash +kubectl get po -A +``` + +You should see output similar to the following: +![alt text](../../../../gen-ai/nvidia-nims/images/all-pod-svc.png) + +:::info +The `Llama3` model deployed is specified in `gen-ai/nvidia-nims/helm-values/nim-llm.yaml` with below config. Please visit https://build.nvidia.com/explore/discover to explore more. You may simply update this image configuration if you want to change to deploy another model. +::: + +```yaml +image: + repository: nvcr.io/nim/meta/llama3-8b-instruct + tag: latest +``` + +**4. Verify the deployed model** + +Once all pods in `nim` namespace is ready with `1/1` status, use below command to verify it's ready to serve the traffic. + +```bash +export INGRESS_URL=$(kubectl get ingress -n nim -o jsonpath='{.items[0].status.loadBalancer.ingress[0].hostname}') + +curl -X 'POST' \ +"http://$INGRESS_URL/v1/completions" \ +-H 'accept: application/json' \ +-H 'Content-Type: application/json' \ +-d '{ +"model": "meta/llama3-8b-instruct", +"prompt": "Once upon a time", +"max_tokens": 64 +}' +``` + +## Cleanup + +To remove all resources created by this deployment, run: + +```bash +./cleanup.sh +``` From 9d5c3d04383571b5f6d68b0ce9536c069e8f5f4d Mon Sep 17 00:00:00 2001 From: hustshawn Date: Sun, 30 Jun 2024 17:27:17 +0800 Subject: [PATCH 03/16] fix: fixed minor doc formating issue based on pre-commit suggestions --- gen-ai/nvidia-nims/.gitignore | 2 +- gen-ai/nvidia-nims/README.md | 2 +- gen-ai/nvidia-nims/cleanup.sh | 5 +++-- gen-ai/nvidia-nims/variables.tf | 1 - 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/gen-ai/nvidia-nims/.gitignore b/gen-ai/nvidia-nims/.gitignore index d6f8a9ea3..f120418f1 100644 --- a/gen-ai/nvidia-nims/.gitignore +++ b/gen-ai/nvidia-nims/.gitignore @@ -1,2 +1,2 @@ nim-llm/ -planfile \ No newline at end of file +planfile diff --git a/gen-ai/nvidia-nims/README.md b/gen-ai/nvidia-nims/README.md index 094a7f7f8..afe156851 100644 --- a/gen-ai/nvidia-nims/README.md +++ b/gen-ai/nvidia-nims/README.md @@ -151,4 +151,4 @@ To remove all resources created by this deployment, run: |------|-------------| | [configure\_kubectl](#output\_configure\_kubectl) | Configure kubectl: make sure you're logged in with the correct AWS profile and run the following command to update your kubeconfig | | [grafana\_secret\_name](#output\_grafana\_secret\_name) | The name of the secret containing the Grafana admin password. | - \ No newline at end of file + diff --git a/gen-ai/nvidia-nims/cleanup.sh b/gen-ai/nvidia-nims/cleanup.sh index f9c5daca6..96ea3fc3c 100755 --- a/gen-ai/nvidia-nims/cleanup.sh +++ b/gen-ai/nvidia-nims/cleanup.sh @@ -3,7 +3,7 @@ read -p "Enter the region: " region export AWS_DEFAULT_REGION=$region -echo "Destroying RayService..." +echo "Destroying Ingress and LoadBalancer type service..." # Delete the Ingress/SVC before removing the addons TMPFILE=$(mktemp) @@ -12,7 +12,8 @@ terraform output -raw configure_kubectl > "$TMPFILE" if [[ ! $(cat $TMPFILE) == *"No outputs found"* ]]; then echo "No outputs found, skipping kubectl delete" source "$TMPFILE" - kubectl delete -f src/service/ray-service.yaml + kubectl delete ingress -n nim --all + kubectl delete svc -n ingress-nginx --all fi diff --git a/gen-ai/nvidia-nims/variables.tf b/gen-ai/nvidia-nims/variables.tf index 157edff13..0189d2253 100644 --- a/gen-ai/nvidia-nims/variables.tf +++ b/gen-ai/nvidia-nims/variables.tf @@ -46,4 +46,3 @@ variable "ngc_api_key" { type = string sensitive = true } - From 2e5a2719f350a8068de4ad7e5024663898917ab2 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Sun, 30 Jun 2024 17:35:10 +0800 Subject: [PATCH 04/16] fix: fixed '' from the website doc HTML formatting based on CI result --- website/docs/gen-ai/inference/nvidia-nim-llama3.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index c48d3e63e..45b8b80df 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -74,7 +74,7 @@ export TF_VAR_ngc_api_key= **2. Installation** -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: +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: Run the installation script: @@ -97,7 +97,7 @@ You should see output similar to the following: ![alt text](../../../../gen-ai/nvidia-nims/images/all-pod-svc.png) :::info -The `Llama3` model deployed is specified in `gen-ai/nvidia-nims/helm-values/nim-llm.yaml` with below config. Please visit https://build.nvidia.com/explore/discover to explore more. You may simply update this image configuration if you want to change to deploy another model. +The `Llama3` model deployed is specified in `gen-ai/nvidia-nims/helm-values/nim-llm.yaml` with below config. Please visit to explore more. You may simply update this image configuration if you want to change to deploy another model. ::: ```yaml From 8af984649657093972155a36e241931f0e8bb17b Mon Sep 17 00:00:00 2001 From: hustshawn Date: Sun, 30 Jun 2024 18:15:37 +0800 Subject: [PATCH 05/16] fix: fixed the url format in website doc --- gen-ai/nvidia-nims/README.md | 6 ++++++ website/docs/gen-ai/inference/nvidia-nim-llama3.md | 8 +++++++- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/gen-ai/nvidia-nims/README.md b/gen-ai/nvidia-nims/README.md index afe156851..8ffb4b92c 100644 --- a/gen-ai/nvidia-nims/README.md +++ b/gen-ai/nvidia-nims/README.md @@ -60,6 +60,12 @@ curl -X 'POST' \ }' ``` +you will see similar output like the following + +```json +{"id":"cmpl-63a0b66aeda1440c8b6ca1ce3583b173","object":"text_completion","created":1719742336,"model":"meta/llama3-8b-instruct","choices":[{"index":0,"text":", there was a young man named Jack who lived in a small village at the foot of a vast and ancient forest. Jack was a curious and adventurous soul, always eager to explore the world beyond his village. One day, he decided to venture into the forest, hoping to discover its secrets.\nAs he wandered deeper into","logprobs":null,"finish_reason":"length","stop_reason":null}],"usage":{"prompt_tokens":5,"total_tokens":69,"completion_tokens":64}} +``` + ## Scaling You can scale up and down to number of replicas with following: diff --git a/website/docs/gen-ai/inference/nvidia-nim-llama3.md b/website/docs/gen-ai/inference/nvidia-nim-llama3.md index 45b8b80df..231ba08de 100644 --- a/website/docs/gen-ai/inference/nvidia-nim-llama3.md +++ b/website/docs/gen-ai/inference/nvidia-nim-llama3.md @@ -97,7 +97,7 @@ You should see output similar to the following: ![alt text](../../../../gen-ai/nvidia-nims/images/all-pod-svc.png) :::info -The `Llama3` model deployed is specified in `gen-ai/nvidia-nims/helm-values/nim-llm.yaml` with below config. Please visit to explore more. You may simply update this image configuration if you want to change to deploy another model. +The `Llama3` model deployed is specified in `gen-ai/nvidia-nims/helm-values/nim-llm.yaml` with below config. Please visit [this page](https://build.nvidia.com/explore/discover) to explore more. You may simply update this image configuration if you want to change to deploy another model. ::: ```yaml @@ -124,6 +124,12 @@ curl -X 'POST' \ }' ``` +you will see similar output like the following + +```json +{"id":"cmpl-63a0b66aeda1440c8b6ca1ce3583b173","object":"text_completion","created":1719742336,"model":"meta/llama3-8b-instruct","choices":[{"index":0,"text":", there was a young man named Jack who lived in a small village at the foot of a vast and ancient forest. Jack was a curious and adventurous soul, always eager to explore the world beyond his village. One day, he decided to venture into the forest, hoping to discover its secrets.\nAs he wandered deeper into","logprobs":null,"finish_reason":"length","stop_reason":null}],"usage":{"prompt_tokens":5,"total_tokens":69,"completion_tokens":64}} +``` + ## Cleanup To remove all resources created by this deployment, run: From bc323861e71db070e474ddc1df23b8ea73239717 Mon Sep 17 00:00:00 2001 From: hustshawn Date: Thu, 4 Jul 2024 16:17:00 +0800 Subject: [PATCH 06/16] refactor: reuse triton-server pattern for NIM --- .../nvidia-triton-server}/.gitignore | 0 ai-ml/nvidia-triton-server/addons.tf | 19 ++ .../helm-values/nim-llm.yaml | 53 +++ .../helm-values/prometheus-adapter.yaml | 14 + ai-ml/nvidia-triton-server/nvidia-nim.tf | 126 +++++++ .../nvidia-triton-server.tf | 35 +- ai-ml/nvidia-triton-server/outputs.tf | 4 +- ai-ml/nvidia-triton-server/variables.tf | 28 ++ .../inference/nvidia-nim/nim-client/client.py | 148 +++++++++ .../nvidia-nim/nim-client/prompts.txt | 20 ++ gen-ai/nvidia-nims/README.md | 160 --------- gen-ai/nvidia-nims/addons.tf | 314 ------------------ gen-ai/nvidia-nims/cleanup.sh | 75 ----- gen-ai/nvidia-nims/efs.tf | 72 ---- gen-ai/nvidia-nims/eks.tf | 118 ------- .../helm-values/ingress-nginx-values.yaml | 12 - .../helm-values/kube-prometheus.yaml | 48 --- gen-ai/nvidia-nims/helm-values/nim-llm.yaml | 24 -- gen-ai/nvidia-nims/images/all-pod-svc.png | Bin 438473 -> 0 bytes gen-ai/nvidia-nims/install.sh | 44 --- gen-ai/nvidia-nims/main.tf | 51 --- gen-ai/nvidia-nims/outputs.tf | 19 -- gen-ai/nvidia-nims/variables.tf | 48 --- gen-ai/nvidia-nims/versions.tf | 37 --- gen-ai/nvidia-nims/vpc.tf | 53 --- .../gen-ai/inference/nvidia-nim-llama3.md | 138 +++++++- 26 files changed, 567 insertions(+), 1093 deletions(-) rename {gen-ai/nvidia-nims => ai-ml/nvidia-triton-server}/.gitignore (100%) create mode 100644 ai-ml/nvidia-triton-server/helm-values/nim-llm.yaml create mode 100644 ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml create mode 100644 ai-ml/nvidia-triton-server/nvidia-nim.tf create mode 100644 gen-ai/inference/nvidia-nim/nim-client/client.py create mode 100644 gen-ai/inference/nvidia-nim/nim-client/prompts.txt delete mode 100644 gen-ai/nvidia-nims/README.md delete mode 100644 gen-ai/nvidia-nims/addons.tf delete mode 100755 gen-ai/nvidia-nims/cleanup.sh delete mode 100644 gen-ai/nvidia-nims/efs.tf delete mode 100644 gen-ai/nvidia-nims/eks.tf delete mode 100644 gen-ai/nvidia-nims/helm-values/ingress-nginx-values.yaml delete mode 100644 gen-ai/nvidia-nims/helm-values/kube-prometheus.yaml delete mode 100644 gen-ai/nvidia-nims/helm-values/nim-llm.yaml delete mode 100644 gen-ai/nvidia-nims/images/all-pod-svc.png delete mode 100755 gen-ai/nvidia-nims/install.sh delete mode 100644 gen-ai/nvidia-nims/main.tf delete mode 100644 gen-ai/nvidia-nims/outputs.tf delete mode 100644 gen-ai/nvidia-nims/variables.tf delete mode 100644 gen-ai/nvidia-nims/versions.tf delete mode 100644 gen-ai/nvidia-nims/vpc.tf diff --git a/gen-ai/nvidia-nims/.gitignore b/ai-ml/nvidia-triton-server/.gitignore similarity index 100% rename from gen-ai/nvidia-nims/.gitignore rename to ai-ml/nvidia-triton-server/.gitignore diff --git a/ai-ml/nvidia-triton-server/addons.tf b/ai-ml/nvidia-triton-server/addons.tf index a232fdc02..1e23f825f 100644 --- a/ai-ml/nvidia-triton-server/addons.tf +++ b/ai-ml/nvidia-triton-server/addons.tf @@ -78,6 +78,11 @@ module "eks_blueprints_addons" { vpc-cni = {} } + #--------------------------------------- + # AWS EFS CSI Add-on + #--------------------------------------- + enable_aws_efs_csi_driver = true + #--------------------------------------- # AWS Load Balancer Controller Add-on #--------------------------------------- @@ -140,6 +145,20 @@ module "eks_blueprints_addons" { ], } + helm_releases = { + "prometheus-adapter" = { + repository = "https://prometheus-community.github.io/helm-charts" + chart = "prometheus-adapter" + namespace = module.eks_blueprints_addons.kube_prometheus_stack.namespace + version = "4.10.0" + values = [ + templatefile( + "${path.module}/helm-values/prometheus-adapter.yaml", {} + ) + ] + } + } + } #--------------------------------------------------------------- diff --git a/ai-ml/nvidia-triton-server/helm-values/nim-llm.yaml b/ai-ml/nvidia-triton-server/helm-values/nim-llm.yaml new file mode 100644 index 000000000..1cf3abe20 --- /dev/null +++ b/ai-ml/nvidia-triton-server/helm-values/nim-llm.yaml @@ -0,0 +1,53 @@ +# ref: https://github.com/NVIDIA/nim-deploy/blob/main/helm/nim-llm/values.yaml +image: + repository: nvcr.io/nim/meta/llama3-8b-instruct + tag: latest +model: + ngcAPIKey: ${ngc_api_key} + nimCache: /model-store +resources: + limits: + nvidia.com/gpu: 1 + requests: + nvidia.com/gpu: 1 +statefulSet: + enabled: true +persistence: + enabled: true + existingClaim: ${pvc_name} +nodeSelector: + NodeGroupType: g5-gpu-karpenter + type: karpenter +tolerations: +- key: "nvidia.com/gpu" + operator: "Exists" + effect: "NoSchedule" +metrics: + enabled: true + serviceMonitor: + enabled: true + additionalLabels: + release: prometheus + app: prometheus +autoscaling: + enabled: true + minReplicas: 1 + maxReplicas: 5 + scaleDownStabilizationSecs: 300 + metrics: + - type: Pods + pods: + metric: + name: num_requests_running + target: + type: Value + averageValue: 5 +ingress: + enabled: true + className: nginx + annotations: {} + hosts: + - paths: + - path: / + pathType: ImplementationSpecific + serviceType: openai diff --git a/ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml b/ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml new file mode 100644 index 000000000..9311c37db --- /dev/null +++ b/ai-ml/nvidia-triton-server/helm-values/prometheus-adapter.yaml @@ -0,0 +1,14 @@ +# ref: https://github.com/prometheus-community/helm-charts/blob/main/charts/prometheus-adapter/values.yaml +prometheus: + url: http://kube-prometheus-stack-prometheus.kube-prometheus-stack + port: 9090 +rules: + default: false + custom: + - seriesQuery: '{__name__=~"num_requests_running"}' + resources: + template: <<.Resource>> + name: + matches: "num_requests_running" + as: "" + metricsQuery: sum(<<.Series>>{<<.LabelMatchers>>}) by (<<.GroupBy>>) diff --git a/ai-ml/nvidia-triton-server/nvidia-nim.tf b/ai-ml/nvidia-triton-server/nvidia-nim.tf new file mode 100644 index 000000000..5bf7d3937 --- /dev/null +++ b/ai-ml/nvidia-triton-server/nvidia-nim.tf @@ -0,0 +1,126 @@ +#--------------------------------------------------------------- +# EFS +#--------------------------------------------------------------- +module "efs" { + count = var.enable_nvidia_nim ? 1 : 0 + source = "terraform-aws-modules/efs/aws" + version = "~> 1.6" + + creation_token = local.name + name = local.name + + # Mount targets / security group + mount_targets = { + for k, v in zipmap(local.azs, slice(module.vpc.private_subnets, length(module.vpc.private_subnets) - 2, length(module.vpc.private_subnets))) : k => { subnet_id = v } + } + security_group_description = "${local.name} EFS security group" + security_group_vpc_id = module.vpc.vpc_id + security_group_rules = { + vpc = { + # relying on the defaults provided for EFS/NFS (2049/TCP + ingress) + description = "NFS ingress from VPC private subnets" + cidr_blocks = module.vpc.private_subnets_cidr_blocks + } + } + + tags = local.tags +} + +resource "kubernetes_storage_class_v1" "efs" { + count = var.enable_nvidia_nim ? 1 : 0 + metadata { + name = "efs" + } + + storage_provisioner = "efs.csi.aws.com" + parameters = { + provisioningMode = "efs-ap" # Dynamic provisioning + fileSystemId = module.efs[count.index].id + directoryPerms = "777" + } + + mount_options = [ + "iam" + ] + + depends_on = [ + module.eks_blueprints_addons.aws_efs_csi_driver + ] +} + +resource "kubernetes_namespace" "nim" { + count = var.enable_nvidia_nim ? 1 : 0 + metadata { + name = "nim" + } + + depends_on = [module.eks] +} + +resource "kubernetes_persistent_volume_claim_v1" "efs_pvc" { + count = var.enable_nvidia_nim ? 1 : 0 + metadata { + # name = "nim-llm-pvc" + name = kubernetes_namespace.nim[count.index].metadata[0].name + namespace = "nim" + } + spec { + access_modes = ["ReadWriteMany"] + # storage_class_name = kubernetes_storage_class_v1.efs.id + storage_class_name = kubernetes_storage_class_v1.efs[count.index].metadata[0].name + resources { + requests = { + storage = "100Gi" + } + } + } +} + + +resource "null_resource" "download_nim_deploy" { + count = var.enable_nvidia_nim ? 1 : 0 + # This trigger ensures the script runs only when the file doesn't exist + triggers = { + script_executed = fileexists("${path.module}/nim-llm/Chart.yaml") ? "false" : "true" + } + + provisioner "local-exec" { + command = <<-EOT + if [ ! -d "${path.module}/nim-llm" ]; then + echo "Downloading nim-deploy repo ..." + TEMP_DIR=$(mktemp -d) + git clone https://github.com/NVIDIA/nim-deploy.git "$TEMP_DIR/nim-deploy" + cp -r "$TEMP_DIR/nim-deploy/helm/nim-llm" ${path.module}/nim-llm + rm -rf "$TEMP_DIR" + echo "Download completed." + else + echo "nim-llm directory already exists. Skipping download." + fi + EOT + } +} + + +resource "helm_release" "nim_llm" { + count = var.enable_nvidia_nim ? 1 : 0 + name = "nim-llm" + chart = "${path.module}/nim-llm" + create_namespace = true + namespace = kubernetes_namespace.nim[count.index].metadata[0].name + timeout = 360 + wait = false + values = [ + templatefile( + "${path.module}/helm-values/nim-llm.yaml", + { + ngc_api_key = var.ngc_api_key + # pvc_name = kubernetes_persistent_volume_claim_v1.efs_pvc.metadata[0].name + pvc_name = kubernetes_persistent_volume_claim_v1.efs_pvc[count.index].metadata[0].name + } + ) + ] + + depends_on = [ + null_resource.download_nim_deploy + ] +} diff --git a/ai-ml/nvidia-triton-server/nvidia-triton-server.tf b/ai-ml/nvidia-triton-server/nvidia-triton-server.tf index e4993e307..65a329aea 100644 --- a/ai-ml/nvidia-triton-server/nvidia-triton-server.tf +++ b/ai-ml/nvidia-triton-server/nvidia-triton-server.tf @@ -8,6 +8,7 @@ locals { # Data on EKS Kubernetes Addons #--------------------------------------------------------------- module "triton_server_vllm_llama2" { + count = var.enable_nvidia_triton_server ? 1 : 0 depends_on = [module.eks_blueprints_addons.kube_prometheus_stack] source = "aws-ia/eks-data-addons/aws" version = "~> 1.32.0" # ensure to update this to the latest/desired version @@ -20,7 +21,7 @@ module "triton_server_vllm_llama2" { version = "1.0.0" timeout = 120 wait = false - namespace = kubernetes_namespace_v1.triton.metadata[0].name + namespace = kubernetes_namespace_v1.triton[count.index].metadata[0].name values = [ <<-EOT replicaCount: 1 @@ -29,8 +30,8 @@ module "triton_server_vllm_llama2" { tag: "24.02-vllm-python-py3" serviceAccount: create: false - name: ${kubernetes_service_account_v1.triton.metadata[0].name} - modelRepositoryPath: s3://${module.s3_bucket.s3_bucket_id}/model_repository + name: ${kubernetes_service_account_v1.triton[count.index].metadata[0].name} + modelRepositoryPath: s3://${module.s3_bucket[count.index].s3_bucket_id}/model_repository environment: - name: model_name value: ${local.default_model_name} @@ -50,7 +51,7 @@ module "triton_server_vllm_llama2" { value: "auto" secretEnvironment: - name: "HUGGING_FACE_TOKEN" - secretName: ${kubernetes_secret_v1.huggingface_token.metadata[0].name} + secretName: ${kubernetes_secret_v1.huggingface_token[count.index].metadata[0].name} key: "HF_TOKEN" resources: limits: @@ -79,9 +80,10 @@ module "triton_server_vllm_llama2" { # Replace the value with your Hugging Face token #--------------------------------------------------------------- resource "kubernetes_secret_v1" "huggingface_token" { + count = var.enable_nvidia_triton_server ? 1 : 0 metadata { name = "huggingface-secret" - namespace = kubernetes_namespace_v1.triton.metadata[0].name + namespace = kubernetes_namespace_v1.triton[count.index].metadata[0].name } data = { @@ -94,6 +96,7 @@ resource "kubernetes_secret_v1" "huggingface_token" { #--------------------------------------------------------------- #tfsec:ignore:* module "s3_bucket" { + count = var.enable_nvidia_triton_server ? 1 : 0 source = "terraform-aws-modules/s3-bucket/aws" version = "4.1.2" @@ -115,14 +118,15 @@ module "s3_bucket" { # Use null_resource to sync local files to the S3 bucket resource "null_resource" "sync_local_to_s3" { + count = var.enable_nvidia_triton_server ? 1 : 0 # Re-run the provisioner if the bucket name changes triggers = { always_run = uuid(), - bucket_name = module.s3_bucket.s3_bucket_id + bucket_name = module.s3_bucket[count.index].s3_bucket_id } provisioner "local-exec" { - command = "aws s3 sync ../../gen-ai/inference/vllm-nvidia-triton-server-gpu/ s3://${module.s3_bucket.s3_bucket_id}" + command = "aws s3 sync ../../gen-ai/inference/vllm-nvidia-triton-server-gpu/ s3://${module.s3_bucket[count.index].s3_bucket_id}" } } @@ -130,6 +134,7 @@ resource "null_resource" "sync_local_to_s3" { # IAM role for service account (IRSA) #--------------------------------------------------------------- resource "kubernetes_namespace_v1" "triton" { + count = var.enable_nvidia_triton_server ? 1 : 0 metadata { name = local.triton_model } @@ -142,10 +147,11 @@ resource "kubernetes_namespace_v1" "triton" { # Service account for Triton model #--------------------------------------------------------------- resource "kubernetes_service_account_v1" "triton" { + count = var.enable_nvidia_triton_server ? 1 : 0 metadata { name = local.triton_model - namespace = kubernetes_namespace_v1.triton.metadata[0].name - annotations = { "eks.amazonaws.com/role-arn" : module.triton_irsa.iam_role_arn } + namespace = kubernetes_namespace_v1.triton[count.index].metadata[0].name + annotations = { "eks.amazonaws.com/role-arn" : module.triton_irsa[count.index].iam_role_arn } } automount_service_account_token = true @@ -155,12 +161,13 @@ resource "kubernetes_service_account_v1" "triton" { # Secret for Triton model #--------------------------------------------------------------- resource "kubernetes_secret_v1" "triton" { + count = var.enable_nvidia_triton_server ? 1 : 0 metadata { name = "${local.triton_model}-secret" - namespace = kubernetes_namespace_v1.triton.metadata[0].name + namespace = kubernetes_namespace_v1.triton[count.index].metadata[0].name annotations = { - "kubernetes.io/service-account.name" = kubernetes_service_account_v1.triton.metadata[0].name - "kubernetes.io/service-account.namespace" = kubernetes_namespace_v1.triton.metadata[0].name + "kubernetes.io/service-account.name" = kubernetes_service_account_v1.triton[count.index].metadata[0].name + "kubernetes.io/service-account.namespace" = kubernetes_namespace_v1.triton[count.index].metadata[0].name } } @@ -171,6 +178,7 @@ resource "kubernetes_secret_v1" "triton" { # IRSA for Triton model server pods #--------------------------------------------------------------- module "triton_irsa" { + count = var.enable_nvidia_triton_server ? 1 : 0 source = "aws-ia/eks-blueprints-addon/aws" version = "~> 1.0" @@ -182,7 +190,7 @@ module "triton_irsa" { role_name = "${var.name}-${local.triton_model}" create_policy = false role_policies = { - triton_policy = aws_iam_policy.triton.arn + triton_policy = aws_iam_policy.triton[count.index].arn } oidc_providers = { @@ -198,6 +206,7 @@ module "triton_irsa" { # Creates IAM policy for IRSA. Provides IAM permissions for Triton model server pods #--------------------------------------------------------------- resource "aws_iam_policy" "triton" { + count = var.enable_nvidia_triton_server ? 1 : 0 description = "IAM role policy for Triton models" name = "${local.name}-${local.triton_model}-irsa" policy = data.aws_iam_policy_document.triton_model.json diff --git a/ai-ml/nvidia-triton-server/outputs.tf b/ai-ml/nvidia-triton-server/outputs.tf index 98fed1666..9276e1b87 100644 --- a/ai-ml/nvidia-triton-server/outputs.tf +++ b/ai-ml/nvidia-triton-server/outputs.tf @@ -5,12 +5,12 @@ output "configure_kubectl" { output "s3_bucket_name" { description = "The name of the S3 bucket." - value = module.s3_bucket.s3_bucket_id + value = var.enable_nvidia_triton_server ? module.s3_bucket[0].s3_bucket_id : null } output "s3_bucket_region" { description = "The AWS region this bucket resides in." - value = module.s3_bucket.s3_bucket_region + value = var.enable_nvidia_triton_server ? module.s3_bucket[0].s3_bucket_region : null } output "grafana_secret_name" { diff --git a/ai-ml/nvidia-triton-server/variables.tf b/ai-ml/nvidia-triton-server/variables.tf index b017eddf0..6ba751b56 100644 --- a/ai-ml/nvidia-triton-server/variables.tf +++ b/ai-ml/nvidia-triton-server/variables.tf @@ -32,6 +32,12 @@ variable "secondary_cidr_blocks" { type = list(string) } +variable "enable_nvidia_triton_server" { + description = "Toggle to enable or disable NVIDIA Triton server resource creation" + default = true + type = bool +} + #------------------------------------------------------------------- # Instructions for Securely Setting the Huggingface Token # ------------------------------------------------------------------- @@ -47,3 +53,25 @@ variable "huggingface_token" { default = "DUMMY_TOKEN_REPLACE_ME" sensitive = true } + +variable "enable_nvidia_nim" { + description = "Toggle to enable or disable NVIDIA NIM pattern resource creation" + default = true + type = bool +} + +#------------------------------------------------------------------- +# Instructions for Securely Setting the NVIDIA NGC API key +# ------------------------------------------------------------------- +# 1. Obtain your NVIDIA NGC API key from https://docs.nvidia.com/ai-enterprise/deployment-guide-spark-rapids-accelerator/0.1.0/appendix-ngc.html +# 2. Before running 'terraform apply', set the environment variable: +# * Linux/macOS: +# export TF_VAR_ngc_api_key= +# 3. Now you can safely run 'terraform apply' +#------------------------------------------------------------------- +variable "ngc_api_key" { + description = "NGC API Key" + type = string + default = "DUMMY_NGC_API_KEY_REPLACE_ME" + sensitive = true +} diff --git a/gen-ai/inference/nvidia-nim/nim-client/client.py b/gen-ai/inference/nvidia-nim/nim-client/client.py new file mode 100644 index 000000000..d4cb0719d --- /dev/null +++ b/gen-ai/inference/nvidia-nim/nim-client/client.py @@ -0,0 +1,148 @@ +import argparse +import asyncio +import time +from os import system + +import openai + + +async def generate(client, prompt, sampling_parameters): + try: + response = await client.chat.completions.create( + model="meta/llama3-8b-instruct", # You can change this to the appropriate model + messages=[ + { + "role": "system", + "content": "Keep short answers of no more than 100 sentences.", + }, + {"role": "user", "content": prompt}, + ], + max_tokens=sampling_parameters["max_tokens"], + temperature=float(sampling_parameters["temperature"]), + top_p=float(sampling_parameters["top_p"]), + n=1, + stream=False, + ) + return response.choices[0].message.content + except Exception as e: + print(f"Error in generate function: {e}") + return None + + +async def process_prompt(client, prompt, prompt_id, sampling_parameters, results_dict): + start_time = time.time() + response = await generate(client, prompt.strip(), sampling_parameters) + end_time = time.time() + + if response: + results_dict[str(prompt_id)].append(response.encode("utf-8")) + duration = (end_time - start_time) * 1000 + print(f"Model {FLAGS.model_name} - Request {prompt_id}: {duration:.2f} ms") + return end_time - start_time + else: + print(f"Error processing prompt {prompt_id}") + return 0 + + +async def main(FLAGS): + sampling_parameters = { + "temperature": "0.01", + "top_p": "1.0", + "top_k": 20, + "max_tokens": 512, + } + client = openai.AsyncOpenAI(base_url=FLAGS.url) + with open(FLAGS.input_prompts, "r") as file: + print(f"Loading inputs from `{FLAGS.input_prompts}`...") + prompts = file.readlines() + + results_dict = {} + total_time_sec = 0 + + for iter in range(FLAGS.iterations): + tasks = [] + for i, prompt in enumerate(prompts): + prompt_id = FLAGS.offset + (len(prompts) * iter) + i + results_dict[str(prompt_id)] = [] + task = process_prompt( + client, prompt, prompt_id, sampling_parameters, results_dict + ) + tasks.append(task) + + times = await asyncio.gather(*tasks) + total_time_sec += sum(times) + + with open(FLAGS.results_file, "w") as file: + for id in results_dict.keys(): + for result in results_dict[id]: + file.write(result.decode("utf-8")) + file.write("\n") + file.write("\n=========\n\n") + print(f"Storing results into `{FLAGS.results_file}`...") + + if FLAGS.verbose: + print(f"\nContents of `{FLAGS.results_file}` ===>") + system(f"cat {FLAGS.results_file}") + + total_time_ms = total_time_sec * 1000 + print( + f"Total time for all requests: {total_time_sec:.2f} seconds ({total_time_ms:.2f} milliseconds)" + ) + print("PASS: NVIDIA NIM example") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-u", + "--url", + type=str, + required=False, + default="http://localhost:8000/v1", + help="Inference server URL. Default is http://localhost:8000/v1 ", + ) + parser.add_argument( + "-v", + "--verbose", + action="store_true", + required=False, + default=False, + help="Enable verbose output", + ) + parser.add_argument( + "--offset", + type=int, + required=False, + default=0, + help="Add offset to request IDs used", + ) + parser.add_argument( + "--input-prompts", + type=str, + required=False, + default="prompts.txt", + help="Text file with input prompts", + ) + parser.add_argument( + "--results-file", + type=str, + required=False, + default="results.txt", + help="The file with output results", + ) + parser.add_argument( + "--iterations", + type=int, + required=False, + default=1, + help="Number of iterations through the prompts file", + ) + parser.add_argument( + "--model-name", + type=str, + required=False, + default="meta/llama3-8b-instruct", + help="Name of the model to test", + ) + FLAGS = parser.parse_args() + asyncio.run(main(FLAGS)) diff --git a/gen-ai/inference/nvidia-nim/nim-client/prompts.txt b/gen-ai/inference/nvidia-nim/nim-client/prompts.txt new file mode 100644 index 000000000..95d067ae7 --- /dev/null +++ b/gen-ai/inference/nvidia-nim/nim-client/prompts.txt @@ -0,0 +1,20 @@ +What are the key differences between traditional machine learning models and very large language models (vLLM)? +Can you explain how TensorRT optimizes LLM inference on NVIDIA hardware? +What are the latest breakthroughs in general AI that could significantly impact everyday technology applications? +How do vLLMs handle the complexity of multiple languages in a single inference scenario? +What are the ethical considerations associated with deploying very large language models in public sectors? +Describe the role of TensorRT in enhancing the performance of neural networks for real-time applications. +What are some common challenges faced when training very large language models and how can they be overcome? +Can general AI achieve true cognitive capabilities similar to human intelligence? Discuss with examples. +Explain the process of fine-tuning a pre-trained vLLM for a specific industry application. +What infrastructure is required to efficiently run LLM inference at scale? +Discuss the impact of AI and machine learning innovations on data privacy and security. +How does TensorRT integrate with existing AI frameworks to improve inference speed and efficiency? +What advancements in AI hardware are crucial for supporting the next generation of vLLMs? +Illustrate how vLLMs can be used to generate creative content such as poetry or music. +What is the potential of general AI in transforming healthcare diagnostics? +Discuss the significance of model quantization in TensorRT for deploying lightweight models on edge devices. +How can businesses leverage vLLM capabilities to improve customer service and engagement? +What are some successful case studies of TensorRT-LLM implementations in commercial products? +Explain how continual learning could be integrated into vLLMs to adapt to new data over time. +What are the future prospects of integrating vLLMs with augmented reality technologies? diff --git a/gen-ai/nvidia-nims/README.md b/gen-ai/nvidia-nims/README.md deleted file mode 100644 index 8ffb4b92c..000000000 --- a/gen-ai/nvidia-nims/README.md +++ /dev/null @@ -1,160 +0,0 @@ -# NVIDIA NIM LLM on Amazon EKS - -This guide provides instructions for deploying NVIDIA NIM (NVIDIA Inference Microservices) Large Language Models (LLMs) on Amazon Elastic Kubernetes Service (EKS). - -## Prerequisites - -- An active AWS account with admin equivalent permissions -- [aws cli](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html) -- [kubectl](https://Kubernetes.io/docs/tasks/tools/) -- [Terraform](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli) installed -- NVIDIA NGC account and API key - -## Setup - -1. Configure the NGC API Key - -Retrieve your NGC API key from [NVIDIA](https://docs.nvidia.com/ai-enterprise/deployment-guide-spark-rapids-accelerator/0.1.0/appendix-ngc.html) and set it as an environment variable: - -```bash -export TF_VAR_ngc_api_key= -``` - -2. Installation - -Run the installation script: - -```bash -cd ai-ml/trainimum-inferentia -./install.sh -``` - -This process will take approximately 20 minutes to complete. - -3. Verify the Installation - -Check the status of your pods deployed - -```bash -kubectl get po -A -``` - -You should see output similar to the following: -![alt text](images/all-pod-svc.png) - -4. Verify the deployed model - -Once all pods in `nim` namespace is ready with `1/1` status, use below command to verify it's ready to serve the traffic. - -```bash -export INGRESS_URL=$(kubectl get ingress -n nim -o jsonpath='{.items[0].status.loadBalancer.ingress[0].hostname}') - -curl -X 'POST' \ -"http://$INGRESS_URL/v1/completions" \ --H 'accept: application/json' \ --H 'Content-Type: application/json' \ --d '{ -"model": "meta/llama3-8b-instruct", -"prompt": "Once upon a time", -"max_tokens": 64 -}' -``` - -you will see similar output like the following - -```json -{"id":"cmpl-63a0b66aeda1440c8b6ca1ce3583b173","object":"text_completion","created":1719742336,"model":"meta/llama3-8b-instruct","choices":[{"index":0,"text":", there was a young man named Jack who lived in a small village at the foot of a vast and ancient forest. Jack was a curious and adventurous soul, always eager to explore the world beyond his village. One day, he decided to venture into the forest, hoping to discover its secrets.\nAs he wandered deeper into","logprobs":null,"finish_reason":"length","stop_reason":null}],"usage":{"prompt_tokens":5,"total_tokens":69,"completion_tokens":64}} -``` - -## Scaling - -You can scale up and down to number of replicas with following: - -```bash -kubectl scale sts nim-llm -n --replicas=2 -``` - -## Clean up - -To remove all resources created by this deployment, run: - -```bash -./cleanup.sh -``` - -## Additional Resources - -- [NVIDIA NIM Documentation](https://docs.nvidia.com/nim/) -- [Amazon EKS Documentation](https://docs.aws.amazon.com/eks/) -- [NVIDIA NGC Catalog](https://catalog.ngc.nvidia.com/) - ---- - - -## Requirements - -| Name | Version | -|------|---------| -| [terraform](#requirement\_terraform) | >= 1.0.0 | -| [aws](#requirement\_aws) | >= 3.72 | -| [helm](#requirement\_helm) | >= 2.4.1 | -| [kubectl](#requirement\_kubectl) | >= 1.14 | -| [kubernetes](#requirement\_kubernetes) | >= 2.10 | -| [null](#requirement\_null) | >= 3.1 | -| [random](#requirement\_random) | >= 3.1 | - -## Providers - -| Name | Version | -|------|---------| -| [aws](#provider\_aws) | 5.56.1 | -| [aws.ecr](#provider\_aws.ecr) | 5.56.1 | -| [kubernetes](#provider\_kubernetes) | 2.31.0 | -| [random](#provider\_random) | 3.6.2 | - -## Modules - -| Name | Source | Version | -|------|--------|---------| -| [data\_addons](#module\_data\_addons) | aws-ia/eks-data-addons/aws | ~> 1.32.0 | -| [ebs\_csi\_driver\_irsa](#module\_ebs\_csi\_driver\_irsa) | terraform-aws-modules/iam/aws//modules/iam-role-for-service-accounts-eks | ~> 5.20 | -| [efs](#module\_efs) | terraform-aws-modules/efs/aws | ~> 1.6 | -| [eks](#module\_eks) | terraform-aws-modules/eks/aws | ~> 19.15 | -| [eks\_blueprints\_addons](#module\_eks\_blueprints\_addons) | aws-ia/eks-blueprints-addons/aws | ~> 1.2 | -| [vpc](#module\_vpc) | terraform-aws-modules/vpc/aws | ~> 5.0 | - -## Resources - -| Name | Type | -|------|------| -| [aws_secretsmanager_secret.grafana](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/secretsmanager_secret) | resource | -| [aws_secretsmanager_secret_version.grafana](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/secretsmanager_secret_version) | resource | -| [kubernetes_annotations.disable_gp2](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/annotations) | resource | -| [kubernetes_namespace.nim](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/namespace) | resource | -| [kubernetes_persistent_volume_claim_v1.efs_pvc](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/persistent_volume_claim_v1) | resource | -| [kubernetes_storage_class.default_gp3](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/storage_class) | resource | -| [kubernetes_storage_class_v1.efs](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/storage_class_v1) | resource | -| [random_password.grafana](https://registry.terraform.io/providers/hashicorp/random/latest/docs/resources/password) | resource | -| [aws_availability_zones.available](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/availability_zones) | data source | -| [aws_ecrpublic_authorization_token.token](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/ecrpublic_authorization_token) | data source | -| [aws_eks_cluster_auth.this](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/eks_cluster_auth) | data source | -| [aws_secretsmanager_secret_version.admin_password_version](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/secretsmanager_secret_version) | data source | - -## Inputs - -| Name | Description | Type | Default | Required | -|------|-------------|------|---------|:--------:| -| [eks\_cluster\_version](#input\_eks\_cluster\_version) | EKS Cluster version | `string` | `"1.29"` | no | -| [name](#input\_name) | Name of the VPC and EKS Cluster | `string` | `"nvidia-nims"` | no | -| [ngc\_api\_key](#input\_ngc\_api\_key) | NGC API Key | `string` | n/a | yes | -| [region](#input\_region) | region | `string` | `"us-west-2"` | no | -| [secondary\_cidr\_blocks](#input\_secondary\_cidr\_blocks) | Secondary CIDR blocks to be attached to VPC | `list(string)` |