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Single Cluster Open Source Observability - Inferentia-based cluster

AWS Inferentia is an accelerated Machine Learning (ML) chip, designed by AWS.

Amazon Elastic Compute Cloud (Amazon EC2) Inf1 and Inf2 instances feature AWS Inferentia chips and support high-performance and low-latency inference.

AWS Neuron is an SDK with a compiler, runtime, and profiling tools that helps developers deploy models on both AWS Inferentia accelerators and train them on AWS Trainium chips. It integrates natively with popular ML frameworks, such as PyTorch and TensorFlow.

This pattern shows you how to monitor the performance of ML chips, used in an Amazon EKS cluster running on Amazon EC2 Inf1 and Inf2 instances.

Amazon Managed Service for Prometheus and Amazon Managed Grafana are open source tools used in this pattern to collect and visualise metrics respectively.

Amazon Managed Service for Prometheus is a Prometheus-compatible service that monitors and provides alerts on containerized applications and infrastructure at scale.

Amazon Managed Grafana is a managed service for Grafana, a popular open-source analytics platform that enables you to query, visualize, and alert on your metrics, logs, and traces.

Objective

This pattern deploys an Amazon EKS cluster with a node group that includes Inf1/Inf2 instances.

The AMI type of the node group is AL2_x86_64_GPU AMI, which uses the Amazon EKS-optimized accelerated AMI. In addition to the standard Amazon EKS-optimized AMI configuration, the accelerated AMI includes the NeuronX container runtime.

To access the ML chips from Kubernetes, the pattern deploys the Neuron device plugin.

Metrics are exposed to Amazon Managed Service for Prometheus by the neuron-monitor DaemonSet, which deploys a minimal container, with the Neuron Tools installed. Specifically, the neuron-monitor DaemonSet runs the neuron-monitor command piped into the neuron-monitor-prometheus.py companion script (both commands are part of the container):

neuron-monitor | neuron-monitor-prometheus.py --port <port>

neuron-monitor collects metrics and stats from the Neuron Applications running on the system and streams the collected data to stdout in JSON format.

neuron-monitor-prometheus.py maps and exposes the telemetry data from JSON format into Prometheus compatible format.

Data is visualised in Amazon Managed Grafana by the corresponding dashboard.

The rest of the setup to collect and visualise metrics with Amazon Managed Service for Prometheus and Amazon Managed Grafana, is similar to that used in other open-source based patterns, included in this repository.

Prerequisites:

Ensure that you have installed the following tools on your machine.

  1. aws cli
  2. kubectl
  3. cdk
  4. npm

Deploying

  1. Clone your forked repository
git clone https://github.com/aws-observability/cdk-aws-observability-accelerator.git
  1. Install the AWS CDK Toolkit globally on your machine using
npm install -g aws-cdk
  1. Amazon Managed Grafana workspace: To visualize metrics collected, you need an Amazon Managed Grafana workspace. If you have an existing workspace, create an environment variable as described below. To create a new workspace, visit our supporting example for Amazon Managed Grafana

Note

For the URL https://g-xyz.grafana-workspace.us-east-1.amazonaws.com, the workspace ID would be g-xyz

export AWS_REGION=<YOUR AWS REGION>
export COA_AMG_WORKSPACE_ID=g-xxx
export COA_AMG_ENDPOINT_URL=https://g-xyz.grafana-workspace.us-east-1.amazonaws.com

Warning

Setting up environment variables COA_AMG_ENDPOINT_URL and AWS_REGION is mandatory for successful execution of this pattern.

  1. GRAFANA API KEY: Amazon Managed Grafana provides a control plane API for generating Grafana API keys or Service Account Tokens.
# IMPORTANT NOTE: skip this command if you already have a service token
GRAFANA_SA_ID=$(aws grafana create-workspace-service-account \
  --workspace-id $COA_AMG_WORKSPACE_ID \
  --grafana-role ADMIN \
  --name cdk-accelerator-eks \
  --query 'id' \
  --output text)

# creates a new token
export AMG_API_KEY=$(aws grafana create-workspace-service-account-token \
  --workspace-id $COA_AMG_WORKSPACE_ID \
  -name "grafana-operator-key" \
  --seconds-to-live 432000 \
  --service-account-id $GRAFANA_SA_ID \
  --query 'serviceAccountToken.key' \
  --output text)

export AMG_API_KEY=$(aws grafana create-workspace-api-key \
  --key-name "grafana-operator-key" \
  --key-role "ADMIN" \
  --seconds-to-live 432000 \
  --workspace-id $COA_AMG_WORKSPACE_ID \
  --query key \
  --output text)
  1. AWS SSM Parameter Store for GRAFANA API KEY: Update the Grafana API key secret in AWS SSM Parameter Store using the above new Grafana API key. This will be referenced by Grafana Operator deployment of our solution to access Amazon Managed Grafana from Amazon EKS Cluster
aws ssm put-parameter --name "/cdk-accelerator/grafana-api-key" \
    --type "SecureString" \
    --value $AMG_API_KEY \
    --region $AWS_REGION

  1. Install project dependencies by running npm install in the main folder of this cloned repository.

  2. The actual settings for dashboard urls and EKS managed node group are expected to be specified in the CDK context. Generically it is inside the cdk.json file of the current directory or in ~/.cdk.json in your home directory.

Example settings: Update the context in cdk.json file located in cdk-eks-blueprints-patterns directory

  "context": {
    "fluxRepository": {
      "name": "grafana-dashboards",
      "namespace": "grafana-operator",
      "repository": {
        "repoUrl": "https://github.com/aws-observability/aws-observability-accelerator",
        "name": "grafana-dashboards",
        "targetRevision": "main",
        "path": "./artifacts/grafana-operator-manifests/eks/infrastructure"
      },
      "values": {
        "GRAFANA_CLUSTER_DASH_URL" : "https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/grafana-dashboards/eks/infrastructure/cluster.json",
        "GRAFANA_KUBELET_DASH_URL" : "https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/grafana-dashboards/eks/infrastructure/kubelet.json",
        "GRAFANA_NSWRKLDS_DASH_URL" : "https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/grafana-dashboards/eks/infrastructure/namespace-workloads.json",
        "GRAFANA_NODEEXP_DASH_URL" : "https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/grafana-dashboards/eks/infrastructure/nodeexporter-nodes.json",
        "GRAFANA_NODES_DASH_URL" : "https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/grafana-dashboards/eks/infrastructure/nodes.json",
        "GRAFANA_WORKLOADS_DASH_URL" : "https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/grafana-dashboards/eks/infrastructure/workloads.json",
        "GRAFANA_NEURON_DASH_URL" : "https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/grafana-dashboards/eks/neuron/neuron-monitor.json"
      },
      "kustomizations": [
        {
          "kustomizationPath": "./artifacts/grafana-operator-manifests/eks/infrastructure"
        },
        {
          "kustomizationPath": "./artifacts/grafana-operator-manifests/eks/neuron"
        }
      ]
    },
     "neuronNodeGroup": {
      "instanceClass": "inf1",
      "instanceSize": "2xlarge",
      "desiredSize": 1, 
      "minSize": 1, 
      "maxSize": 3,
      "ebsSize": 512
    },
  }

Note: you can replace the inf1 instance type with inf2 and the size as you prefer; to check availability in your selected Region, you can run the following command (amend Values below as you see fit):

aws ec2 describe-instance-type-offerings \
    --filters Name=instance-type,Values="inf1*" \
    --query "InstanceTypeOfferings[].InstanceType" \
    --region $AWS_REGION
  1. Once all pre-requisites are set you are ready to deploy the pipeline. Run the following command from the root of this repository to deploy the pipeline stack:
make build
make pattern single-new-eks-inferentia-opensource-observability deploy

Verify the resources

Run update-kubeconfig command. You should be able to get the command from CDK output message.

aws eks update-kubeconfig --name single-new-eks-inferentia-opensource... --region <your region> --role-arn arn:aws:iam::xxxxxxxxx:role/single-new-eks-inferentia-opensource-singleneweksgpuopensourc...

Let’s verify the resources created by steps above:

kubectl get pods -A

Output:

Neuron_Pods

Specifically, ensure neuron-device-plugin-daemonset DaemonSet is running:

kubectl get ds neuron-device-plugin-daemonset --namespace kube-system

Output:

NAME                             DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR   AGE
neuron-device-plugin-daemonset   1         1         1       1            1           <none>          2h

And also that neuron-monitor DaemonSet is running:

kubectl get ds neuron-monitor --namespace kube-system

Output:

NAME             DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR   AGE
neuron-monitor   1         1         1       1            1           <none>          2h

Next, let's verify that Neuron devices and cores are visible, by running neuron-ls and neuron-top commands from e.g. your neuron-monitor pod:

kubectl exec -it {your neuron-monitor pod} -n kube-system -- /bin/bash -c "neuron-ls"

Output:

Neuron_ls

kubectl exec -it {your neuron-monitor pod} -n kube-system -- /bin/bash -c "neuron-top"

Output:

Neuron_top

Visualization

Grafana Neuron Dashboard

Login to your Amazon Managed Grafana workspace and navigate to the Dashboards panel. You should see a dashboard named Neuron / Monitor.

To actually see some interesting metrics on the Grafana dashboard, we will apply the following manifest:

curl https://raw.githubusercontent.com/aws-observability/aws-observability-accelerator/main/artifacts/k8s-deployment-manifest-templates/neuron/pytorch-inference-resnet50.yml | kubectl apply -f -

This is just a sample workload that compiles the torchvision ResNet50 model and runs repetitive inference in a loop to generate telemetry data.

To verify the Pod was successfully deployed, please run:

kubectl get pods

You should see a pod named pytorch-inference-resnet50.

After a few minutes, looking into the Neuron / Monitor, you should see the gathered metrics, similar to:

GPU_dcgm_Dashboard_1

GPU_dcgm_Dashboard_2

GPU_dcgm_Dashboard_3

GPU_dcgm_Dashboard_4

Grafana Operator and Flux always work together to synchronize your dashboards with Git. If you delete your dashboards by accident, they will be re-provisioned automatically.

Teardown

You can teardown the whole CDK stack with the following command:

make pattern single-new-eks-inferentia-opensource-observability destroy