Allow pod-based GPU metrics in Amazon CloudWatch

In February 2022, Amazon Net Providers added support for NVIDIA GPU metrics in Amazon CloudWatch, making it potential to push metrics from the Amazon CloudWatch Agent to Amazon CloudWatch and monitor your code for optimum GPU utilization. Since then, this function has been built-in into lots of our managed Amazon Machine Photographs (AMIs), such because the Deep Learning AMI and the AWS ParallelCluster AMI. To acquire instance-level metrics of GPU utilization, you should utilize Packer or the Amazon ImageBuilder to bootstrap your personal customized AMI and use it in varied managed service choices like AWS Batch, Amazon Elastic Container Service (Amazon ECS), or Amazon Elastic Kubernetes Service (Amazon EKS). Nonetheless, for a lot of container-based service choices and workloads, it’s excellent to seize utilization metrics on the container, pod, or namespace degree.

This put up particulars methods to arrange container-based GPU metrics and offers an instance of gathering these metrics from EKS pods.

Answer overview

To show container-based GPU metrics, we create an EKS cluster with g5.2xlarge situations; nonetheless, this can work with any supported NVIDIA accelerated occasion household.

We deploy the NVIDIA GPU operator to allow use of GPU assets and the NVIDIA DCGM Exporter to allow GPU metrics assortment. Then we discover two architectures. The primary one connects the metrics from NVIDIA DCGM Exporter to CloudWatch through a CloudWatch agent, as proven within the following diagram.

The second structure (see the next diagram) connects the metrics from DCGM Exporter to Prometheus, then we use a Grafana dashboard to visualise these metrics.

Conditions

To simplify reproducing your entire stack from this put up, we use a container that has all of the required tooling (aws cli, eksctl, helm, and many others.) already put in. To be able to clone the container project from GitHub, you will have git. To construct and run the container, you will have Docker. To deploy the structure, you will have AWS credentials. To allow entry to Kubernetes companies utilizing port-forwarding, additionally, you will want kubectl.

These stipulations will be put in in your native machine, EC2 instance with NICE DCV, or AWS Cloud9. On this put up, we’ll use a c5.2xlarge Cloud9 occasion with a 40GB native storage quantity. When utilizing Cloud9, please disable AWS managed short-term credentials by visiting Cloud9->Preferences->AWS Settings as proven on the screenshot beneath.

Construct and run the aws-do-eks container

Open a terminal shell in your most popular surroundings and run the next instructions:

git clone https://github.com/aws-samples/aws-do-eks
cd aws-do-eks
./construct.sh
./run.sh
./exec.sh

The result’s as follows:

You now have a shell in a container surroundings that has all of the instruments wanted to finish the duties beneath. We are going to seek advice from it as “aws-do-eks shell”. You may be working the instructions within the following sections on this shell, until particularly instructed in any other case.

Create an EKS cluster with a node group

This group features a GPU occasion household of your alternative; on this instance, we use the g5.2xlarge occasion kind.

The aws-do-eks project comes with a group of cluster configurations. You may set your required cluster configuration with a single configuration change.

Within the container shell, run ./env-config.sh after which set CONF=conf/eksctl/yaml/eks-gpu-g5.yaml
To confirm the cluster configuration, run ./eks-config.sh

It’s best to see the next cluster manifest:

apiVersion: eksctl.io/v1alpha5
form: ClusterConfig
metadata:
  identify: do-eks-yaml-g5
  model: "1.25"
  area: us-east-1
availabilityZones:
  - us-east-1a
  - us-east-1b
  - us-east-1c
  - us-east-1d
managedNodeGroups:
  - identify: sys
    instanceType: m5.xlarge
    desiredCapacity: 1
    iam:
      withAddonPolicies:
        autoScaler: true
        cloudWatch: true
  - identify: g5
    instanceType: g5.2xlarge
    instancePrefix: g5-2xl
    privateNetworking: true
    efaEnabled: false
    minSize: 0
    desiredCapacity: 1
    maxSize: 10
    volumeSize: 80
    iam:
      withAddonPolicies:
        cloudWatch: true
iam:
  withOIDC: true

To create the cluster, run the next command within the container

The output is as follows:

root@e5ecb162812f:/eks# ./eks-create.sh 
/eks/impl/eksctl/yaml /eks

./eks-create.sh

Mon Might 22 20:50:59 UTC 2023
Creating cluster utilizing /eks/conf/eksctl/yaml/eks-gpu-g5.yaml ...

eksctl create cluster -f /eks/conf/eksctl/yaml/eks-gpu-g5.yaml

2023-05-22 20:50:59 [ℹ]  eksctl model 0.133.0
2023-05-22 20:50:59 [ℹ]  utilizing area us-east-1
2023-05-22 20:50:59 [ℹ]  subnets for us-east-1a - public:192.168.0.0/19 personal:192.168.128.0/19
2023-05-22 20:50:59 [ℹ]  subnets for us-east-1b - public:192.168.32.0/19 personal:192.168.160.0/19
2023-05-22 20:50:59 [ℹ]  subnets for us-east-1c - public:192.168.64.0/19 personal:192.168.192.0/19
2023-05-22 20:50:59 [ℹ]  subnets for us-east-1d - public:192.168.96.0/19 personal:192.168.224.0/19
2023-05-22 20:50:59 [ℹ]  nodegroup "sys" will use "" [AmazonLinux2/1.25]
2023-05-22 20:50:59 [ℹ]  nodegroup "g5" will use "" [AmazonLinux2/1.25]
2023-05-22 20:50:59 [ℹ]  utilizing Kubernetes model 1.25
2023-05-22 20:50:59 [ℹ]  creating EKS cluster "do-eks-yaml-g5" in "us-east-1" area with managed nodes
2023-05-22 20:50:59 [ℹ]  2 nodegroups (g5, sys) had been included (based mostly on the embody/exclude guidelines)
2023-05-22 20:50:59 [ℹ]  will create a CloudFormation stack for cluster itself and 0 nodegroup stack(s)
2023-05-22 20:50:59 [ℹ]  will create a CloudFormation stack for cluster itself and a couple of managed nodegroup stack(s)
2023-05-22 20:50:59 [ℹ]  should you encounter any points, examine CloudFormation console or strive 'eksctl utils describe-stacks --region=us-east-1 --cluster=do-eks-yaml-g5'
2023-05-22 20:50:59 [ℹ]  Kubernetes API endpoint entry will use default of {publicAccess=true, privateAccess=false} for cluster "do-eks-yaml-g5" in "us-east-1"
2023-05-22 20:50:59 [ℹ]  CloudWatch logging won't be enabled for cluster "do-eks-yaml-g5" in "us-east-1"
2023-05-22 20:50:59 [ℹ]  you'll be able to allow it with 'eksctl utils update-cluster-logging --enable-types={SPECIFY-YOUR-LOG-TYPES-HERE (e.g. all)} --region=us-east-1 --cluster=do-eks-yaml-g5'
2023-05-22 20:50:59 [ℹ]  
2 sequential duties: { create cluster management aircraft "do-eks-yaml-g5", 
    2 sequential sub-tasks: { 
        4 sequential sub-tasks: { 
            await management aircraft to turn into prepared,
            affiliate IAM OIDC supplier,
            2 sequential sub-tasks: { 
                create IAM position for serviceaccount "kube-system/aws-node",
                create serviceaccount "kube-system/aws-node",
            },
            restart daemonset "kube-system/aws-node",
        },
        2 parallel sub-tasks: { 
            create managed nodegroup "sys",
            create managed nodegroup "g5",
        },
    } 
}
2023-05-22 20:50:59 [ℹ]  constructing cluster stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:51:00 [ℹ]  deploying stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:51:30 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:52:00 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:53:01 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:54:01 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:55:01 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:56:02 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:57:02 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:58:02 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 20:59:02 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 21:00:03 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 21:01:03 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 21:02:03 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 21:03:04 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-cluster"
2023-05-22 21:05:07 [ℹ]  constructing iamserviceaccount stack "eksctl-do-eks-yaml-g5-addon-iamserviceaccount-kube-system-aws-node"
2023-05-22 21:05:10 [ℹ]  deploying stack "eksctl-do-eks-yaml-g5-addon-iamserviceaccount-kube-system-aws-node"
2023-05-22 21:05:10 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-addon-iamserviceaccount-kube-system-aws-node"
2023-05-22 21:05:40 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-addon-iamserviceaccount-kube-system-aws-node"
2023-05-22 21:05:40 [ℹ]  serviceaccount "kube-system/aws-node" already exists
2023-05-22 21:05:41 [ℹ]  up to date serviceaccount "kube-system/aws-node"
2023-05-22 21:05:41 [ℹ]  daemonset "kube-system/aws-node" restarted
2023-05-22 21:05:41 [ℹ]  constructing managed nodegroup stack "eksctl-do-eks-yaml-g5-nodegroup-sys"
2023-05-22 21:05:41 [ℹ]  constructing managed nodegroup stack "eksctl-do-eks-yaml-g5-nodegroup-g5"
2023-05-22 21:05:42 [ℹ]  deploying stack "eksctl-do-eks-yaml-g5-nodegroup-sys"
2023-05-22 21:05:42 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-sys"
2023-05-22 21:05:42 [ℹ]  deploying stack "eksctl-do-eks-yaml-g5-nodegroup-g5"
2023-05-22 21:05:42 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-g5"
2023-05-22 21:06:12 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-sys"
2023-05-22 21:06:12 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-g5"
2023-05-22 21:06:55 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-sys"
2023-05-22 21:07:11 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-g5"
2023-05-22 21:08:29 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-g5"
2023-05-22 21:08:45 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-sys"
2023-05-22 21:09:52 [ℹ]  ready for CloudFormation stack "eksctl-do-eks-yaml-g5-nodegroup-g5"
2023-05-22 21:09:53 [ℹ]  ready for the management aircraft to turn into prepared
2023-05-22 21:09:53 [✔]  saved kubeconfig as "/root/.kube/config"
2023-05-22 21:09:53 [ℹ]  1 process: { set up Nvidia gadget plugin }
W0522 21:09:54.155837    1668 warnings.go:70] spec.template.metadata.annotations[scheduler.alpha.kubernetes.io/critical-pod]: non-functional in v1.16+; use the "priorityClassName" discipline as an alternative
2023-05-22 21:09:54 [ℹ]  created "kube-system:DaemonSet.apps/nvidia-device-plugin-daemonset"
2023-05-22 21:09:54 [ℹ]  as you're utilizing the EKS-Optimized Accelerated AMI with a GPU-enabled occasion kind, the Nvidia Kubernetes gadget plugin was robotically put in.
        to skip putting in it, use --install-nvidia-plugin=false.
2023-05-22 21:09:54 [✔]  all EKS cluster assets for "do-eks-yaml-g5" have been created
2023-05-22 21:09:54 [ℹ]  nodegroup "sys" has 1 node(s)
2023-05-22 21:09:54 [ℹ]  node "ip-192-168-18-137.ec2.inside" is prepared
2023-05-22 21:09:54 [ℹ]  ready for at the very least 1 node(s) to turn into prepared in "sys"
2023-05-22 21:09:54 [ℹ]  nodegroup "sys" has 1 node(s)
2023-05-22 21:09:54 [ℹ]  node "ip-192-168-18-137.ec2.inside" is prepared
2023-05-22 21:09:55 [ℹ]  kubectl command ought to work with "/root/.kube/config", strive 'kubectl get nodes'
2023-05-22 21:09:55 [✔]  EKS cluster "do-eks-yaml-g5" in "us-east-1" area is prepared

Mon Might 22 21:09:55 UTC 2023
Executed creating cluster utilizing /eks/conf/eksctl/yaml/eks-gpu-g5.yaml

/eks

To confirm that your cluster is created efficiently, run the next command

kubectl get nodes -L node.kubernetes.io/instance-type

The output is much like the next:

NAME                              STATUS   ROLES    AGE   VERSION               INSTANCE_TYPE
ip-192-168-18-137.ec2.inside    Prepared    <none>   47m   v1.25.9-eks-0a21954   m5.xlarge
ip-192-168-214-241.ec2.inside   Prepared    <none>   46m   v1.25.9-eks-0a21954   g5.2xlarge

On this instance, we’ve got one m5.xlarge and one g5.2xlarge occasion in our cluster; due to this fact, we see two nodes listed within the previous output.

Throughout the cluster creation course of, the NVIDIA gadget plugin will get put in. You will have to take away it after cluster creation as a result of we’ll use the NVIDIA GPU Operator as an alternative.

Delete the plugin with the next command

kubectl -n kube-system delete daemonset nvidia-device-plugin-daemonset

We get the next output:

daemonset.apps "nvidia-device-plugin-daemonset" deleted

Set up the NVIDIA Helm repo

Set up the NVIDIA Helm repo with the next command:

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia && helm repo replace

Deploy the DCGM exporter with the NVIDIA GPU Operator

To deploy the DCGM exporter, full the next steps:

Put together the DCGM exporter GPU metrics configuration

curl https://uncooked.githubusercontent.com/NVIDIA/dcgm-exporter/fundamental/and many others/dcp-metrics-included.csv > dcgm-metrics.csv

You will have the choice to edit the dcgm-metrics.csv file. You may add or take away any metrics as wanted.

Create the gpu-operator namespace and DCGM exporter ConfigMap

kubectl create namespace gpu-operator && /
kubectl create configmap metrics-config -n gpu-operator --from-file=dcgm-metrics.csv

The output is as follows:

namespace/gpu-operator created
configmap/metrics-config created

Apply the GPU operator to the EKS cluster

helm set up --wait --generate-name -n gpu-operator --create-namespace nvidia/gpu-operator 
--set dcgmExporter.config.identify=metrics-config 
--set dcgmExporter.env[0].identify=DCGM_EXPORTER_COLLECTORS 
--set dcgmExporter.env[0].worth=/and many others/dcgm-exporter/dcgm-metrics.csv 
--set toolkit.enabled=false

The output is as follows:

NAME: gpu-operator-1684795140
LAST DEPLOYED: Day Month Date HH:mm:ss YYYY
NAMESPACE: gpu-operator
STATUS: deployed
REVISION: 1
TEST SUITE: None

Affirm that the DCGM exporter pod is working

kubectl -n gpu-operator get pods | grep dcgm

The output is as follows:

nvidia-dcgm-exporter-lkmfr       1/1     Operating    0   1m

In the event you examine the logs, it’s best to see the “Beginning webserver” message:

kubectl -n gpu-operator logs -f $(kubectl -n gpu-operator get pods | grep dcgm | minimize -d ' ' -f 1)

The output is as follows:

Defaulted container "nvidia-dcgm-exporter" out of: nvidia-dcgm-exporter, toolkit-validation (init)
time="2023-05-22T22:40:08Z" degree=data msg="Beginning dcgm-exporter"
time="2023-05-22T22:40:08Z" degree=data msg="DCGM efficiently initialized!"
time="2023-05-22T22:40:08Z" degree=data msg="Accumulating DCP Metrics"
time="2023-05-22T22:40:08Z" degree=data msg="No configmap knowledge specified, falling again to metric file /and many others/dcgm-exporter/dcgm-metrics.csv"
time="2023-05-22T22:40:08Z" degree=data msg="Initializing system entities of kind: GPU"
time="2023-05-22T22:40:09Z" degree=data msg="Initializing system entities of kind: NvSwitch"
time="2023-05-22T22:40:09Z" degree=data msg="Not gathering swap metrics: no switches to observe"
time="2023-05-22T22:40:09Z" degree=data msg="Initializing system entities of kind: NvLink"
time="2023-05-22T22:40:09Z" degree=data msg="Not gathering hyperlink metrics: no switches to observe"
time="2023-05-22T22:40:09Z" degree=data msg="Kubernetes metrics assortment enabled!"
time="2023-05-22T22:40:09Z" degree=data msg="Pipeline beginning"
time="2023-05-22T22:40:09Z" degree=data msg="Beginning webserver"

NVIDIA DCGM Exporter exposes a Prometheus metrics endpoint, which will be ingested by the CloudWatch agent. To see the endpoint, use the next command:

kubectl -n gpu-operator get companies | grep dcgm

We get the next output:

nvidia-dcgm-exporter    ClusterIP   10.100.183.207   <none>   9400/TCP   10m

To generate some GPU utilization, we deploy a pod that runs the gpu-burn binary

kubectl apply -f https://uncooked.githubusercontent.com/aws-samples/aws-do-eks/fundamental/Container-Root/eks/deployment/gpu-metrics/gpu-burn-deployment.yaml

The output is as follows:

deployment.apps/gpu-burn created

This deployment makes use of a single GPU to provide a steady sample of 100% utilization for 20 seconds adopted by 0% utilization for 20 seconds.

To verify the endpoint works, you’ll be able to run a brief container that makes use of curl to learn the content material of http://nvidia-dcgm-exporter:9400/metrics

kubectl -n gpu-operator run -it --rm curl --restart="By no means" --image=curlimages/curl --command -- curl http://nvidia-dcgm-exporter:9400/metrics

We get the next output:

# HELP DCGM_FI_DEV_SM_CLOCK SM clock frequency (in MHz).
# TYPE DCGM_FI_DEV_SM_CLOCK gauge
DCGM_FI_DEV_SM_CLOCK{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 1455
# HELP DCGM_FI_DEV_MEM_CLOCK Reminiscence clock frequency (in MHz).
# TYPE DCGM_FI_DEV_MEM_CLOCK gauge
DCGM_FI_DEV_MEM_CLOCK{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 6250
# HELP DCGM_FI_DEV_GPU_TEMP GPU temperature (in C).
# TYPE DCGM_FI_DEV_GPU_TEMP gauge
DCGM_FI_DEV_GPU_TEMP{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 65
# HELP DCGM_FI_DEV_POWER_USAGE Energy draw (in W).
# TYPE DCGM_FI_DEV_POWER_USAGE gauge
DCGM_FI_DEV_POWER_USAGE{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 299.437000
# HELP DCGM_FI_DEV_TOTAL_ENERGY_CONSUMPTION Whole power consumption since boot (in mJ).
# TYPE DCGM_FI_DEV_TOTAL_ENERGY_CONSUMPTION counter
DCGM_FI_DEV_TOTAL_ENERGY_CONSUMPTION{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 15782796862
# HELP DCGM_FI_DEV_PCIE_REPLAY_COUNTER Whole variety of PCIe retries.
# TYPE DCGM_FI_DEV_PCIE_REPLAY_COUNTER counter
DCGM_FI_DEV_PCIE_REPLAY_COUNTER{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_GPU_UTIL GPU utilization (in %).
# TYPE DCGM_FI_DEV_GPU_UTIL gauge
DCGM_FI_DEV_GPU_UTIL{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 100
# HELP DCGM_FI_DEV_MEM_COPY_UTIL Reminiscence utilization (in %).
# TYPE DCGM_FI_DEV_MEM_COPY_UTIL gauge
DCGM_FI_DEV_MEM_COPY_UTIL{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 38
# HELP DCGM_FI_DEV_ENC_UTIL Encoder utilization (in %).
# TYPE DCGM_FI_DEV_ENC_UTIL gauge
DCGM_FI_DEV_ENC_UTIL{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_DEC_UTIL Decoder utilization (in %).
# TYPE DCGM_FI_DEV_DEC_UTIL gauge
DCGM_FI_DEV_DEC_UTIL{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_XID_ERRORS Worth of the final XID error encountered.
# TYPE DCGM_FI_DEV_XID_ERRORS gauge
DCGM_FI_DEV_XID_ERRORS{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_FB_FREE Framebuffer reminiscence free (in MiB).
# TYPE DCGM_FI_DEV_FB_FREE gauge
DCGM_FI_DEV_FB_FREE{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 2230
# HELP DCGM_FI_DEV_FB_USED Framebuffer reminiscence used (in MiB).
# TYPE DCGM_FI_DEV_FB_USED gauge
DCGM_FI_DEV_FB_USED{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 20501
# HELP DCGM_FI_DEV_NVLINK_BANDWIDTH_TOTAL Whole variety of NVLink bandwidth counters for all lanes.
# TYPE DCGM_FI_DEV_NVLINK_BANDWIDTH_TOTAL counter
DCGM_FI_DEV_NVLINK_BANDWIDTH_TOTAL{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_VGPU_LICENSE_STATUS vGPU License standing
# TYPE DCGM_FI_DEV_VGPU_LICENSE_STATUS gauge
DCGM_FI_DEV_VGPU_LICENSE_STATUS{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_UNCORRECTABLE_REMAPPED_ROWS Variety of remapped rows for uncorrectable errors
# TYPE DCGM_FI_DEV_UNCORRECTABLE_REMAPPED_ROWS counter
DCGM_FI_DEV_UNCORRECTABLE_REMAPPED_ROWS{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_CORRECTABLE_REMAPPED_ROWS Variety of remapped rows for correctable errors
# TYPE DCGM_FI_DEV_CORRECTABLE_REMAPPED_ROWS counter
DCGM_FI_DEV_CORRECTABLE_REMAPPED_ROWS{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_DEV_ROW_REMAP_FAILURE Whether or not remapping of rows has failed
# TYPE DCGM_FI_DEV_ROW_REMAP_FAILURE gauge
DCGM_FI_DEV_ROW_REMAP_FAILURE{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0
# HELP DCGM_FI_PROF_GR_ENGINE_ACTIVE Ratio of time the graphics engine is energetic (in %).
# TYPE DCGM_FI_PROF_GR_ENGINE_ACTIVE gauge
DCGM_FI_PROF_GR_ENGINE_ACTIVE{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0.808369
# HELP DCGM_FI_PROF_PIPE_TENSOR_ACTIVE Ratio of cycles the tensor (HMMA) pipe is energetic (in %).
# TYPE DCGM_FI_PROF_PIPE_TENSOR_ACTIVE gauge
DCGM_FI_PROF_PIPE_TENSOR_ACTIVE{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0.000000
# HELP DCGM_FI_PROF_DRAM_ACTIVE Ratio of cycles the gadget reminiscence interface is energetic sending or receiving knowledge (in %).
# TYPE DCGM_FI_PROF_DRAM_ACTIVE gauge
DCGM_FI_PROF_DRAM_ACTIVE{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 0.315787
# HELP DCGM_FI_PROF_PCIE_TX_BYTES The speed of information transmitted over the PCIe bus - together with each protocol headers and knowledge payloads - in bytes per second.
# TYPE DCGM_FI_PROF_PCIE_TX_BYTES gauge
DCGM_FI_PROF_PCIE_TX_BYTES{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 3985328
# HELP DCGM_FI_PROF_PCIE_RX_BYTES The speed of information obtained over the PCIe bus - together with each protocol headers and knowledge payloads - in bytes per second.
# TYPE DCGM_FI_PROF_PCIE_RX_BYTES gauge
DCGM_FI_PROF_PCIE_RX_BYTES{gpu="0",UUID="GPU-ff76466b-22fc-f7a9-abe2-ce3ac453b8b3",gadget="nvidia0",modelName="NVIDIA A10G",Hostname="nvidia-dcgm-exporter-48cwd",DCGM_FI_DRIVER_VERSION="470.182.03",container="fundamental",namespace="kube-system",pod="gpu-burn-c68d8c774-ltg9s"} 21715174
pod "curl" deleted

Configure and deploy the CloudWatch agent

To configure and deploy the CloudWatch agent, full the next steps:

Obtain the YAML file and edit it

curl -O https://uncooked.githubusercontent.com/aws-samples/amazon-cloudwatch-container-insights/k8s/1.3.15/k8s-deployment-manifest-templates/deployment-mode/service/cwagent-prometheus/prometheus-eks.yaml

The file accommodates a cwagent configmap and a prometheus configmap. For this put up, we edit each.

Edit the prometheus-eks.yaml file

Open the prometheus-eks.yaml file in your favourite editor and exchange the cwagentconfig.json part with the next content material:

apiVersion: v1
knowledge:
  # cwagent json config
  cwagentconfig.json: |
    {
      "logs": {
        "metrics_collected": {
          "prometheus": {
            "prometheus_config_path": "/and many others/prometheusconfig/prometheus.yaml",
            "emf_processor": {
              "metric_declaration": [
                {
                  "source_labels": ["Service"],
                  "label_matcher": ".*dcgm.*",
                  "dimensions": [["Service","Namespace","ClusterName","job","pod"]],
                  "metric_selectors": [
                    "^DCGM_FI_DEV_GPU_UTIL$",
                    "^DCGM_FI_DEV_DEC_UTIL$",
                    "^DCGM_FI_DEV_ENC_UTIL$",
                    "^DCGM_FI_DEV_MEM_CLOCK$",
                    "^DCGM_FI_DEV_MEM_COPY_UTIL$",
                    "^DCGM_FI_DEV_POWER_USAGE$",
                    "^DCGM_FI_DEV_ROW_REMAP_FAILURE$",
                    "^DCGM_FI_DEV_SM_CLOCK$",
                    "^DCGM_FI_DEV_XID_ERRORS$",
                    "^DCGM_FI_PROF_DRAM_ACTIVE$",
                    "^DCGM_FI_PROF_GR_ENGINE_ACTIVE$",
                    "^DCGM_FI_PROF_PCIE_RX_BYTES$",
                    "^DCGM_FI_PROF_PCIE_TX_BYTES$",
                    "^DCGM_FI_PROF_PIPE_TENSOR_ACTIVE$"
                  ]
                }
              ]
            }
          }
        },
        "force_flush_interval": 5
      }
    }

Within the prometheus config part, append the next job definition for the DCGM exporter

- job_name: 'kubernetes-pod-dcgm-exporter'
      sample_limit: 10000
      metrics_path: /api/v1/metrics/prometheus
      kubernetes_sd_configs:
      - position: pod
      relabel_configs:
      - source_labels: [__meta_kubernetes_pod_container_name]
        motion: maintain
        regex: '^DCGM.*$'
      - source_labels: [__address__]
        motion: exchange
        regex: ([^:]+)(?::d+)?
        substitute: ${1}:9400
        target_label: __address__
      - motion: labelmap
        regex: __meta_kubernetes_pod_label_(.+)
      - motion: exchange
        source_labels:
        - __meta_kubernetes_namespace
        target_label: Namespace
      - source_labels: [__meta_kubernetes_pod]
        motion: exchange
        target_label: pod
      - motion: exchange
        source_labels:
        - __meta_kubernetes_pod_container_name
        target_label: container_name
      - motion: exchange
        source_labels:
        - __meta_kubernetes_pod_controller_name
        target_label: pod_controller_name
      - motion: exchange
        source_labels:
        - __meta_kubernetes_pod_controller_kind
        target_label: pod_controller_kind
      - motion: exchange
        source_labels:
        - __meta_kubernetes_pod_phase
        target_label: pod_phase
      - motion: exchange
        source_labels:
        - __meta_kubernetes_pod_node_name
        target_label: NodeName

Save the file and apply the cwagent-dcgm configuration to your cluster

kubectl apply -f ./prometheus-eks.yaml

We get the next output:

namespace/amazon-cloudwatch created
configmap/prometheus-cwagentconfig created
configmap/prometheus-config created
serviceaccount/cwagent-prometheus created
clusterrole.rbac.authorization.k8s.io/cwagent-prometheus-role created
clusterrolebinding.rbac.authorization.k8s.io/cwagent-prometheus-role-binding created
deployment.apps/cwagent-prometheus created

Affirm that the CloudWatch agent pod is working

kubectl -n amazon-cloudwatch get pods

We get the next output:

NAME                                  READY   STATUS    RESTARTS   AGE
cwagent-prometheus-7dfd69cc46-s4cx7   1/1     Operating   0          15m

Visualize metrics on the CloudWatch console

To visualise the metrics in CloudWatch, full the next steps:

On the CloudWatch console, beneath Metrics within the navigation pane, select All metrics
Within the Customized namespaces part, select the brand new entry for ContainerInsights/Prometheus

For extra details about the ContainerInsights/Prometheus namespace, seek advice from Scraping additional Prometheus sources and importing those metrics.

Drill all the way down to the metric names and select DCGM_FI_DEV_GPU_UTIL
On the Graphed metrics tab, set Interval to 5 seconds

Set the refresh interval to 10 seconds

You will notice the metrics collected from DCGM exporter that visualize the gpu-burn sample on and off every 20 seconds.

On the Browse tab, you’ll be able to see the information, together with the pod identify for every metric.

The EKS API metadata has been mixed with the DCGM metrics knowledge, ensuing within the supplied pod-based GPU metrics.

This concludes the primary strategy of exporting DCGM metrics to CloudWatch through the CloudWatch agent.

Within the subsequent part, we configure the second structure, which exports the DCGM metrics to Prometheus, and we visualize them with Grafana.

Use Prometheus and Grafana to visualise GPU metrics from DCGM

Full the next steps:

Add the Prometheus group helm chart

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts

This chart deploys each Prometheus and Grafana. We have to make some edits to the chart earlier than working the set up command.

Save the chart configuration values to a file in /tmp

helm examine values prometheus-community/kube-prometheus-stack > /tmp/kube-prometheus-stack.values

Edit the char configuration file

Edit the saved file (/tmp/kube-prometheus-stack.values) and set the next possibility by in search of the setting identify and setting the worth:

prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false

Add the next ConfigMap to the additionalScrapeConfigs part

additionalScrapeConfigs:
- job_name: gpu-metrics
  scrape_interval: 1s
  metrics_path: /metrics
  scheme: http
  kubernetes_sd_configs:
  - position: endpoints
    namespaces:
      names:
      - gpu-operator
  relabel_configs:
  - source_labels: [__meta_kubernetes_pod_node_name]
    motion: exchange
    target_label: kubernetes_node

Deploy the Prometheus stack with the up to date values

helm set up prometheus-community/kube-prometheus-stack 
--create-namespace --namespace prometheus 
--generate-name 
--values /tmp/kube-prometheus-stack.values

We get the next output:

NAME: kube-prometheus-stack-1684965548
LAST DEPLOYED: Wed Might 24 21:59:14 2023
NAMESPACE: prometheus
STATUS: deployed
REVISION: 1
NOTES:
kube-prometheus-stack has been put in. Test its standing by working:
  kubectl --namespace prometheus get pods -l "launch=kube-prometheus-stack-1684965548"

Go to https://github.com/prometheus-operator/kube-prometheus
 for directions on methods to create & configure Alertmanager 
and Prometheus situations utilizing the Operator.

Affirm that the Prometheus pods are working

kubectl get pods -n prometheus

We get the next output:

NAME                                                              READY   STATUS    RESTARTS   AGE
alertmanager-kube-prometheus-stack-1684-alertmanager-0            2/2     Operating   0          6m55s
kube-prometheus-stack-1684-operator-6c87649878-j7v55              1/1     Operating   0          6m58s
kube-prometheus-stack-1684965548-grafana-dcd7b4c96-bzm8p          3/3     Operating   0          6m58s
kube-prometheus-stack-1684965548-kube-state-metrics-7d856dptlj5   1/1     Operating   0          6m58s
kube-prometheus-stack-1684965548-prometheus-node-exporter-2fbl5   1/1     Operating   0          6m58s
kube-prometheus-stack-1684965548-prometheus-node-exporter-m7zmv   1/1     Operating   0          6m58s
prometheus-kube-prometheus-stack-1684-prometheus-0                2/2     Operating   0          6m55s

Prometheus and Grafana pods are within the Operating state.

Subsequent, we validate that DCGM metrics are flowing into Prometheus.

Port-forward the Prometheus UI

There are other ways to reveal the Prometheus UI working in EKS to requests originating outdoors of the cluster. We are going to use kubectl port-forwarding. Up to now, we’ve got been executing instructions contained in the aws-do-eks container. To entry the Prometheus service working within the cluster, we’ll create a tunnel from the host. Right here the aws-do-eks container is working by executing the next command outdoors of the container, in a brand new terminal shell on the host. We are going to seek advice from this as “host shell”.

kubectl -n prometheus port-forward svc/$(kubectl -n prometheus get svc | grep prometheus | grep -v alertmanager | grep -v operator | grep -v grafana | grep -v metrics | grep -v exporter | grep -v operated | minimize -d ' ' -f 1) 8080:9090 &

Whereas the port-forwarding course of is working, we’re in a position to entry the Prometheus UI from the host as described beneath.

Open the Prometheus UI
- If you’re utilizing Cloud9, please navigate to Preview->Preview Operating Software to open the Prometheus UI in a tab contained in the Cloud9 IDE, then click on the icon within the upper-right nook of the tab to come out in a brand new window.
- If you’re in your native host or linked to an EC2 occasion through distant desktop open a browser and go to the URL http://localhost:8080.

Enter DCGM to see the DCGM metrics which can be flowing into Prometheus
Choose DCGM_FI_DEV_GPU_UTIL, select Execute, after which navigate to the Graph tab to see the anticipated GPU utilization sample

Cease the Prometheus port-forwarding course of

Run the next command line in your host shell:

kill -9 $(ps -aef | grep port-forward | grep -v grep | grep prometheus | awk '{print $2}')

Now we are able to visualize the DCGM metrics through Grafana Dashboard.

Retrieve the password to log in to the Grafana UI

kubectl -n prometheus get secret $(kubectl -n prometheus get secrets and techniques | grep grafana | minimize -d ' ' -f 1) -o jsonpath="{.knowledge.admin-password}" | base64 --decode ; echo

Port-forward the Grafana service

Run the next command line in your host shell:

kubectl port-forward -n prometheus svc/$(kubectl -n prometheus get svc | grep grafana | minimize -d ' ' -f 1) 8080:80 &

Entry the Grafana UI login display screen the identical means as you accessed the Prometheus UI earlier. If utilizing Cloud9, choose Preview->Preview Operating Software, then come out in a brand new window. If utilizing your native host or an EC2 occasion with distant desktop go to URL http://localhost:8080. Login with the consumer identify admin and the password you retrieved earlier.

Within the navigation pane, select Dashboards

Select New and Import

We’re going to import the default DCGM Grafana dashboard described in NVIDIA DCGM Exporter Dashboard.

Within the discipline import through grafana.com, enter 12239 and select Load
Select Prometheus as the information supply
Select Import

You will notice a dashboard much like the one within the following screenshot.

To show that these metrics are pod-based, we’re going to modify the GPU Utilization pane on this dashboard.

Select the pane and the choices menu (three dots)
Broaden the Choices part and edit the Legend discipline
Exchange the worth there with Pod {{pod}}, then select Save

The legend now exhibits the gpu-burn pod identify related to the displayed GPU utilization.

Cease port-forwarding the Grafana UI service

Run the next in your host shell:

kill -9 $(ps -aef | grep port-forward | grep -v grep | grep prometheus | awk '{print $2}')

On this put up, we demonstrated utilizing open-source Prometheus and Grafana deployed to the EKS cluster. If desired, this deployment will be substituted with Amazon Managed Service for Prometheus and Amazon Managed Grafana.

Clear up

To wash up the assets you created, run the next script from the aws-do-eks container shell:

Conclusion

On this put up, we utilized NVIDIA DCGM Exporter to gather GPU metrics and visualize them with both CloudWatch or Prometheus and Grafana. We invite you to make use of the architectures demonstrated right here to allow GPU utilization monitoring with NVIDIA DCGM in your personal AWS surroundings.

Extra assets

In regards to the authors

Amr Ragab is a former Principal Options Architect, EC2 Accelerated Computing at AWS. He’s dedicated to serving to prospects run computational workloads at scale. In his spare time, he likes touring and discovering new methods to combine expertise into each day life.

Alex Iankoulski is a Principal Options Architect, Self-managed Machine Studying at AWS. He’s a full-stack software program and infrastructure engineer who likes to do deep, hands-on work. In his position, he focuses on serving to prospects with containerization and orchestration of ML and AI workloads on container-powered AWS companies. He’s additionally the writer of the open-source do framework and a Docker captain who loves making use of container applied sciences to speed up the tempo of innovation whereas fixing the world’s greatest challenges. Throughout the previous 10 years, Alex has labored on democratizing AI and ML, combating local weather change, and making journey safer, healthcare higher, and power smarter.

Keita Watanabe is a Senior Options Architect of Frameworks ML Options at Amazon Net Providers the place he helps develop the business’s greatest cloud based mostly Self-managed Machine Studying options. His background is in Machine Studying analysis and improvement. Previous to becoming a member of AWS, Keita was working within the e-commerce business. Keita holds a Ph.D. in Science from the College of Tokyo.

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