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Proposed Solution:

  • Utilizing NVIDIA Data Center GPU Manager (DCGM), suite of tools for managing and monitoring NVIDIA datacenter GPUs in cluster environments.
  • Has a dashboard which the customer can track the history of usages, and set alerts such as when GPU utilization suddenly increases or drops.
  • All tools that are utilized are open sourced.

Components:

  • DCGM Exporter: It exposes GPU metrics exporter for Prometheus leveraging NVIDIA DCGM. Running on each GPU machine under docker container. Lightweight.
  • Prometheus: monitoring system with a dimensional data model, flexible query language, efficient time series database and modern alerting approach.
  • Grafana Dashboard: platform for monitoring and observability
  • Prometheus and Grafana running under a Kubernetes cluster on a separate CPU node within the same Private Network.
  • Kubernetes running on k3s.

Prerequisites

  • You will need a CORE Paperspace Account that has permission to create A4000X4 and C5 droplets.
  • A local environment where you can run Terraform.

Tutorial

The tutorial begins now. Please follow each steps sequentially.

Obtain API Keys for your Paperspace Account

Obtain your API Keys for your CORE Paperspace Account by following the instructions at https://docs.digitalocean.com/reference/paperspace/api-keys/.

Edit the main.tf file

Install Terraform

Create a working directory for A4000 and run Terraform

  • Go to your home directory (e.g. cd /home/)
  • mkdir A4000
  • cd A0000
  • Place the main.tf file that you just edited above in this directory.
  • terraform init
  • terraform plan, to double check and ensure that it passes with the values configured in the main.tf (it should be 2 to add, 0 to change, 0 to destroy)
  • terraform apply, then enter "yes"
  • Wait for a few minutes until it finishes, then go to the Paperspace Console to ensure that A4000x4 machine is created.

Create a working directory for C5 and run Terraform

  • Go to your home directory (e.g. cd /home/)
  • mkdir C5
  • cd C5
  • Copy the main.tf from A4000 directory
  • Edit line 16 to change it to "tkni3aa4", this changes the OS to Ubuntu 20.04 Server
  • Edit line 39 to change the name to "C5 Machine"
  • Edit line 40 to change it to "C5"
  • terraform init
  • terraform plan, to double check and ensure that it passes with the values configured in the main.tf (it should be 2 to add, 0 to change, 0 to destroy)
  • terraform apply, then enter "yes"
  • Wait for a few minutes until it finishes, then go to the Paperspace Console to ensure that C5 machines is created.

On A4000 machine

  • ssh into your A4000 machine
  • sudo su
  • Run the DCGM Exporter Docker command: docker run -d --gpus all --rm -p 9400:9400 -c 300 nvcr.io/nvidia/k8s/dcgm-exporter:3.3.6-3.4.2-ubuntu22.04
  • The above container will output the promethesus metrics to localhost:9400 every 300ms. Run curl http://localhost:9400/metrics and you should see a bunch of metrics in Prometheus formatting.
  • Downlaod installgb.sh and run chmod +x installgb.sh and then run ./installgb.sh. This script will install the GPU Burn Container for later use.
  • Run hostname -I | cut -d" " -f 1 and grab the IP address to use in the next steps (on C5 machine, naming it as )

On C5 machine

  • ssh into your C5 machine
  • Download the install.sh script and run chmod +x install.sh and then run ./install.sh. This script performs the following:
    • Install k3s
    • Install Helm Chart and update the helm charts
  • Download the override.yaml script
  • Edit line 11 of the override.yaml to change it to <IP>:9400, where is obtained from the last step on the A4000 machine.
  • Download installpg.sh script and place it within the same directory as override.yaml
  • Run chmod +x installpg.sh and run source ./installpg.sh. This script performs the following:
    • Install prometheus
    • Install grafana
    • Configure nodeport for prometheus and grafana
  • Run kubectl get svc -o json | jq '.items[] | select(.metadata.name == "prometheus-server-ext" or .metadata.name == "grafana-ext") | { name: .metadata.name, nodePort: .spec.ports[].nodePort }' to output the port for gradana and prometheus dashboards. The dashboards are accessible through http://<public_ip>:<port>, where <public_ip> is the Public IP for the C5 Machine, and <port> is the output of the port.
  • Run hostname -I | cut -d" " -f 1 and grab the IP address for later use (naming it as ).
  • Run kubectl get secret --namespace default grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo to obtain the Grafana password

Access Prometheus

  • Access Prometheus that is running on the C5 machine through http://<public_ip>:<port> for the prometheus-server-ext
  • Go to http://<public_ip>:<port>/targets and ensure that dcgm endpoint has State UP. If the state is not up, double check the override.yaml part and if the DCGM exporter is running.

Access Grafana

  • Access Grafana that is running on the C5 machine through http://<public_ip>:<port> for the grafana-ext
  • Go to http://<public_ip>:<port> and login using the following credentials:
    • username: admin
    • password was obtained from the last step on the C5 maine
  • Go to Connections -> Data sources -> Add data source -> Select Time Series Database Prometheus -> Within the Connection field, Edit the Prometheus server URL to http://<IP2>:<port> -> scroll down to the bottom and click Save & test
  • Go to Dashboards -> Create Dashboard -> Import Dashboard -> Use the ID 12239 -> Load -> Select the Prometheus datasource that was just created -> Import
  • ssh into your A4000 machine, Run sudo su and then run docker run --rm --gpus all gpu_burn
  • Wait for 5 minutes, then go back to the Grafana Dashboards, you should see GPU Utilization, Temperature all jumped for a small period of time.

Destroy everything

  • Destroy the A4000 machine by cd into A4000 directory and run terraform destroy
  • Destroy the C5 machine by cs into C5 directory and run terraform destroy

Disclaimer

  • The A4000 and C5 machines, if spinned up with the Terraform script as indicated, will shut down in 42 hours. But the machines won't be deactivated unless you destory them.
  • Please file a GitHub Issue for any questions or comments.
  • Please note that this tutorial is only used for demoing purposes and further configuration and refinement is required for production usage.