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Example: Aggregating GPU Workload running on OpenShift AI Jupyter Notebook

The following instructions show step by step how to use SusQL to aggregate energy data consumed by a GPU utilizing Jupyter notebook running on OpenShift AI.

Prerequisites

The following are assumed to be installed and available.

  • OpenShift Cluster (verified on version 4.14)
  • OpenShift AI (verified on version 2.11)
  • Kepler (verified with community version 0.13.)
  • SusQL (verified with community version 0.0.22)
  • Command line access to the OpenShift Cluster (e.g., logged in with oc command) To use GPU functionality seamlessly within the cluster, a GPU and necessary software must be available:
  • NVIDIA GPU Operator (verified with version 24.3)
  • Node Feature Discovery Operator (verified with version 4.14)
  • Kernel Module Management Operator (verified with version 2.1)

Create a Jupyter Notebook

Any code that runs in a Jupyter notebook can be aggregated by SusQL. The following sample code demonstrates the use of GPU resources. This is also a good test case to verify that GPU is configured correctly.

pip install pycaret[full]

import torch
import time

if torch.cuda.is_available():
    device = torch.device('cuda')
else:
    device = torch.device('cpu')

matrix_size = 16384

x = torch.randn(matrix_size, matrix_size)
y = torch.randn(matrix_size, matrix_size)

x_gpu = x.to(device)
y_gpu = y.to(device)
torch.cuda.synchronize()

for i in range(10):
    start = time.time()
    result_gpu = torch.matmul(x_gpu, y_gpu)
    print("Run time using device",result_gpu.device,"is","{:.7f}".format(time.time() - start))

A Jupyter Notebook can be created and run through the following steps:

  • Log into OpenShift
  • Open the OpenShift AI Console by clicking the app tile, and then clicking "Red Hat OpenShift AI".
    • If this is your first time, or if you want a refresher, you can scroll down to the "Get oriented with learning resources" section and follow the "Creating a Jupyter notebook" tutorial. Otherwise continue with the instructions below.
  • Click "Applications" on the menu on the left hand side of the window. Click "Enabled". Then click "Launch Application" on the newly displayed tile.
    • The first time you will need to create a notebook server. These instructions were verified with the PyTorch image using CUDA v11.8, Python v3.9, and PyTorch v2.2.
  • Within the Jupyter notebook server, click the "+" sign and click on the "Python 3.9" tile. Copy the code above into cells in the notebook. (It is probably best to put the pip command in its own cell to run once before running the rest of the Python code.)
  • Verify that the code runs and uses the GPU.

Attach a SusQL label to the Jupyter Notebook server:

Although the OpenShift Web Console can be used to set labels on existing workloads, this is also easy to do from the command line:

The following command removes a SusQL label on the Jupyter Notebook Server pod, in case one happens to be defined.

$ oc label pod $(oc get po -n rhods-notebooks | grep jupyter | head -1 |  cut -f 1 -d" ") -n rhods-notebooks "susql.label/1-"
pod/jupyter-nb-kube-3aadmin-0 unlabeled

Next, this command sets the label Susql.label/1 to openshiftaij for the Jupyter notebook server running in namespace rhods-notebooks.

$ oc label pod $(oc get po -n rhods-notebooks | grep jupyter | head -1 |  cut -f 1 -d" ") -n rhods-notebooks "susql.label/1=openshiftaij"
pod/jupyter-nb-kube-3aadmin-0 labeled

And, finally, this command can verify that the label has been set

$ oc describe pod $(oc get po -n rhods-notebooks | grep jupyter | head -1 |  cut -f 1 -d" ") -n rhods-notebooks | grep -i susql
                  susql.label/1=openshiftaij

Create the SusQL LabelGroup

First create a LabelGroup definition file called openshiftaij.yaml as follows:

---
apiVersion: susql.ibm.com/v1
kind: LabelGroup
metadata:
    name: openshiftaij
    namespace: rhods-notebooks
spec:
    labels:
        - openshiftaij
---

And apply the file:

$ oc apply -f openshiftaij.yaml
labelgroup.susql.ibm.com/openshiftaij created

Visualize

  • Go back to the Jupyter Notebook Server, and run the workload. (Possibly change the number of times the program loops to a very large number to make the energy usage obvious.)
  • Then on the OpenShift Web GUI, click "Observe" on the left hand side menu, then click "Metrics" and enter the following search query, and click "Run queries":
susql_total_energy_joules{susql_label_1="openshiftaij"}

If you have cloned the GitHub susql-operator repository, you could also run the test/susqltop command to view energy aggregation from the command line.

$ test/susqltop
NameSpace           LabelGroup                              Labels                                                    TotalEnergy (J)
rhods-notebooks     openshiftaij                            ["openshiftaij"]                                          17963.00