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Benchmark

This user guide shows how to run benchmarks against a vLLM deployment, by using both the Gateway API inference extension, and a Kubernetes service as the load balancing strategy. The benchmark uses the Latency Profile Generator (LPG) tool to generate load and collect results.

Run benchmarks manually

Prerequisite: have an endpoint ready to server inference traffic

To serve via a Gateway using the inference extension, follow this user guide to deploy the sample vLLM application, and the inference extension.

You will more likely to see the benefits of the inference extension when there are a decent number of replicas to make the optimal routing decision. So consider scaling the sample application with more replicas:

kubectl scale --replicas=8 -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/vllm/gpu-deployment.yaml

To serve via a Kubernetes LoadBalancer service as a baseline comparison, you can expose the sample application:

kubectl expose -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/vllm/gpu-deployment.yaml --port=8081 --target-port=8000 --type=LoadBalancer

Run benchmark

The LPG benchmark tool works by sending traffic to the specified target IP and port, and collect results. Follow the steps below to run a single benchmark. You can deploy multiple LPG instances if you want to run benchmarks in parallel against different targets.

  1. Check out the repo.

    git clone https://github.com/kubernetes-sigs/gateway-api-inference-extension
cd gateway-api-inference-extension

  2. Get the target IP. Examples below show how to get the IP of a gateway or a LoadBalancer k8s service.

    # Get gateway IP
GW_IP=$(kubectl get gateway/inference-gateway -o jsonpath='{.status.addresses[0].value}')
# Get LoadBalancer k8s service IP
SVC_IP=$(kubectl get gateway/inference-gateway -o jsonpath='{.status.addresses[0].value}')

echo $GW_IP
echo $SVC_IP

  3. Then update the <target-ip> in ./config/manifests/benchmark/benchmark.yaml to your target IP. Feel free to adjust other parameters such as request_rates as well. For a complete list of LPG configurations, pls refer to the LPG user guide.

  4. Start the benchmark tool. kubectl apply -f ./config/manifests/benchmark/benchmark.yaml

  5. Wait for benchmark to finish and download the results. Use the benchmark_id environment variable to specify what this benchmark is for. For instance, inference-extension or k8s-svc. When the LPG tool finishes benchmarking, it will print a log line LPG_FINISHED, the script below will watch for that log line and then start downloading results.

    benchmark_id='my-benchmark' ./tools/benchmark/scripts/download-benchmark-results.bash

  6. After the script finishes, you should see benchmark results under ./tools/benchmark/output/default-run/my-benchmark/results/json folder.

Tips

  • You can specify run_id="runX" environment variable when running the download-benchmark-results.bash script. This is useful when you run benchmarks multiple times to get a more statistically meaningful results and group the results accordingly.
  • Update the request_rates that best suit your benchmark environment.

Run benchmarks automatically

The benchmark automation tool enables defining benchmarks via a config file and running the benchmarks automatically. It's currently experimental. To try it, refer to its user guide.

Advanced Benchmark Configurations

Pls refer to the LPG user guide for a detailed list of configuration knobs.

Analyze the results

This guide shows how to run the jupyter notebook using vscode.

  1. Create a python virtual environment.

    python3 -m venv .venv
source .venv/bin/activate

  2. Install the dependencies.

    pip install -r ./tools/benchmark/requirements.txt

  3. Open the notebook ./tools/benchmark/benchmark.ipynb, and run each cell. At the end you should see a bar chart like below:

    alt text