mirror of
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openai mock server, Split of "k8s bench, locust"
# What does this PR do? ## Test Plan # What does this PR do? ## Test Plan # What does this PR do? ## Test Plan
This commit is contained in:
Eric Huang
parent
78a59a4dbe
commit
9db924adae
11 changed files with 1234 additions and 3 deletions
57
docs/source/distributions/k8s-benchmark/apply.sh
Executable file
57
docs/source/distributions/k8s-benchmark/apply.sh
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#!/usr/bin/env bash
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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# Deploys the benchmark-specific components on top of the base k8s deployment (../k8s/apply.sh).
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export MOCK_INFERENCE_PORT=8080
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export STREAM_DELAY_SECONDS=0.005
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export POSTGRES_USER=llamastack
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export POSTGRES_DB=llamastack
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export POSTGRES_PASSWORD=llamastack
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export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
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export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
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export MOCK_INFERENCE_MODEL=mock-inference
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# Use llama-stack-benchmark-service as the benchmark server
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export LOCUST_HOST=http://llama-stack-benchmark-service:8323
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export LOCUST_BASE_PATH=/v1/openai/v1
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# Use vllm-service as the benchmark server
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# export LOCUST_HOST=http://vllm-server:8000
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# export LOCUST_BASE_PATH=/v1
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export BENCHMARK_INFERENCE_MODEL=$INFERENCE_MODEL
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set -euo pipefail
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set -x
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# Deploy benchmark-specific components
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# Deploy OpenAI mock server
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kubectl create configmap openai-mock --from-file=openai-mock-server.py \
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--dry-run=client -o yaml | kubectl apply --validate=false -f -
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envsubst < openai-mock-deployment.yaml | kubectl apply --validate=false -f -
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# Create configmap with our custom stack config
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kubectl create configmap llama-stack-config --from-file=stack_run_config.yaml \
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--dry-run=client -o yaml > stack-configmap.yaml
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kubectl apply --validate=false -f stack-configmap.yaml
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# Deploy our custom llama stack server (overriding the base one)
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envsubst < stack-k8s.yaml.template | kubectl apply --validate=false -f -
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# Deploy Locust load testing
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kubectl create configmap locust-script --from-file=locustfile.py \
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--dry-run=client -o yaml | kubectl apply --validate=false -f -
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envsubst < locust-k8s.yaml | kubectl apply --validate=false -f -
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131
docs/source/distributions/k8s-benchmark/locust-k8s.yaml
Normal file
131
docs/source/distributions/k8s-benchmark/locust-k8s.yaml
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@ -0,0 +1,131 @@
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: locust-master
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labels:
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app: locust
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role: master
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: locust
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role: master
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template:
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metadata:
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labels:
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app: locust
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role: master
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spec:
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containers:
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- name: locust-master
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image: locustio/locust:2.31.8
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ports:
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- containerPort: 8089 # Web UI
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- containerPort: 5557 # Master communication
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env:
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- name: LOCUST_HOST
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value: "${LOCUST_HOST}"
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- name: LOCUST_LOCUSTFILE
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value: "/locust/locustfile.py"
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- name: LOCUST_WEB_HOST
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value: "0.0.0.0"
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- name: LOCUST_MASTER
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value: "true"
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- name: LOCUST_BASE_PATH
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value: "${LOCUST_BASE_PATH}"
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- name: INFERENCE_MODEL
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value: "${BENCHMARK_INFERENCE_MODEL}"
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volumeMounts:
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- name: locust-script
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mountPath: /locust
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command: ["locust"]
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args:
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- "--master"
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- "--web-host=0.0.0.0"
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- "--web-port=8089"
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- "--host=${LOCUST_HOST}"
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- "--locustfile=/locust/locustfile.py"
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volumes:
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- name: locust-script
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configMap:
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name: locust-script
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: locust-worker
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labels:
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app: locust
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role: worker
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spec:
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replicas: 2 # Start with 2 workers, can be scaled up
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selector:
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matchLabels:
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app: locust
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role: worker
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template:
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metadata:
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labels:
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app: locust
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role: worker
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spec:
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containers:
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- name: locust-worker
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image: locustio/locust:2.31.8
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env:
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- name: LOCUST_HOST
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value: "${LOCUST_HOST}"
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- name: LOCUST_LOCUSTFILE
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value: "/locust/locustfile.py"
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- name: LOCUST_MASTER_HOST
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value: "locust-master-service"
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- name: LOCUST_MASTER_PORT
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value: "5557"
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- name: INFERENCE_MODEL
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value: "${BENCHMARK_INFERENCE_MODEL}"
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- name: LOCUST_BASE_PATH
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value: "${LOCUST_BASE_PATH}"
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volumeMounts:
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- name: locust-script
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mountPath: /locust
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command: ["locust"]
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args:
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- "--worker"
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- "--master-host=locust-master-service"
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- "--master-port=5557"
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- "--locustfile=/locust/locustfile.py"
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volumes:
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- name: locust-script
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configMap:
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name: locust-script
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: locust-master-service
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spec:
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selector:
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app: locust
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role: master
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ports:
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- name: web-ui
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port: 8089
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targetPort: 8089
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- name: master-comm
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port: 5557
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targetPort: 5557
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type: ClusterIP
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: locust-web-ui
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spec:
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selector:
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app: locust
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role: master
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ports:
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- port: 8089
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targetPort: 8089
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type: ClusterIP # Keep internal, use port-forward to access
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78
docs/source/distributions/k8s-benchmark/locustfile.py
Normal file
78
docs/source/distributions/k8s-benchmark/locustfile.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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"""
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Locust load testing script for Llama Stack with Prism mock OpenAI provider.
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"""
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import random
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from locust import HttpUser, task, between
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import os
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base_path = os.getenv("LOCUST_BASE_PATH", "/v1/openai/v1")
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MODEL_ID = os.getenv("INFERENCE_MODEL")
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class LlamaStackUser(HttpUser):
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wait_time = between(0.0, 0.0001)
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def on_start(self):
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"""Setup authentication and test data."""
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# No auth required for benchmark server
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self.headers = {
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"Content-Type": "application/json"
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}
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# Test messages of varying lengths
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self.test_messages = [
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[{"role": "user", "content": "Hi"}],
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[{"role": "user", "content": "What is the capital of France?"}],
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[{"role": "user", "content": "Explain quantum physics in simple terms."}],
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[{"role": "user", "content": "Write a short story about a robot learning to paint."}],
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[
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{"role": "user", "content": "What is machine learning?"},
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{"role": "assistant", "content": "Machine learning is a subset of AI..."},
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{"role": "user", "content": "Can you give me a practical example?"}
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]
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]
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@task(weight=100)
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def chat_completion_streaming(self):
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"""Test streaming chat completion (20% of requests)."""
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messages = random.choice(self.test_messages)
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payload = {
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"model": MODEL_ID,
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"messages": messages,
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"stream": True,
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"max_tokens": 100
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}
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with self.client.post(
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f"{base_path}/chat/completions",
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headers=self.headers,
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json=payload,
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stream=True,
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catch_response=True
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) as response:
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if response.status_code == 200:
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chunks_received = 0
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try:
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for line in response.iter_lines():
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if line:
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line_str = line.decode('utf-8')
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if line_str.startswith('data: '):
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chunks_received += 1
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if line_str.strip() == 'data: [DONE]':
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break
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if chunks_received > 0:
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response.success()
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else:
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response.failure("No streaming chunks received")
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except Exception as e:
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response.failure(f"Streaming error: {e}")
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else:
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response.failure(f"HTTP {response.status_code}: {response.text}")
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: openai-mock
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labels:
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app: openai-mock
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: openai-mock
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template:
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metadata:
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labels:
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app: openai-mock
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spec:
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containers:
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- name: openai-mock
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image: python:3.12-slim
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ports:
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- containerPort: ${MOCK_INFERENCE_PORT}
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env:
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- name: PORT
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value: "${MOCK_INFERENCE_PORT}"
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- name: MOCK_MODELS
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value: "${MOCK_INFERENCE_MODEL}"
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- name: STREAM_DELAY_SECONDS
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value: "${STREAM_DELAY_SECONDS}"
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command: ["sh", "-c"]
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args:
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- |
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pip install flask &&
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python /app/openai-mock-server.py --port ${MOCK_INFERENCE_PORT}
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volumeMounts:
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- name: openai-mock-script
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mountPath: /app
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volumes:
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- name: openai-mock-script
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configMap:
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name: openai-mock
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: openai-mock-service
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spec:
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selector:
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app: openai-mock
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ports:
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- port: 8080
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targetPort: 8080
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type: ClusterIP
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190
docs/source/distributions/k8s-benchmark/openai-mock-server.py
Normal file
190
docs/source/distributions/k8s-benchmark/openai-mock-server.py
Normal file
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#!/usr/bin/env python3
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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"""
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OpenAI-compatible mock server that returns:
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- Hardcoded /models response for consistent validation
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- Valid OpenAI-formatted chat completion responses with dynamic content
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"""
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from flask import Flask, request, jsonify, Response
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import time
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import random
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import uuid
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import json
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import argparse
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import os
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app = Flask(__name__)
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# Models from environment variables
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def get_models():
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models_str = os.getenv("MOCK_MODELS", "mock-inference")
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model_ids = [m.strip() for m in models_str.split(",") if m.strip()]
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return {
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"object": "list",
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"data": [
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{
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"id": model_id,
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"object": "model",
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"created": 1234567890,
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"owned_by": "vllm"
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}
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for model_id in model_ids
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]
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}
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def generate_random_text(length=50):
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"""Generate random but coherent text for responses."""
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words = [
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"Hello", "there", "I'm", "an", "AI", "assistant", "ready", "to", "help", "you",
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"with", "your", "questions", "and", "tasks", "today", "Let", "me","know", "what",
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"you'd", "like", "to", "discuss", "or", "explore", "together", "I", "can", "assist",
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"with", "various", "topics", "including", "coding", "writing", "analysis", "and", "more"
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]
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return " ".join(random.choices(words, k=length))
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@app.route('/models', methods=['GET'])
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def list_models():
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models = get_models()
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print(f"[MOCK] Returning models: {[m['id'] for m in models['data']]}")
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return jsonify(models)
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@app.route('/chat/completions', methods=['POST'])
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def chat_completions():
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"""Return OpenAI-formatted chat completion responses."""
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data = request.get_json()
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default_model = get_models()['data'][0]['id']
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model = data.get('model', default_model)
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messages = data.get('messages', [])
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stream = data.get('stream', False)
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print(f"[MOCK] Chat completion request - model: {model}, stream: {stream}")
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if stream:
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return handle_streaming_completion(model, messages)
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else:
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return handle_non_streaming_completion(model, messages)
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def handle_non_streaming_completion(model, messages):
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response_text = generate_random_text(random.randint(20, 80))
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# Calculate realistic token counts
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prompt_tokens = sum(len(str(msg.get('content', '')).split()) for msg in messages)
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completion_tokens = len(response_text.split())
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response = {
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"id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
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"object": "chat.completion",
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"created": int(time.time()),
|
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"model": model,
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"choices": [
|
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{
|
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"index": 0,
|
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"message": {
|
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"role": "assistant",
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"content": response_text
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||||
},
|
||||
"finish_reason": "stop"
|
||||
}
|
||||
],
|
||||
"usage": {
|
||||
"prompt_tokens": prompt_tokens,
|
||||
"completion_tokens": completion_tokens,
|
||||
"total_tokens": prompt_tokens + completion_tokens
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}
|
||||
}
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||||
|
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return jsonify(response)
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|
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def handle_streaming_completion(model, messages):
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def generate_stream():
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# Generate response text
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full_response = generate_random_text(random.randint(30, 100))
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words = full_response.split()
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|
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# Send initial chunk
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initial_chunk = {
|
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"id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
"object": "chat.completion.chunk",
|
||||
"created": int(time.time()),
|
||||
"model": model,
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"role": "assistant", "content": ""}
|
||||
}
|
||||
]
|
||||
}
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yield f"data: {json.dumps(initial_chunk)}\n\n"
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|
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# Send word by word
|
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for i, word in enumerate(words):
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chunk = {
|
||||
"id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
"object": "chat.completion.chunk",
|
||||
"created": int(time.time()),
|
||||
"model": model,
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"content": f"{word} " if i < len(words) - 1 else word}
|
||||
}
|
||||
]
|
||||
}
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yield f"data: {json.dumps(chunk)}\n\n"
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# Configurable delay to simulate realistic streaming
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stream_delay = float(os.getenv("STREAM_DELAY_SECONDS", "0.005"))
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time.sleep(stream_delay)
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||||
# Send final chunk
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||||
final_chunk = {
|
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"id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
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"object": "chat.completion.chunk",
|
||||
"created": int(time.time()),
|
||||
"model": model,
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"content": ""},
|
||||
"finish_reason": "stop"
|
||||
}
|
||||
]
|
||||
}
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||||
yield f"data: {json.dumps(final_chunk)}\n\n"
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yield "data: [DONE]\n\n"
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||||
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||||
return Response(
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||||
generate_stream(),
|
||||
mimetype='text/event-stream',
|
||||
headers={
|
||||
'Cache-Control': 'no-cache',
|
||||
'Connection': 'keep-alive',
|
||||
'Access-Control-Allow-Origin': '*',
|
||||
}
|
||||
)
|
||||
|
||||
@app.route('/health', methods=['GET'])
|
||||
def health():
|
||||
return jsonify({"status": "healthy", "type": "openai-mock"})
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='OpenAI-compatible mock server')
|
||||
parser.add_argument('--port', type=int, default=8081,
|
||||
help='Port to run the server on (default: 8081)')
|
||||
args = parser.parse_args()
|
||||
|
||||
port = args.port
|
||||
|
||||
models = get_models()
|
||||
print("Starting OpenAI-compatible mock server...")
|
||||
print(f"- /models endpoint with: {[m['id'] for m in models['data']]}")
|
||||
print("- OpenAI-formatted chat/completion responses with dynamic content")
|
||||
print("- Streaming support with valid SSE format")
|
||||
print(f"- Listening on: http://0.0.0.0:{port}")
|
||||
app.run(host='0.0.0.0', port=port, debug=False)
|
||||
143
docs/source/distributions/k8s-benchmark/stack-configmap.yaml
Normal file
143
docs/source/distributions/k8s-benchmark/stack-configmap.yaml
Normal file
|
|
@ -0,0 +1,143 @@
|
|||
apiVersion: v1
|
||||
data:
|
||||
stack_run_config.yaml: |
|
||||
version: '2'
|
||||
image_name: kubernetes-benchmark-demo
|
||||
apis:
|
||||
- agents
|
||||
- inference
|
||||
- safety
|
||||
- telemetry
|
||||
- tool_runtime
|
||||
- vector_io
|
||||
providers:
|
||||
inference:
|
||||
- provider_id: vllm-inference
|
||||
provider_type: remote::vllm
|
||||
config:
|
||||
url: ${env.VLLM_URL:=http://localhost:8000/v1}
|
||||
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
|
||||
api_token: ${env.VLLM_API_TOKEN:=fake}
|
||||
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
|
||||
- provider_id: vllm-safety
|
||||
provider_type: remote::vllm
|
||||
config:
|
||||
url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1}
|
||||
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
|
||||
api_token: ${env.VLLM_API_TOKEN:=fake}
|
||||
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
|
||||
- provider_id: mock-vllm-inference
|
||||
provider_type: remote::vllm
|
||||
config:
|
||||
url: http://openai-mock-service:${env.MOCK_INFERENCE_PORT}
|
||||
max_tokens: 4096
|
||||
api_token: fake
|
||||
tls_verify: false
|
||||
- provider_id: sentence-transformers
|
||||
provider_type: inline::sentence-transformers
|
||||
config: {}
|
||||
vector_io:
|
||||
- provider_id: ${env.ENABLE_CHROMADB:+chromadb}
|
||||
provider_type: remote::chromadb
|
||||
config:
|
||||
url: ${env.CHROMADB_URL:=}
|
||||
kvstore:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
safety:
|
||||
- provider_id: llama-guard
|
||||
provider_type: inline::llama-guard
|
||||
config:
|
||||
excluded_categories: []
|
||||
agents:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
persistence_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
responses_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
telemetry:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
|
||||
sinks: ${env.TELEMETRY_SINKS:=console}
|
||||
tool_runtime:
|
||||
- provider_id: brave-search
|
||||
provider_type: remote::brave-search
|
||||
config:
|
||||
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
|
||||
max_results: 3
|
||||
- provider_id: tavily-search
|
||||
provider_type: remote::tavily-search
|
||||
config:
|
||||
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
|
||||
max_results: 3
|
||||
- provider_id: rag-runtime
|
||||
provider_type: inline::rag-runtime
|
||||
config: {}
|
||||
- provider_id: model-context-protocol
|
||||
provider_type: remote::model-context-protocol
|
||||
config: {}
|
||||
metadata_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
table_name: llamastack_kvstore
|
||||
inference_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
models:
|
||||
- metadata:
|
||||
embedding_dimension: 384
|
||||
model_id: all-MiniLM-L6-v2
|
||||
provider_id: sentence-transformers
|
||||
model_type: embedding
|
||||
- model_id: ${env.INFERENCE_MODEL}
|
||||
provider_id: vllm-inference
|
||||
model_type: llm
|
||||
- model_id: ${env.SAFETY_MODEL}
|
||||
provider_id: vllm-safety
|
||||
model_type: llm
|
||||
- model_id: ${env.MOCK_INFERENCE_MODEL}
|
||||
provider_id: mock-vllm-inference
|
||||
model_type: llm
|
||||
shields:
|
||||
- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}
|
||||
vector_dbs: []
|
||||
datasets: []
|
||||
scoring_fns: []
|
||||
benchmarks: []
|
||||
tool_groups:
|
||||
- toolgroup_id: builtin::websearch
|
||||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
server:
|
||||
port: 8323
|
||||
kind: ConfigMap
|
||||
metadata:
|
||||
creationTimestamp: null
|
||||
name: llama-stack-config
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
apiVersion: v1
|
||||
kind: PersistentVolumeClaim
|
||||
metadata:
|
||||
name: llama-benchmark-pvc
|
||||
spec:
|
||||
accessModes:
|
||||
- ReadWriteOnce
|
||||
resources:
|
||||
requests:
|
||||
storage: 1Gi
|
||||
---
|
||||
apiVersion: apps/v1
|
||||
kind: Deployment
|
||||
metadata:
|
||||
name: llama-stack-benchmark-server
|
||||
spec:
|
||||
replicas: 1
|
||||
selector:
|
||||
matchLabels:
|
||||
app.kubernetes.io/name: llama-stack-benchmark
|
||||
app.kubernetes.io/component: server
|
||||
template:
|
||||
metadata:
|
||||
labels:
|
||||
app.kubernetes.io/name: llama-stack-benchmark
|
||||
app.kubernetes.io/component: server
|
||||
spec:
|
||||
containers:
|
||||
- name: llama-stack-benchmark
|
||||
image: llamastack/distribution-starter:latest
|
||||
imagePullPolicy: Always # since we have specified latest instead of a version
|
||||
env:
|
||||
- name: ENABLE_CHROMADB
|
||||
value: "true"
|
||||
- name: CHROMADB_URL
|
||||
value: http://chromadb.default.svc.cluster.local:6000
|
||||
- name: POSTGRES_HOST
|
||||
value: postgres-server.default.svc.cluster.local
|
||||
- name: POSTGRES_PORT
|
||||
value: "5432"
|
||||
- name: INFERENCE_MODEL
|
||||
value: "${INFERENCE_MODEL}"
|
||||
- name: SAFETY_MODEL
|
||||
value: "${SAFETY_MODEL}"
|
||||
- name: TAVILY_SEARCH_API_KEY
|
||||
value: "${TAVILY_SEARCH_API_KEY}"
|
||||
- name: MOCK_INFERENCE_PORT
|
||||
value: "${MOCK_INFERENCE_PORT}"
|
||||
- name: VLLM_URL
|
||||
value: http://vllm-server.default.svc.cluster.local:8000/v1
|
||||
- name: VLLM_MAX_TOKENS
|
||||
value: "3072"
|
||||
- name: VLLM_SAFETY_URL
|
||||
value: http://vllm-server-safety.default.svc.cluster.local:8001/v1
|
||||
- name: VLLM_TLS_VERIFY
|
||||
value: "false"
|
||||
- name: MOCK_INFERENCE_MODEL
|
||||
value: "${MOCK_INFERENCE_MODEL}"
|
||||
command: ["python", "-m", "llama_stack.core.server.server", "/etc/config/stack_run_config.yaml", "--port", "8323"]
|
||||
ports:
|
||||
- containerPort: 8323
|
||||
volumeMounts:
|
||||
- name: llama-storage
|
||||
mountPath: /root/.llama
|
||||
- name: llama-config
|
||||
mountPath: /etc/config
|
||||
volumes:
|
||||
- name: llama-storage
|
||||
persistentVolumeClaim:
|
||||
claimName: llama-benchmark-pvc
|
||||
- name: llama-config
|
||||
configMap:
|
||||
name: llama-stack-config
|
||||
---
|
||||
apiVersion: v1
|
||||
kind: Service
|
||||
metadata:
|
||||
name: llama-stack-benchmark-service
|
||||
spec:
|
||||
selector:
|
||||
app.kubernetes.io/name: llama-stack-benchmark
|
||||
app.kubernetes.io/component: server
|
||||
ports:
|
||||
- name: http
|
||||
port: 8323
|
||||
targetPort: 8323
|
||||
type: ClusterIP
|
||||
136
docs/source/distributions/k8s-benchmark/stack_run_config.yaml
Normal file
136
docs/source/distributions/k8s-benchmark/stack_run_config.yaml
Normal file
|
|
@ -0,0 +1,136 @@
|
|||
version: '2'
|
||||
image_name: kubernetes-benchmark-demo
|
||||
apis:
|
||||
- agents
|
||||
- inference
|
||||
- safety
|
||||
- telemetry
|
||||
- tool_runtime
|
||||
- vector_io
|
||||
providers:
|
||||
inference:
|
||||
- provider_id: vllm-inference
|
||||
provider_type: remote::vllm
|
||||
config:
|
||||
url: ${env.VLLM_URL:=http://localhost:8000/v1}
|
||||
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
|
||||
api_token: ${env.VLLM_API_TOKEN:=fake}
|
||||
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
|
||||
- provider_id: vllm-safety
|
||||
provider_type: remote::vllm
|
||||
config:
|
||||
url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1}
|
||||
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
|
||||
api_token: ${env.VLLM_API_TOKEN:=fake}
|
||||
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
|
||||
- provider_id: mock-vllm-inference
|
||||
provider_type: remote::vllm
|
||||
config:
|
||||
url: http://openai-mock-service:${env.MOCK_INFERENCE_PORT}
|
||||
max_tokens: 4096
|
||||
api_token: fake
|
||||
tls_verify: false
|
||||
- provider_id: sentence-transformers
|
||||
provider_type: inline::sentence-transformers
|
||||
config: {}
|
||||
vector_io:
|
||||
- provider_id: ${env.ENABLE_CHROMADB:+chromadb}
|
||||
provider_type: remote::chromadb
|
||||
config:
|
||||
url: ${env.CHROMADB_URL:=}
|
||||
kvstore:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
safety:
|
||||
- provider_id: llama-guard
|
||||
provider_type: inline::llama-guard
|
||||
config:
|
||||
excluded_categories: []
|
||||
agents:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
persistence_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
responses_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
telemetry:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
|
||||
sinks: ${env.TELEMETRY_SINKS:=console}
|
||||
tool_runtime:
|
||||
- provider_id: brave-search
|
||||
provider_type: remote::brave-search
|
||||
config:
|
||||
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
|
||||
max_results: 3
|
||||
- provider_id: tavily-search
|
||||
provider_type: remote::tavily-search
|
||||
config:
|
||||
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
|
||||
max_results: 3
|
||||
- provider_id: rag-runtime
|
||||
provider_type: inline::rag-runtime
|
||||
config: {}
|
||||
- provider_id: model-context-protocol
|
||||
provider_type: remote::model-context-protocol
|
||||
config: {}
|
||||
metadata_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
table_name: llamastack_kvstore
|
||||
inference_store:
|
||||
type: postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
db: ${env.POSTGRES_DB:=llamastack}
|
||||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
models:
|
||||
- metadata:
|
||||
embedding_dimension: 384
|
||||
model_id: all-MiniLM-L6-v2
|
||||
provider_id: sentence-transformers
|
||||
model_type: embedding
|
||||
- model_id: ${env.INFERENCE_MODEL}
|
||||
provider_id: vllm-inference
|
||||
model_type: llm
|
||||
- model_id: ${env.SAFETY_MODEL}
|
||||
provider_id: vllm-safety
|
||||
model_type: llm
|
||||
- model_id: ${env.MOCK_INFERENCE_MODEL}
|
||||
provider_id: mock-vllm-inference
|
||||
model_type: llm
|
||||
shields:
|
||||
- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}
|
||||
vector_dbs: []
|
||||
datasets: []
|
||||
scoring_fns: []
|
||||
benchmarks: []
|
||||
tool_groups:
|
||||
- toolgroup_id: builtin::websearch
|
||||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
server:
|
||||
port: 8323
|
||||
|
|
@ -40,19 +40,19 @@ spec:
|
|||
value: "3072"
|
||||
- name: VLLM_SAFETY_URL
|
||||
value: http://vllm-server-safety.default.svc.cluster.local:8001/v1
|
||||
- name: VLLM_TLS_VERIFY
|
||||
value: "false"
|
||||
- name: POSTGRES_HOST
|
||||
value: postgres-server.default.svc.cluster.local
|
||||
- name: POSTGRES_PORT
|
||||
value: "5432"
|
||||
- name: VLLM_TLS_VERIFY
|
||||
value: "false"
|
||||
- name: INFERENCE_MODEL
|
||||
value: "${INFERENCE_MODEL}"
|
||||
- name: SAFETY_MODEL
|
||||
value: "${SAFETY_MODEL}"
|
||||
- name: TAVILY_SEARCH_API_KEY
|
||||
value: "${TAVILY_SEARCH_API_KEY}"
|
||||
command: ["python", "-m", "llama_stack.core.server.server", "--config", "/etc/config/stack_run_config.yaml", "--port", "8321"]
|
||||
command: ["python", "-m", "llama_stack.core.server.server", "/etc/config/stack_run_config.yaml", "--port", "8321"]
|
||||
ports:
|
||||
- containerPort: 8321
|
||||
volumeMounts:
|
||||
|
|
|
|||
|
|
@ -140,6 +140,9 @@ docs = [
|
|||
"requests",
|
||||
]
|
||||
codegen = ["rich", "pydantic", "jinja2>=3.1.6"]
|
||||
benchmark = [
|
||||
"locust>=2.37.14",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://github.com/meta-llama/llama-stack"
|
||||
|
|
|
|||
354
uv.lock
generated
354
uv.lock
generated
|
|
@ -290,6 +290,15 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/a9/cf/45fb5261ece3e6b9817d3d82b2f343a505fd58674a92577923bc500bd1aa/bcrypt-4.3.0-cp39-abi3-win_amd64.whl", hash = "sha256:e53e074b120f2877a35cc6c736b8eb161377caae8925c17688bd46ba56daaa5b", size = 152799, upload-time = "2025-02-28T01:23:53.139Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "bidict"
|
||||
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|
||||
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||||
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|||
Loading…
Add table
Add a link
Reference in a new issue