237 lines
No EOL
6.1 KiB
Markdown
237 lines
No EOL
6.1 KiB
Markdown
## Deployment bigdl-llm serving service in K8S environment
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## Image
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To deploy BigDL-LLM-serving cpu in Kubernetes environment, please use this image: `intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT`
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## Before deployment
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### Models
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In this document, we will use `vicuna-7b-v1.5` as the deployment model.
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After downloading the model, please change name from `vicuna-7b-v1.5` to `vicuna-7b-v1.5-bigdl` to use `bigdl-llm` as the backend. The `bigdl-llm` backend will be used if model path contains `bigdl`. Otherwise, the original transformer-backend will be used.
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You can download the model from [here](https://huggingface.co/lmsys/vicuna-7b-v1.5).
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For ChatGLM models, users do not need to add `bigdl` into model path. We have already used the `BigDL-LLM` backend for this model.
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### Kubernetes config
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We recommend to setup your kubernetes cluster before deployment. Mostly importantly, please set `cpu-management-policy` to `static` by using this [tutorial](https://kubernetes.io/docs/tasks/administer-cluster/cpu-management-policies/). Also, it would be great to also set the `topology management policy` to `single-numa-node`.
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### Machine config
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Set hyper-threading to off, ensure that only physical cores are used during deployment.
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## Deployment
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### Reminder on `OMP_NUM_THREADS`
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The entrypoint of the image will try to set `OMP_NUM_THREADS` to the correct number by reading configs from the `runtime`. However, this only happens correctly if the `core-binding` feature is enabled. If not, please set environment variable `OMP_NUM_THREADS` manually in the yaml file.
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### Controller
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We use the following yaml file for controller deployment:
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```yaml
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apiVersion: v1
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kind: Pod
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metadata:
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name: bigdl-fschat-a1234bd-controller
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labels:
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fastchat-appid: a1234bd
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fastchat-app-type: controller
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spec:
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dnsPolicy: "ClusterFirst"
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containers:
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- name: fastchat-controller # fixed
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image: intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
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imagePullPolicy: IfNotPresent
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env:
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- name: CONTROLLER_HOST # fixed
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value: "0.0.0.0"
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- name: CONTROLLER_PORT # fixed
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value: "21005"
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- name: API_HOST # fixed
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value: "0.0.0.0"
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- name: API_PORT # fixed
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value: "8000"
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ports:
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- containerPort: 21005
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name: con-port
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- containerPort: 8000
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name: api-port
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resources:
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requests:
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memory: 16Gi
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cpu: 4
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limits:
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memory: 16Gi
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cpu: 4
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args: ["-m", "controller"]
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restartPolicy: "Never"
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---
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# Service for the controller
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apiVersion: v1
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kind: Service
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metadata:
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name: bigdl-a1234bd-fschat-controller-service
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spec:
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# You may also want to change this to use the cluster's feature
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type: NodePort
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selector:
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fastchat-appid: a1234bd
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fastchat-app-type: controller
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ports:
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- name: cont-port
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protocol: TCP
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port: 21005
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targetPort: 21005
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- name: api-port
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protocol: TCP
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port: 8000
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targetPort: 8000
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```
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### Worker
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We use the following deployment for worker deployment:
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```yaml
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: bigdl-fschat-a1234bd-worker-deployment
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spec:
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# Change this to the number you want
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replicas: 1
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selector:
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matchLabels:
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fastchat: worker
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template:
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metadata:
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labels:
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fastchat: worker
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spec:
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dnsPolicy: "ClusterFirst"
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containers:
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- name: fastchat-worker # fixed
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image: intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
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imagePullPolicy: IfNotPresent
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env:
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- name: CONTROLLER_HOST # fixed
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value: bigdl-a1234bd-fschat-controller-service
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- name: CONTROLLER_PORT # fixed
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value: "21005"
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- name: WORKER_HOST # fixed
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valueFrom:
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fieldRef:
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fieldPath: status.podIP
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- name: WORKER_PORT # fixed
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value: "21841"
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- name: MODEL_PATH # Change this
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value: "/llm/models/vicuna-7b-v1.5-bigdl/"
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- name: OMP_NUM_THREADS
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value: "16"
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resources:
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requests:
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memory: 32Gi
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cpu: 16
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limits:
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memory: 32Gi
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cpu: 16
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args: ["-m", "worker"]
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volumeMounts:
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- name: llm-models
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mountPath: /llm/models/
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restartPolicy: "Always"
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volumes:
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- name: llm-models
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hostPath:
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path: /home/llm/models # change this in other envs
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```
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You may want to change the `MODEL_PATH` variable in the yaml. Also, please remember to change the volume path accordingly.
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### Testing
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#### Using openai-python
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First, install openai-python:
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```bash
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pip install --upgrade openai
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```
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Then, interact with model vicuna-7b-v1.5-bigdl:
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```python
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import openai
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openai.api_key = "EMPTY"
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openai.api_base = "http://localhost:8000/v1"
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model = "vicuna-7b-v1.5-bigdl"
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prompt = "Once upon a time"
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# create a completion
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completion = openai.Completion.create(model=model, prompt=prompt, max_tokens=64)
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# print the completion
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print(prompt + completion.choices[0].text)
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# create a chat completion
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completion = openai.ChatCompletion.create(
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model=model,
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messages=[{"role": "user", "content": "Hello! What is your name?"}]
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)
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# print the completion
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print(completion.choices[0].message.content)
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```
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#### cURL
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cURL is another good tool for observing the output of the api.
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For the following examples, you may also change the service deployment address.
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List Models:
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```bash
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curl http://localhost:8000/v1/models
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```
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If you have `jq` installed, you can use it to format the output like this:
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```bash
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curl http://localhost:8000/v1/models | jq
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```
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Chat Completions:
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```bash
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "YOUR_MODEL",
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"messages": [{"role": "user", "content": "Hello! What is your name?"}]
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}'
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```
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Text Completions:
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```bash
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curl http://localhost:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "YOUR_MODEL",
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"prompt": "Once upon a time",
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"max_tokens": 41,
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"temperature": 0.5
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}'
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```
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Embeddings:
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```bash
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curl http://localhost:8000/v1/embeddings \
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-H "Content-Type: application/json" \
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-d '{
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"model": "YOUR_MODEL",
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"input": "Hello world!"
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}'
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``` |