部署经过微调的模型进行测试（预览版）

若要部署候选模型，请选择要部署的微调模型，然后选择“ 部署”。

此时会打开“部署模型”对话框。 在对话框中，输入 部署名称 ，然后从部署类型下拉列表中选择 “开发人员 ”。 选择“ 创建 ”以开始部署自定义模型。

可以在 Azure AI Foundry 门户中的 “部署 ”窗格中监视新部署的进度。

import json
import os
import requests

token = os.getenv("<TOKEN>") 
subscription = "<YOUR_SUBSCRIPTION_ID>"  
resource_group = "<YOUR_RESOURCE_GROUP_NAME>"
resource_name = "<YOUR_AZURE_OPENAI_RESOURCE_NAME>"
model_deployment_name = "gpt41-mini-candidate-01" # custom deployment name that you will use to reference the model when making inference calls.

deploy_params = {'api-version': "2024-10-21"} 
deploy_headers = {'Authorization': 'Bearer {}'.format(token), 'Content-Type': 'application/json'}

deploy_data = {
    "sku": {"name": "developer", "capacity": 50}, 
    "properties": {
        "model": {
            "format": "OpenAI",
            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt41-mini-candidate-01.ft-b044a9d3cf9c4228b5d393567f693b83
            "version": "1"
        }
    }
}
deploy_data = json.dumps(deploy_data)

request_url = f'https://management.azure.com/subscriptions/{subscription}/resourceGroups/{resource_group}/providers/Microsoft.CognitiveServices/accounts/{resource_name}/deployments/{model_deployment_name}'

print('Creating a new deployment...')

r = requests.put(request_url, params=deploy_params, headers=deploy_headers, data=deploy_data)

print(r)
print(r.reason)
print(r.json())

以下示例演示如何使用 REST API 为自定义模型创建模型部署。 REST API 为自定义模型的部署生成名称。

curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-21" \
  -H "Authorization: Bearer <TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "sku": {"name": "developer", "capacity": 50},
    "properties": {
        "model": {
            "format": "OpenAI",
            "name": "<FINE_TUNED_MODEL>",
            "version": "1"
        }
    }
}'

使用 Azure CLI 部署模型

以下示例演示如何使用 Azure CLI 部署自定义模型。 使用 Azure CLI 时，必须为自定义模型的部署指定名称。 有关使用 Azure CLI 部署自定义模型的详细信息，请参阅 az cognitiveservices account deployment。

若要在控制台窗口中运行此 Azure CLI 命令，必须将以下 <占位符> 替换为自定义模型的相应值：

az cognitiveservices account deployment create 
    --resource-group <YOUR_RESOURCE_GROUP>
    --name <YOUR_RESOURCE_NAME>  
    --deployment-name <YOUR_DEPLOYMENT_NAME>
    --model-name <YOUR_FINE_TUNED_MODEL_ID>
    --model-version "1" 
    --model-format OpenAI 
    --sku-capacity "50" 
    --sku-name "Developer"

部署自定义模型后，可像使用任何其他已部署的模型一样使用它。 可以使用 Azure AI Foundry 门户中的 Playgrounds 来试验新部署。 可以继续对自定义模型使用相同的参数，例如 temperature 和 max_tokens，就像对其他已部署的模型一样。

还可以使用 评估 服务针对已部署的模型候选模型和其他模型版本创建和运行模型评估。

import os
from openai import AzureOpenAI

client = AzureOpenAI(
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
  api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
  api_version="2024-02-01"
)

response = client.chat.completions.create(
    model="gpt41-mini-candidate-01", # model = "Custom deployment name you chose for your fine-tuning model"
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
        {"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},
        {"role": "user", "content": "Do other Azure AI services support this too?"}
    ]
)

print(response.choices[0].message.content)

curl $AZURE_OPENAI_ENDPOINT/openai/deployments/<deployment_name>/chat/completions?api-version=2024-10-21 \
  -H "Content-Type: application/json" \
  -H "api-key: $AZURE_OPENAI_API_KEY" \
  -d '{"messages":[{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},{"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},{"role": "user", "content": "Do other Azure AI services support this too?"}]}'