Cerebrium¶
vLLM 可以在基於雲的 GPU 機器上執行,藉助 Cerebrium 平臺,這是一個無伺服器人工智慧基礎設施平臺,可幫助公司更輕鬆地構建和部署基於人工智慧的應用程式。
要安裝 Cerebrium 客戶端,請執行
接下來,建立您的 Cerebrium 專案,執行
接下來,要安裝所需的包,請將以下內容新增到您的 cerebrium.toml
[cerebrium.deployment]
docker_base_image_url = "nvidia/cuda:12.1.1-runtime-ubuntu22.04"
[cerebrium.dependencies.pip]
vllm = "latest"
接下來,讓我們新增程式碼來處理您選擇的 LLM 的推理(本例中使用 mistralai/Mistral-7B-Instruct-v0.1
),將以下程式碼新增到您的 main.py
中
程式碼
from vllm import LLM, SamplingParams
llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.1")
def run(prompts: list[str], temperature: float = 0.8, top_p: float = 0.95):
sampling_params = SamplingParams(temperature=temperature, top_p=top_p)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
results = []
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
results.append({"prompt": prompt, "generated_text": generated_text})
return {"results": results}
然後,執行以下程式碼將其部署到雲端
如果成功,您將收到一個 CURL 命令,可以對其進行推理呼叫。請記住在 URL 末尾加上您正在呼叫的函式名(本例中是 /run
)
命令
您應該會收到如下響應
響應
{
"run_id": "52911756-3066-9ae8-bcc9-d9129d1bd262",
"result": {
"result": [
{
"prompt": "Hello, my name is",
"generated_text": " Sarah, and I'm a teacher. I teach elementary school students. One of"
},
{
"prompt": "The president of the United States is",
"generated_text": " elected every four years. This is a democratic system.\n\n5. What"
},
{
"prompt": "The capital of France is",
"generated_text": " Paris.\n"
},
{
"prompt": "The future of AI is",
"generated_text": " bright, but it's important to approach it with a balanced and nuanced perspective."
}
]
},
"run_time_ms": 152.53663063049316
}
您現在擁有一個自動伸縮的端點,只需為您使用的計算付費!