llama-stack-mirror/llama_stack/providers/remote/inference/vllm/config.py
2024-11-15 14:17:16 -08:00

80 lines
2.4 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
from llama_stack.providers.utils.docker.service_config import DockerComposeServiceConfig
DEFAULT_VLLM_PORT = 8000
@json_schema_type
class VLLMInferenceAdapterConfig(BaseModel):
url: Optional[str] = Field(
default=None,
description="The URL for the vLLM model serving endpoint",
)
max_tokens: int = Field(
default=4096,
description="Maximum number of tokens to generate.",
)
api_token: Optional[str] = Field(
default="fake",
description="The API token",
)
@classmethod
def sample_run_config(
cls,
url: str = "${env.VLLM_URL:http://host.docker.internal:5100/v1}",
):
return {
"url": url,
"max_tokens": "${env.VLLM_MAX_TOKENS:4096}",
"api_token": "${env.VLLM_API_TOKEN:fake}",
}
@classmethod
def sample_docker_compose_config(
cls,
port: int = DEFAULT_VLLM_PORT,
cuda_visible_devices: str = "0",
model: str = "meta-llama/Llama-3.2-3B-Instruct",
) -> Optional[DockerComposeServiceConfig]:
return DockerComposeServiceConfig(
image="vllm/vllm-openai:latest",
volumes=["$HOME/.cache/huggingface:/root/.cache/huggingface"],
devices=["nvidia.com/gpu=all"],
deploy={
"resources": {
"reservations": {
"devices": [{"driver": "nvidia", "capabilities": ["gpu"]}]
}
}
},
runtime="nvidia",
ports=[f"{port}:{port}"],
environment={
"CUDA_VISIBLE_DEVICES": cuda_visible_devices,
"HUGGING_FACE_HUB_TOKEN": "$HF_TOKEN",
},
command=(
" ".join(
[
"--gpu-memory-utilization 0.75",
f"--model {model}",
"--enforce-eager",
"--max-model-len 8192",
"--max-num-seqs 16",
f"--port {port}",
]
)
),
)