Source code for ray.serve.http_adapters

from io import BytesIO
from typing import Any, Dict, List, Optional, Union

from fastapi import File
from pydantic import BaseModel, Field
import numpy as np

from ray.serve.utils import require_packages
import starlette.requests

_1DArray = List[float]
_2DArray = List[List[float]]
_3DArray = List[List[List[float]]]
_4DArray = List[List[List[List[float]]]]

[docs]class NdArray(BaseModel): """Schema for numeric array input.""" array: Union[_1DArray, _2DArray, _3DArray, _4DArray] = Field( ..., description=( "The array content as a nested list. " "You can pass in 1D to 4D array as nested list, or flatten them. " "When you flatten the array, you can use the `shape` parameter to perform " "reshaping." ), ) shape: Optional[List[int]] = Field( default=None, description=("The shape of the array. If present, the array will be reshaped."), ) dtype: Optional[str] = Field( default=None, description=( "The numpy dtype of the array. If present, the array will be cast " "by `astype`." ), )
[docs]def json_to_ndarray(payload: NdArray) -> np.ndarray: """Accepts an NdArray JSON from an HTTP body and converts it to a numpy array.""" arr = np.array(payload.array) if payload.shape: arr = arr.reshape(*payload.shape) if payload.dtype: arr = arr.astype(payload.dtype) return arr
[docs]def starlette_request( request: starlette.requests.Request, ) -> starlette.requests.Request: """Returns the raw request object.""" # NOTE(simon): This adapter is used for ease of getting started. return request
[docs]async def json_request(request: starlette.requests.Request) -> Dict[str, Any]: """Return the JSON object from request body.""" return await request.json()
[docs]@require_packages(["PIL"]) def image_to_ndarray(img: bytes = File(...)) -> np.ndarray: """Accepts a PIL-readable file from an HTTP form and converts it to a numpy array. """ from PIL import Image image = return np.array(image)