Source code for ray.serve.utils

import asyncio
from functools import singledispatch
from itertools import groupby
import json
import logging
import random
import string
import time
from typing import List, Dict
import io
import os
from ray.serve.exceptions import RayServeException
from collections import UserDict

import requests
import numpy as np
import pydantic
import flask

import ray
from ray.serve.constants import HTTP_PROXY_TIMEOUT
from ray.serve.context import TaskContext
from ray.serve.http_util import build_flask_request


class ServeMultiDict(UserDict):
    """Compatible data structure to simulate Flask.Request.args API."""

    def getlist(self, key):
        """Return the list of items for a given key."""
        return, [])

[docs]class ServeRequest: """The request object used in Python context. ServeRequest is built to have similar API as Flask.Request. You only need to write your model serving code once; it can be queried by both HTTP and Python. """ def __init__(self, data, kwargs, headers, method): self._data = data self._kwargs = ServeMultiDict(kwargs) self._headers = headers self._method = method @property def headers(self): """The HTTP headers from ``handle.option(http_headers=...)``.""" return self._headers @property def method(self): """The HTTP method data from ``handle.option(http_method=...)``.""" return self._method @property def args(self): """The keyword arguments from ``handle.remote(**kwargs)``.""" return self._kwargs @property def json(self): """The request dictionary, from ``handle.remote(dict)``.""" if not isinstance(self._data, dict): raise RayServeException("Request data is not a dictionary. " f"It is {type(self._data)}.") return self._data @property def form(self): """The request dictionary, from ``handle.remote(dict)``.""" if not isinstance(self._data, dict): raise RayServeException("Request data is not a dictionary. " f"It is {type(self._data)}.") return self._data @property def data(self): """The request data from ``handle.remote(obj)``.""" return self._data
def parse_request_item(request_item): if request_item.metadata.request_context == TaskContext.Web: asgi_scope, body_bytes = request_item.args return build_flask_request(asgi_scope, io.BytesIO(body_bytes)) else: arg = request_item.args[0] if len(request_item.args) == 1 else None # If the input data from handle is web request, we don't need to wrap # it in ServeRequest. if isinstance(arg, flask.Request): return arg return ServeRequest( arg, request_item.kwargs, headers=request_item.metadata.http_headers, method=request_item.metadata.http_method, ) def _get_logger(): logger = logging.getLogger("ray.serve") # TODO(simon): Make logging level configurable. if os.environ.get("SERVE_LOG_DEBUG"): logger.setLevel(logging.DEBUG) else: logger.setLevel(logging.INFO) return logger logger = _get_logger() class ServeEncoder(json.JSONEncoder): """Ray.Serve's utility JSON encoder. Adds support for: - bytes - Pydantic types - Exceptions - numpy.ndarray """ def default(self, o): # pylint: disable=E0202 if isinstance(o, bytes): return o.decode("utf-8") if isinstance(o, pydantic.BaseModel): return o.dict() if isinstance(o, Exception): return str(o) if isinstance(o, np.ndarray): if o.dtype.kind == "f": # floats o = o.astype(float) if o.dtype.kind in {"i", "u"}: # signed and unsigned integers. o = o.astype(int) return o.tolist() return super().default(o) @ray.remote(num_cpus=0) def block_until_http_ready(http_endpoint, backoff_time_s=1, timeout=HTTP_PROXY_TIMEOUT): http_is_ready = False start_time = time.time() while not http_is_ready: try: resp = requests.get(http_endpoint) assert resp.status_code == 200 http_is_ready = True except Exception: pass if 0 < timeout < time.time() - start_time: raise TimeoutError( "HTTP proxy not ready after {} seconds.".format(timeout)) time.sleep(backoff_time_s) def get_random_letters(length=6): return "".join(random.choices(string.ascii_letters, k=length)) def format_actor_name(actor_name, controller_name=None, *modifiers): if controller_name is None: name = actor_name else: name = "{}:{}".format(controller_name, actor_name) for modifier in modifiers: name += "-{}".format(modifier) return name @singledispatch def chain_future(src, dst): """Base method for chaining futures together. Chaining futures means the output from source future(s) are written as the results of the destination future(s). This method can work with the following inputs: - src: Future, dst: Future - src: List[Future], dst: List[Future] """ raise NotImplementedError() @chain_future.register(asyncio.Future) def _chain_future_single(src: asyncio.Future, dst: asyncio.Future): asyncio.futures._chain_future(src, dst) @chain_future.register(list) def _chain_future_list(src: List[asyncio.Future], dst: List[asyncio.Future]): if len(src) != len(dst): raise ValueError( "Source and destination list doesn't have the same length. " "Source: {}. Destination: {}.".foramt(len(src), len(dst))) for s, d in zip(src, dst): chain_future(s, d) def unpack_future(src: asyncio.Future, num_items: int) -> List[asyncio.Future]: """Unpack the result of source future to num_items futures. This function takes in a Future and splits its result into many futures. If the result of the source future is an exception, then all destination futures will have the same exception. """ dest_futures = [ asyncio.get_event_loop().create_future() for _ in range(num_items) ] def unwrap_callback(fut: asyncio.Future): exception = fut.exception() if exception is not None: [f.set_exception(exception) for f in dest_futures] return result = fut.result() assert len(result) == num_items for item, future in zip(result, dest_futures): future.set_result(item) src.add_done_callback(unwrap_callback) return dest_futures def try_schedule_resources_on_nodes( requirements: List[dict], ray_resource: Dict[str, Dict] = None, ) -> List[bool]: """Test given resource requirements can be scheduled on ray nodes. Args: requirements(List[dict]): The list of resource requirements. ray_nodes(Optional[Dict[str, Dict]]): The resource dictionary keyed by node id. By default it reads from ``ray.state.state._available_resources_per_node()``. Returns: successfully_scheduled(List[bool]): A list with the same length as requirements. Each element indicates whether or not the requirement can be satisied. """ if ray_resource is None: ray_resource = ray.state.state._available_resources_per_node() successfully_scheduled = [] for resource_dict in requirements: # Filter out zero value resource_dict = {k: v for k, v in resource_dict.items() if v > 0} for node_id, node_resource in ray_resource.items(): # Check if we can schedule on this node feasible = True for key, count in resource_dict.items(): if node_resource.get(key, 0) - count < 0: feasible = False # If we can, schedule it on this node if feasible: for key, count in resource_dict.items(): node_resource[key] -= count successfully_scheduled.append(True) break else: successfully_scheduled.append(False) return successfully_scheduled def get_all_node_ids(): """Get IDs for all nodes in the cluster. Handles multiple nodes on the same IP by appending an index to the node_id, e.g., 'node_id-index'. Returns a list of ('node_id-index', 'node_id') tuples (the latter can be used as a resource requirement for actor placements). """ node_ids = [] # We need to use the node_id and index here because we could # have multiple virtual nodes on the same host. In that case # they will have the same IP and therefore node_id. for _, node_id_group in groupby(sorted(ray.state.node_ids())): for index, node_id in enumerate(node_id_group): node_ids.append(("{}-{}".format(node_id, index), node_id)) return node_ids