使用 Ray Serve 进行可扩展的在线 XGBoost 推理#
本教程将启动一个在线服务，该服务：
部署训练好的 XGBoost 模型工件以生成预测。
根据实时传入流量自动扩展。
涵盖了围绕该服务的可观测性和调试。
请注意，本笔记本要求您先运行 XGBoost 模型分布式训练 教程，以生成本教程将获取的预训练模型工件。
Ray Serve 是一个高度可扩展且灵活的模型服务库，用于构建在线推理 API。您可以：
避免一个受网络和计算限制的大型服务，以及资源使用效率低下。
利用异构的 分片资源（SageMaker、Vertex、KServe 等 **不可能** 实现这一点），并通过 `num_replicas` 进行水平扩展。
根据流量 自动扩展。
与 FastAPI 和 HTTP 集成。
设置 gRPC 服务 来构建分布式系统和微服务。
启用基于批量大小、时间等的 动态批处理。
访问用于 服务 LLM 的实用工具集，这些工具集与推理引擎无关，并对 LLM 特定的功能（如多 LoRA 支持）提供了开箱即用的支持。
%load_ext autoreload
%autoreload all
# Enable loading of the dist_xgboost module.
import os
import sys
sys.path.append(os.path.abspath(".."))
# Enable Ray Train v2.
os.environ["RAY_TRAIN_V2_ENABLED"] = "1"
# Now it's safe to import from ray.train.
import ray
import dist_xgboost
# Initialize Ray with the dist_xgboost package.
ray.init(runtime_env={"py_modules": [dist_xgboost]})
加载模型#
接下来，从 MLflow 注册表中加载预训练的预处理器和 XGBoost 模型，如验证笔记本中所述。
创建 Ray Serve 部署#
接下来，定义 Ray Serve 端点。使用可重用类来避免为每个请求重新加载模型和预处理器。该部署支持 Pythonic 和 HTTP 请求。
import pandas as pd
import xgboost
from ray import serve
from starlette.requests import Request
from dist_xgboost.data import load_model_and_preprocessor
@serve.deployment(num_replicas=2, max_ongoing_requests=25, ray_actor_options={"num_cpus": 2})
class XGBoostModel:
def __init__(self):
self.preprocessor, self.model = load_model_and_preprocessor()
@serve.batch(max_batch_size=16, batch_wait_timeout_s=0.1)
async def predict_batch(self, input_data: list[dict]) -> list[float]:
print(f"Batch size: {len(input_data)}")
# Convert list of dictionaries to DataFrame.
input_df = pd.DataFrame(input_data)
# Preprocess the input.
preprocessed_batch = self.preprocessor.transform_batch(input_df)
# Create DMatrix for prediction.
dmatrix = xgboost.DMatrix(preprocessed_batch)
# Get predictions.
predictions = self.model.predict(dmatrix)
return predictions.tolist()
async def __call__(self, request: Request):
# Parse the request body as JSON.
input_data = await request.json()
return await self.predict_batch(input_data)
🧱 模型组合
Ray Serve 使得 模型组合 变得极其容易，您可以将包含 ML 模型或业务逻辑的多个部署组合成一个应用程序。您可以独立扩展分片资源，并配置每个部署。
首先确保您没有任何现有部署，可以使用 serve.shutdown()。
if "default" in serve.status().applications and serve.status().applications["default"].status == "RUNNING":
print("Shutting down existing serve application")
serve.shutdown()
2025-04-16 21:35:03,819 INFO worker.py:1660 -- Connecting to existing Ray cluster at address: 10.0.23.200:6379...
2025-04-16 21:35:03,828 INFO worker.py:1843 -- Connected to Ray cluster. View the dashboard at https://session-1kebpylz8tcjd34p4sv2h1f9tg.i.anyscaleuserdata.com
2025-04-16 21:35:03,833 INFO packaging.py:367 -- Pushing file package 'gcs://_ray_pkg_dbf2a602028d604b4b1f9474b353f0574c4a48ce.zip' (0.08MiB) to Ray cluster...
2025-04-16 21:35:03,834 INFO packaging.py:380 -- Successfully pushed file package 'gcs://_ray_pkg_dbf2a602028d604b4b1f9474b353f0574c4a48ce.zip'.
现在您已经定义了部署，您可以使用 `.bind()` 方法创建 ray.serve.Application。
# Define the app.
xgboost_model = XGBoostModel.bind()
准备测试数据#
准备一些示例数据来测试部署。使用保留集中的样本。
sample_input = {
"mean radius": 14.9,
"mean texture": 22.53,
"mean perimeter": 102.1,
"mean area": 685.0,
"mean smoothness": 0.09947,
"mean compactness": 0.2225,
"mean concavity": 0.2733,
"mean concave points": 0.09711,
"mean symmetry": 0.2041,
"mean fractal dimension": 0.06898,
"radius error": 0.253,
"texture error": 0.8749,
"perimeter error": 3.466,
"area error": 24.19,
"smoothness error": 0.006965,
"compactness error": 0.06213,
"concavity error": 0.07926,
"concave points error": 0.02234,
"symmetry error": 0.01499,
"fractal dimension error": 0.005784,
"worst radius": 16.35,
"worst texture": 27.57,
"worst perimeter": 125.4,
"worst area": 832.7,
"worst smoothness": 0.1419,
"worst compactness": 0.709,
"worst concavity": 0.9019,
"worst concave points": 0.2475,
"worst symmetry": 0.2866,
"worst fractal dimension": 0.1155,
}
sample_target = 0 # Ground truth label
运行服务#
有两种运行 Ray Serve 服务的方式：
Serve API：使用 `serve run` CLI 命令，例如 `serve run tutorial:xgboost_model`。
Pythonic API：使用 `ray.serve` 的
serve.run命令，例如 `serve.run(xgboost_model)`。
此示例使用 Pythonic API。
from ray.serve.handle import DeploymentHandle
handle: DeploymentHandle = serve.run(xgboost_model, name="xgboost-breast-cancer-classifier")
INFO 2025-04-16 21:35:08,246 serve 30790 -- Started Serve in namespace "serve".
INFO 2025-04-16 21:35:13,363 serve 30790 -- Application 'xgboost-breast-cancer-classifier' is ready at http://127.0.0.1:8000/.
(ProxyActor pid=31032) INFO 2025-04-16 21:35:08,167 proxy 10.0.23.200 -- Proxy starting on node dc30e171b93f61245644ba4d0147f8b27f64e9e1eaf34d1bb63c9c99 (HTTP port: 8000).
(ProxyActor pid=31032) INFO 2025-04-16 21:35:08,226 proxy 10.0.23.200 -- Got updated endpoints: {}.
(ServeController pid=30973) INFO 2025-04-16 21:35:08,307 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ProxyActor pid=31032) INFO 2025-04-16 21:35:08,310 proxy 10.0.23.200 -- Got updated endpoints: {Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier'): EndpointInfo(route='/', app_is_cross_language=False)}.
(ProxyActor pid=31032) INFO 2025-04-16 21:35:08,323 proxy 10.0.23.200 -- Started <ray.serve._private.router.SharedRouterLongPollClient object at 0x77864005ee70>.
(ServeController pid=30973) INFO 2025-04-16 21:35:08,411 controller 30973 -- Adding 2 replicas to Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier').
(ServeController pid=30973) INFO 2025-04-16 21:35:09,387 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ServeController pid=30973) INFO 2025-04-16 21:35:10,337 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ServeController pid=30973) INFO 2025-04-16 21:35:10,550 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ServeController pid=30973) INFO 2025-04-16 21:35:11,395 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ServeController pid=30973) INFO 2025-04-16 21:35:12,449 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ServeController pid=30973) INFO 2025-04-16 21:35:13,402 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ServeController pid=30973) INFO 2025-04-16 21:35:13,613 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
您应该会看到一些日志表明服务正在本地运行。
INFO 2025-04-09 14:06:55,760 serve 31684 -- Started Serve in namespace "serve".
INFO 2025-04-09 14:06:57,875 serve 31684 -- Application 'default' is ready at http://127.0.0.1:8000/.
您还可以使用 serve.status() 来检查它是否正在运行。
serve.status().applications["xgboost-breast-cancer-classifier"].status == "RUNNING"
True
查询服务#
使用 HTTP#
查询服务的最常见方式是通过 HTTP 请求。此请求会调用前面定义的 `__call__` 方法。
import requests
url = "http://127.0.0.1:8000/"
prediction = requests.post(url, json=sample_input).json()
print(f"Prediction: {prediction:.4f}")
print(f"Ground truth: {sample_target}")
Prediction: 0.0503
Ground truth: 0
这种方法适用于处理单个查询，但不适用于您有许多查询的情况。由于 `requests.post` 是一个阻塞调用，如果您在 for 循环中运行它，您将无法获得 Ray Serve 动态批处理的好处。
相反，您希望使用异步请求并发地发送许多请求，并让 Ray Serve 对它们进行缓冲和批量处理。您可以使用 `aiohttp` 来实现此方法。
import asyncio
import aiohttp
async def fetch(session, url, data):
async with session.post(url, json=data) as response:
return await response.json()
async def fetch_all(requests: list):
async with aiohttp.ClientSession() as session:
tasks = [fetch(session, url, input_item) for input_item in requests]
responses = await asyncio.gather(*tasks)
return responses
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) Batch size: 1
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:13,834 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 0ddcd27d-d671-4365-b7e3-6e4cae856d9b -- POST / 200 117.8ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,352 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 aeb83339-359a-41e2-99c4-4ab06252d0b9 -- POST / 200 94.7ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,353 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 8c80adfd-2033-41d3-a718-aecbd5bcb996 -- POST / 200 93.9ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,354 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 7ed45f79-c665-4a17-94f7-6d02c56ab504 -- POST / 200 93.8ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,355 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 56fd016b-497a-43cc-b500-edafe878cda8 -- POST / 200 88.6ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,356 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 4910e208-d042-4fcb-aba9-330400fba538 -- POST / 200 85.5ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,356 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 b4999d9c-72fd-4bd2-aa9c-3c854ebe7457 -- POST / 200 84.7ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,358 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 04bc7c27-ae22-427f-8bee-c9dbc48a0b82 -- POST / 200 85.3ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,358 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 dcbbe5fa-d278-4568-a0fb-ea9347889990 -- POST / 200 84.3ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,359 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 22683613-16a5-479a-92bc-14f07dc317aa -- POST / 200 83.3ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,360 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 b773626c-8607-4572-bb87-8d8f80964de5 -- POST / 200 82.8ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,361 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 bceee2b4-ff30-4866-a300-7591e0cdc598 -- POST / 200 79.2ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,362 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 edaeb2f7-8de3-494d-8db0-8ebf2009acf7 -- POST / 200 74.7ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,362 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 09a38fe8-47d3-4c0e-8f5e-c312cded2c35 -- POST / 200 74.6ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,363 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 7f0d2f52-e59b-4f26-8931-61a1e9e4f988 -- POST / 200 72.9ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,363 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 269b045d-0b42-407d-a52f-7222cafce0d6 -- POST / 200 71.5ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) INFO 2025-04-16 21:35:14,364 xgboost-breast-cancer-classifier_XGBoostModel cxd4bxd1 98b7ef19-f5a1-4ab2-a71c-a2b7f6a6c1ad -- POST / 200 71.1ms
(ServeController pid=30973) INFO 2025-04-16 21:35:14,457 controller 30973 -- Deploying new version of Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier') (initial target replicas: 2).
(ProxyActor pid=5012, ip=10.0.240.129) INFO 2025-04-16 21:35:14,484 proxy 10.0.240.129 -- Proxy starting on node 9d22416ba66c129a3b66c96533eaa5455f7e882c37408b4fe7dc81f8 (HTTP port: 8000).
sample_input_list = [sample_input] * 100
# Notebook is already running an asyncio event loop in background, so use `await`.
# In other cases, you would use `asyncio.run(fetch_all(sample_input_list))`.
responses = await fetch_all(sample_input_list)
print(f"Finished processing {len(responses)} queries. Example result: {responses[0]}")
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) Batch size: 16
Finished processing 100 queries. Example result: 0.05025313049554825
(ProxyActor pid=5012, ip=10.0.240.129) INFO 2025-04-16 21:35:14,555 proxy 10.0.240.129 -- Got updated endpoints: {Deployment(name='XGBoostModel', app='xgboost-breast-cancer-classifier'): EndpointInfo(route='/', app_is_cross_language=False)}.
(ProxyActor pid=5012, ip=10.0.240.129) INFO 2025-04-16 21:35:14,576 proxy 10.0.240.129 -- Started <ray.serve._private.router.SharedRouterLongPollClient object at 0x7835f2b9acc0>.
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,619 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 24933cc1-07b4-4680-bb84-adcd54ff2de3 -- POST / 200 139.5ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,620 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 15167894-ceac-4464-bbb6-0556c8299d8a -- POST / 200 138.3ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,621 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x e4bb73d9-6b5b-4cd0-8dc0-5bbe5329c29e -- POST / 200 138.6ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,621 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 004be5f3-9ce7-4708-8579-31da77926491 -- POST / 200 94.1ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,621 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 233fc1bb-6486-4704-bf03-8599176e539c -- POST / 200 92.7ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,621 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x cd417685-cad4-4c9d-ab51-fcd33babe57c -- POST / 200 88.5ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,622 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 0ea1c55a-6722-4cb6-a9ab-9e0ffa156ef4 -- POST / 200 84.6ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,622 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 3315400d-9213-46ac-9abd-baa576c73107 -- POST / 200 77.9ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,622 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 25054e1f-e3e7-4106-910b-f6ba94f111be -- POST / 200 76.9ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,623 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x a0dbd826-c595-455f-8869-7c567c0dfac2 -- POST / 200 75.6ms
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) INFO 2025-04-16 21:35:14,623 xgboost-breast-cancer-classifier_XGBoostModel ep2o1d1x 136060ac-9705-49a5-b743-dc29164a3eee -- POST / 200 75.4ms
使用 Python#
要更直接地以 Pythonic 方式查询模型，您可以使用部署句柄。
response = await handle.predict_batch.remote(sample_input)
print(response)
INFO 2025-04-16 21:35:14,803 serve 30790 -- Started <ray.serve._private.router.SharedRouterLongPollClient object at 0x7156ffcf6d80>.
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4874, ip=10.0.240.129) Batch size: 11
(ServeReplica:xgboost-breast-cancer-classifier:XGBoostModel pid=4875, ip=10.0.240.129) Batch size: 1
0.05025313049554825
如果您需要与同一 Ray 集群中的另一个进程交互服务，则此方法很有用。如果您需要重新生成服务句柄，可以使用 serve.get_deployment_handle。
handle = serve.get_deployment_handle("XGBoostModel", "xgboost-breast-cancer-classifier")
🔎 服务的可观测性
Ray 仪表板会在 Serve 视图 中自动捕获 Ray Serve 应用程序的可观测性。您可以查看服务 部署及其副本 以及有关服务运行状况的时间序列指标。
# Shutdown service.
serve.shutdown()
Anyscale Services
Anyscale Services 提供了一种容错、可扩展且经过优化的方式来服务 Ray Serve 应用程序。有关更多详细信息，请参阅 API 参考。您可以：
通过专用服务页面、统一日志查看器、追踪等方式监控服务，并设置警报。
使用 `num_replicas=auto` 扩展服务，并利用副本压缩来整合碎片化利用的节点。
实现 主节点容错。OSS Ray 可以从失败的工作节点和副本中恢复，但不能从主节点崩溃中恢复。
在一个服务中提供 多个应用程序。
Anyscale 上的 RayTurbo Serve 在 Ray Serve 之上提供了更多功能：
快速自动扩展和模型加载，使服务即使对于 LLM 也能以 5 倍的改进 速度启动和运行。
为高流量服务用例提供 54% 的 **更高 QPS** 和高达 3 倍的 **每秒流式传输令牌数**，没有代理瓶颈。
副本压缩，尽可能合并到更少的节点中，以减少资源碎片并提高硬件利用率。
零停机 增量滚动更新，从而不会中断服务。
在多服务应用程序中为每个服务提供不同的环境 不同的环境。
多可用区感知 Ray Serve 副本调度，为可用区故障提供更高的冗余度。
注意:
此示例使用 `containerfile` 定义依赖项，但您也可以轻松使用预构建镜像。
当您未指定计算资源并从工作区启动时，默认使用工作区的计算配置。
# Production online service.
anyscale service deploy dist_xgboost.serve:xgboost_model --name=xgboost-breast_cancer_all_features \
--containerfile="${WORKING_DIR}/containerfile" \
--working-dir="${WORKING_DIR}" \
--exclude=""
请注意，要使此命令成功，您需要配置 MLflow 将工件存储在集群之间可读的存储中。Anyscale 提供多种开箱即用的存储选项，例如 默认存储桶，以及在集群、用户和云级别共享的 自动挂载的网络存储。您也可以设置自己的网络挂载或存储桶。
运行此命令将在生产环境中启动服务。在此过程中，Anyscale 会创建并保存容器镜像，以便将来能够快速启动此服务。终端节点链接和身份验证令牌将显示在日志中。在服务远程运行时，您需要使用身份验证令牌来查询它。以下是如何修改前面的 `requests` 代码以使用此令牌：
# Service specific config. Replace with your own values from the preceding logs.
base_url = "https://xgboost-breast-cancer-all-features-jgz99.cld-kvedzwag2qa8i5bj.s.anyscaleuserdata.com"
token = "tXhmYYY7qMbrb1ToO9_J3n5_kD7ym7Nirs8djtip7P0"
# Requests config.
path = "/"
full_url = f"{base_url}{path}"
headers = {"Authorization": f"Bearer {token}"}
prediction = requests.post(url, json=sample_input, headers=headers).json()
请记住，一旦不再需要服务，请将其停止。
anyscale service terminate --name e2e-xgboost
