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libsvm python模型秒变API？MLServer这招绝了，不用Flask也能跑

2026-07-29 分类：服务器技术 阅读(37) 评论(0)

你有没有碰到过这种情形, 就是当你训练好了一个新的模型之后, 有时候你不想费神去编写那个Flask Code（也就是web框架）, 也不想把模型进行容器化并且在某个环境里运行它, 只是想要借助API马上就能使用这个模型?

要是你存在这样的需求, 那必然是会希望去了解的。它属于是一个基于某种情况的推理服务器, 近期推出了有着GA（遗传算法）的版本, 而这是一款专门为生产环境所设计构造的具备高性能的服务器。

使用它, 能够确保, 在本地构建模型, 且这种构建的模型, 与投入生产环境后的模型, 是保持一致状态的。

本文以几个图像模型为例，向您介绍如何使用 。

数据集

我们所要运用的数据集是MNIST, 其所包含的是张数为70,000的灰度呈现的、像素为28×28的有关服装的图像, 这些图像覆盖了10个不一样的类别具体像上衣、连衣裙、外套、裤子等等。

若您打算令本文里的代码再现, 务必要保证把文件下载下来, 且将其解开压缩, 放置到名为 data 的地方, 鉴于文件个头极度大, 存储库里把它们给省略掉了。

1.训练 -learn 模型

首先, 我们会运用 -learn 构架去训练一个支持向量机, 简称为 SVM, 的模型。而后, 我们要把模型存放至一个称作 -MNIST 的文件里边。

import pandas as pd
from sklearn import svm
import time
import joblib
#Load Training Data
train = pd.read_csv('../../data/fashion-mnist_train.csv', header=0)
y_train = train['label']
X_train = train.drop(['label'], axis=1)
classifier = svm.SVC(kernel="poly", degree=4, gamma=0.1)
#Train Model
start = time.time()
classifier.fit(X_train.values, y_train.values)
end = time.time()
exec_time = end-start
print(f'Execution time: {exec_time} seconds')
#Save Model
joblib.dump(classifier, "Fashion-MNIST.joblib")1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.

须留意, SVM算法并非格外适配大型数据集, 缘由在于其具备二次性质。依据您的硬件状况, 在此示例里的模型将会耗费几分钟时间来进行训练。

2.服务于 -learn 模型

经循着上面那些步骤, 我们获取到了一个名为MNIST的模型文件, 紧接着, 来瞧瞧怎样借助它去提供服务。

首先，通过如下命令安装 ：pip 。

尽管额外的运行时组件属于可选范畴, 然而在服务模型之际会更为顺畅, 故而我们也会去安装 -Learn 以及 的扩展。

完成pip – -操作后，就需要添加如下两个配置文件：

（1）.json- 这包含服务器本身的配置。

（2）.model-.json- , 按字面意思理解, 这个文件里面涵盖着将要去运行的模型配置。

对于.json文件，只定义一个参数就足够了：

{
"debug": "true"
}1.2.3.

这一model-.json的文件, 是需要更多信息的, 原因在于它务必要知晓服务之中的模型信息。

{
"name": "fashion-sklearn",
"implementation": "mlserver_sklearn.SKLearnModel",
"parameters": {
"uri": "./Fashion_MNIST.joblib",
"version": "v1"
}
}1.2.3.4.5.6.7.8.

mlserver scikit-learn model deployment_libsvm python_mlserver xgboost model serving

name参数给出了唯一识标, 它在多个模型的服务情形之际很有用处, 这一点过后会提及。要去界定以便预建起服务器, 假若存在这种服务器的话。它跟在训练模型所运用的机器学习框架紧密相连。于我们的实例当中运用 -learn去训练模型, 因而会借助 -learn来达成。配置之时得给出模型文件的所在位置以及版本编号。

经过之上提及的配置, 便能够运用如下命令来给我们的模型予以服务: start。

简单来讲, 当下已然使得模型于本地服务器那儿运行起来了。此刻, 其现已能够接纳诸如 HTTP 以及 gRPC（默认端口是 8080 和 8081）这般的请求了。

3.测试模型

模型已然启动, 且在正常运行着, 此刻, 让我们去发送一些请求, 以此来测试它的运行状况。

我们会通过如下URL 发送一个 POST 请求：

:8080/v2/////infer

此URL所表达的意思是, 去访问先前训练好的那个-lea模型, 在此处, 仅仅需要把带有-的模型名字予以替换, 与此同时, 还得把之以v1去进行替换才行。

下述代码呈现怎样去导入测试数据, 朝着模型服务器发出请求, 接着把结果跟实际标签予以比较。

import pandas as pd
import requests
#Import test data, grab the first row and corresponding label
test = pd.read_csv('../../data/fashion-mnist_test.csv', header=0)
y_test = test['label'][0:1]
X_test = test.drop(['label'],axis=1)[0:1]
#Prediction request parameters
inference_request = {
"inputs": [
{
"name": "predict",
"shape": X_test.shape,
"datatype": "FP64",
"data": X_test.values.tolist()
}
]
}
endpoint = "http://localhost:8080/v2/models/fashion-sklearn/versions/v1/infer"
#Make request and print response
response = requests.post(endpoint, json=inference_request)
print(response.text)
print(y_test.values)1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.

运行上面的test.py之后，会从 得到以下响应：

"model_name": "fashion-sklearn",
"model_version": "v1",
"id": "31c3fa70-2e56-49b1-bcec-294452dbe73c",
"parameters": null,
"outputs": [
{
"name": "predict",
"shape": [
1
],
"datatype": "INT64",
"parameters": null,
"data": [
0
]
}
]
}1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.

知道从响应里能够晓得, 生成可了一个请求身份标识, 并且自动添加上了给服务请求所用的模型以及版本的元数据。一旦模型投入进产品生产环节, 收集类似这般的的元数据就变得格外重要起来。它能让我们记录每一个请求, 进而便利地开展审计以及故障排查工作。

您或许也留意到, 返回一个数组。尽管于请求里仅发送了一行数据, 然而处理批量请求, 且一并返回。我们甚至能够借助一种称作自适应批处理的技术, 来优化在生产环境中处理多个请求的方式。

于上述其中示例里, 亦能够找寻到模型所预测出的结果。.data显示模型已将这个样本标记成了类别0（数值0对应着类别t-shirt/top）此般情况下此样本的真实的标签0也是如此, 所以能够证实模型得到的是正确的预测。

4.训练 模型

依循上面所举的例子, 我们知悉了怎样去运用, 从而创建单个模型, 紧接着, 让我们瞧瞧该如何处置, 在不同框架里训练的多个模型。

依旧使用 MNIST 数据集，但这次将训练模型。

import pandas as pd
import xgboost as xgb
import time
#Load Training Data
train = pd.read_csv('../../data/fashion-mnist_train.csv', header=0)
y_train = train['label']
X_train = train.drop(['label'], axis=1)
dtrain = xgb.DMatrix(X_train.values, label=y_train.values)
#Train Model
params = {
'max_depth': 5,
'eta': 0.3,
'verbosity': 1,
'objective': 'multi:softmax',
'num_class' : 10
}
num_round = 50
start = time.time()
bstmodel = xgb.train(params, dtrain, num_round, evals=[(dtrain, 'label')], verbose_eval=10)
end = time.time()
exec_time = end-start
print(f'Execution time: {exec_time} seconds')
#Save Model
bstmodel.save_model('Fashion_MNIST.json')1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.

上边用来训练模型的代码, 跟之前用来训练 -learn 模型的代码相似, 然而这次, 为了让我们的模型兼容 的格式, 把它存成了.json 文件。

5.服务多个模型

mlserver scikit-learn model deployment_libsvm python_mlserver xgboost model serving

主要的一个优点在于能够支持多模型服务, 这所蕴含的意思是, 不需要针对所部署的每个ML模型去创建或者运行新的服务器, 借助上述构建而成的模型, 会运用这个功能同时为它们供给服务。

一旦启动, 它会于目录以及其中任何子目录里搜寻model-.json文件。要是您存在多个model-.json文件, 那它会自动给所有文件赋予服务。

留意: 您依旧仅需指明根目录里的那种, （服务器配置）文件.json。

这是目录结构的细分以供参考：

.
├── data
│ ├── fashion-mnist_test.csv
│ └── fashion-mnist_train.csv
├── models
│ ├── sklearn
│ │ ├── Fashion_MNIST.joblib
│ │ ├── model-settings.json
│ │ ├── test.py
│ │ └── train.py
│ └── xgboost
│ ├── Fashion_MNIST.json
│ ├── model-settings.json
│ ├── test.py
│ └── train.py
├── README.md
├── settings.json
└── test_models.py1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.

提请留意着, 存在着两个名为model-.json的文件, 其中一个是应用于-learn模型的, 另一个是应用于另一模型的。

现在可以运行 start .，它将开始处理两个模型的请求。

[mlserver] INFO – Loaded model 'fashion-sklearn' succesfully.
[mlserver] INFO – Loaded model 'fashion-xgboost' succesfully.1.2.

6.测试多个模型的准确性

当前, 有着两个模型, 它们都处于运行的状态, 且运行的平台是特定的那个, 在这里, 我们能够运用测试集中所包含的样本, 以此用来验证每一个模型所具备的准确性。

有这样一段代码, 它会向每一个模型送去一个批处理请求, 此请求涵盖完整的测试集, 将预测值跟真实标签予以比较, 在整个测试集上去执行这个操作, 这为我们给出了衡量每个模型准确性的方式, 并且会把最终结果打印出来。

import pandas as pd
import requests
import json
#Import the test data and split the data from the labels
test = pd.read_csv('./data/fashion-mnist_test.csv', header=0)
y_test = test['label']
X_test = test.drop(['label'],axis=1)
#Build the inference request
inference_request = {
"inputs": [
{
"name": "predict",
"shape": X_test.shape,
"datatype": "FP64",
"data": X_test.values.tolist()
}
]
}
#Send the prediction request to the relevant model, compare responses to training labels and calculate accuracy
def infer(model_name, version):
endpoint = f"http://localhost:8080/v2/models/{model_name}/versions/{version}/infer"
response = requests.post(endpoint, json=inference_request)
#calculate accuracy
correct = 0
for i, prediction in enumerate(json.loads(response.text)['outputs'][0]['data']):
if y_test[i] == prediction:
correct += 1
accuracy = correct / len(y_test)
print(f'Model Accuracy for {model_name}: {accuracy}')
infer("fashion-xgboost", "v1")
infer("fashion-sklearn", "v1")1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.

结果表明， 模型略微优于 SVM -learn 模型：

Model Accuracy for fashion-xgboost: 0.8953
Model Accuracy for fashion-sklearn: 0.8641.2.

总结

望以之上所述, 您已明晰运用服务模型之大体流程。若欲知悉更多讯息, 您得去研读相关文档, 还要查看不同框架之示例。

若是用户, 能够运用来在里提供予以模型, 倘若当作用户, 即应明确知晓Core, 它是一个把模型部署到的开源工具, 其在后台运用。WWw.M.nffqi.cN/Article/details/309390.shtml
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