MLflow【部署 01】MLflow官网Quick Start实操安装、模型训练、数据预测（一篇学会部署使用MLflow）

news2024/9/22 4:04:25

一篇学会部署使用MLflow

1.版本及环境
2.官方步骤
- Step 1 - Get MLflow
- Step 2 - Start a Tracking Server
- Step 3 - Train a model and prepare metadata for logging
- Step 4 - Log the model and its metadata to MLflow
- Step 5 - Load the model as a Python Function (pyfunc) and use it for inference
- Step 6 - View the Run in the MLflow UI
3.模型使用
4.总结
5.更新日志

Learn in 5 minutes how to log,register,and load a model for inference. 在5分钟内学习如何记录、注册和加载模型用于推理。

1.版本及环境

本文基于2.9.2版本进行说明，内容来自官方文档：https://www.mlflow.org/docs/2.9.2/getting-started/intro-quickstart/index.html，测试环境说明：

# 1.服务器系统版本
CentOS Linux release 7.9.2009 (Core)

# 2.使用conda创建的虚拟环境【conda create -n mlflow python=3.8】
(mlflow) [root@tcloud /]# python -V
Python 3.8.18

2.官方步骤

Step 1 - Get MLflow

# 官方步骤
pip install mlflow

# 实际操作【限制版本 否则会安装最新版本】
pip install mlflow==2.9.2

Step 2 - Start a Tracking Server

# 官方步骤
mlflow server --host 127.0.0.1 --port 8080
# 启动日志【删除了时间信息】
[5027] [INFO] Starting gunicorn 21.2.0
[5027] [INFO] Listening at: http://127.0.0.1:8080 (5027)
[5027] [INFO] Using worker: sync
[5030] [INFO] Booting worker with pid: 5030
[5031] [INFO] Booting worker with pid: 5031
[5032] [INFO] Booting worker with pid: 5032
[5033] [INFO] Booting worker with pid: 5033

# 实际操作【使用的是腾讯云服务器】
mlflow server --host 0.0.0.0 --port 9090
# 启动日志【删除了时间信息】
[13020] [INFO] Starting gunicorn 21.2.0
[13020] [INFO] Listening at: http://0.0.0.0:9090 (13020)
[13020] [INFO] Using worker: sync
[13023] [INFO] Booting worker with pid: 13023
[13024] [INFO] Booting worker with pid: 13024
[13025] [INFO] Booting worker with pid: 13025
[13026] [INFO] Booting worker with pid: 13026

–host 0.0.0.0 to listen on all network interfaces (or a specific interface address).

启动后，访问http://<host>:<port>可查看到页面：

如果使用的是 Databricks 未提供的托管 MLflow 跟踪服务器，或者运行本地跟踪服务器，请确保使用以下命令设置跟踪服务器的 URI：

import mlflow

mlflow.set_tracking_uri(uri="http://<host>:<port>")

如果未在运行时环境中设置此项，则运行将记录到本地文件系统。

Step 3 - Train a model and prepare metadata for logging

在本部分中，我们将使用 MLflow 记录模型。这些步骤的快速概述如下：

加载并准备用于建模的 Iris 数据集。
训练逻辑回归模型并评估其性能。
准备模型超参数并计算日志记录指标。

官方代码如下：

import mlflow
from mlflow.models import infer_signature

import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score


# Load the Iris dataset
X, y = datasets.load_iris(return_X_y=True)

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Define the model hyperparameters
params = {
    "solver": "lbfgs",
    "max_iter": 1000,
    "multi_class": "auto",
    "random_state": 8888,
}

# Train the model
lr = LogisticRegression(**params)
lr.fit(X_train, y_train)

# Predict on the test set
y_pred = lr.predict(X_test)

# Calculate metrics
accuracy = accuracy_score(y_test, y_pred)

Step 4 - Log the model and its metadata to MLflow

这个步骤将使用我们训练的模型、为模型拟合指定的超参数，以及通过评估模型对要记录到 MLflow 的测试数据的性能来计算的损失指标。步骤如下：

启动 MLflow 运行上下文以启动新运行，我们将模型和元数据记录到该运行。
记录模型参数和性能指标。
标记运行以便于检索。
在记录（保存）模型时，在 MLflow 模型注册表中注册模型。

官方代码如下：

# Set our tracking server uri for logging
mlflow.set_tracking_uri(uri="http://127.0.0.1:8080")

# Create a new MLflow Experiment
mlflow.set_experiment("MLflow Quickstart")

# Start an MLflow run
with mlflow.start_run():
    # Log the hyperparameters
    mlflow.log_params(params)

    # Log the loss metric
    mlflow.log_metric("accuracy", accuracy)

    # Set a tag that we can use to remind ourselves what this run was for
    mlflow.set_tag("Training Info", "Basic LR model for iris data")

    # Infer the model signature
    signature = infer_signature(X_train, lr.predict(X_train))

    # Log the model
    model_info = mlflow.sklearn.log_model(
        sk_model=lr,
        artifact_path="iris_model",
        signature=signature,
        input_example=X_train,
        registered_model_name="tracking-quickstart",
    )

Step 5 - Load the model as a Python Function (pyfunc) and use it for inference

记录模型后，我们可以通过以下方式执行推理：

使用 MLflow 的 pyfunc 风格加载模型。
使用加载的模型对新数据运行 Predict。

官方源码如下：

# Load the model back for predictions as a generic Python Function model
loaded_model = mlflow.pyfunc.load_model(model_info.model_uri)

predictions = loaded_model.predict(X_test)

iris_feature_names = datasets.load_iris().feature_names

result = pd.DataFrame(X_test, columns=iris_feature_names)
result["actual_class"] = y_test
result["predicted_class"] = predictions

result[:4]

Step 6 - View the Run in the MLflow UI

官方带注释的示例：

实际执行示例：

官方运行详情图片：

实际运行详情图片：

查看生成的模型：

恭喜你完成了 MLflow 跟踪快速入门！

3.模型使用

模型文件可以从MLflow的页面上下载，调用代码如下：

import pickle

from sklearn import datasets
from sklearn.model_selection import train_test_split

# 使用鸢尾数据集
# Load the Iris dataset
X, y = datasets.load_iris(return_X_y=True)
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 使用自己收集的数据
# X_test = [[5.1, 3.5, 1.4, 0.2], [7, 3.2, 4.7, 1.4], [5.8, 2.7, 5.1, 1.9], [5, 3.4, 1.5, 0.2]]

# 加载模型文件
with open('model.pkl', 'rb') as f:
    model = pickle.load(f)

# 预测类别
predict_res = model.predict(X_test)
print(predict_res)
# 类别的概率值
predict_proba_res = model.predict_proba_res(X_test)
print(predict_proba_res)

运行结果：

[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0]
[[3.78540528e-03 8.27210036e-01 1.69004558e-01]
 [9.46727168e-01 5.32726324e-02 1.99986485e-07]
 [8.72385678e-09 1.55691543e-03 9.98443076e-01]
 [6.43448541e-03 7.92126720e-01 2.01438794e-01]
 [1.44095504e-03 7.74344678e-01 2.24214367e-01]
 [9.55768195e-01 4.42316282e-02 1.76849618e-07]
 [7.76122925e-02 9.08082089e-01 1.43056184e-02]
 [1.61401761e-04 1.55690379e-01 8.44148220e-01]
 [2.20736358e-03 7.62834309e-01 2.34958328e-01]
 [2.83154097e-02 9.45798683e-01 2.58859075e-02]
 [4.39679072e-04 2.43281233e-01 7.56279088e-01]
 [9.68307933e-01 3.16919884e-02 7.80649442e-08]
 [9.72933931e-01 2.70660354e-02 3.33361479e-08]
 [9.62096918e-01 3.79029707e-02 1.10921553e-07]
 [9.79272618e-01 2.07273173e-02 6.47441645e-08]
 [4.54276481e-03 7.12605196e-01 2.82852039e-01]
 [7.22631014e-06 2.42037680e-02 9.75789006e-01]
 [2.73294494e-02 9.47693264e-01 2.49772862e-02]
 [8.23322627e-03 8.31120340e-01 1.60646433e-01]
 [1.41951887e-05 3.59416995e-02 9.64044105e-01]
 [9.64370490e-01 3.56293171e-02 1.92868716e-07]
 [1.31386507e-03 3.99100235e-01 5.99585900e-01]
 [9.61627956e-01 3.83717831e-02 2.61167666e-07]
 [1.85433787e-05 4.58639912e-02 9.54117465e-01]
 [1.63757839e-06 2.58619974e-02 9.74136365e-01]
 [9.32586134e-05 1.05067524e-01 8.94839218e-01]
 [8.68953654e-06 5.83511361e-02 9.41640174e-01]
 [4.29911372e-06 1.88516061e-02 9.81144095e-01]
 [9.66838022e-01 3.31618418e-02 1.35794380e-07]
 [9.56304600e-01 4.36951677e-02 2.32419972e-07]]