qlib的工作流管理：mlflow机器学习生命周期管理平台

mlflow不是机器学习框架，而是与机器学习框架无关的机器学习过程管理平台。

它是轻量极，侵入性比较低。

我把它单独拿出来是因为qlib平台的workflow工作流是使用mlflow来管理的。

https://www.mlflow.org/

MLflow Documentation — MLflow 1.28.0 documentation

用来管理机器学习生命周期的平台，我们平常使用的sklearn和pytorch都兼容。

01 安装

pip install mlflow

依赖包还比较多，numpy, scipy， 还有整个flask技术栈，这个是给mlflow ui用的。

Installing collected packages: numpy, zipp, importlib-resources, MarkupSafe, typing-extensions, importlib-metadata, Mako, greenlet, sqlalchemy, alembic, pyyaml, pytz, pyparsing, packaging, click, pyjwt, oauthlib, charset-normalizer, idna, urllib3, certifi, requests, tabulate, six, databricks-cli, gunicorn, smmap, gitdb, gitpython, Werkzeug, itsdangerous, Jinja2, Flask, sqlparse, querystring-parser, scipy, cloudpickle, protobuf, entrypoints, websocket-client, docker, python-dateutil, pandas, prometheus-client, prometheus-flask-exporter, mlflow

02 mlflow UI

mlflow ui

打开本机的5000端口，可以看到精美的界面：

 目前还没有在运行的实验与模型。

03 hello mlflow

import os
from random import random, randint
from mlflow import log_metric, log_param, log_artifacts
 
if __name__ == "__main__":
    # 给参数打日志 (key-value 对)， 参数1：随机整形
    log_param("param1", randint(0, 100))
 
    # Log a metric; metrics can be updated throughout the run
    log_metric("foo", random())
    log_metric("foo", random() + 1)
    log_metric("foo", random() + 2)
 
    # 当前目录下创建outputs文件夹，并写text.txt。
    if not os.path.exists("outputs"):
        os.makedirs("outputs")
    with open("outputs/test.txt", "w") as f:
        f.write("hello world2!")
    log_artifacts("outputs")

写入三种日志： 参数，metric-指标，artifacts一些输出文件。

param,metric都是key-value的形式写入。

artifacts是备份文件夹。

04 tutorial

到github上下载代码：

GitHub - mlflow/mlflow: Open source platform for the machine learning lifecycle

github如果不稳定，可以通过gitee下载：

MLflow: MLflow 是由 Apache Spark 技术团队开源的一个机器学习平台，主打开放性： 开放接口：可与任意 ML 库、算法、部署工具或编程语言一起使用

pip install mlflow[extras]

这里会安装sklearn。

 或者单独安装sklearn:

pip install scikit-learn

在examples目录下会有不少官方的例子可以学习。

# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
 
import os
import warnings
import sys
 
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
 
import mlflow
import mlflow.sklearn
 
 
def eval_metrics(actual, pred):
    rmse = np.sqrt(mean_squared_error(actual, pred))
    mae = mean_absolute_error(actual, pred)
    r2 = r2_score(actual, pred)
    return rmse, mae, r2
 
 
 
if __name__ == "__main__":
    warnings.filterwarnings("ignore")
    np.random.seed(40)
 
    # Read the wine-quality csv file (make sure you're running this from the root of MLflow!)
    wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv")
    data = pd.read_csv(wine_path)
 
    # Split the data into training and test sets. (0.75, 0.25) split.
    train, test = train_test_split(data)
 
    # The predicted column is "quality" which is a scalar from [3, 9]
    train_x = train.drop(["quality"], axis=1)
    test_x = test.drop(["quality"], axis=1)
    train_y = train[["quality"]]
    test_y = test[["quality"]]
 
    alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
    l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
 
    with mlflow.start_run():
        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
        lr.fit(train_x, train_y)
 
        predicted_qualities = lr.predict(test_x)
 
        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
 
        print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
        print("  RMSE: %s" % rmse)
        print("  MAE: %s" % mae)
        print("  R2: %s" % r2)
 
        mlflow.log_param("alpha", alpha)
        mlflow.log_param("l1_ratio", l1_ratio)
        mlflow.log_metric("rmse", rmse)
        mlflow.log_metric("r2", r2)
        mlflow.log_metric("mae", mae)
 
        mlflow.sklearn.log_model(lr, "model")

sklearn里fit, predict之后，把参数，结果指标还是模型一并保存。

相当于帮我们管理整个实验过程。