執行 MLflow Projects

MLflow 入門

Weston Bassler

Senior MLOps Engineer

API 與命令列

工作流程

MLflow 入門

Projects API

mlflow.projects

mlflow.projects.run()

  • uri - MLproject 檔案的 URI

  • entry_point - 從 MLproject 啟動的進入點

  • experiment_name - 用於追蹤訓練執行的實驗

  • env_manager - Python 環境管理器：local 或 virtualenv

# 執行 MLflow Project
mlflow.projects.run(

    uri='./',

    entry_point='main',

    experiment_name='My Experiment',

    env_manager='virtualenv'

)
MLflow 入門

MLproject

name: salary_model
python_env: python_env.yaml
entry_points:
  main:
    command: "python train_model.py"
MLflow 入門

train_model.py

# 匯入函式庫與模組
import mlflow
import mlflow.sklearn
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split


# 訓練資料
df = pd.read_csv('Salary_predict.csv')
X = df[["experience", "age", "interview_score"]]
y = df[["Salary"]]
MLflow 入門

train_model.py

# 訓練/測試切分
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7,
                                                    random_state=0)


# 啟用 Scikit-learn 風格的自動記錄
mlflow.sklearn.autolog()

# 訓練模型
lr = LinearRegression()
lr.fit(X_train, y_train)
MLflow 入門

Projects 執行

# 匯入 MLflow 模組
import mlflow


# 執行本機 Project
mlflow.projects.run(uri='./', entry_point='main', 
                    experiment_name='Salary Model')
MLflow 入門

執行輸出

# 執行本機 Project
mlflow.projects.run(uri='./', entry_point='main', 
                    experiment_name='Salary Model')

2023/04/02 14:33:23 INFO mlflow.projects: 'Salary Model' does not exist. 
Creating a new experiment

2023/04/02 14:33:23 INFO mlflow.utils.virtualenv: Installing python 3.10.8 if it 
does not exist
2023/04/02 14:33:23 INFO mlflow.utils.virtualenv: Creating a new environment 
/.mlflow/envs/mlflow-44f5094bba686a8d4a5c772
created virtual environment CPython3.10.8.final.0-64 in 236ms
2023/04/02 14:33:23 INFO mlflow.utils.virtualenv: Installing dependencies
MLflow 入門

執行輸出

2023/04/02 14:33:59 INFO mlflow.projects.backend.local: === Running command 
'source /.mlflow/envs/mlflow-44f5094bba686a8d4a5c772/bin/activate && python 
train_model.py' in run with ID '562916d45aeb48ec84c1c393d6e3f5b6' ===

2023/04/02 14:34:34 INFO mlflow.projects: === Run (ID 
'562916d45aeb48ec84c1c393d6e3f5b6') succeeded ===
MLflow 入門

MLflow 追蹤

MLflow 追蹤介面

MLflow 入門

命令列

mlflow run

  • --entry-point - 從 MLproject 啟動的進入點

  • --experiment-name - 用於追蹤訓練執行的實驗

  • --env-manager - Python 環境管理器：local 或 virtualenv

  • URI - MLproject 檔案的 URI

MLflow 入門

執行指令

# 從 Salary Model 實驗執行 main 進入點
mlflow run --entry-point main --experiment-name "Salary Model" ./

2023/04/02 15:23:34 INFO mlflow.utils.virtualenv: Installing python 3.10.8 if it 
does not exist
2023/04/02 15:23:34 INFO mlflow.utils.virtualenv: Environment 
/.mlflow/envs/mlflow-44f5094bba686a8d4a5c772 already exists

2023/04/02 15:23:34 INFO mlflow.projects.backend.local: === Running command 'source
/Users/weston/.mlflow/envs/mlflow-44f5094bba686a8d4a5c772/bin/activate && python 
train_model.py' in run with ID 'da5b37b6f53245e5bca59ba8ed6d7dc1' ===

2023/04/02 15:23:38 INFO mlflow.projects: === Run 
(ID 'da5b37b6f53245e5bca59ba8ed6d7dc1') succeeded ===
MLflow 入門

一起來練習吧！

MLflow 入門

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