Menjalankan Proyek MLflow

Pengantar MLflow

Weston Bassler

Senior MLOps Engineer

API dan baris perintah

Alur kerja

API Projects

mlflow.projects

mlflow.projects.run()

uri - URI ke file MLproject
entry_point - Titik awal untuk memulai run dari MLproject
experiment_name - Eksperimen untuk melacak run pelatihan
env_manager - Pengelola environment Python: local atau virtualenv

# Jalankan Proyek MLflow
mlflow.projects.run(

    uri='./',

    entry_point='main',

    experiment_name='My Experiment',

    env_manager='virtualenv'

)

MLproject

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

train_model.py

# Impor library dan modul
import mlflow
import mlflow.sklearn
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split


# Data pelatihan
df = pd.read_csv('Salary_predict.csv')
X = df[["experience", "age", "interview_score"]]
y = df[["Salary"]]

train_model.py

# Train test split 
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7,
                                                    random_state=0)


# Aktifkan autolog untuk flavor Scikit-learn
mlflow.sklearn.autolog()

# Latih model
lr = LinearRegression()
lr.fit(X_train, y_train)

Menjalankan Projects

# Impor modul MLflow
import mlflow


# Jalankan Proyek lokal
mlflow.projects.run(uri='./', entry_point='main', 
                    experiment_name='Salary Model')

Output run

# Jalankan Proyek lokal
mlflow.projects.run(uri='./', entry_point='main', 
                    experiment_name='Salary Model')

2023/04/02 14:33:23 INFO mlflow.projects: 'Salary Model' tidak ada. 
Membuat eksperimen baru

2023/04/02 14:33:23 INFO mlflow.utils.virtualenv: Menginstal python 3.10.8 jika 
tidak ada
2023/04/02 14:33:23 INFO mlflow.utils.virtualenv: Membuat environment baru 
/.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: Menginstal dependensi

Output run

2023/04/02 14:33:59 INFO mlflow.projects.backend.local: === Menjalankan perintah 
'source /.mlflow/envs/mlflow-44f5094bba686a8d4a5c772/bin/activate && python 
train_model.py' pada run dengan ID '562916d45aeb48ec84c1c393d6e3f5b6' ===

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

Pelacakan MLflow

UI Pelacakan MLflow

Baris perintah

mlflow run

--entry-point - Titik awal untuk memulai run dari MLproject
--experiment-name - Eksperimen untuk melacak run pelatihan
--env-manager - Pengelola environment Python: local atau virtualenv
URI - URI ke file MLproject

Perintah run

# Jalankan entry point main dari eksperimen Salary Model
mlflow run --entry-point main --experiment-name "Salary Model" ./

2023/04/02 15:23:34 INFO mlflow.utils.virtualenv: Menginstal python 3.10.8 jika 
tidak ada
2023/04/02 15:23:34 INFO mlflow.utils.virtualenv: Environment 
/.mlflow/envs/mlflow-44f5094bba686a8d4a5c772 sudah ada

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

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

Ayo berlatih!

Pengantar MLflow