Running MLflow Projects

Introduction to MLflow

Weston Bassler

Senior MLOps Engineer

API and command line

Workflows

Introduction to MLflow

Projects API

mlflow.projects

mlflow.projects.run()

  • uri - URI to MLproject file

  • entry_point - Entry point to start run from MLproject

  • experiment_name - Experiment to track training run

  • env_manager - Python environment manager: local or virtualenv

# Run MLflow Project
mlflow.projects.run(

    uri='./',

    entry_point='main',

    experiment_name='My Experiment',

    env_manager='virtualenv'

)
Introduction to MLflow

MLproject

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

train_model.py

# Import libraries and modules
import mlflow
import mlflow.sklearn
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split


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

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)


# Set Auto logging for Scikit-learn flavor
mlflow.sklearn.autolog()

# Train model
lr = LinearRegression()
lr.fit(X_train, y_train)
Introduction to MLflow

Projects run

# Import MLflow Module
import mlflow


# Run local Project
mlflow.projects.run(uri='./', entry_point='main', 
                    experiment_name='Salary Model')
Introduction to MLflow

Run output

# Run local 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
Introduction to MLflow

Run output

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 ===
Introduction to MLflow

MLflow Tracking

MLflow Tracking UI

Introduction to MLflow

Command line

mlflow run

  • --entry-point - Entry point to start run from MLproject

  • --experiment-name - Experiment to track training run

  • --env-manager - Python environment manager: local or virtualenv

  • URI - URI to MLproject file

Introduction to MLflow

Run command

# Run main entry point from Salary Model experiment
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 ===
Introduction to MLflow

Let's practice!

Introduction to MLflow

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