Een run loggen

In deze oefening train je een model met scikit-learns Linear Regression om de winst uit de Unicorn-gegevensset te voorspellen. Je hebt een experiment met de naam Unicorn Sklearn Experiment gemaakt en een nieuwe run gestart. Je gaat metrics voor r2_score loggen en parameters voor n_jobs, en je logt ook de trainingscode als een artefact.

Het Linear Regression-model wordt getraind met de parameter n_jobs ingesteld op 1. De r2_score-metric is berekend met r2_score() uit scikit-learn op basis van de variabele y_pred, die afkomstig is van voorspellingen op X_test.

model = LinearRegression(n_jobs=1)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

r2_score = r2_score(y_test, y_pred)

De mlflow-module en de modules LinearRegression, train_test_split en metrics uit scikit-learn worden geïmporteerd.

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

Cursus bekijken

Oefeninstructies

Log de variabele r2_score als een metric met de naam "r2_score".
Log een parameter met de naam "n_jobs" naar de Tracking Server.
Log het bestand "train.py" als een artefact in de run.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Log the metric r2_score as "r2_score"
____.____("____", ____)

# Log parameter n_jobs as "n_jobs"
____.____("____", ____)

# Log the training code
____.____("train.py")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

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Begin de cursus gratis

In this Chapter, you will be introduced to MLflow and how it aims to assist with some difficulties of the Machine Learning lifecycle. You will be introduced to the four main concepts that make up MLflow with a main focus on MLflow Tracking. You will learn to create experiments and runs as well as how to track metrics, parameters, and artifacts. Finally, you will search MLflow programmatically to find experiment runs that fit certain criteria.

Exercise 1: Wat is MLflow?Exercise 2: Componenten van MLflow Exercise 3: MLflow-experiments Exercise 4: MLflow Tracking Exercise 5: Een run starten Exercise 6: Een run loggen

Huidige oefening

Exercise 7: Hoe haal je gegevens van de actieve run op?Exercise 8: Runs opvragen Exercise 9: Zoekopties voor runs Exercise 10: Runs zoeken

In this Chapter, you will be introduced to MLflow Models. The MLflow Models component of MLflow plays an essential role in the Model Evaluation and Model Engineering steps of the Machine Learning lifecycle. You will learn how MLflow Models standardizes the packaging of ML models as well as how to save, log and load them. You will learn how to create custom MLflow Models to provide more flexibility to your use cases as well as how to evaluate model performance. You will utilize the powerful concept of “Flavors” and finally use the MLflow command line tool for model deployment.

Exercise 1: Introduction to MLflow Models Exercise 2: Package a machine learning model Exercise 3: Storage Format Exercise 4: What's in an MLmodel file?Exercise 5: Model API Exercise 6: Saving and loading a model Exercise 7: Logging and loading a model Exercise 8: Custom models Exercise 9: Creating a custom Python Class Exercise 10: Custom scikit-learn model Exercise 11: Scikit-learn flavor and evaluation Exercise 12: Model serving Exercise 13: Serving a model Exercise 14: Score from a served model

This Chapter introduces the concept of MLflow called the Model Registry. You will be introduced to how the Model Registry is used to manage the lifecycle of ML models. You will learn how to create and search for models in the Model Registry. You then learn how to register models to the Model Registry and learn how to transition models between predefined stages. Finally, you will also learn how to deploy models from the Model Registry.

Exercise 1: Introduction to MLflow Model Registry Exercise 2: Create a Model Exercise 3: Searching Models Exercise 4: Registering Models Exercise 5: Registering existing models Exercise 6: Registering new models Exercise 7: Model stages Exercise 8: Model stages in Model Registry Exercise 9: Transitioning model stages Exercise 10: Model deployment Exercise 11: Serving models from the Model Registry Exercise 12: Loading models from the Model Registry

In this chapter, you'll gain valuable knowledge on how to streamline your data science code for reusability and reproducibility using MLflow Projects. The chapter begins by introducing the concept of MLflow Projects and walking you through creating an MLproject file. From there, you'll learn how to run MLflow Projects through both the command-line and the MLflow Projects module while also discovering the power of using parameters for added flexibility in your code. Finally, you will learn how to manage steps of the machine learning lifecycle by creating a multi-step workflow using MLflow Projects.

Exercise 1: Introduction to MLflow Projects Exercise 2: MLproject file layout Exercise 3: Creating an MLproject Exercise 4: Running MLflow Projects Exercise 5: MLflow run command Exercise 6: MLflow projects module Exercise 7: Specifying parameters Exercise 8: Adding parameters to MLproject Exercise 9: Adding parameters to project run Exercise 10: Workflows Exercise 11: Creating an MLproject for the ML Lifecycle: Model Engineering Exercise 12: Creating an MLproject for the ML Lifecycle: Model Evaluation Exercise 13: Creating a multi-step workflow: Model Engineering Exercise 14: Creating a multi-step workflow: Model Evaluation Exercise 15: Wrap-up