DVC pipeline yürütme kavramları

DVC pipeline'ları Machine Learning iş akışlarını sadeleştirir; verinin ve kodun etkin şekilde sürümlenmesini, izlenmesini ve önbelleğe alınmasını sağlar. Pipeline yürütme bilgisini kavramak, etkili iş akışları tasarlamana yardımcı olur. Bu egzersizde, DVC'nin yeniden üretilebilir pipeline'ları nasıl çalıştırdığını ne kadar anladığını kontrol edeceksin.

Bu egzersiz, kursun bir parçasıdır

DVC ile Veri Sürümlendirmeye Giriş

Kursa Göz Atın

Uygulamalı etkileşimli egzersiz

Teoriyi etkileşime dönüştürün, interaktif egzersizlerimizden biriyle

Egzersize başla

Bu egzersiz, kursun bir parçasıdır

DVC ile Veri Sürümlendirmeye Giriş

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Kursa Ücretsiz Başla

This chapter provides a comprehensive introduction to Data Version Control (DVC), a tool essential for data versioning in machine learning. Learners will explore the motivation behind data versioning, understand its differences from code versioning, and experiment with a simple classification problem. They will review basic Git commands, learn about DVC, and practice setting up a repository. The chapter concludes with an overview of DVC’s features and use cases, including versioning data and models, CI/CD for machine learning, experiment tracking, pipelines, and more.

Exercise 1: Data Versioning Motivation Exercise 2: Anatomy of a Machine Learning Model Exercise 3: Differences Between Data and Code Versioning Exercise 4: Understanding Hyperparameters Exercise 5: Introduction to DVC Exercise 6: Working with Git CLI Exercise 7: Review DVC CLI Exercise 8: DVC features and use cases Exercise 9: DVC pipelines Exercise 10: CI/CD for machine learning

This chapter delves into the setup of DVC, encompassing aspects such as installation, initialization of the repository, and the utilization of the .dvcignore file. It further navigates through the exploration of DVC cache and staging files, imparting knowledge on how to add and remove files, manage caches, and comprehend the underlying mechanisms using the MD5 hash. The chapter also elucidates on DVC remotes, distinguishing them from Git remotes, and guides you on how to add, list, and modify them. Lastly, it teaches you how to interact with these remotes by pushing and pulling data, checking out specific versions, and fetching data to the cache.

Exercise 1: DVC Setup and Initialization Exercise 2: Setting up DVC Exercise 3: .dvcignore Patterns Exercise 4: DVC Cache and Staging Files Exercise 5: Working with DVC Cache Exercise 6: Understanding .dvc Files Exercise 7: Configuring DVC Remotes Exercise 8: Purpose of DVC Remotes Exercise 9: Setup a DVC Remote Exercise 10: Interacting with DVC Remotes Exercise 11: Versioning Data using DVC Remote Exercise 12: Checking out Versioned Data

This chapter focuses on automating ML pipelines using DVC. Learners create a configuration file containing settings and hyperparameters. They also learn about pipeline visualization using directed acyclic graphs and use commands to describe dependencies, commands, and outputs. Execution of DVC pipelines is covered, including local model training and how Git tracks DVC metadata. Additionally, learners explore metrics and plots tracking in DVC, including how to print metrics, create plot files, and compare metrics and plots across different pipeline stages.

Exercise 1: Kod organizasyonu ve yeniden düzenleme Exercise 2: DVC’de parametre dosyalarını anlama Exercise 3: Bir parametre dosyası yaz Exercise 4: DVC pipeline’larını yazmak ve görselleştirmek Exercise 5: Bir DVC pipeline’ı tasarlama Exercise 6: Bir DVC pipeline’ını görselleştirme Exercise 7: DVC boru hatlarını çalıştırma Exercise 8: DVC pipeline yürütme kavramları

Geçerli egzersiz

Exercise 9: Bir ML model eğitim hattını çalıştır Exercise 10: Değerlendirme: DVC'de metrikler ve grafikler Exercise 11: DVC Metriklerini Takip Etme Exercise 12: dvc.yaml'a grafikler ekleme Exercise 13: Tebrikler!