DVC Remote kullanarak verileri versiyonlama

Bu düzenleyici egzersizinde, veri kümelerini nasıl versiyonlayacağını ve DVC remote’a nasıl göndereceğini pratik yapacaksın. Veri versiyonlama ve depolama, DVC’nin temel değer önerisidir; bunu başarmak için Git ve DVC’nin nasıl birlikte çalıştığının mekanizmasını öğreneceksin. Üzerinde çalışacağın veri kümesi, atmosferik koşullar verilerek yağış tahmini için kullanılan bir hava durumu veri kümesidir.

Biz senin için DVC’yi başlattık, /tmp/dvc konumunda yerel bir remote yapılandırdık ve bir kurulum commit’i ekledik.

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 Kurulumu ve Başlatma Exercise 2: DVC Kurulumu Exercise 3: .dvcignore Kalıpları Exercise 4: DVC Önbelleği ve Aşama Dosyaları Exercise 5: DVC Önbelleği ile Çalışma Exercise 6: `.dvc` Dosyalarını Anlama Exercise 7: DVC Uzaklarını Yapılandırma Exercise 8: DVC Remotes'in Amacı Exercise 9: Bir DVC Remote’u Ayarla Exercise 10: DVC Uzaklarıyla Etkileşim Exercise 11: DVC Remote kullanarak verileri versiyonlama

Geçerli egzersiz

Exercise 12: Sürümlendirilmiş Verileri Checkout Etme

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: Code organization and refactoring Exercise 2: Understanding parameter files in DVC Exercise 3: Write a parameter file Exercise 4: Writing and visualizing DVC pipelines Exercise 5: Designing a DVC pipeline Exercise 6: Visualizing a DVC pipeline Exercise 7: Executing DVC pipelines Exercise 8: DVC pipeline execution concepts Exercise 9: Execute a ML model training pipeline Exercise 10: Evaluation: Metrics and plots in DVC Exercise 11: Tracking DVC Metrics Exercise 12: Adding plots to dvc.yaml Exercise 13: Congratulations!