Configurare DVC

Data Version Control (DVC) offre un metodo strutturato per gestire il versioning dei dati, un aspetto cruciale spesso trascurato. Con DVC puoi monitorare con precisione le modifiche ai tuoi insiemi di dati, garantendo riproducibilità, collaborazione e troubleshooting più semplice. Funziona come uno scudo protettivo contro gli ostacoli legati ai dati, favorendo fiducia ed efficienza nel tuo lavoro data‑centric

In questo esercizio metterai in pratica l’inizializzazione di un progetto DVC e verificherai come è installato DVC. Git è già stato inizializzato per questo progetto.

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Introduzione al versionamento dei dati con DVC

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Introduzione al versionamento dei dati con DVC

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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: Configurazione e inizializzazione di DVC Exercise 2: Configurare DVC

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Exercise 3: Pattern di .dvcignore Exercise 4: Cache DVC e file in staging Exercise 5: Lavorare con la cache di DVC Exercise 6: Capire i file .dvc Exercise 7: Configurare i remoti DVC Exercise 8: Scopo dei remoti DVC Exercise 9: Configura un DVC Remote Exercise 10: Interagire con i DVC Remote Exercise 11: Versionare i dati usando un DVC Remote Exercise 12: Eseguire il checkout dei dati versionati

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!