Bekerja dengan variabel lingkungan

Dalam latihan ini, Anda akan bekerja dengan variabel lingkungan global dan lokal. Workflow akan berjalan saat membuka pull request terhadap branch main.

GitHub Actions memampukan kustomisasi workflow melalui variabel lingkungan. Mempelajari cara menetapkan variabel lingkungan tingkat global dan tingkat job untuk pemicu tertentu akan meningkatkan pemahaman Anda tentang kontrol dan otomatisasi workflow.

Setelah menyelesaikan latihan ini, Anda akan mahir mengelola variabel lingkungan dalam workflow GitHub Actions. Anda akan memahami pentingnya variabel global dan lokal, cakupannya, serta perannya dalam meningkatkan perilaku workflow. Pengetahuan ini memungkinkan Anda menyesuaikan workflow secara efisien, mengoptimalkan proses otomatisasi, dan menangani konfigurasi spesifik lingkungan secara efektif.

Latihan ini adalah bagian dari kursus

CI/CD untuk Machine Learning

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

CI/CD untuk Machine Learning

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4.8+

Mulai Kursus Gratis

In this chapter, you will explore the essential principles of Continuous Integration/Continuous Delivery (CI/CD) and YAML. You'll grasp the software development life cycle and key terms like build, test, and deploy. Discover the differences between Continuous Integration, Continuous Delivery, and Continuous Deployment. Moreover, you'll investigate the significance of CI/CD in machine learning and experimentation.

Exercise 1: Introduction to Continuous Integration/Continuous Delivery for Machine Learning Exercise 2: Continuous deployment and delivery Exercise 3: Machine learning workflow Exercise 4: Introduction to YAML Exercise 5: YAML syntax Exercise 6: YAML mappings and sequences Exercise 7: Introduction to GitHub Actions Exercise 8: Utility of GitHub Actions Exercise 9: Anatomy of GitHub Actions

Get ready to explore GitHub Actions (GHA), an influential platform for executing CI/CD workflows. Uncover the diverse components of GHA, encompassing events, actions, jobs, steps, runners, and context. Gain insights into crafting workflows that activate upon events like push and pull requests, and tailor runner machines. Dive into hands-on learning as you establish fundamental CI pipelines and grasp the intricacies of the GHA log.

Exercise 1: YAML Menengah Exercise 2: Temukan kombinasi yang benar Exercise 3: Rancang workflow Continuous Integration Exercise 4: Menyusun pipeline CI dasar Exercise 5: Menginterpretasikan Workflow GitHub Actions Exercise 6: Tulis Workflow GitHub Actions Exercise 7: Menjalankan kode repositori Exercise 8: Feature branches dalam model repositori bersama Exercise 9: Menjalankan kode Python di GitHub Actions Exercise 10: Variabel Lingkungan dan Secret Exercise 11: Apa itu GITHUB_TOKEN?Exercise 12: Bekerja dengan variabel lingkungan

Latihan Saat Ini

Exercise 13: Bekerja dengan secrets

In this chapter, you'll explore the integration of machine learning model training into a GitHub Action pipeline using Continuous Machine Learning GitHub Action. You'll generate a comprehensive markdown report including model metrics and plots. You will also delve into data versioning in Machine Learning by adopting Data Version Control (DVC) to track data changes. The chapter also covers setting DVC remotes and dataset transfers. Finally, you'll explore DVC pipelines, configuring a DVC YAML file to orchestrate reproducible model training.

Exercise 1: Model training with GitHub Actions Exercise 2: Develop a classification model Exercise 3: Train a classification model Exercise 4: Setup model training using CML Exercise 5: Versioning datasets with Data Version Control Exercise 6: Why are .dvc files needed?Exercise 7: Data versioning in action Exercise 8: Interacting with DVC remotes Exercise 9: Exploring the Benefits of DVC Remotes Exercise 10: DVC remotes in action Exercise 11: DVC Pipelines Exercise 12: Creating a DVC pipeline Exercise 13: Train ML models with DVC

In this chapter, you will direct your attention towards the analysis of model performance and the fine-tuning of hyperparameters. You will acquire practical expertise in comparing metrics and visualizations across different branches to assess changes in model performance. You will conduct hyperparameter tuning using scikit-learn's GridSearchCV. Furthermore, you will delve into the automation of pull requests using the optimal model configuration.

Exercise 1: Comparing metrics and plots in DVC Exercise 2: Adding metrics and plots to dvc.yaml Exercise 3: Comparing metrics across Git branches Exercise 4: Run DVC pipeline in GitHub Actions Exercise 5: Hyperparameter Tuning with DVC Exercise 6: Adding Hyperparameter tuning to dvc.yaml Exercise 7: Running Hyperparameter tuning DVC pipelines Exercise 8: GitHub Actions workflow for Hyperparameter Tuning Exercise 9: Loose Coupling Exercise 10: Setup Hyperparameter Tuning in GitHub Actions Exercise 11: Congratulations!