GitHub Copilot Tutorial | How useful is it for Cloud and DevOps?

GitHub Copilot, an AI coding assistant, has garnered attention for its potential to revolutionize software development and cloud DevOps engineering. This tutorial investigates its practicality, comparing its utility in software development with its application in cloud and DevOps. The video demonstrates GitHub Copilot's integration with Visual Studio Code and its ability to suggest code based on context, though it emphasizes the tool's limitation due to outdated information as of September 2021. Despite this, the video showcases how GitHub Copilot can assist with code generation, infrastructure as code translation, and even offers a voice command feature. The tutorial also contrasts GitHub Copilot with Pulumi AI, highlighting the latter's more up-to-date and preferred approach for infrastructure provisioning with Pulumi. The video concludes by stressing the importance of developers' knowledge and the role of GitHub Copilot as a co-pilot, not a replacement, in the coding process.

Takeaways

• 🤖 GitHub Copilot is an AI assistant for coding that integrates into code editors like Visual Studio Code, providing code suggestions and helping write code more efficiently.
• 🔍 Copilot uses an open AI large language model trained on billions of lines of code and text, offering context-aware assistance based on the current code files open in the editor.
• 📚 The context provided to GitHub Copilot is limited to the files currently open in the editor, ensuring relevance to the task at hand without exposing the entire workspace.
• 🚀 Copilot can predict what code a user might write next and offer autocomplete suggestions, including comments and even security configurations for certain instances.
• 🛠️ While useful for general coding, GitHub Copilot has limitations, particularly in infrastructure as code scenarios where tools and libraries evolve rapidly, potentially leading to outdated suggestions.
• 🆓 For certain groups like open source maintainers, teachers, and students, GitHub Copilot is available for free, encouraging wider use and experimentation.
• 📝 Users have the option to control whether GitHub Copilot can generate code suggestions from public repositories and whether the context used by Copilot is collected and retained.
• 🔗 To integrate GitHub Copilot with Visual Studio Code, users need to install the official GitHub Copilot extension and sign in to authorize access.
• ➡️ Copilot can be instructed via comments within the code or through a chat interface, allowing for natural language requests for code generation.
• 🔄 For migrating projects from other tools like Terraform to Pulumi, Copilot can assist in translating code, although the resulting code may require updates to be current.
• ⚠️ It's crucial for engineers to validate Copilot's suggestions against current documentation due to the risk of receiving outdated information.
• 🎤 A unique feature showcased is the ability to use voice commands with an extension like Visual Studio Code Speech to interact with GitHub Copilot in a hands-free manner.

Q & A

What is GitHub Copilot and how does it assist in coding?

GitHub Copilot is an AI assistant for coding that integrates into a code editor or an integrated development environment like Visual Studio Code. It assists by providing code suggestions and auto-completions based on the context of the code you're currently working on, making the coding process potentially faster and more efficient.

How does GitHub Copilot use context to provide better code suggestions?

GitHub Copilot considers the existing code you have written and what you are currently writing as context to provide relevant code suggestions. It takes into account the files you currently have open in your editor, assuming these are relevant to your current task.

What are some limitations of using GitHub Copilot for infrastructure as code scenarios?

One significant limitation is that GitHub Copilot's knowledge is outdated as of September 2021, which can be problematic given the rapid pace of development in infrastructure as code tools. This means that code suggestions may use older versions of libraries or functions that are no longer best practice or even supported.

How can GitHub Copilot be useful for someone new to a project or a particular coding language?

GitHub Copilot can help explain complex code segments and provide translations between different coding languages or tools. For instance, it can translate Terraform code into Pulumi code, which can be particularly useful for someone looking to migrate a project or understand code written in a different tool.

What are some best practices when using GitHub Copilot for infrastructure provisioning?

It's important to double-check and reference the up-to-date documentation for each configuration option provided by GitHub Copilot. While it can speed up the coding process, it should not replace a developer's knowledge and understanding of the tools and languages they are working with.

How does GitHub Copilot handle the privacy and security of the code it assists with?

Users can configure GitHub Copilot's settings to decide whether the prompts and context used to generate code are collected, retained, and processed by GitHub and shared with Microsoft. This is an important consideration for maintaining the security of sensitive information within the code.

What is the role of GitHub Copilot in the context of version control and collaboration?

GitHub Copilot can assist developers in writing code that integrates well with version control systems like GitHub, by providing suggestions and automating some of the coding process. However, it does not directly handle version control or collaboration tasks; these are still managed by the developer using GitHub's platform.

How does GitHub Copilot differ from other AI code assistants in terms of its approach to coding assistance?

GitHub Copilot stands out by being integrated directly into the code editor, which streamlines the process of receiving and implementing code suggestions. It also uses a large language model trained on billions of lines of code and text, allowing it to provide suggestions based on best practices and a deep understanding of coding patterns.

What are the prerequisites for using GitHub Copilot?

To use GitHub Copilot, you need a GitHub account, the ability to install extensions on your code editor (like Visual Studio Code), and a programming environment set up for your chosen language. For cloud and DevOps scenarios, you may also need specific tools like Pulumi and an AWS account.

Can GitHub Copilot be used for free under certain conditions?

Yes, GitHub Copilot is available for free to open-source maintainers, teachers, and students. These statuses are checked on a monthly basis, so if you fall into one of these categories, you can use GitHub Copilot without incurring costs.

How does GitHub Copilot handle the suggestion of comments within the code?

GitHub Copilot can suggest comments to accompany code blocks, which can be helpful for explaining the purpose and functionality of the code. This feature can be particularly useful for documenting code as it is written.

Outlines

00:00

🚀 Introduction to GitHub Copilot for Cloud and DevOps Engineers

This paragraph introduces GitHub Copilot as an AI assistant for coding, emphasizing its integration within code editors like Visual Studio Code. It contrasts GitHub Copilot with other AI tools like Cat GPT, highlighting its ability to provide code suggestions and explanations within the editor itself. The paragraph also explains how GitHub Copilot uses context from the user's current codebase to offer relevant suggestions and predicts the next steps in coding. It mentions the tool's reliance on an open AI large language model trained on billions of lines of code and text.

05:02

💻 Setting Up GitHub Copilot and Prerequisites

The second paragraph outlines the setup process for using GitHub Copilot, including the need for an AWS account, Pulumi installation, Python, Visual Studio Code, and the GitHub Copilot extension. It details the steps to install Pulumi using a package manager, the necessity of having Python 3, and the process of obtaining GitHub Copilot through a GitHub account. The paragraph also discusses the free trial option and the availability of GitHub Copilot for free for certain groups like open source maintainers, teachers, and students.

10:03

🔒 Privacy and Security Considerations for GitHub Copilot

This section addresses privacy and security settings for GitHub Copilot, allowing users to choose whether code suggestions can match code from public repositories on GitHub and if the prompts and context can be collected and retained by GitHub and Microsoft. It raises the importance of this setting, especially for private applications or when sensitive information is involved in the code.

15:03

📝 Activating GitHub Copilot and Starting a Pulumi Project

The paragraph demonstrates how to activate GitHub Copilot in Visual Studio Code and start a new Pulumi project. It guides through the process of installing the GitHub Copilot extension, initializing a Pulumi project with a specific template, and setting up an access token for Pulumi. The paragraph also mentions the convenience of auto-generated boilerplate code and the inclusion of the Pulumi AWS library for creating AWS resources.

20:04

🔁 Using GitHub Copilot for Coding Suggestions and Loops

The speaker explores using GitHub Copilot for generating code loops and handling infrastructure configuration. It shows how to request code that creates multiple S3 buckets and how to use inline suggestions for code configuration. The paragraph also discusses the limitations of GitHub Copilot for infrastructure as code, noting that it is more helpful for writing code than for infrastructure provisioning.

25:05

📚 Translating Terraform to Pulumi with GitHub Copilot

This section focuses on using GitHub Copilot to translate Terraform code into Pulumi code. It explains how to ask for an explanation of Terraform code and then requests a translation into Pulumi Python code. The paragraph points out the limitations of GitHub Copilot due to outdated information and emphasizes the need for validation against up-to-date documentation.

30:08

🗣️ Utilizing GitHub Copilot with Voice Commands

The final paragraph showcases a unique use case of GitHub Copilot: using voice commands to generate code. It mentions the use of the Visual Studio Code Speech extension to interact with GitHub Copilot via speech. The speaker tries out the feature by asking GitHub Copilot to create an EKS cluster with specific node group configurations, demonstrating an alternative way to interact with the AI tool.

35:09

📈 Conclusion and Real-world Application of GitHub Copilot

The speaker concludes by discussing the practical applications and limitations of GitHub Copilot, especially for rapidly evolving tools like Pulumi. They compare GitHub Copilot's output with that of Pulumi AI, noting the latter's more up-to-date information. The paragraph ends with a call to action for viewers to share their experiences with GitHub Copilot or other AI tools in the comments.

Keywords

💡GitHub Copilot

GitHub Copilot is an AI-powered code assistant that integrates directly into a code editor or integrated development environment (IDE), such as Visual Studio Code. It helps developers write code by providing suggestions and auto-completions based on the context of the code being written. In the video, it is discussed in the context of its utility for software engineers and Cloud/DevOps engineers, highlighting its ability to ease and speed up the coding process.

💡DevOps

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle and provide continuous delivery with high software quality. The video explores how GitHub Copilot can be particularly useful for DevOps engineers by automating certain aspects of infrastructure code provisioning.

💡Cloud Engineering

Cloud Engineering refers to the practice of building, testing, deploying, and managing cloud-native applications and services. The video discusses the potential benefits and limitations of using GitHub Copilot in the context of cloud engineering, particularly when working with infrastructure as code (IaC).

💡Infrastructure as Code (IaC)

IaC is a methodology for managing and provisioning computer infrastructure (as opposed to physical hardware) through machine-readable definition files, rather than interactive configuration. The video script provides examples of how GitHub Copilot can assist in writing IaC scripts, despite being potentially outdated.

💡Pulumi

Pulumi is an open-source infrastructure as code (IaC) platform that enables developers to manage cloud resources using programming languages like Python, TypeScript, and Go. The video demonstrates using Pulumi with GitHub Copilot to create and manage cloud resources, showcasing its integration with Python.

💡Code Suggestions

Code suggestions are a feature of GitHub Copilot that provides developers with autocomplete-style code snippets as they type. These suggestions are based on the current context of the code being written. The video illustrates how these suggestions can speed up the coding process by predicting what the developer might want to write next.

💡Security Group

In cloud computing, a security group acts as a virtual firewall that controls inbound and outbound traffic to AWS resources like EC2 instances. The video mentions GitHub Copilot suggesting the creation of a security group with firewall configuration for EC2 instances, demonstrating its application in IaC.

💡EKS (Amazon Elastic Kubernetes Service)

EKS is a fully managed service by AWS to run Kubernetes clusters. The video discusses the process of migrating an EKS cluster configuration from Terraform to Pulumi and how GitHub Copilot can assist in understanding and translating the configuration.

💡Outdated Information

The video points out that one of the limitations of GitHub Copilot is that its training data is outdated, which can be problematic for rapidly evolving tools and languages. This is particularly relevant when working with IaC tools, where new versions and best practices are released frequently.

💡Pulumi AI

Pulumi AI is a tool that provides up-to-date infrastructure configurations for Pulumi projects. The video contrasts GitHub Copilot with Pulumi AI, noting that Pulumi AI has more current information and is better suited for generating configurations for Pulumi projects.

💡Voice Assistant

The video script briefly mentions the capability of using voice commands with GitHub Copilot through a Visual Studio Code extension called 'Speech'. This feature allows developers to dictate commands to the editor, which can then be translated into code by GitHub Copilot.

Highlights

GitHub Copilot is an AI assistant for coding that integrates within a code editor or IDE, such as Visual Studio Code.

Copilot provides code suggestions and explanations based on the context of your current code files and project.

It uses an open AI large language model trained with billions of lines of code and text for suggestions.

GitHub Copilot can predict what you're going to write and provide autocomplete suggestions as you type.

For infrastructure as code, Copilot is more helpful for writing code than for keeping up with the latest tools and libraries.

The AI tool can generate code for creating multiple AWS resources, such as S3 buckets, in a loop.

GitHub Copilot can help translate code from one infrastructure as code tool (like Terraform) to another (like Pulumi).

Copilot's information is only up-to-date until September 2021, which can be a limitation for rapidly evolving tools.

It is important for engineers to double-check Copilot's suggestions against the latest documentation to avoid outdated configurations.

GitHub Copilot is not a replacement for in-depth knowledge but can speed up certain coding processes.

Pulumi AI is a more up-to-date tool for generating Pulumi configurations compared to GitHub Copilot.

Pulumi's AI tool can provide configurations with better defaults and autoscaling features for EKS clusters.

GitHub Copilot can explain complex code and break down what different parts of a script are doing.

Using inline comments, you can guide Copilot to provide specific code suggestions within your editor.

Copilot can help with importing necessary modules for your code, based on your project's context.

Visual Studio Code's Speech extension can be used with GitHub Copilot for voice commands, offering a unique way to interact with the editor.

GitHub Copilot offers a chat interface for asking questions and getting code explanations or suggestions.

The tool can be particularly useful for explaining and converting configurations when migrating between cloud service providers or tools.

It is crucial to validate configurations generated by Copilot due to the risk of using outdated information.