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New PAServer Docker Image: Smaller, Faster, Better

Docker

Deploying and debugging RAD Studio applications has never been easier. With our new Docker image and our revamped run.sh script, this process is now streamlined and more user-friendly. This post introduces our optimized PAServer Docker solution, highlighting significant improvements, including a whopping 64% reduction in image size.

What is PAServer?

PAServer (Platform Assistant Server) is a tool that facilitates cross-platform development and deployment for RAD Studio, Delphi, and C++Builder. It enables remote compilation and debugging, allowing developers to build and debug applications on different platforms, such as Windows, Linux, macOS, iOS, and Android, from a single development environment. This integration streamlines the development process, enhancing efficiency and productivity by managing file transfers and remote sessions easily.

Why use Docker for Linux development?

Using Docker for development offers several key benefits that streamline the development process and enhance productivity. The key factors for using Docker are:

What’s New?

Key Features

Our Docker image and run.sh script offer a range of features to make developing and deploying RAD Studio applications easier:

All these options have been integrated into the new run.sh script and you can easily access all the info through the handy --help arg.

Accessing the new image and repo code

The new image is available on DockerHub, and the run.sh script and the rest of the code to build your own images can be downloaded from GitHub.

Using run.sh Script

Pull the repo from GitHub and navigate to the directory containing run.sh in your terminal. Execute the script with your preferred options:

./run.sh [OPTIONS]

Options

Example 1: Production Mode

[crayon-673f55da0d84c511524107/]

Example 2: Detach Mode with Specific Version

[crayon-673f55da0d857504377223/]

Other functionalities

The updated GitHub repo contains a brand new, in-depth readme that explains more advanced solutions in detail. Using docker run instead of the new script, docker compose or create your own image version customizing the Dockerfile are just some examples.

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