RUN dotnet publish 'WebApp.csproj' -c Release -o /app/publish /p:UseAppHost=falseĪs you can see, there are several FROM instructions, also known as stages, with different names of base, build, publish, and final. RUN dotnet build 'WebApp.csproj' -c Release -o /app/build RUN dotnet restore 'WebApp/WebApp.csproj' Let’s look at an example of a common Dockerfile for an ASP.NET Core application: FROM /dotnet/aspnet:7.0 AS baseįROM /dotnet/sdk:7.0 AS build In short, multi-stage builds create environment checkpoints that can be reused between rebuilds. JetBrains Rider’s Docker fast mode approach is based on Docker’s multi-stage build feature. We use the same Docker Compose and Dockerfiles that you use for all environments, but apply some overrides to start the container quicker during development. Ugh, so much to remember!Īnd this is where our Docker fast mode can help. Keeping track of differences between environments can be a hassle. For example, having the same configuration for all environments can lead to inadvertently opening development ports in production, exposing your application to malicious attackers. The drawback is that it is inconvenient to have different Docker Compose files for different environments. In other words, you get a much quicker response after making your changes. You don’t need to rebuild the application container image and wait for it.

The advantage of this approach is that it lets you apply changes to the application much faster. database, queue or something similar) with Docker Compose, open local ports, and then run the. A typical scenario would be to run their backing services (e.g.

Some developers prefer to run their container applications as regular. In this post, we’ll look at what Rider is doing under the hood to make fast mode work, and how it launches your containers when you start a run configuration. With fast mode, Rider builds your application locally and then mounts it within a Docker container for a production-like experience. JetBrains Rider’s Docker fast mode helps speed up the feedback loop when building, debugging, and running containerized applications.

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