![]() We want to build our initial images and see them running. We are going to run docker-compose directly. We don’t have those now since we want to be a bit more technical and dive in. To run the project, we (by we, I mean my colleague and myself) usually go into the deployment folder and run make scripts to spin up the server. The django_project is where the actual Django app is located. The deployment folder contains an orchestration script to run the project. The repo contains two folders, django_project and deployment. We are going to set up a small microservice-based django server. Clone the repo to start experimenting with it. To demonstrate how P圜harm enhances our development workflow - and not ruining it or conflicting with the current workflow - I create a small sample repo here so you can see our basic Django setup. In addition to that, since we are using P圜harm, you need P圜harm Pro Edition to use its debugging features. Linux based environment, or MacOS, or WSL.Docker, Docker-compose, and how to use their CLI.Git and how to use it to clone a project from Github.We are going to assume this basic understanding of technical skills: You can skim through, but it is best if you do it at your own pace. The article from here is going to be a sort of hands on lab/workshop. So, you won’t need to redeclare your environment variables again. When you create a Run Configuration, the environment the interpreter uses is the one coming from docker-compose.You can map a local directory to a directory inside container (so that P圜harm knows it's the same thing).You are allowed to include an environment file (they fixed the main deal breaker from the previous issue).You can now include multiple docker-compose recipes (useful for overriding local config on top of production config).So, fed up with this, I decided to try the docker-compose-based interpreter again. I’ve also tried a different approach, such as creating the SSH configuration first, then set it as remote interpreter, but still it generates the deployment configuration. You are only allowed to delete the deployment configuration after you’ve made the configuration (funny, eh?). However the new interface is quite confusing because you can’t disable the setting at first. This is not needed if you are using a container, because the files are already mounted there and you don’t need to copy them again using sftp, etc. As of now, in version 2020.2, when we set up a remote interpreter, P圜harm implicitly defines a deployment configuration. Recent P圜harm versions are making it more difficult to set up a remote interpreter. So we keep doing the old way, using a remote interpreter. Since Django used environment variables to override its settings file, this setup was unusable. The first issue that I remember is that the interpreter forgot all the environment variables declared in the docker-compose file. Things didn’t transition smoothly back then. Then finally JetBrains released support to allow interpreters inside a docker-compose configuration. This worked for quite a long time (almost 5 years). Then we setup P圜harm so that it treated the docker container as a remote interpreter. To debug with this kind of setup, we used an SSH daemon inside our container. We already managed some of our projects as microservices described by one or more docker-compose files. However, back then the notion of developing a Django project from inside a container was not so common. JetBrains generously gave us a free licence to use the whole suite of JetBrains tools as their way of supporting open source projects. Back then the standard approach of attaching your debug interpreter was by creating a virtual environment in your python project. I was involved with different kinds of Django projects in the past.
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