I'm trying to develop a database manager in Django and want to develop and deploy it in Docker. As my IDE, I'd like to continue using PyCharm, but I'm having trouble understanding how it interacts with Docker.
I am new to Docker and its integration in PyCharm. My system runs Windows 10 and Docker for Windows.
I already tried using PyCharm's remote interpreter, but I have to activate the port forwarding manually (using Kitematic), since PyCharm does somehow not forward the exposed port automatically.
I also tried using a "Docker Deployment" run configuration. However, I can't get requests to localhost:8000 to get through to the Django server. All I get are empty response errors.
(Note: The bold issue was addressed in the accepted answer.)
It would really help me to have an explanation of how PyCharm's two options (remote interpreter and docker deployment) really work and ideally have an up-to-date tutorial for setting up Django with it. Unfortunately I could only find outdated tutorials and JetBrain's help pages are either outdated or do not explain it in enough detail.
Could someone help me out and guide me through this or point me to good resources?
Assuming you have the latest Docker (for Mac or for Windows) along with an updated version of PyCharm, you could achieve the port forwarding (binding) this way:
Create a new run configuration
Select your Docker server in the Deployment tab. If nothing shows, create a new one. Test that it actually works by clicking View > Tools Windows > Docker and connecting to the docker server. You should see the existing images and running containers.
In the Container tab, make sure to add the right Ports bindings.
An important note
Make sure that you are running your Django server on 0.0.0.0:8000 and not localhost:8000
Related
I'm new to coding and been fiddling around with docker containers and services
I have installed a temporary vscode server on my raspberry and deployed on my local lan for accessing it from various machines
Now i've been trying to create a flask app and run it from the container and trying to figure out how to publish and run the flask web server since i can't figure out what ip should i host it on (the default i always used was host=127.0.0.1 port=8080, but that would bring me to the local machine i'm visiting it from)
So while i was troubleshooting to understand what to do with exposed ports etc i've stopped the container and changed the docker-compose file, (i have a path set for the config's permanent storage, so my vscode setting are actually saved and persistent between deployments)
But I'm having the problem that every time i stop and re deploy the container i loose my python3 installation, and have to re run apt-update, apt-upgrade, apt install python3-pip and every python packages i need for the project
Where am i going wrong?
Silly question, but where does python get installed, and why isn't it persistent since i have my config path set?
I read that python gets installed in usr/local/lib, should i also map those directories to the persistent storage folder?
how should i do that?
thanks
I set up an ubuntu vagrant instance as virtual machine and installed docker in it. I also have python as docker container within vagrant instance. Is there anyway to debug django application using pycharm directively with python in this case?
Thanks!
It is possible indeed but I very much doubt you really need to use such a complex setup. Isn't it possible to run Docker on your OS directly?
Anyway, if you are confident. PyCharm at the moment does not "natively" support remote Docker daemon usage, so you have to tweak a bunch of options manually. I wrote a detailed guide in the dedicated ticket in PyCharm's bug tracker: https://youtrack.jetbrains.com/issue/PY-33489 Remote machine in your case is a Vagrant VM.
P.S. Please vote for the PY-33489 ticket if you want such support in PyCharm simplified.
I'm using jupyter notebooks to prototype and I write the majority of my code as python packages using vscode and installed as so:
pip install -e .
This works well as I can test rapidly prototype in jupyter but still maintain reusable / testable code by keeping most of the heavy lifting in the package(s)
I'd like to move my python/jupyter environment to docker. Is there any way to configure vscode to work well with a "remote" development environment running in a docker container?
Since May 2019 (version 1.35), VScode remote development feature is present in the stable release. It splits the VScode program in two:
a server part that can be runned on a remote computer, container, or WSL environment
a client part, mainly the GUI, that is runned locally
When properly configured, debugging/linting/... operations will be executed inside the container. To answer your specific question, you can get a debug experience identical the one of an uncontainerized setup.
See here for a quick overview of this feature. You can find a vscode-issued tutorial on how to setup vscode with docker here.
If you expose the Jupyter instance running in the container to your machine, you may be able to specify it as a remote Jupyter server.
So my team and I have bought into Docker - it is fantastic for deployment and testing. My real question is how to set up a great developer experience, specifically around writing Python apps, but this question could be generalized to nodejs, Java, etc.
The problem: When writing a Python app, I really like having decent linting/autocomplete functionality, there are some really good editors out there (Atom, VSCode, PyCharm) that provide these, but most really want a Python install on the local disk. The real advantage of Docker is that all of the core language and any project libraries can all be in the container, so reproducing all of that on the host machine just for developing is a pain.
I know that PyCharm pro does support Docker and docker-compose, but I found it quite sluggish and a lot of the test running capabilities were busted. On top of that, I really would like something that I can commit to version control so that the team can share dev setup and people don't have to repeat all of the steps for their own system.
A few Ideas that I had were:
Install an editor (like Atom) in a sidecar Docker container and use X11 forwarding
Use a browser based editor such as https://c9.io/ in a container - this seems most promising
Install some agent in a dev container that could handle autocomplete/linting, etc. and connect to it from a locally running editor - I think this would be the best solution, but I also think that right now it actually doesn't exist.
Has anyone had luck setting up a more productive development environment besides just mounting volumes and editing text?
You should use an 'advanced' IDE like IntelliJ (Pycharm) and configure a remote Python SDK using SSH-Access to your App-Docker-Container (using a shared ssh-key to auth against the app-container with a preinstalled openssh server and preconfigured authorized_keys file).
You can share this SDK information in your project file with all devs, so they wlll have this setup out of the box
1) This will ensure, your IDE knows about all the python libs/symbols available/installed in your docker-container during runtime. It will also enable you to properly debug remotely at the same time
2) This ensures, you have an IDE at your hand including a lot of important additional features like the inspector, 3way duff, search in path.. . hardly any of the Browser-Based IDEs will catch up with Pycharm at this point IMHO
Of course, as already mentioned in the comments, you need to share aka mount your code into the container. On linux, you plainly use host-volume-mounts from your local src folder to the container.
On OSX, you will run into performance issues when using host mounts. You might use something like http://docker-sync.io ( i am biased - there are also a lot of other similar tools )
I know this is an old question, but as I stumbled across it while trying to see what other editors might offer in this space, I would like to point out Visual Studio Code's notion of a Dev Container, which seems to provide the best level of integration I've seen for this so far. I'm hoping to see this turn into an industry trend myself.
Could use x11docker
x11docker allows to run graphical desktop applications (and entire desktops) in Docker Linux containers.
Docker allows to run applications in an isolated container environment. Containers need much less resources than virtual machines for similar tasks.
Docker does not provide a display server that would allow to run applications with a graphical user interface.
x11docker fills the gap. It runs an X display server on the host system and provides it to Docker containers.
Additionally x11docker does some security setup to enhance container isolation and to avoid X security leaks. This allows a sandbox environment that fairly well protects the host system from possibly malicious or buggy software.
https://github.com/mviereck/x11docker
https://github.com/mviereck/x11docker/wiki (extensive! knowledge)
https://dev.to/brickpop/my-dream-come-true-launching-gui-docker-sessions-with-dx11-in-seconds-1a53
What I want to know are actually 2 things.
Part 1:
I have a developed source code for an application in python that uses Django framework.
What I want is to be able to run the code on a developer machine and see the result. What do I need for it?
(my guesses):
Python development enironment (Eclipse/PyDev/Aptana Studio 3 seem to be the better ones for windows not sure linux yet),
I also have a postgre database already setup (I know there's a file where I have to specify connection information)
- something installed from django or would this be already included in the code that I have?
Part II:
I also want to make a dev server accessible through internet.
- this is the major part of the question and the most important. How do I publish the app?
- I have a linux machine that I would do this on, but unsure of all the things I need. Apache server?
To answer your questions:
What you need: A list of requirements and instructions to get started with Django is available here: http://djangobook.com/en/2.0/chapter02/.
Database: that chapter also includes a section on configuring access to your database, with a specific section on postgreSQL.
Dev server: To start a basic development server, see this tutorial section
Deploying django (production): For instructions on how to deploy Django for production, see chapter on deploying Django.
Publishing on internet: as for making your dev server accessible through the internet, ask on https://serverfault.com/. Make sure you provide more information about your network setup, what you've tried, what isn't working, etc. (Briefly, you need to make sure that the host you are running your server on is on a publicly accessible IP, or has port 80 forwarded to it from such a host. If in doubt, speak to your sys/network admin if you have one. Or use a django hosting service such as those listed on http://djangohosting.com)
IDE : Regarding IDE, it's down to personal preference. What you mentioned are fine and can run on Linux too.
As a first step, I suggest you follow the tutorial which guides you through the process of starting a development server and developing a basic app.
Even if your goal is to deploy an existing app, the tutorial will give you an idea of how the different components work together (apps, models, urls, templates, etc) which will help with debugging when something goes wrong with your deployment.
Good luck.
You need Python, Django, a WSGI container (e.g. mod_wsgi, uWSGI, Paste Deploy), and a database server. You make the Django project available as a WSGI app, bound to the appropriate interface on the machine.