gunicorn

A lot of Django app deployments over Amazon's EC2 use HTTP servers NGINX and Gunicorn. I was wondering what they actually do and why both are used in parallel. What is the purpose of running them both in parallel? They aren't used in parallel. NGINX is a reverse proxy . It's first in line. It accepts incoming connections and decides where they should go next. It also (usually) serves static media such as CSS, JS and images. It can also do other things such as encryption via SSL, caching etc. Gunicorn is the next layer and is an application server . NGINX sees that the incoming connection is

I'm trying to deploy my Django Project through different application server : Apache2 , Nginx/gunicorn , ... It works fine with Apache2 or Nginx , but I would like to dissociate these application server with my Django Projet . I would like to get an environment like this : Server 1 (172.30.10.92) : Django Project & wsgi Server 2 (172.30.10.93) : Nginx/gunicorn Why ? Because later I will have some Django applications, but I would like to use just one application server. I think I'm making a mistake with my configuration files syntax. I have in /etc/nginx/sites-available/DatasystemsCORE.conf :

I have a Django app that is running on a domain e.g. www.example.com I want to create a Wordpress landing page, and point this landing page to the home url www.example.com and the wordpress admin site to www.example.com/admin or www.example.com/wp-admin. All the other URLs should be served by Django. So, I want: www.example.com -> wordpress www.example.com/admin or www.example.com/wp-admin -> wordpress All the other URLs to be served by Django Till now, this is my Nginx configuration using Django: upstream django_server { server unix:/path/to/gunicorn.sock fail_timeout=0; } server { listen 80;

While running the django project locally, I can access my home, admin, app1, app2 directory (i.e localhost:portnum , localhost:portnum/admin , localhost:portnum/app1 , localhost:portnum/app2 ) The problem begins when I deployed the app in a server ( I used nginx and gunicorn for django deployment with the help of this guide ) Problem : - I'm unable able to access example.com/admin, example.com/app1 , example.com/app2. I'm able to access my home example.com anyway. When I trying to access example.com/app1/ the page give an error 403 forbidden 2018/11/17 18:00:55 [error] 28459#28459: *8

I'm experiencing a very strange issue on my Digitalocean Ubuntu droplet, where Python 2.7 gives me the following error: from instagram import InstagramAuthentication, InstagramAPI File "/root/instagram_insights/socialgraphs-final-assignment/instagram.py", line 1, in <module> import requests File "/usr/lib/python2.7/dist-packages/requests/__init__.py", line 55, in <module> import urllib3 File "/usr/lib/python2.7/dist-packages/urllib3/__init__.py", line 10, in <module> from .connectionpool import ( File "/usr/lib/python2.7/dist-packages/urllib3/connectionpool.py", line 2, in <module> import

I am setting up a web server with gunicorn + django. There are two deployment options: either use regular WSGI, or use gunicorn's django-integration. I'm tempted to use the latter, because it simplifies configuration, but the django documentation says this: If you are using Django 1.4 or newer, it’s highly recommended to simply run your application with the WSGI interface using the gunicorn command as described above. They give no explanation, so I wonder why it's "highly recommended" to go with WSGI? Any ideas? Thanks a lot. Starting with Django 1.4, your project will already have a wsgi.py,

I'm wrapping my tensorflow model in a simple flask server and I'm adding gunicorn wsgi for the flask server. When I ran the gunicorn and tried to send a request to call my train function that has been import to the flask server, I got an error from command line arguments parsing: absl.flags._exceptions.UnrecognizedFlagError: Unknown command line flag 'b' I know this flags is passed when gunicorn bind the address arguments, because I have no flags named as 'b' for tensorflow. So my question is how does tensorflow ignore these undefined flags that the tf.app.run() function will not complain? FYI