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Deploying Python Applications with Gunicorn

Deploying Python Applications with Gunicorn
Hostman Team
Technical writer
Python
23.10.2024
Reading time: 7 min

In this article, we’ll show how to set up an Ubuntu 20.04 server and install and configure the components required for deploying Python applications. We’ll configure the WSGI server Gunicorn to interact with our application. Gunicorn will serve as an interface that converts client requests via the HTTP protocol into Python function calls executed by the application. Then, we will configure Nginx as a reverse proxy server for Gunicorn, which will forward requests to the Gunicorn server. Additionally, we will cover securing HTTP connections with an SSL certificate or using other features like load balancing, caching, etc. These details can be helpful when working with cloud services like those provided by Hostman.

Creating a Python Virtual Environment

To begin, we need to update all packages:

sudo apt update

Ubuntu provides the latest version of the Python interpreter by default. Let’s check the installed version using the following command:

python3 --version

Example output:

Python 3.10.12

We’ll set up a virtual environment to ensure that our project has its own dependencies, separate from other projects. First, install the virtualenv package, which allows you to create virtual environments:

sudo apt-get install python3-venv python3-dev

Next, create a folder for your project and navigate into it:

mkdir myapp
cd myapp

Now, create a virtual environment:

python3 -m venv venv

And create a folder for your project:

mkdir app

Your project directory should now contain two items: app and venv.

You can verify this using the standard Linux command to list directory contents:

ls

Expected output:

myapp venv

You need to activate the virtual environment so that all subsequent components are installed locally for the project:

source venv/bin/activate

Installing and Configuring Gunicorn

Gunicorn (Green Unicorn) is a Python WSGI HTTP server for UNIX. It is compatible with various web frameworks, fast, easy to implement, and uses minimal server resources.

To install Gunicorn, run the following command:

pip install gunicorn

WSGI and Python

WSGI (Web Server Gateway Interface) is the standard interface between a Python application running on the server side and the web server itself, such as Nginx. A WSGI server interacts with the application, allowing you to run code when handling requests. Typically, the application is provided as an object named application in a Python module, which is made available to the server.

In the standard wsgi.py file, there is usually a callable application. For example, let’s create such a file using the nano text editor:

nano wsgi.py

Add the following simple code to the file:

from aiohttp import web

async def index(request):
    return web.Response(text="Welcome home!")

app = web.Application()
app.router.add_get('/', index)

In the code above, we import aiohttp, a library that provides an asynchronous HTTP client built on top of asyncio. HTTP requests are a classic example of where asynchronous handling is ideal, as they involve waiting for server responses, during which other code can execute efficiently. This library allows sequential requests to be made without waiting for the first response before sending a new one. It’s common to run aiohttp servers behind Nginx.

Running the Gunicorn Server

You can launch the server using the following command template:

gunicorn [OPTIONS] [WSGI_APP]

Here, [WSGI_APP] consists of $(MODULE_NAME):$(VARIABLE_NAME) and [OPTIONS] is a set of parameters for configuring Gunicorn.

A simple command would look like this:

gunicorn wsgi:app

To restart Gunicorn, you can use:

sudo systemctl restart gunicorn

Systemd Integration

systemd is a system and service manager that allows for strict control over processes, resources, and permissions. We’ll create a socket that systemd will listen to, automatically starting Gunicorn in response to traffic.

Configuring the Gunicorn Service and Socket

First, create the service configuration file:

sudo nano /etc/systemd/system/gunicorn.service

Add the following content to the file:

[Unit]
Description=gunicorn daemon
Requires=gunicorn.socket
After=network.target

[Service]
Type=notify
User=someuser
Group=someuser
RuntimeDirectory=gunicorn
WorkingDirectory=/home/someuser/myapp
ExecStart=/path/to/venv/bin/gunicorn wsgi:app
ExecReload=/bin/kill -s HUP $MAINPID
KillMode=mixed
TimeoutStopSec=5
PrivateTmp=true

[Install]
WantedBy=multi-user.target

Make sure to replace /path/to/venv/bin/gunicorn with the actual path to the Gunicorn executable within your virtual environment. It will likely look something like this: /home/someuser/myapp/venv/bin/gunicorn.

Next, create the socket configuration file:

sudo nano /etc/systemd/system/gunicorn.socket

Add the following content:

[Unit]
Description=gunicorn socket

[Socket]
ListenStream=/run/gunicorn.sock
SocketUser=www-data

[Install]
WantedBy=sockets.target

Enable and start the socket with:

systemctl enable --now gunicorn.socket

Configuring Gunicorn

Let's review some useful parameters for Gunicorn in Python 3. You can find all possible parameters in the official documentation.

Sockets

  • -b BIND, --bind=BIND — Specifies the server socket. You can use formats like: $(HOST), $(HOST):$(PORT).

Example:

gunicorn --bind=127.0.0.1:8080 wsgi:app

This command will run your application locally on port 8080.

Worker Processes

  • -w WORKERS, --workers=WORKERS — Sets the number of worker processes. Typically, this number should be between 2 to 4 per server core.

Example:

gunicorn --workers=2 wsgi:app

Process Type

  • -k WORKERCLASS, --worker-class=WORKERCLASS — Specifies the type of worker process to run.

By default, Gunicorn uses the sync worker type, which is a simple synchronous worker that handles one request at a time. Other worker types may require additional dependencies.

Asynchronous worker processes are available using Greenlets (via Eventlet or Gevent). Greenlets are a cooperative multitasking implementation for Python. The corresponding parameters are eventlet and gevent.

We will use an asynchronous worker type compatible with aiohttp:

gunicorn wsgi:app --bind localhost:8080 --worker-class aiohttp.GunicornWebWorker

Access Logging

You can enable access logging using the --access-logfile flag.

Example:

gunicorn wsgi:app --access-logfile access.log

Error Logging

To specify an error log file, use the following command:

gunicorn wsgi:app --error-logfile error.log

You can also set the verbosity level of the error log output using the --log-level flag. Available log levels in Gunicorn are:

  • debug

  • info

  • warning

  • error

  • critical

By default, the info level is set, which omits debug-level information.

Installing and Configuring Nginx

First, install Nginx with the command:

sudo apt install nginx

Let’s check if the Nginx service can connect to the socket created earlier:

sudo -u www-data curl --unix-socket /run/gunicorn.sock http

If successful, Gunicorn will automatically start, and you'll see the HTML code from the server in the terminal.

Nginx configuration involves adding config files for virtual hosts. Each proxy configuration should be stored in the /etc/nginx/sites-available directory.

To enable each proxy server, create a symbolic link to it in /etc/nginx/sites-enabled. When Nginx starts, it automatically loads all proxy servers in this directory.

Create a new configuration file:

sudo nano /etc/nginx/sites-available/myconfig.conf

Then create a symbolic link with the command:

sudo ln -s /etc/nginx/sites-available/myconfig.conf /etc/nginx/sites-enabled

Nginx must be restarted after any changes to the configuration file to apply the new settings.

First, check the syntax of the configuration file:

nginx -t

Then reload Nginx:

nginx -s reload

Conclusion

Gunicorn is a robust and versatile WSGI server for deploying Python applications, offering flexibility with various worker types and integration options like Nginx for load balancing and reverse proxying. Its ease of installation and configuration, combined with detailed logging and scaling options, make it an excellent choice for production environments. By utilizing Gunicorn with frameworks like aiohttp and integrating it with Nginx, you can efficiently serve Python applications with improved performance and resource management.

Python
23.10.2024
Reading time: 7 min

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Command-Line Option and Argument Parsing using argparse in Python

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Step 3: Add Inputs and Options Define the parameters and features your program accepts via add_argument() function: parser.add_argument('filename', type=str, help="Name of the file to process") parser.add_argument('--verbose', action='store_true', help="Enable verbose mode") Here: filename is a mandatory option. --verbose is optional, to allow you to set the flag to make it verbose. 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This description is displayed when the user runs the program via the --help option. Including --timeout: The --timeout option is not obligatory (indicated by the -- prefix). The type=int makes the argument for --timeout an integer. The default=30 is provided so that in case the user does not enter a value, then the timeout would be 30 seconds. The help parameter adds a description to the argument, and it will also appear in the help documentation. Parsing Process: The parse_args() function processes user inputs and makes them accessible as attributes of the args object. In our example, we access args.timeout and print out its value. 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How to Get the Length of a List in Python

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17 July 2025 · 3 min to read
Python

Understanding the main() Function in Python

In any complex program, it’s crucial to organize the code properly: define a starting point and separate its logical components. In Python, modules can be executed on their own or imported into other modules, so a well‑designed program must detect the execution context and adjust its behavior accordingly.  Separating run‑time code from import‑time code prevents premature execution, and having a single entry point makes it easier to configure launch parameters, pass command‑line arguments, and set up tests. When all important logic is gathered in one place, adding automated tests and rolling out new features becomes much more convenient.  For exactly these reasons it is common in Python to create a dedicated function that is called only when the script is run directly. Thanks to it, the code stays clean, modular, and controllable. That function, usually named main(), is the focus of this article. All examples were executed with Python 3.10.12 on a Hostman cloud server running Ubuntu 22.04. Each script was placed in a separate .py file (e.g., script.py) and started with: python script.py The scripts are written so they can be run just as easily in any online Python compiler for quick demonstrations. What Is the main() Function in Python The simplest Python code might look like: print("Hello, world!")  # direct execution Or a script might execute statements in sequence at file level: print("Hello, world!")       # action #1 print("How are you, world?") # action #2 print("Good‑bye, world...")  # action #3 That trivial arrangement works only for the simplest scripts. As a program grows, the logic quickly becomes tangled and demands re‑organization: # function containing the program’s main logic (entry point) def main():     print("Hello, world!") # launch the main logic if __name__ == "__main__":     main()                    # call the function with the main logic With more actions the code might look like: def main(): print("Hello, world!") print("How are you, world?") print("Good‑bye, world...") if __name__ == "__main__": main() This implementation has several important aspects, discussed below. The main() Function The core program logic lives inside a separate function. Although the name can be anything, developers usually choose main, mirroring C, C++, Java, and other languages.  Both helper code and the main logic are encapsulated: nothing sits “naked” at file scope. # greeting helper def greet(name): print(f"Hello, {name}!") # program logic def main(): name = input("Enter your name: ") greet(name) # launch the program if __name__ == "__main__": main() Thus main() acts as the entry point just as in many other languages. The if __name__ == "__main__" Check Before calling main() comes the somewhat odd construct if __name__ == "__main__":.  Its purpose is to split running from importing logic: If the script runs directly, the code inside the if block executes. If the script is imported, the block is skipped. Inside that block, you can put any code—not only the main() call: if __name__ == "__main__":     print("Any code can live here, not only main()") __name__ is one of Python’s built‑in “dunder” (double‑underscore) variables, often called magic or special. All dunder objects are defined and used internally by Python, but regular users can read them too. Depending on the context, __name__ holds: "__main__" when the module runs as a standalone script. The module’s own name when it is imported elsewhere. This lets a module discover its execution context. Advantages of Using  main() Organization Helper functions and classes, as well as the main function, are wrapped separately, making them easy to find and read. Global code is minimal—only initialization stays at file scope: def process_data(data): return [d * 2 for d in data] def main(): raw = [1, 2, 3, 4] result = process_data(raw) print("Result:", result) if __name__ == "__main__": main() A consistent style means no data manipulation happens at the file level. Even in a large script you can quickly locate the start of execution and any auxiliary sections. Isolation When code is written directly at the module level, every temporary variable, file handle, or connection lives in the global namespace, which can be painful for debugging and testing. Importing such a module pollutes the importer’s globals: # executes immediately on import values = [2, 4, 6] doubles = [] for v in values: doubles.append(v * 2) print("Doubled values:", doubles) With main() everything is local; when the function returns, its variables vanish: def double_list(items): return [x * 2 for x in items] # create a new list with doubled elements def main(): values = [2, 4, 6] result = double_list(values) print("Doubled values:", result) if __name__ == "__main__": main() That’s invaluable for unit testing, where you might run specific functions (including  main()) without triggering the whole program. Safety Without the __name__ check, top‑level code runs even on import—usually undesirable and potentially harmful. some.py: print("This code will execute even on import!") def useful_function(): return 42 main.py: import some print("The logic of the imported module executed itself...") Console: This code will execute even on import! The logic of the imported module executed itself... The safer some.py: def useful_function():     return 42 def main():     print("This code will not run on import") main() plus the __name__ check guard against accidental execution. Inside main() you can also verify user permissions or environment variables. How to Write main() in Python Remember: main() is not a language construct, just a regular function promoted to “entry point.” To ensure it runs only when the script starts directly: Tools – define helper functions with business logic. Logic – assemble them inside main() in the desired order. Check – add the if __name__ == "__main__" guard.  This template yields structured, import‑safe, test‑friendly code—excellent practice for any sizable Python project. Example Python Program Using main() # import the standard counter from collections import Counter # runs no matter how the program starts print("The text‑analysis program is active") # text‑analysis helper def analyze_text(text): words = text.split() # split text into words total = len(words) # total word count unique = len(set(words)) # unique word count avg_len = sum(len(w) for w in words) / total if total else 0 freq = Counter(words) # build frequency counter top3 = freq.most_common(3) # top three words return { 'total': total, 'unique': unique, 'avg_len': avg_len, 'top3': top3 } # program’s main logic def main(): print("Enter text (multiple lines). Press Enter on an empty line to finish:") lines = [] while True: line = input() if not line: break lines.append(line) text = ' '.join(lines) stats = analyze_text(text) print(f"\nTotal number of words: {stats['total']}") print(f"Unique words: {stats['unique']}") print(f"Average word length: {stats['avg_len']:.2f}") print("Top‑3 most frequent words:") for word, count in stats['top3']: print(f" {word!r}: {count} time(s)") # launch program if __name__ == "__main__": main() Running the script prints a prompt: Enter text (multiple lines). Press Enter on an empty line to finish: Input first line: Star cruiser Orion glided silently through the darkness of intergalactic space. Second line: Signals of unknown life‑forms flashed on the onboard sensors where the nebula glowed with a phosphorescent light. Third line: The cruiser checked the sensors, then the cruiser activated the defense system, and the cruiser returned to its course. Console output: The text‑analysis program is active Total number of words: 47 Unique words: 37 Average word length: 5.68 Top‑3 most frequent words: 'the': 7 time(s) 'cruiser': 4 time(s) 'of': 2 time(s) If you import this program (file program.py) elsewhere: import program         # importing program.py Only the code outside main() runs: The text‑analysis program is active So, a moderately complex text‑analysis utility achieves clear logic separation and context detection. When to Use main() and When Not To Use  main() (almost always appropriate) when: Medium/large scripts – significant code with non‑trivial logic, multiple functions/classes. Libraries or CLI utilities – you want parts of the module importable without side effects. Autotests – you need to test pure logic without extra boilerplate. You can skip main() when: Tiny one‑off scripts – trivial logic for a quick data tweak. Educational snippets – short examples illustrating a few syntax features. In short, if your Python program is a standalone utility or app with multiple processing stages, command‑line arguments, and external resources—introduce  main(). If it’s a small throw‑away script, omitting main() keeps things concise. Conclusion The  main() function in Python serves two critical purposes: Isolates the program’s core logic from the global namespace. Separates standalone‑execution logic from import logic. Thus, a Python file evolves from a straightforward script of sequential actions into a fully‑fledged program with an entry point, encapsulated logic, and the ability to detect its runtime environment.
14 July 2025 · 8 min to read

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