In Django, Daphne is an ASGI server used for running Django applications. By default, Daphne starts with a single worker process. To increase the number of worker processes, you can use the --workers option when starting Daphne. The number of workers determines how many concurrent requests Daphne can handle.

Here's how you can increase the number of workers in Daphne with Django:

  1. Using the Command Line: To start Daphne with multiple worker processes, run the following command:

    bash
    daphne -b 0.0.0.0 -p 8000 myproject.asgi:application --workers 4

    In this example, we are starting Daphne with four worker processes (--workers 4). Adjust the number as per your application's requirements and available resources.

  2. Using a Configuration File: If you prefer using a configuration file to start Daphne, you can create a daphne.py file in your Django project directory with the following content:

    python
    # daphne.py import os from django.core.asgi import get_asgi_application # Adjust the number of workers as per your requirements NUM_WORKERS = 4 os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'myproject.settings') application = get_asgi_application()

    Then, start Daphne with the configuration file using the following command:

    bash
    daphne -b 0.0.0.0 -p 8000 myproject.daphne:application

    Replace myproject with the actual name of your Django project.

Increasing the number of workers allows Daphne to handle more concurrent requests and can improve the performance of your Django application, especially under heavy load. However, keep in mind that increasing the number of workers will also consume more system resources, so you should choose an appropriate value based on your server's capabilities and the expected traffic to your application.

Additionally, you might want to consider using a process manager like daphne.prometheus for monitoring the performance of your Daphne workers and the overall health of your Django application. Monitoring helps you tune the number of workers to find the optimal balance between performance and resource usage.

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