issue Dockerise Django Cuda application using docker compose - django

Question

I am trying to dockerize a Django Cuda application that runs on Nginx and Gunicorn.Problem is when I go to do prediction .. I get an error cuda drivers not found
My DockerFile:
FROM nvidia/cuda
FROM python:3.6.8
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
WORKDIR /app
COPY ./requirements.txt /app/requirements.txt
RUN python -m pip install --upgrade pip
RUN pip install cmake
RUN pip install opencv-python==4.2.0.32
# RUN pip install pywin32==227
RUN pip install -r requirements.txt
COPY . /app
RUN python manage.py collectstatic --noinput
RUN pip install gunicorn
RUN mkdir -p /home/app/staticfiles/
Ngnix DockerFile
FROM nginx:1.21-alpine
RUN rm /etc/nginx/conf.d/default.conf
COPY nginx.conf /etc/nginx/conf.d
Ngnix config file
upstream project_settings {
server web:8000;
}
server {
listen 80;
client_max_body_size 0;
location / {
proxy_pass http://project_settings;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header Host $host;
proxy_redirect off;
}
location /static/ {
alias /home/app/staticfiles/;
}
}
Main Docker compose file
services:
nginx:
build: ./nginx
ports:
- 1300:80
volumes:
- static_volume:/home/app/staticfiles/
depends_on:
- web
web:
build: .
command: gunicorn project_settings.wsgi:application --bind 0.0.0.0:8000
volumes:
- static_volume:/home/app/staticfiles/
image: sampleapp1121asa
expose:
- 8000
deploy:
resources:
reservations:
devices:
- capabilities: [ gpu ]
volumes:
static_volume:
Things are not working with docker compose, when I try to build the dockerfile seperately and then run using docker run --rm --gpus all -p 8000:8000 deefakedetectiondockerimage python3 manage.py runserver 0.0.0.0:8000 it works but the problem with this approach is I can not serve static file in docker. Ngnix is required to serve the static file, it means I need to run this through docker compose only

Answer 1

I found the solution for the same. Actually, things become difficult to run from docker-compose when you are trying to run multiple images in single container.
So I build the image using DockerFile for application and separate image for Ngnix and enable communication of both the container with unix socket connections.
My updated dockerfile for application:
#pull the nvidia cuda GPU docker image
FROM nvidia/cuda
#pull python 3.6.8 docker image
FROM python:3.6.8
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
#create a directory to serve static files
RUN mkdir -p /home/app/staticfiles/app/uploaded_videos/
WORKDIR /app
COPY ./requirements.txt /app/requirements.txt
RUN python -m pip install --upgrade pip
RUN pip install cmake
RUN pip install opencv-python==4.2.0.32
RUN pip install -r requirements.txt
COPY . /app
RUN python manage.py collectstatic --noinput
RUN pip install gunicorn
RUN mkdir -p /app/uploaded_videos/app/uploaded_videos/
VOLUME /app/run/
ENTRYPOINT ["/app/bin/gunicorn_start.sh"]
gunicorn_start.sh script
#!/bin/bash
NAME="project_settings" # Name of the application
DJANGODIR=/app # Django project directory
SOCKFILE=/app/run/gunicorn.sock # we will communicte using this unix socket
NUM_WORKERS=3 # how many worker processes should Gunicorn spawn
DJANGO_SETTINGS_MODULE=project_settings.settings # which settings file should Django use
DJANGO_WSGI_MODULE=project_settings.wsgi # WSGI module name
echo "Starting $NAME as `whoami`"
# Create the run directory if it doesn't exist
RUNDIR=$(dirname $SOCKFILE)
test -d $RUNDIR || mkdir -p $RUNDIR
# Start your Django Gunicorn
gunicorn project_settings.wsgi:application --bind=unix:$SOCKFILE --workers $NUM_WORKERS --timeout 600
My updated docker file for Nginx
FROM nginx
WORKDIR /etc/nginx/
RUN rm /etc/nginx/conf.d/default.conf
COPY nginx.conf /etc/nginx/conf.d
EXPOSE 80
For step by step process you can follow this blog