PyTorch in Docker

I’m getting into the fray that is Generative AI since, according to some, my job as a programmer will soon be taken over by some literal code cranking machine.

There are a few things to set up:

Like most tools and libraries, the instructions assume that I have nothing else happening on my machine, so install just Anaconda globally according to their simplistic assumptions. All the other dependencies like even the Python version on my machine can go to hell.

Dockerizing the environment

Well. Until I have enough $$$ to buy a new machine for each new tool I want to try out, I’ll be using Docker (think goodness I don’t need an actual VM) to isolate an environment to play with.

After some trial and error, this is a template Dockerfile and docker-compose.yml I’m using:

Dockerfile

FROM python:3.11-bookworm
ENV PYTHONUNBUFFERED 1

WORKDIR /code

RUN apt update && apt install -y \
    vim

RUN curl -O https://repo.anaconda.com/miniconda/Miniconda3-py311_24.1.2-0-Linux-x86_64.sh
RUN sh Miniconda3-py311_24.1.2-0-Linux-x86_64.sh -b
ENV PATH="$PATH:/root/miniconda3/bin"

RUN conda install -y pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
RUN conda init

The above environment has:

  • Vim
  • Python 3.11
  • Miniconda3
  • PyTorch with CUDA 12.1

docker-compose.yml

services:
  app:
    build: .
    volumes:
      - ./:/code
    tty: true
    stdin_open: true

The docker-compose.yml is a template one I use for other things. Nothing special here.

Redux Toolkit w/ Vite in Docker

Notes on setting up a Docker/Vite/React/Redux Toolkit service in Docker. That’s a mouthful.

Docker Files

Start with a name for the project (e.g. “myproject”). Then create these Docker files:

Dockerfile

FROM node:20.10

RUN apt update && apt install -y \
  xdg-utils

EXPOSE 5173

docker-compose.yml

version: '3'
services:
  app:
    build: .
    command: >
      sh -c "npm run dev"
    ports:
      - "5173:5173"
    expose:
      - "5173"
    volumes:
      - .:/app
    tty: true
    stdin_open: true

Now build the working image and shell into the container:

docker-compose build

Redux Toolkit Setup

Shell into a container of the working image and initialize the project:

docker-compose run --rm app /bin/bash
...
npx degit reduxjs/redux-templates/packages/vite-template-redux myproject

Of course, use the name of the project instead of myproject.

This will create the starter files for the project under myproject/.

Exit the shell.

File Updates

Dockerfile

Modify Dockerfile to build the image with the project:

FROM node:20.10

RUN apt update && apt install -y \
  xdg-utils

EXPOSE 5173

WORKDIR /app/myproject

RUN npm install -g npm 

vite.config.ts

Modify the Vite config to allow it to host from a Docker container:

import { defineConfig } from "vitest/config"
import react from "@vitejs/plugin-react"

// https://vitejs.dev/config/
export default defineConfig({
  plugins: [react()],
  server: {
    host: true,  // <-- ADD THIS
    open: true,
  },
  ....

Start

Finally, to start the server, first build the image again:

docker-compose build

Then bring up the app:

docker-compose up

This should now bring up the app at http://localhost:5173/

To work on the project’s settings (e.g. installing packages, running tests, etc.), shell in with:

docker-compose run --rm --service-ports app /bin/bash

The --service-ports option will allow commands like npm run dev to start the app correctly (i.e. so that http://localhost:5173/ works). Without it, the port 5173 will not be mapped to the host, and docker-compose up will be the only way to run the app.

Celery with RabbitMQ on Docker

Picking up from Django app template w/ Docker, here are the steps to add Celery to the Django app.

Add RabbitMQ as the message queue

Modify docker-compose.yml to include:

services:
  ...
  rabbitmq:
    image: rabbitmq:3-management
    ports:
      - "15672:15672"
    expose:
      - "15672"

I use the “3-management” tag so that it includes a management plugin (accessible at http://localhost:15672/). However, simpler tags (e.g. “3” or “latest“) can be used if the management UI is not needed.

Add a couple of files to set up Celery in our Django project

The Celery stuff will be added into the myapp Django app.

myapp/celery.py

from celery import Celery


app = Celery(
    'celery_app',
    broker='amqp://rabbitmq',
    # backend='rpc://',

    # This should include modules/files that define tasks. This is a list of strs 
    # to be evaluated later in order to get around circular dependencies, I suspect.
    include=[  
        'myapp.tasks',  # This is our file containing our task
    ]
)

# Optional configuration, see the application user guide.
app.conf.update(result_expires=3600)


if __name__ == '__main__':
    app.start()

myapp/tasks.py

import logging

from myapp.celery import app


logger = logging.getLogger(__name__)


# This is where Celery tasks are defined


@app.task
def add(x: int, y: int) -> int:
    logger.info(f"add({x}, {y}) called.")
    return x + y

Add a Celery service to docker-compose.yml

Modify docker-compose.yml again to add a Celery service. This can be done together with the RabbitMQ service above, but it is shown here separately for readability.

services:
  ...
  rabbitmq:
    ...
  app-celery:
    build: .
    command: >
      sh -c "celery -A myapp.celery worker --loglevel=INFO"
    volumes:
      - ./:/code
    depends_on:
      rabbitmq:
        condition: service_started

Things to watch out for

A bunch of things to highlight to show where the connection points are:

  • The broker URL when instantiating the Celery app is amqp://rabbitmq (not amqp://localhost) because that’s how networking in Docker works. The “rabbitmq” in this case the name of the service we use for the RabbitMQ container. So if a different container name is used, this AMQP URL needs to use that corresponding name.
  • The Celery app parameter (-A myapp.celery) is the path to the myapp/celery.py file where the Celery app (app = Celery('celery_app', ...) ) is created.
  • Speaking of which, when defining the Celery app, its include=[ ... ] should include str values that point to files where Celery tasks are defined.
  • And the task files that define the Celery tasks need to import the Celery app and use its @app.task decorator for the task functions.

Complete docker-compose.yml

The entire file looks like:

services:
  app:
    build: .
    command: >
      sh -c "python manage.py migrate &&
        python manage.py runserver 0.0.0.0:8000"
    ports:
      - "8000:8000"
    expose:
      - "8000"
    volumes:
      - ./:/code
    depends_on:
      rabbitmq:
        condition: service_started
    tty: true
    stdin_open: true
  app-celery:
    build: .
    command: >
      sh -c "celery -A myapp.celery worker --loglevel=INFO"
    volumes:
      - ./:/code
    depends_on:
      rabbitmq:
        condition: service_started
  rabbitmq:
    image: rabbitmq:3-management
    ports:
      - "15672:15672"
    expose:
      - "15672"