2024-06-12

Getting Started with Docker

Docker basics and some of the best practices to keep in mind

Dockers a tool that allows devs to deploy their apps easily in a container to run on any hostOS. The benefit of docker is that it packages an app with all ist dependencies into a container.

Containers are highly efficient in terms of mem usage unlike VMs.

VMs

Provides isolation but has high resource overhead and slow boot times.

Containers

Lightweight as they share OS, can be easily deployed.

This mechanism decouples the app from the environment in which they actually run allowing devs to easily deploy into public cloud, private data center or even in their own laptop reagrdless of whichOS they use.

docker deamon is the heart and manages. Similar to k8s api server. docker client is used to interact with the deamon. images are the blueprints. containers run the actual app. docker hub is the registry. Similar to AWS ECR.

Installation

Install docker based on theOS you have.

Once you install docker, you can test if it is installed correctly by running:

$ docker run hello-world

Pull

[registry-url]/[namespace]/[image]:[tag]

$ docker pull docker pull pytorch/pytorch:2.3.1-cuda11.8-cudnn8-devel

only downloads those layers that haven't been cached locally

Run

run the image in an interactive mode

$ docker run -it pytorch/pytorch:2.3.1-cuda11.8-cudnn8-devel

exit with CTRL+D

you can ssh into running containers docker exec -ti <container_id> bash

listing all containers

$ docker ps

useful variant is to use -a

to run with GPUs pass --gpus=all

Volumes

persists data beyond container lifecycle

add -v HOST_PATH:CONTAINER_PATHto docker run command

for example, map current dir to /opt/local

$ docker run \
    -v `pwd`:/opt/local \
    -it pytorch/pytorch:2.3.1-cuda11.8-cudnn8-devel

Clean up

$ docker stop
 
$ docker stop $(docker ps -a -q)
 
$ docker rm
 
$ docker rmi

Dockerfile

It's a text file that has all the commands

We start with specifying our base image:

FROM pytorch/pytorch:2.3.1-cuda11.8-cudnn8-devel

We can pass ARG

ARG key=value

for example

ARG CUDA="11.8"
 
FROM pytorch/pytorch:2.3.1-cuda${CUDA}-cudnn8-devel
 
# set up a directory
WORKDIR /usr/src/app

We can also use ENV which persists within the docker image like ENV DEBIAN_FRONTEND=noninteractive

installing dependencies

RUN pip install --no-cache-dir -r requirements.txt

next, copy files

# copy all the files to the container
COPY . .

expose the port

EXPOSE 5000

Best practices

Use multi-stage builds to create leaner and more secure images
Order commands properly
Use smaller base images like python:3.8-slim
Use COPY (copy local files from the docker host to the image) over ADD (downloading external files)
Cache python packages using --mount=type=cache,target=/root/.cache/pip
Run only one process per container
Use ENTRYPOINT instead of CMD
Use a .dockerignore file
Use docker scout to scan image for vulnerabilities