mohbattharani/docker-cheatsheet

Cheat Sheet for docker

Docker-cheatsheet

Build a Docker image docker build -t [image_name]:[tag] .

Run a Docker container specifying a name docker run --name [container_name] [image_name]:[tag]

Fetch the logs of a container docker logs -f [container_id_or_name]

Run a command in a running container docker exec -it [container_id_or_name] bash

Show running containers docker ps

Show all containers docker ps -a

Show Docker images docker images

Stop a Docker container docker stop [container_id_or_name]

Remove a Docker container docker rm [container_id_or_name]

Remove a Docker image docker rmi [image_id_or_name]

docker file/location/in/base/os container-id:/file-location </br>

Remove untagged Docker images docker image prune -fa

Docker tensorflow run Need nvidia-docker installed to run gpu on containers
Nvidia-docker: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)

docker run --runtime=nvidia -it --rm tensorflow/tensorflow:devel-gpu-py3 bash <br/>

Jupyter notebook with Docker

install notebook on docker image as: pip install notebook

Run follwing command to run docker image docker run -it --rm -p 8080:8080 image </br>

Following command start notebook jupyter notebook --ip 0.0.0.0 --no-browser --allow-root </br>

Install graphviz on Docker to render images in notebook.

pip install graphviz

Run (ref)

apt-get update \
  && apt-get install -yq --no-install-recommends libfuse-dev nano fuse vim git \
  && apt-get install -yq --no-install-recommends libfuse-dev nano fuse vim git graphviz \
  && apt-get clean \
  && rm -rf /var/lib/apt/lists/*
USER $NB_USER 

Push Docker image to Docker hub

docker tag id [name]

docker push [name]