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Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

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# I have no idea who is the author of the code
# in this cell that saves an image from a webcam
# inside google colab.
# It comes from a notebook called "📸 Take a photo with your webcam"

from IPython.display import HTML, Audio
from google.colab.output import eval_js
from base64 import b64decode
import numpy as np
import io

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FROM balenalib/rpi-raspbian:stretch

# This is responsible for making your current docker work with arm images:
# $ sudo apt install qemu qemu-user-static binfmt-support-

# And this line is responsible for enabling it (MUST run after a restart):
# $ docker run --privileged linuxkit/binfmt:v0.7

ENV OPENCV_VERSION=4.1.0 

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import io
import time
import picamera
#from PIL import Image
import cv2 as cv
import numpy as np

NoI = 200

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FROM balenalib/rpi-raspbian:stretch

# This is responsible for making your current docker work with arm images:
# $ sudo apt install qemu qemu-user-static binfmt-support-

# And this line is responsible for enabling it (MUST run after a restart):
# $ docker run --privileged linuxkit/binfmt:v0.7
  
RUN apt-get update
RUN apt-get upgrade -y

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FROM arm32v6/alpine

RUN apk add --update --no-cache \
      build-base \
      unzip \
      wget \
      cmake \
      # A C language family front-end for LLVM (development files)
      clang-dev \
      linux-headers

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Building TensorFlow 1.3.0-rc1 for Raspberry Pi/Ubuntu 16.04: a Step-By-Step Guide

Here you'll learn how to build Tensorflow for the raspberry pi 3 with either the Python API or as a standalone shared library which can be interfaced from the C++ API and eventually as a library which can be used in other languages.

For the C++ library this tutorial will show you how extract tensorflow library and headers to use in any environment you want.

(This tutorial couldn't be possible without the help of the people from the References section)

What You Need

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print('This piece of code came straight from a GitHub Gist!')

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#
# Dockerfile for Tensorflow 2.0 (GPU) and opengl enabled (nvidia-docker2)
#

FROM nvcr.io/nvidia/cudagl:10.0-runtime-ubuntu18.04 as base
# Tensorflow 2 has problems with CUDA 10.1

ARG DEBIAN_FRONTEND=noninteractive