These are some sample docker containers for learning purposes.
- dl-classifier: An executable container with all of PyTorch's convolutional neural networks (CNNs).
- dl-darknet: YOLO object detection with darknet in a box!
- dl-pose-yolo: A modified YOLO v3 Pytorch implementation used for detecting humans for pose estimation.
- dl-pose-hrn: A modified Deep High-Resolution Network implementation used for pose estimation.
- dl-transfer: An executable neural style transfer Docker image.
- conda-deeplearning: A container with conda environment + Jupyter for Deep Learning.
- conda-nlp: A container with conda environment + Jupyter for Natural Language Processing (NLP).
- nginx-env: A container showing how to acquire environment variables at runtime for application configuration (externalizing properties).
- nginx-port: A container showing how to run nginx on a port specified at runtime through an environment variable.
- nginx-wsgi: A container showing how to run flask behind gunicorn and nginx.
- nginx-stream-binary: A container showing how to stream back binary files (e.g. Word, Excel, PowerPoint and PDF).
- docker-exe: A project showing how to build a container that may be used as an executable.
- spark-jupyter: A project with Hadoop, Spark and Python that may be used to learn massively parallel processing.
- java-jupyter: A project to learn Java 12.
- cpp-cicd: A C++ Docker image for building C++ projects using CMake and BOOST.
- rpi-python37: A container with Python 3.7 for use with Raspberry Pi 4.
- rpi-base: A container for use with Raspberry Pi 4.
- rpi-miniconda: A container with Miniconda and Python 3.6 for use with Raspberry Pi 4.
- rpi-jupyterlab: A container with Python 3.6 + Jupyter Lab (and Notebook) for Raspberry Pi 4.
- rpi-scikit: A container with Python 3.6 + Scikit-Learn + Jupyter Lab for Raspberry Pi 4.
- rpi-nlp: A container with Python 3.6 + NLTK + gensim + Stanford Core NLP + textblob + Jupyter Lab for Raspberry Pi 4.
- rpi-deeplearning: A container with Python 3.6 + Scikit-Learn + NLP + Tensorflow + Jupyter Lab for Raspberry Pi 4.
- rpi-darknet: YOLO object detection with darknet in a box for Raspberry Pi 4! Be careful, not for the faint of heart!
Here are the dependencies between the RPi Docker images.
- rpi-base
- rpi-miniconda, rpi-darknet (from rpi-base)
- rpi-jupyterlab (from rpi-miniconda)
- rpi-scikit (from rpi-jupyterlab)
- rpi-nlp (from rpi-scikit)
- rpi-deeplearning (from rpi-nlp)
The rpi-python37 docker image does not depend on the others.
- db-nlp: Customized Natural Language Processing (NLP) container for use with Databricks.
- db-java: Template Docker container showing how to manage Java/Scala dependencies for use with Databricks.
All the images are published on Docker Hub.
Remove all exited docker images
docker rm $(docker ps -q -f status=exited)Remove all docker images that are dangling.
docker image rm $(docker images -f dangling=true -q)Remove stopped container and images without any container associations.
docker system prune -a -f@misc{oneoffcoder_docker_containers_2019,
title={Docker Containers},
url={https://github.com/oneoffcoder/docker-containers},
journal={GitHub},
author={One-Off Coder},
year={2019},
month={Jul}}
Copyright 2019 One-Off Coder
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