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
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
