/docker-images

Out-of-box Data Science / AI platform | AI/数据科学的瑞士军刀

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QPod - Docker Image Stack

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Your Swiss Army Knife for AI & Data Science

In a nutshell, QPod ( DockerHub | GitHub ) is an out-of-box Data Science / AI environment and platform at your fingertip which you would love 💕.

AI/数据科学的瑞士军刀——QPod提供了一站式、开箱即用、可自由定制的，基于容器的、开源AI/数据科学开发、分析工具。

With Docker and QPod, you

  • 📦 can start your data science / AI projects with nearly zero configuration - QPod puts everything about installing (latest) packages and configuring environment into standard docker images and set you free from these tedious work.
  • 🌍 will find your work more easy-to-reproduce - QPod standard images make scientific research or data analysis project as reproducible pipelines and help you share your work with others.
  • 🆙 can easily scale-up and scale-out your algorithms and key innovations - QPod help you move forward smoothly from the development stage to deployment stage by re-using these images to either to provide RESTful APIs or orchestrate map/reduce operations on big data.

Screenshot of QPod

What's actually there

QPod curates and maintains a series of Docker images including interactive computing environment to run a Jupyter Notebook (or JupyterLab) with Python, R, OpenJDK, NodeJS, Go, Julia, etc. Other IDE-like tools (e.g VS Code, R-Studio) are also included.

QPod supports use cases of both research and production:

  • (Stand-alone) Use it on your laptop as default data science / develop environment.
  • (Multi-tenant) Use it on a server/cluster to host multiple users to exploit hardware resources like GPU.
  • (Deployment/Production) Use it as the base image to host RESTful APIs or work as executors or map/reduce operations.

QPod-tech-arch

How to use? 1-2-3-GO🎉

0. Have docker installed on your laptop/server - Linux (e.g.: Ubuntu LTS) / Windows (>=10) / macOS

  • Install Docker >= 19.03: docker-ce ( community version & free: Linux | macOS | Windows ) on your laptop/server. Docker installed from default Ubuntu/CentOS repository probably won't work for GPU!
  • If you want to use NVIDIA GPUs with QPod, Linux server or latest Windows WSL2 is required. After installing Docker >= 19.03, also install both the NVIDIA driver and the latest version of nvidia-container-toolkit to use the GPUs in containers.

1. Choose the features and choose a folder on your disk

  • Choose a folder on your laptop/server to server as the base directory (e.g.: /root, /User/me, or D:/work). Use an absolute path instead of relative path -- files in this folder are visible in the environment (files outside this folder are not).

  • Choose an tag from the table below (e.g full for your laptop, or full-cuda for a Linux server with NVIDIA GPU), depends on what features/moduels do you want. Typically, you can choose full / full-cuda if you have enough disk space and no worry about your network speed.

👉 Click here to see a list of Docker Images run on CPUs only
Image Name (Feature Spectrum) DockerHub Link Based On Description
atom https://hub.docker.com/r/qpod/atom ubuntu (Not for final usage, add basic utilities based on ubuntu.)
base https://hub.docker.com/r/qpod/base qpod/atom The image add some basic OS libs and Python3 (conda), as well as tini.
py-data https://hub.docker.com/r/qpod/py-data qpod/base Python environment customized for Data Science tasks.
py-nlp https://hub.docker.com/r/qpod/py-nlp qpod/base Python environment customized for NLP tasks.
py-cv https://hub.docker.com/r/qpod/py-cv qpod/base Python environment customized for Computer Vision tasks.
py-chem https://hub.docker.com/r/qpod/py-chem qpod/base Python environment customized for Computational Chemistry tasks.
py-std https://hub.docker.com/r/qpod/py-std qpod/base Python environment including all the packages mentioned above installed.
py-jdk https://hub.docker.com/r/qpod/py-jdk qpod/base py-std plus OpenJDK. (no LaTex)
r-base https://hub.docker.com/r/qpod/r-base qpod/base Minimal R environment -- no JDK, no R data science packages, no LaTex.
r-std https://hub.docker.com/r/qpod/r-std qpod/base Standard R environment for data science -- including popular R data science packages. (OpenJDK included since many R packages need Java, no LaTex.)
r-latex https://hub.docker.com/r/qpod/r-latex qpod/base r-std + LaTex -- this is the full R environment if you do not need RStudio.
r-studio https://hub.docker.com/r/qpod/r-studio qpod/base Full R environment if you want to use RStudio. r-latex + RStudio + RShiny.
node https://hub.docker.com/r/qpod/node qpod/base Minimal NodeJS environment (including npm and yarn).
jdk https://hub.docker.com/r/qpod/jdk qpod/base Minimal Java environment (OpenJDK)
go https://hub.docker.com/r/qpod/go qpod/base Minimal Golang environment.
julia https://hub.docker.com/r/qpod/julia qpod/base Minimal Julia environment.
core https://hub.docker.com/r/qpod/core qpod/base ➕ Full Python environment (data + nlp + cv + chem + tensorflow + pytorch)
➕ Full R environment (datascience + RStudio + RShiny) + LaTex
➕ Base NodeJS environment
➕ Base Java environment (OpenJDK + maven)
➕ Minimal Julia environment
core-dev, full https://hub.docker.com/r/qpod/core-dev qpod/core All features and packages (Python, R, RStudio, OpenJDK, NodeJS, Go, Julia, LaTex) ➕ IDE tools: JupyterLab / Jupyter Notebook, VSCode Server
👉 Click here to see a list of Docker Images run on GPUs + CPUs
Image Name (Feature Set) DockerHub Link Based On Description
cuda_11.7, cuda https://hub.docker.com/r/qpod/cuda_11.7 qpod/base Version 11.7 of NVIDIA cuda and cudnn libs, including runtime and devel.
cuda_11.2 https://hub.docker.com/r/qpod/cuda_11.2 qpod/base Version 11.2 of NVIDIA cuda and cudnn libs, including runtime and devel.
cuda_10.2 https://hub.docker.com/r/qpod/cuda_10.2 qpod/base Version 10.2 of NVIDIA cuda and cudnn libs, including runtime and devel.
cuda_10.0 https://hub.docker.com/r/qpod/cuda_10.0 qpod/base Version 10.0 of NVIDIA cuda and cudnn libs, including runtime and devel. (Specifically retained for tensorflow 1.15)
tf1-cuda100, tf1 https://hub.docker.com/r/qpod/tf1 qpod/cuda_10.0 Tensorflow 1.1x environment with GPU (cuda 10.0).
tf2-cuda112, tf2 https://hub.docker.com/r/qpod/tf2 qpod/cuda_11.2 Tensorflow 2.x environment with GPU (cuda 11.2).
torch-cuda117, torch https://hub.docker.com/r/qpod/torch qpod/torch Pytorch 1.x environment with GPU (cuda 11.7).
torch-cuda112 https://hub.docker.com/r/qpod/torch-cuda112 qpod/torch-cuda12 Pytorch 1.x environment with GPU (cuda 10.2).
core-cuda, full-cuda-11.7 https://hub.docker.com/r/qpod/core-cuda qpod/cuda_11.7 Tensorflow 2.x + Pytorch environment with cuda.
cuda-dev, full-cuda https://hub.docker.com/r/qpod/cuda-dev qpod/coe-cuda core-cuda + IDE tools: JupyterLab / Jupyter Notebook + VSCode Server.

2. Start the container

Change the value of IMG and WORKDIR to your choices in the script below, and run the script. Shutdown Jupyter or other service/program which are using port 8888 or 9999.

For Linux/macOS/Windows WSL, run this in bash/terminal

IMG="qpod/full:latest"
WORKDIR="/root"  # <- macOS change this to /Users/your_user_name

docker run -d --restart=always \
    --name=QPod \
    --hostname=QPod \
    -p 8888:8888 -p 9999:9999 \
    -v $WORKDIR:/root \
    $IMG
sleep 10s && docker logs QPod 2>&1|grep token=

⚠️ To use QPod with NVIDIA GPU machines with nvidia-docker, be sure to:

  • 👉 Use Docker >= 19.03 and the command nvidia-smi works well on host machine
  • 👉 Add option (after --restart=always) in the docker run command to enable GPU access: --gpus all (for older version of nvidia-container, use --runtime nvidia)
  • 👉 Use IMG="qpod/full-cuda" or other images with cuda support

For Windows, run this in Terminal or CMD

Docker on Windows doesn't support GPU yet (cuda WSL support is coming soon).

SET IMG="qpod/full:latest"
SET WORKDIR="D:/work"

docker run -d --restart=always ^
    --name=QPod ^
    --hostname=QPod ^
    -p 8888:8888 9999:9999 ^
    -v %WORKDIR%:/root ^
    %IMG%
timeout 10 && docker logs QPod 2>&1|findstr token=

3. Sit back for minutes and get the first-time login token

The commands in the last step will:

  • trigger a docker image download process which may take minutes
  • start a docker container named QPod
  • print a string contains a URL, which includes a 48-digit hexadecimal number

Copy the printed hexadecimal string after ?token= as the first-time login token.

Go! 🎉

Access http://localhost:8888 (or http://ip-address:8888) in your browser and paste the token you just copied to start the journey.

Additional Information

FAQ

For a list of FAQ or other information, please refer to the wiki page of this repo.

Hardware

The images are built based on ubuntu:latest and only tested on the x86 platform. Minor modifications are expected to port to arm64, ppc64le platform.

Package Management

Although conda is installed, we do not recommend to use conda to install a lib/package, because:

  • conda repo mirrors are generally not avaliable in restricted enterprise LAN, especially in fincial/medical related companies.
  • conda does not reuse the existing system library yet if a system lib is already installed -- conda installs it again.
  • An open-source alternative, mamba is installed.

Customization

These images are highly customizable. If you find a system lib / Python module / R packages is missing, you can easily add one in the install_XX.list in the work folder. Utilites scripts and functions in /opt/utils folder will be helpful for custimize images.