jupyter/README.md

# jupyter-image-stacks

English | [中文](README_CN.md)

**THIS doc for non-CN USER**

**dockerhub: https://hub.docker.com/r/ben0i0d/jupyter**

## Jupyter For Data-Science

we have decide to remove some images from the repo, because they are not maintained and are not suitable for production use.

we decide add coder or denv for these 

### HOW TO USE
**Docker**
* No data is used persistently：`docker run -d -p 8888:8888 ben0i0d/jupyter:<tag>`  
* Provide data for persistent use：`docker run -d -p 8888:8888 -v "${PWD}":/home/jovyan ben0i0d/jupyter:<tag>`

**Jupyterhub**  
Specify the image in the profile of the singleuser
```
- description:  SCIPython, for scientific research and engineering applications.
    display_name: Scipy
    kubespawner_override:
        image: ben0i0d/jupyter:scipy-c
```
### Global description
1. If you build or fork the image yourself, replace the base image in the Dockerfile with the image on DockerHub
2. For commercial software such as Mathematica, MATLAB, etc., we only provide packaging, and the specific activation method and possible consequences are borne by the user
3. We hide '__pycache__' by default, i.e. it is not visible in the file browser view
5. For ohmyzsh, you only need to execute the following code once inside the terminal
```
git clone https://github.com/ohmyzsh/ohmyzsh.git ~/.oh-my-zsh
cp ~/.oh-my-zsh/templates/zshrc.zsh-template ~/.zshrc
```
7. The following code is applicable to add support for conda virtual directories so that custom environments are not lost
    1. Data persistence
        * docker add `-v "DATA-VOLUME":/opt/conda/envs/`
        * Jupyterhub add mountpoint`/opt/conda/envs/`
    2. Install the kernel in a virtual environment  
        * Use `conda create -n NAME *** ipykernel` create this env,use`source activate NAME`switch，use`python -m ipykernel install --user --name NAME --display-name "DISPLAY-NAME"`done
### List of images that are currently being built
* Upstream: Mirror upstream, benchmarking against the jupyter official minimal-notebook image
    * Description
        1. Upstream has switched to `debian:trixie`, and GPU upstream images are also built based on `debian:trixie`
        2. By default, we add the domain certificate of eoelab.org, which does not pose any security issues
        3. Sudo is added for passwordless use. In scenarios with high security requirements, do not allow privilege escalation
        4. Provided packages: SHELL (zsh), file compression/extraction (.bz2|.zip|.7z), project management (git|git-lfs), certificate management (ca-certificates), editor (vim), network interaction (curl|wget), Chinese fonts (fonts-wqy-zenhei)
* Pyjo: Supports Python and Mojo, combining Python syntax with the ecosystem for production and research. Mojo features metaprogramming capabilities. We look forward to the improvements this environment will bring.
    * Description
        1. Mojo has been merged into Python. Due to network reasons, manual execution of `modular install mojo && python /opt/modular/pkg/packages.modular.com_mojo/jupyter/manage_kernel.py install` is required to add the kernel
    * Scipyjo: Provides a scientific computing environment for Python, offering rich numerical computation, optimization, signal processing, statistical analysis, etc., for scientific research and engineering applications.
    * Scrpyjo: Provides a web scraping environment for Python, used for extracting data from the internet, automating information gathering and analysis tasks, suitable for data mining, web scraping, and business intelligence collection applications.
    * Pyjospark: Provides a Python-based Spark programming interface for large-scale data processing and analysis, offering powerful parallel computing capabilities and rich data manipulation functions, suitable for efficient data processing and machine learning tasks in distributed environments.
    * Pyjoflink: Provides a Python-based Flink programming interface for stateful computation of unbounded and bounded data streams. It also offers batch processing APIs for processing batch data computations based on stream computing engines.
    * Pyjoai (With GPU): Provides a common AI toolchain, offering a rich set of deep learning frameworks and NLP model libraries, enabling developers to easily build and train various AI models for applications in image recognition, natural language processing, etc.
* Julia: Supports Julia, a high-performance, dynamic programming language designed for scientific computing and data analysis, with readability similar to Python and execution speed similar to C.
    * Description:
        1. Environment variable `JULIA_NUM_THREADS` in Julia image, please configure according to desired concurrency threads at startup.
* SciR: Supports R's scientific computing environment, a programming language for statistical analysis and data visualization, with rich data processing libraries and powerful statistical functions, widely used in data science and research fields.
  * Rspark: Provides an R-based Spark programming interface for running R code on Spark. It offers R language capabilities in big data processing and distributed environments, enabling efficient data manipulation, machine learning, and statistical analysis for large-scale data processing and analysis tasks.
* Sagemath: An open-source mathematical computation system that combines multiple mathematical packages, providing extensive mathematical functions such as numerical computation, symbolic computation, discrete mathematics, and statistical analysis. It is also an interactive computing environment, convenient for mathematical modeling, algorithm design, and academic research.
* Dotnet: A cross-platform development framework that supports C#, F#, and PW, used for building various types of applications including web applications, desktop applications, and mobile applications. It provides rich class libraries and tools, simplifying development processes, and has high performance and scalability.
* Scilab (With Desktop-GUI): An open-source numerical computing software, suitable for numerical analysis, data visualization, simulation, and modeling in scientific and engineering fields. It provides a rich set of mathematical functions and toolboxes, supports matrix computation, symbolic computation, and plotting functions, is a powerful mathematical tool, especially suitable for education and research fields, provides Xfce desktop support, and includes all plugins available through APT.
* Octave: An open-source numerical computing software similar to Matlab, used for scientific computing, data analysis, and numerical simulation. It provides powerful matrix operations, plotting functions, and rich numerical analysis functions, a free and convenient tool suitable for mathematical modeling, algorithm development, and teaching tasks, includes all plugins available through APT.
* Maple: A mathematical software that provides a powerful mathematical engine through an intelligent file interface, enabling easy analysis, exploration, visualization, and solving of mathematical problems.
    * Description
        1. Upload `license.dat libmaple.so` to the main directory. Each time the environment is started, run `cp license.dat /opt/maple/license && cp libmaple.so /opt/maple/bin.X86_64_LINUX/` to activate before use.
* Mathematica: A scientific computing software that provides powerful and convenient features for data analysis, mathematical computation, and other fields.
    * Description
        1. Each time the environment is started, run `WolframKernel` to manually activate. View activation code at `https://ibug.io/blog/2019/05/mathematica-keygen/`. If activation fails multiple times, run `rm /home/jovyan/.Mathematica/Licensing/mathpass` to delete previous license records.
* MATLAB: A programming and numerical computing platform that supports data analysis, algorithm development, and modeling.
    * Description
        1. Upload `license.lic libmwlmgrimpl.so` to the main directory. Each time the environment is started, run `cp license.lic /opt/matlab/r2023b/licenses/ && cp libmwlmgrimpl.so /opt/matlab/r2023b/bin/glnxa64/matlab_startup_plugins/lmgrimpl/` to activate before use.
    * minimal: Contains only `Product:MATLAB`
    * mcm: Contains toolboxes required for mathematical modeling.


### List of plugins

**Global**
* jupyterlab-language-pack-zh-CN:Support for Chinese
* jupyterlab_tabnine：It is used for autocompletion, parameter suggestion, function document query, and jump definition

**Part**

### Image dependencies
```mermaid
graph LR
BASE-->PROGRAM{PROGRAMLANG}
PROGRAM-->PC(Julia)
PROGRAM-->PD(SciR)-->PDA(Rspark)
PROGRAM-->PR(Pyjo)
PR-->PRA(Scipyjo)
PR-->PRB(Scrpyjo)
PRA-->PRAA(Pyjoai)  
PRA-->PRAB(Pyjospark)  
PRA-->PRAC(Pyjoflink)  
BASE-->MATH{MATH-TOOL}-->MA(Octave)
MATH-->MB(Scilab)
MATH-->MC(Sagemath)
MATH-->ME(Mathematica)
MATH-->MD(MATLAB)-->MDA(minimal)-->MDAA(mcm)
```

## Upstream

**Package version**
* cuda 12.2.0
* Python 3.11
* Julia 1.9.4
* spark 3.5.0
* jupyterlab 4
* Matlab R2023b
* Mathematica 13.3.1
* Maple 2023

**Mirror source**
* conda bfsu：https://mirrors.bfsu.edu.cn/help/anaconda/
* pip bfsu：https://mirrors.bfsu.edu.cn/help/pypi/
* apt ustc：https://mirrors.ustc.edu.cn/help/debian.html
* apache tuna: https://mirrors.tuna.tsinghua.edu.cn/apache/
* julia-pkg ustc: https://mirrors.ustc.edu.cn/julia/

### Upstream of the project
jupyter团队项目 https://github.com/jupyter/docker-stacks

**However, we are quite different from the upstream in terms of sources, packages, localizations, extensions, etc., so if you have a problem with this project, please do not ask the Jupyter team questions, as it will increase their workload**

### kernel
* Python：https://ipython.org/
* Julia: https://github.com/JuliaLang/IJulia.jl
* R: http://irkernel.github.io/
* Octave: https://github.com/Calysto/octave_kernel
* MATLAB: https://github.com/mathworks/jupyter-matlab-proxy

## Necessary copyright notice
For code derived from other teams, we added the original copyright notice to the file header, and we retain and support the copyrights of other development teams