mirror of
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a883ce1794
- added versions to ggplot2 and sparklyr for all-spark-notebook - added tidymodels to r-notebook and docs closes #1102
206 lines
14 KiB
Markdown
206 lines
14 KiB
Markdown
# Selecting an Image
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* [Core Stacks](#core-stacks)
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* [Image Relationships](#image-relationships)
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* [Community Stacks](#community-stacks)
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Using one of the Jupyter Docker Stacks requires two choices:
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1. Which Docker image you wish to use
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2. How you wish to start Docker containers from that image
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This section provides details about the first.
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## Core Stacks
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The Jupyter team maintains a set of Docker image definitions in the [https://github.com/jupyter/docker-stacks](https://github.com/jupyter/docker-stacks) GitHub
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repository. The following sections describe these images including their contents, relationships, and versioning strategy.
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### jupyter/base-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/base-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/base-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/base-notebook/tags/)
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`jupyter/base-notebook` is a small image supporting the [options common across all core stacks](common.md). It is the basis for all other stacks.
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* Minimally-functional Jupyter Notebook server (e.g., no LaTeX support for saving notebooks as PDFs)
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* [Miniconda](https://conda.io/miniconda.html) Python 3.x in `/opt/conda`
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* No preinstalled scientific computing packages
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* Unprivileged user `jovyan` (`uid=1000`, configurable, see options) in group `users` (`gid=100`) with ownership over the `/home/jovyan` and `/opt/conda` paths
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* `tini` as the container entrypoint and a `start-notebook.sh` script as the default command
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* A `start-singleuser.sh` script useful for launching containers in JupyterHub
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* A `start.sh` script useful for running alternative commands in the container (e.g. `ipython`, `jupyter kernelgateway`, `jupyter lab`)
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* Options for a self-signed HTTPS certificate and passwordless sudo
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### jupyter/minimal-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/minimal-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/minimal-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/minimal-notebook/tags/)
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`jupyter/minimal-notebook` adds command line tools useful when working in Jupyter applications.
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* Everything in `jupyter/base-notebook`
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* [TeX Live](https://www.tug.org/texlive/) for notebook document conversion
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* [git](https://git-scm.com/), [emacs](https://www.gnu.org/software/emacs/) (actually `emacs-nox`), [vi](https://vim.org/) (actually `vim-tiny`), [jed](https://www.jedsoft.org/jed/), [nano](https://www.nano-editor.org/), tzdata, and unzip
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### jupyter/r-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/r-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/r-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/r-notebook/tags/)
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`jupyter/r-notebook` includes popular packages from the R ecosystem.
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* Everything in `jupyter/minimal-notebook` and its ancestor images
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* The [R](https://www.r-project.org/) interpreter and base environment
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* [IRKernel](https://irkernel.github.io/) to support R code in Jupyter notebooks
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* [tidyverse](https://www.tidyverse.org/) packages, including [ggplot2](http://ggplot2.org/), [dplyr](http://dplyr.tidyverse.org/),
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[tidyr](http://tidyr.tidyverse.org/), [readr](http://readr.tidyverse.org/), [purrr](http://purrr.tidyverse.org/), [tibble](http://tibble.tidyverse.org/),
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[stringr](http://stringr.tidyverse.org/), [lubridate](http://lubridate.tidyverse.org/), and
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[broom](https://cran.r-project.org/web/packages/broom/vignettes/broom.html) from [conda-forge](https://conda-forge.github.io/feedstocks)
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* [devtools](https://cran.r-project.org/web/packages/devtools/index.html),
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[shiny](https://shiny.rstudio.com/), [rmarkdown](http://rmarkdown.rstudio.com/), [forecast](https://cran.r-project.org/web/packages/forecast/forecast.pdf),
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[rsqlite](https://cran.r-project.org/web/packages/RSQLite/index.html),
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[nycflights13](https://cran.r-project.org/web/packages/nycflights13/index.html), [caret](http://topepo.github.io/caret/index.html), [tidymodels](https://www.tidymodels.org/),
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[rcurl](https://cran.r-project.org/web/packages/RCurl/index.html), and [randomforest](https://cran.r-project.org/web/packages/randomForest/randomForest.pdf)
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packages from [conda-forge](https://conda-forge.github.io/feedstocks)
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### jupyter/scipy-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/scipy-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/scipy-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/scipy-notebook/tags/)
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`jupyter/scipy-notebook` includes popular packages from the scientific Python ecosystem.
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* Everything in `jupyter/minimal-notebook` and its ancestor images
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* [dask](https://dask.org/), [pandas](https://pandas.pydata.org/), [numexpr](https://github.com/pydata/numexpr), [matplotlib](https://matplotlib.org/), [scipy](https://www.scipy.org/),
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[seaborn](https://seaborn.pydata.org/), [scikit-learn](http://scikit-learn.org/stable/), [scikit-image](http://scikit-image.org/),
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[sympy](http://www.sympy.org/en/index.html), [cython](http://cython.org/), [patsy](https://patsy.readthedocs.io/en/latest/),
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[statsmodel](http://www.statsmodels.org/stable/index.html), [cloudpickle](https://github.com/cloudpipe/cloudpickle), [dill](https://pypi.python.org/pypi/dill),
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[numba](https://numba.pydata.org/), [bokeh](https://bokeh.pydata.org/en/latest/), [sqlalchemy](https://www.sqlalchemy.org/), [hdf5](http://www.h5py.org/),
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[vincent](http://vincent.readthedocs.io/en/latest/), [beautifulsoup](https://www.crummy.com/software/BeautifulSoup/),
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[protobuf](https://developers.google.com/protocol-buffers/docs/pythontutorial), [xlrd](http://www.python-excel.org/), [bottleneck](https://bottleneck.readthedocs.io/en/latest/), and [pytables](https://www.pytables.org/) packages
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* [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/) and [ipympl](https://github.com/matplotlib/jupyter-matplotlib) for interactive visualizations and plots in Python notebooks
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* [Facets](https://github.com/PAIR-code/facets) for visualizing machine learning datasets
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### jupyter/tensorflow-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/tensorflow-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/tensorflow-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/tensorflow-notebook/tags/)
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`jupyter/tensorflow-notebook` includes popular Python deep learning libraries.
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* Everything in `jupyter/scipy-notebook` and its ancestor images
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* [tensorflow](https://www.tensorflow.org/) and [keras](https://keras.io/) machine learning libraries
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### jupyter/datascience-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/datascience-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/datascience-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/datascience-notebook/tags/)
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`jupyter/datascience-notebook` includes libraries for data analysis from the Julia, Python, and R communities.
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* Everything in the `jupyter/scipy-notebook` and `jupyter/r-notebook` images, and their ancestor images
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* The [Julia](https://julialang.org/) compiler and base environment
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* [IJulia](https://github.com/JuliaLang/IJulia.jl) to support Julia code in Jupyter notebooks
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* [HDF5](https://github.com/JuliaIO/HDF5.jl), [Gadfly](http://gadflyjl.org/stable/), and [RDatasets](https://github.com/johnmyleswhite/RDatasets.jl) packages
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### jupyter/pyspark-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/pyspark-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/pyspark-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/pyspark-notebook/tags/)
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`jupyter/pyspark-notebook` includes Python support for Apache Spark.
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* Everything in `jupyter/scipy-notebook` and its ancestor images
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* [Apache Spark](https://spark.apache.org/) with Hadoop binaries
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### jupyter/all-spark-notebook
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[Source on GitHub](https://github.com/jupyter/docker-stacks/tree/master/all-spark-notebook)
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| [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/all-spark-notebook/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/jupyter/all-spark-notebook/tags/)
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`jupyter/all-spark-notebook` includes Python, R, and Scala support for Apache Spark.
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* Everything in `jupyter/pyspark-notebook` and its ancestor images
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* [IRKernel](https://irkernel.github.io/) to support R code in Jupyter notebooks
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* [Apache Toree](https://toree.apache.org/) and [spylon-kernel](https://github.com/maxpoint/spylon-kernel) to support Scala code in Jupyter notebooks
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* [ggplot2](http://ggplot2.org/), [sparklyr](http://spark.rstudio.com/), and [rcurl](https://cran.r-project.org/web/packages/RCurl/index.html) packages
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### Image Relationships
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The following diagram depicts the build dependency tree of the core images. (i.e., the `FROM` statements in their Dockerfiles). Any given image inherits the
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complete content of all ancestor images pointing to it.
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[](http://interactive.blockdiag.com/?compression=deflate&src=eJyFzTEPgjAQhuHdX9Gws5sQjGzujsaYKxzmQrlr2msMGv-71K0srO_3XGud9NNA8DSfgzESCFlBSdi0xkvQAKTNugw4QnL6GIU10hvX-Zh7Z24OLLq2SjaxpvP10lX35vCf6pOxELFmUbQiUz4oQhYzMc3gCrRt2cWe_FKosmSjyFHC6OS1AwdQWCtyj7sfh523_BI9hKlQ25YdOFdv5fcH0kiEMA)
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### Builds
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Pull requests to the `jupyter/docker-stacks` repository trigger builds of all images on Travis CI. These images are for testing purposes only and are not saved for
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use. When pull requests merge to master, all images rebuild on Docker Cloud and become available to `docker pull` from Docker Hub.
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### Versioning
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The `latest` tag in each Docker Hub repository tracks the master branch `HEAD` reference on GitHub. `latest` is a moving target, by definition, and will have
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backward-incompatible changes regularly.
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Every image on Docker Hub also receives a 12-character tag which corresponds with the git commit SHA that triggered the image build. You can inspect the state of
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the `jupyter/docker-stacks` repository for that commit to review the definition of the image (e.g., images with tag 7c45ec67c8e7 were built from
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[https://github.com/jupyter/docker-stacks/tree/7c45ec67c8e7](https://github.com/jupyter/docker-stacks/tree/7c45ec67c8e7)).
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You must refer to git-SHA image tags when stability and reproducibility are important in your work. (e.g. `FROM jupyter/scipy-notebook:7c45ec67c8e7`, `docker run
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-it --rm jupyter/scipy-notebook:7c45ec67c8e7`). You should only use `latest` when a one-off container instance is acceptable (e.g., you want to briefly try a new
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library in a notebook).
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## Community Stacks
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The core stacks are just a tiny sample of what's possible when combining Jupyter with other technologies. We encourage members of the Jupyter community to create
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their own stacks based on the core images and link them below.
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* [csharp-notebook is a community Jupyter Docker Stack image. Try C# in Jupyter Notebooks](https://github.com/tlinnet/csharp-notebook). The image includes more
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than 200 Jupyter Notebooks with example C# code and can readily be tried online via mybinder.org. Click here to launch
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[](https://mybinder.org/v2/gh/tlinnet/csharp-notebook/master).
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* [education-notebook is a community Jupyter Docker Stack image](https://github.com/umsi-mads/education-notebook). The image includes nbgrader and RISE on top of
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the datascience-notebook image. Click here to launch it on
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[](https://mybinder.org/v2/gh/umsi-mads/education-notebook/master).
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* __crosscompass/ihaskell-notebook__
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[Source on GitHub](https://github.com/jamesdbrock/ihaskell-notebook)
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| [Dockerfile commit history](https://github.com/jamesdbrock/ihaskell-notebook/commits/master/Dockerfile)
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| [Docker Hub image tags](https://hub.docker.com/r/crosscompass/ihaskell-notebook/tags)
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`crosscompass/ihaskell-notebook` is based on [IHaskell](https://github.com/gibiansky/IHaskell). Includes popular packages and example notebooks.
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Try it on [](https://mybinder.org/v2/gh/jamesdbrock/learn-you-a-haskell-notebook/master?urlpath=lab/tree/ihaskell_examples/ihaskell/IHaskell.ipynb)
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* [java-notebook is a community Jupyter Docker Stack image](https://github.com/jbindinga/java-notebook). The image includes
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[IJava](https://github.com/SpencerPark/IJava) kernel on top of the minimal-notebook image. Click here to launch it on
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[](https://mybinder.org/v2/gh/jbindinga/java-notebook/master).
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* [sage-notebook](https://github.com/sharpTrick/sage-notebook) is a community Jupyter Docker Stack image with the [sagemath](https://sagemath.org) kernel on top of
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the minimal-notebook image. Click here to launch it on
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[](https://mybinder.org/v2/gh/sharpTrick/sage-notebook/master).
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* [GPU-Jupyter](https://github.com/iot-salzburg/gpu-jupyter/): Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using
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Tensorflow and Pytorch in collaborative notebooks.
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This is done by generating a Dockerfile, that consists of the **nvidia/cuda** base image,
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the well-maintained **docker-stacks** that is integrated as submodule
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and GPU-able libraries like **Tensorflow**, **Keras** and **PyTorch** on top of it.
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* [cgspatial-notebook](https://github.com/SCiO-systems/cgspatial-notebook) is a community Jupyter Docker Stack image. The image includes major geospatial Python &
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R libraries on top of the datascience-notebook image. Try it on
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binder:[](https://mybinder.org/v2/gh/SCiO-systems/cgspatial-notebook/master)
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See the [contributing guide](../contributing/stacks.md) for information about how to create your own Jupyter Docker Stack.
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