mbrede/notebook-gpu
mbrede
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https://xuanyuan.cloud/agents.md
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HAW Kiel JupyterHub Notebook Images
Custom notebook images for the JupyterHub teaching infrastructure at HAW Kiel (Hochschule für Angewandte Wissenschaften Kiel). Built for use with JupyterHub on a Kubernetes (RKE2) cluster and designed for courses in Data Science, Cloud Computing, and Generative AI.
Images
| Image | Base | Description |
|---|---|---|
notebook-base | ubuntu:24.04 | Shared base with all common packages |
notebook-cpu | notebook-base | CPU-only PyTorch for standard workloads |
notebook-gpu | notebook-base | CUDA-enabled PyTorch + GPU monitoring |
Kernels
All images expose three Jupyter kernels:
- Python (uv) — Python 3.12 in a https://github.com/astral-sh/uv-managed virtual environment
- R — R via https://eddelbuettel.github.io/r2u/ ***ary packages for Ubuntu 24.04
- Marimo — Marimo reactive notebook kernel
Python Packages
All images include the following Python packages (installed via uv into a virtual environment at ~/.venv):
jupyterlab, jupyterhub, notebook, ipykernel, ipympl, ipywidgets, altair, beautifulsoup4, bokeh, bottleneck, cloudpickle, dask, dill, h5py, jupyterlab-git, matplotlib, numba, numexpr, numpy, openpyxl, pandas, patsy, plotly, scikit-image, scikit-learn, scipy, seaborn, sentence-transformers, sqlalchemy, statsmodels, sympy, transformers, xlrd, jupyter-resource-usage, marimo, uv
The notebook-cpu image adds CPU-only PyTorch. The notebook-gpu image adds CUDA-enabled PyTorch and jupyterlab-nvdashboard for GPU monitoring.
R Packages
R packages are installed via https://eddelbuettel.github.io/r2u/ as pre-compiled Ubuntu ***aries (significantly faster than install.packages()):
r-base, caret, crayon, devtools, e1071, forecast, hexbin, htmltools, htmlwidgets, IRkernel, nycflights13, randomForest, RCurl, rmarkdown, RODBC, RSQLite, shiny, tidymodels, tidyverse
Attribution
The startup scripts (start.sh, run-hooks.sh, fix-permissions), server configuration (jupyter_server_config.py), and container conventions (jovyan user, NB_UID/NB_GID remapping, before-notebook.d hooks) are adapted from the https://github.com/jupyter/docker-stacks project.
Copyright (c) Jupyter Development Team. Distributed under the terms of the Modified BSD License.
The following files are copied verbatim from jupyter/docker-stacks:
fix-permissions(fromdocker-stacks-foundation)run-hooks.sh(fromdocker-stacks-foundation)start.sh(fromdocker-stacks-foundation)start-singleuser.py(frombase-notebook)docker_healthcheck.py(frombase-notebook)
The following files are adapted:
start-notebook.py— prepends the uv venv toPATHbefore delegating tostart.shjupyter_server_config.py—CONDA_DIRreference replaced withVENVfor SSL certificate path resolution
Key Differences from Jupyter Docker Stacks
- No conda/mamba — Python package management is handled entirely by https://github.com/astral-sh/uv
- uv venv as kernel — the active Python kernel is a uv-managed venv at
~/.venv, not the conda base environment - R via r2u — ***ary R packages from Ubuntu apt instead of conda
- Built from
ubuntu:24.04— no conda base layer
Source
https://github.com/MBrede/haw-kiel-jupyter
镜像拉取方式
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问 标签列表页面。
轩辕镜像加速拉取命令点我查看更多 notebook-gpu 镜像标签
docker pull docker.xuanyuan.run/mbrede/notebook-gpu:<标签>
DockerHub 原生拉取命令
docker pull mbrede/notebook-gpu:<标签>
镜像拉取常见问题
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oldzhang
运维工程师
Linux服务器
5
"Docker访问体验非常流畅，大镜像也能快速完成下载。"