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Update info about images by b-data (#2330)
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README.md

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## Alternatives
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- [b-data](https://github.com/b-data)'s JupyterLab docker stacks - For
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[R](https://github.com/b-data/jupyterlab-r-docker-stack),
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[Python](https://github.com/b-data/jupyterlab-python-docker-stack),
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[MAX/Mojo](https://github.com/b-data/jupyterlab-mojo-docker-stack) and
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[Julia](https://github.com/b-data/jupyterlab-julia-docker-stack).
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With [code-server](https://github.com/coder/code-server) next to JupyterLab.
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Just Python – no [Conda](https://github.com/conda/conda) /
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[Mamba](https://github.com/mamba-org/mamba).
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- [rocker/binder](https://rocker-project.org/images/versioned/binder.html) -
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From the R focused [rocker-project](https://rocker-project.org),
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lets you run both RStudio and Jupyter either standalone or in a JupyterHub

docs/using/selecting.md

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| [GPU-Jupyter][gpu] | Power of your NVIDIA GPU and GPU calculations using Tensorflow and Pytorch in collaborative notebooks. This is done by generating a Dockerfile that consists of the **nvidia/cuda** base image, the well-maintained **docker-stacks** that is integrated as a submodule, and GPU-able libraries like **Tensorflow**, **Keras** and **PyTorch** on top of it. |
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| [myLab TH Lübeck Images][gpu_thl] | Images based on the **jupyter/docker-stacks**, built and maintained at the [myLab TH Lübeck][gpu_mylab] using build scripts similar to iot-salzburg. Several images include GPU libraries. |
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| [PRP-GPU][prp_gpu] | PRP (Pacific Research Platform) maintained [registry][prp_reg] for jupyter stack based on NVIDIA CUDA-enabled image. Added the PRP image with Pytorch and some other Python packages and GUI Desktop notebook based on <https://github.com/jupyterhub/jupyter-remote-desktop-proxy>. |
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| [b-data][b-data] | GPU accelerated, multi-arch (`linux/amd64`, `linux/arm64/v8`) docker images for [R][r_cuda], [Python][python_cuda] and [Julia][julia_cuda]. Derived from nvidia/cuda `devel`-flavored images, including TensortRT and TensorRT plugin libraries. With [code-server][code-server] next to JupyterLab. Just Python – no [Conda][conda]/[Mamba][mamba]. |
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| [b-data][b-data] | GPU accelerated, multi-arch (`linux/amd64`, `linux/arm64/v8`) Docker images for [R][r_cuda], [Python][python_cuda] , [MAX][max_cuda] and [Julia][julia_cuda]. Derived from nvidia/cuda `devel`-flavored images. With [code-server][code-server] next to JupyterLab. Just Python – no [Conda][conda] / [Mamba][mamba]. |
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[gpu]: https://github.com/iot-salzburg/gpu-jupyter
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[gpu_thl]: https://hub.docker.com/r/hanseware/jlab-images
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[b-data]: https://github.com/b-data
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[r_cuda]: https://github.com/b-data/jupyterlab-r-docker-stack/blob/main/CUDA.md
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[python_cuda]: https://github.com/b-data/jupyterlab-python-docker-stack/blob/main/CUDA.md
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[max_cuda]: https://github.com/b-data/jupyterlab-mojo-docker-stack/blob/main/CUDA.md
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[julia_cuda]: https://github.com/b-data/jupyterlab-julia-docker-stack/blob/main/CUDA.md
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[code-server]: https://github.com/coder/code-server
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[conda]: https://github.com/conda/conda

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