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Update selecting.md with TH Lübecks (University of Applied Sciences Lübeck) image collection #2110

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Apr 19, 2024
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Update selecting.md with TH Lübecks (University of Applied Sciences Lübeck) image collection #2110

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13 changes: 8 additions & 5 deletions docs/using/selecting.md
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Expand Up @@ -328,13 +328,16 @@ See the [contributing guide](../contributing/stacks.md) for information about ho

### Other GPU-accelerated notebooks

| Flavor | Description |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [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. |
| [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>. |
| [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]. |
| Flavor | Description |
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [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. |
| [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. |
| [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>. |
| [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]. |

[gpu]: https://github.com/iot-salzburg/gpu-jupyter
[gpu_thl]: https://hub.docker.com/r/hanseware/jhub-images
[gpu_mylab]: https://mylab.th-luebeck.de
[prp_gpu]: https://gitlab.nrp-nautilus.io/prp/jupyter-stack/-/tree/prp
[prp_reg]: https://gitlab.nrp-nautilus.io/prp/jupyter-stack/container_registry
[b-data]: https://github.com/b-data
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