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Add RAPIDS and arrange custom environment directory (#181)
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Co-authored-by: icoxfog417 <[email protected]>
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icoxfog417 and icoxfog417 authored Dec 13, 2022
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22 changes: 16 additions & 6 deletions README.md
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Expand Up @@ -73,16 +73,26 @@ We provide `.yml` files to set up various programming language / framework envir

#### Programming language environment

* [R](custom-environments/R/R.yml)
* [Julia](custom-environments/julia/1-install-julia.ipynb)
* [R environment](custom-environments/R/R.yml)
* [Julia environment](custom-environments/julia/1-install-julia.ipynb)

#### Speficic framework environment

* [AutoGluon (CPU)](custom-environments/AutoGluon/autogluon_cpu.yml)
* [fast.ai](custom-environments/fastai/fastai.yml)
* [AutoGluon (CPU) environment](custom-environments/AutoGluon/autogluon_cpu.yml)
* [AutoGluon](https://auto.gluon.ai/stable/index.html) is AutoML library for quick prototype by state-of-the-art method without expert knowledge.
* [fast.ai environment](custom-environments/fastai/fastai.yml)
* [fast.ai](https://www.fast.ai/) is deep learning library which provides state-of-the-art results with high-level API for practitioners and low-level API for expert.
* [SciPy environment](custom-environments/SciPy/scipy.yml)
* [SciPy](https://scipy.org/) is an open-source software for mathematics, science, and engineering.
* [Diffusers environment](custom-environments/diffusers/diffusers.yml)
* [diffusers](https://github.com/huggingface/diffusers) provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves as a modular toolbox for inference and training of diffusion models.
* [RAPIDS environment](https://studiolab.sagemaker.aws/import/github/rapidsai-community/rapids-smsl/blob/main/rapids-smsl.ipynb) (external link)
* [RAPIDS](https://rapids.ai/index.html) provides GPU accelerated data science libraries.
* [Geospatial environment](custom-environments/Geospatial/geospatial.yml)
* [SciPy](custom-environments/SciPy/scipy.yml)
* [Diffusers](custom-environments/diffusers/diffusers.yml)
* Geospatial environment is built from basic libraries for geospatial analysis such as [geopandas](https://geopandas.org/en/stable/), [shapely](https://github.com/shapely/shapely), and [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started), etc.
* [Medical image AI environment](https://github.com/aws/studio-lab-examples/blob/main/custom-environments/medical-image-ai/environment.yml)
* Medial image AI environment is built from basic libraries for medical image analysis such as [itkwidgets](https://github.com/InsightSoftwareConsortium/itkwidgets), [monai](https://monai.io/).


### Community contents

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2 changes: 1 addition & 1 deletion custom-environments/R/R.yml
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- unixodbc
- r-rmarkdown
- r-tidymodels
- r-tidyverse
- r-tidyverse
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