"docker-datascience-cuda" is a simple/easy-to-use template for setting up a python data science environment using Docker & VScode.
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GPU available in container
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Jupyter notebook with VScode
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Useful extensions & libraries are available
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Ubuntu-22.04 packages
- git
- vim
- powerline
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VScode extensions
- git extensions
- code highlight & completion
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libralies for Japanese Python users
- Mecab (mecab-ipadic-neologd)
- japanese_matplotlib
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workspace that can be shared with the host
- CUDA>=11.8
- Docker>=20.10.16
- Docker-Compose>=2.6.0
- VScode>=1.73.1
- Dev Container>=0.264.0
- git
- Upgrade your CUDA Toolkit >= 11.8 (or your GPU Driver)
- Download Docker-Desktop and setup
- Download VScode and setup
- Download Remote Development in VScode
- Download Git and setup
- Clone this repository
git clone https://github.com/hayashizaki-yu/docker-datascience-cuda.git
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Open this folder with VScode
- Click "files" in the upper left corner of VScode screen
- Click "Open folder"
- Select this folder
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Open Container with Dev container
- Click "><" icon in the lower left corner of VScode screen
- Select "Reopen in container"
- Initially it takes a while to start up
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restart VScode
- Initially, VScode extention for python will not be installed
- You can resolve this problem by restarting VScode
Extra: install pytorch or tensorflow
- pytorch
- run the command in container
pip install torch torch vision torchaudio
- tensorflow
- run the command in container
pip install tensorflow-gpu
"docker-datascience-cuda" is under MIT license.