While Anaconda is powerful enough for managing environments, it is too heavy to use for a product Docker image, especially when compared to more minimalistic Python environments. Therefore, this repo aims to help you build any dev/prod environment for a docker image in the fastest and lightest way.
Currently, it supports the following configurations:
| parameters | info |
|---|---|
| -bi, --baseimage | specify base docker image. For example: nvidia/cuda:11.7.0-cudnn8-runtime-ubuntu20.04; all valid CUDA and cuDNN base images can be found here. |
| -cv, --cudaversion | specify to cuda version to use |
| -t, --tag | specify tag name for docker image |
| -ipex, --build-with-ipex | specify to build with intel_extension_for_pytorch |
| -py, --pythonversion | specify to python version to use: Possible values: 3.8 3.9 3.10, 3.11 |
| -torch, --torch_version | specify pytorch version |
| -torchvision, --torchvision_version | specify torch-vision version |
| -g, --gpu | specify to use gpu |
- exposed port: 22, 8080
- username:
root - password:
root - Default Python path:
/home/venv/bin/python
You can connect with the docker container through ssh in your favorite IDE.
an example:
./docker/build_image.sh -g -cv cu121 -py 3.10 -torch 2.1.1 -torchvision 0.16.1