Comments (1)
Multi-stage builds are useful for situations where you need to build software that is included in the image, since you can setup a development environment in one image, build the software there, then copy the built artifacts across to the actual final image, all in one Dockerfile. However, if you look at e.g. https://github.com/anibali/docker-pytorch/blob/master/dockerfiles/1.7.0-cuda11.0-ubuntu20.04/Dockerfile you'll notice that it just uses binaries and doesn't need to build anything. So I don't see a way of using multi-stage builds to reduce the size of the base docker-pytorch image. Perhaps it could be useful if any of the additional software you're adding requires building.
Unfortunately, I can't see much fat to cut at all from my base images---PyTorch and its dependencies are just really big. I suppose if you don't need torchvision you could cut that from the image yourself.
from docker-pytorch.
Related Issues (20)
- this is the issue which it's showing while running the docker file HOT 2
- Cuda not found after pulling docker image at docker pull anibali/pytorch:1.4.0-cuda10.1 HOT 2
- can we have a docker image with torch 1.4+ cuda 10.2 and ubuntu 16.04 HOT 1
- pytorch 1.3 with cuda 10 on ubuntu 18.04 image HOT 1
- could not install software HOT 5
- Is CUDA being downloaded twice? HOT 4
- python3: can't open file 'script.py': [Errno 2] No such file or directory HOT 4
- CUDA Version Problem HOT 1
- Disable user prompt when building dockerfile HOT 1
- install HOT 1
- docker image failed to build HOT 2
- why do we use miniconda with pytorch cuda image? HOT 2
- Images to work with for Google Cloud SDK HOT 1
- About the permissions of docekr images HOT 1
- Key errors when deriving from 2.0 images & installing packages HOT 2
- How to enter the container with su mode HOT 1
- Size to big: use multi stage docker files HOT 4
- ERROR: failed to solve -> 1.7.0-cuda11.0-ubuntu20.04 HOT 1
- Support arm64 platform image HOT 1
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