If you want to share you're whole raspberry-pi setup, then you have to share a image of your SD-card.
A image contains the whole filesystem and has normally a size of a few gigabytes. It's not recommended to share them to teammates for each little change.
There are different ways to create a image - just google for "create image sd card".
A docker-image is like a image, but is has much less data. Docker-Images does not contain the whole operation-system.
If you want to share your docker-image, then your teammates have to setup their Pi's first.
And you need a repository where you can upload your images (and your mates can download). A good plattform is https://hub.docker.com
It sounds for me, that you want to share code. For code-sharing I recommend git. There are some good tutorials for https://github.com
My hint: Setup a pi with all the things you need (tenserflow, git, ...).
You can share this image with your mates.
The code of the project can you store on GitHub. When you update the code, then your mates can get these updates with git pull
- these changes are normally very small and not some gigabytes for the whole image.
If you train big complex models and want to share them, than a docker-image is the best choice.
In this case share a image for raspberry with docker. Docker-Images are split in different layers. With a good design, these layers are small and good for sharing.
Your questions in your comment:
You can install tenserflow in a docker-image. There are good instructions on their site. https://www.tensorflow.org/install/docker
I would recommend a Dockerfile
like this:
FROM tensorflow/tensorflow # latest stable release
ADD requirements.txt requirements.txt # https://pip.pypa.io/en/stable/user_guide/#requirements-files
RUN pip install -r requirements.txt && rm -f requirements.txt
ADD src/* /app/
WORKDIR /app
ENTRYPOINT["python"]
CMD["-u", "yourscript.py"]
Every time when you change your code, then you can build it with docker build . -t <IMAGE-NAME>
.
You have to push the image somewhere (like hub.docker.com). docker push <IMAGE-NAME>
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