The idea is a docker container which aims to train machine learning models for computer vision. The data which is trained needs to be uploaded to the container, consumed and deleted afterwards.
Is there any way to use a volume and transport data efficiently between the host and the container?
When I searched on the web, most sources mentioned manual transport via bash or something similar, while my application needs to do this in an automated an repeating way for different datasets.
The host machine is a windows and the container is linux.
EDIT: there is a main application running on the machine which is responsible for managing the process.
- send data to container (somehow?)
- trigger training process
the training process runs async to not block the rest api
Any ideas?