This directory contains examples of training models and storing them into a model store over different types of storage.
The Python script in examples-by-ml-model
iterates over all of the supported ML frameworks and all of the supported storage types. For each pair, it trains a model, uploads it to storage, and then downloads/loads it back.
The bash script cli-examples
has exaples of how to run python -m modelstore
commands.
As with the main library, these scripts have been developed using pyenv and pyenv-virtualenv.
Warning: the examples-by-ml-model
virtual environment installs ALL of the machine learning frameworks that are supported by modelstore
. In your own project, you will only need to install the machine learning frameworks that you need.
Start by cd
'ing into the directory containing the example you want to run:
❯ cd examples-by-ml-model/
And then you can use this Makefile
command that creates a new virtual environment
and installs all of the requirements:
❯ make pyenv
After creating a virtual environment, you can run all of the examples using:
❯ make run
This will run all of the examples - you can expect it to take some time!
Start by cd
'ing into the directory containing the example you want to run:
❯ cd examples-by-ml-model/
After creating a virtual environment, you can run all of the examples using:
❯ python main.py --modelstore-in $backend --ml-framework $framework