This project includes an evolving design of the MLOps flow on CPD and the corresponding implementation as a CLI tool.
The current version covers a flow for deep learning models as the follows:
- train: code development in WS, training job on WMLA
- deploy: WMLA Elastic Distributed Inference
- monitor: custom monitor for OpenScale, headless service provider & dummy subscription, only custom monitors enabled for subscription
Next steps:
- add toy model, toy data, and toy custom monitor script for dev and test
- set up unit tests
- extend to WML deployments
- extend to OOTB OpenScale monitors
- move from config yaml to factsheets host metadata shared between services
Python: >= 3.8
Python packages:
- ibm-cloud-sdk-core==3.10.1
- ibm-watson-openscale>=3.0.14
- ibm-watson-machine-learning>=1.0.246
- click
- cpd-sdk-plus>=1.1
No installation needed, but you can install the dependencies as follows:
pip install -r requirements.txt
Download the cli script and the dependency utility scripts. Now you can use it:
python cli_mlops.py --help
For example of available commands, see the cheat sheet.
DCO is suggested to be used. See here for details on how to do it.
- Rich Nieto (rich.nieto@ibm.com)
- Drew McCalmont (drewm@ibm.com)