yamlflow 0.0.7

Yet Another ML flow

yamlflow

Yet Another ML flow

STATUS NOT READY

We follow convention over configuration (also known as coding by convention) software design paradigm.

Here are some of the features the yamlflow provides.

1. Build and publish your ML solution as a RESTful Web Service with yaml.

   • You don't need to write web realated code, or dockerfiles.

   • You don't need to benchmark which python web server or framework is best in terms of performance.

   • WE do it for you. All the best, packed in.

Project structure

.
├── models
│   ├── model_1
│   │   ├── api
│   │   │   └── model.py
│   │   └── data
│   │       ├── model.bin
│   │       └── model.xml
│   └── model_2
│       ├── api
│       │   └── model.py
│       └── data
│           └── model.pt
├── service
│   ├── data
│   ├── predictor.py
│   └── requirements.txt
├── src
│   ├── data
│   ├── requirements.txt
│   └── train.py
├── README.md
└── yamlflow.yaml

example yamlflowflow.yaml

kind: Service
meta:
  project:
    name: ml-project
    version: 0.1.0
  registry: your.docker.registry
  user: dockerusername
backend:
  model_1:
    runtime: openvino
    device: cpu
  model_2:
    runtime: torch
    device: gpu

example predictor.py

User guide

pip install yamlflow
yamlflow init
yamlflow build -f yamlflow.yaml

Developer guide

pyenv install 3.8.6
poetry env use ~/.pyenv/versions/3.8.6/bin/python
poetry shell
poetry install

TODO

• build context