Yet Another ML flow
Project description
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.
-
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
├── train
│ ├── 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
Project details
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