Yet Another ML flow
Project description
yamlflow
Yet Another ML flow
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.
-
Metrics for inference
-
Throughput (How many requests can server process in some interval of time)
-
Latency (How long does it take to get a prediction for a single request)
Python REST API
async
- web-server(ASGI): Uvicorn, Hypercorn, Daphne
- web-(micro)framework: Starlete, ...
- API-framework: FastAPI, ...
sync
- web-server(WSGI): gunicorn, uWSGI, Gevent, Twisted Web
- web-(micro)framework: Flask
- API-framework: None
Project structure
.
├── model
│ ├── ...
│ ├── pipeline.py
│ └── requirements.txt
├── service
│ ├── objects
│ ├── config.py
│ └── predictor.py
├── train
│ ├── ...
│ ├── train.py
│ └── requirements.txt
├── README.md
└── yamlflow.yaml
example yamlflow.yaml
kind: Service
meta:
registry: your.docker.registry
user: dockerusername
project:
name: ml-project
version: 0.1.0
backend:
runtime: torch
device: cpu
port: 8002
Installation guide
pip install yamlflow
User guide
yamlflow init
yamlflow build
yamlflow run
Developer guide
pyenv install 3.8.6
poetry env use ~/.pyenv/versions/3.8.6/bin/python
poetry shell
poetry install
Project details
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