slash_docker's Introduction

Model to Docker

A unified way to package pre-trained models into docker files

Model to Docker in action, or how it works

Installation guide

git clone [email protected]:slashml/slash_docker.git
cd slash_docker
pip install -e .

Quickstart

from sklearn.linear_model import LinearRegression

lm = LinearRegression()
lm.fit([[2], [3], [4]], [4,6,8])

#test the model locally
lm.predict([[1024]])

# serialize and save the model
from model_to_docker import save_model
save_model(lm, 'sklearn_linear_regression')

# start a docker container with the previously saved model
# this will create a docker image, and start a docker container
from model_to_docker import run_model_server

# this will take a few seconds if its the first time
container = run_model_server('sklearn_linear_regression', port=5000)

# perform inference on the locally running docker container
import requests

resp = requests.post('http://127.0.0.1:8080/v1/models/model:predict', '[[1024]]')

print(resp.json())

# you can also use the curl command in terminal to perform inference
curl http://127.0.0.1:8080/v1/models/model:predict -d '[[1025]]'

# stop the docker container
from model_to_docker import stop_model_server
stop_model_server(container.id)

For other examples look inside the examples folder

Supported frameworks

Scikit-learn
XGBoost
PyTorch
Mlflow
TensorFlow
MLFLow
FastAI
HuggingFace Transformers
Keras
LightGBM
ONNX
PyCaret
SegmentAnything

Contact

If you run into any issues, or need a new feature, or any custom help, please feel free to reach out to us at [email protected], [email protected]

Contributing

We are actively looking for contributors. Please look at the CONTRIBUTING.md guide for more details. We have a growing list of good_first_issues, you can reach us at [email protected], to get started.

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