A unified way to package pre-trained models into docker files
git clone git@github.com:slashml/slash_docker.git
cd slash_docker
pip install -e .
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
- Scikit-learn
- XGBoost
- PyTorch
- Mlflow
- TensorFlow
- MLFLow
- FastAI
- HuggingFace Transformers
- Keras
- LightGBM
- ONNX
- PyCaret
- SegmentAnything
If you run into any issues, or need a new feature, or any custom help, please feel free to reach out to us at faizank@slashml.com, support@slashml.com
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 support@slashml.com, to get started.