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/ A minimalistic example of preparing a \
| model for (synchronous) inference in |
\ production. /
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\ ^__^
\ (oo)\_______
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||----w |
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Clone or download this repository:
git clone https://github.com/crocopie/sklearn-docker-api -
Install docker-ce and docker-compose
-
Train an example iris classifier
python experiments/train.py -
Build a docker container and deploy the model
docker-compose -f dev.compose.yml up --build -
This will start a uvicorn web server inside a docker container that will expose a FastAPI endpoint with a
/classifymethod -
Enjoy the autogenerated API docs at http://127.0.0.1:9090/docs. Press the
Try it outbutton for an interactive example.
.
├── data # training data
│ └── iris.csv
├── experiments # here you can keep the training workflows and notebooks
│ └── train.py
├── models # a place for your trained model dumps
│ └── svm.joblib
├── service # api service directory
│ ├── api.py # the main script for our model server
| └── utils.py # utility functions, e.g. a logging helper
├── .dockerignore # list of files that aren't needed inside the docker container
├── conda-env.yml # conda environment configuration
├── Dockerfile # docker image configuration
├── dev.compose.yml # docker-compose configuration for a DEVELOPMENT deployment
└── prod.compose.yml # docker-compose configuration for a PRODUCTION deploymentTo be continued