Scikit-Learn server is an implementation for serving Scikit-learn models, and provides a Scikit-learn model implementation for prediction, pre and post processing.

To start the server locally for development needs, run the following command under this folder in your github repository.

make dev_install

Once Scikit-learn server is up and running, you can check for successful installation by running the following command

python3 -m sklearnserver
usage: __main__.py [-h] [--http_port HTTP_PORT] [--grpc_port GRPC_PORT]
                   --model_dir MODEL_DIR [--model_name MODEL_NAME]
__main__.py: error: the following arguments are required: --model_dir

You can now point to your joblib or pkl model file and use the server to load the model and test for prediction. Models can be on local filesystem, S3 compatible object storage, Azure Blob Storage, or Google Cloud Storage. If both joblib and pickle formats are presented, joblib model will get loaded. Please follow this sample to test your server by generating your own model.

Install the development dependencies with:

make dev_install

To run tests, please change the test file to point to your model.joblib file. Then run the following command:

make test

To run static type checks:

mypy --ignore-missing-imports sklearnserver

An empty result will indicate success.

You can build and publish your own image for development needs. Please ensure that you modify the inferenceservice files for Scikit-Learn in the api directory to point to your own image.

To build your own image, navigate up one directory level to the python directory and run:

docker build -t docker_user_name/sklearnserver -f sklearn.Dockerfile .

Sometimes you may want to build the SKLearnServer image with a different version of SKLearn, you can modify the version "scikit-learn == X.X.X" in setup.py and build the image with tag like docker_user_name/sklearnserver:0.24.

To push your image to your dockerhub repo,

docker push docker_user_name/sklearnserver:latest