FastScore Model Deploy is a containerized Jupyter notebook server with FastScore’s model deployment and Jupyter integration toolkit built in. It is built on top of the Jupyter data science Docker image. Model Deploy provides model creation and deployment tools for R and Python (2 & 3) notebooks, as well as for PFA (through Python 2).
Start Model Deploy with the following command:
docker run -it --rm -p 8888:8888 fastscore/model-deploy:latest
If other services in the FastScore fleet are also running on the same host, it may be advantageous to start Model Deploy with the
--net="host" option, so that these services are accessible from
Model Deploy may also be started with any of the additional configuration options available to the Jupyter base Docker image, see the documentation for more details.
Once the container is created, it will be accessible from port 8888 (by default) on the host machine, using the token generated during the startup process.
Model Deploy is also avaiable via the
fastscoredeploy library and can be installed using
pip install fastscoredeploy from within the Jupyter Notebook terminal.
Model Deploy provides a number of features to make it easy to migrate a model into FastScore:
Modelclass that can be used for validation and testing of a model locally, before deploying to a FastScore engine.
Model_from_string(R) functions provide shortcuts for creating a Model object from a string of code. In Python notebooks, the
%%ppfamodelcell magic commands will automatically convert the contents of a cell into a Python or (P)PFA model object, respectively.
Engineclass allows for direct interaction with a FastScore Engine, including scoring data using a running Engine
Modelobjects may be deployed directly to a FastScore Engine from within the Jupyter notebook, as well as to Model Manage.
codeclibrary is included to make it easy to serialize R and Python objects to JSON and other formats based on an Avro schema.
Example notebooks demonstrating this functionality are included with the Model Deploy container.