Your first API service¶
This page will guide you through the process of your creating your first API service in a DSS design node, testing it, making a package out of it, and deploying on the DSS API node.
The steps to expose a prediction model are:
- Train the model in Analysis
- Deploy the model to Flow
- Create a new API service
- Create a prediction endpoint using the saved model
- Create a package of your service
- Deploy and activate the package on the API node
This section assumes that you already have installed and started a DSS API node instance. Please see Installing the API node if that’s not yet the case.
The first step is to create a model and deploy it to the Flow. This is done using the regular Machine Learning component of DSS. Please refer to the “Machine Learning” tutorial of DSS and to Machine learning for more information.
An API service is created within a project in DSS. From the project Homepage, click on “API SERVICES” in the navigation bar.
- Click on New API Service
- You need to give an identifier to your service. This “service id” will be referenced in APP interaction with the API node, and will be part of the URL to which your clients connect.
- Click on your newly-created service
Our service is created but does not yet have any endpoint, ie. it does not yet expose any model. See Concepts for what endpoints are.
- Click on the “Create new endpoint” button
We are going to create a “Prediction model” endpoint. You need to select the model that this endpoint will use. This is a saved model (ie. a model which has been deployed to the Flow). You also need to give an identifier to your endpoint. The endpoint id will be part of the URL to which your clients connect.
If your model is java-compatible (See: Machine learning training engines), you may select “Java scoring.” This will make the deployed model use java to score new records, resulting in extremely improved performance and throughput for your endpoint.
For a simple service, that’s it! You don’t need any further configuration.
Now that your service is properly configured in DSS, the next step is to create a package (See Concepts).
- Click on the “Prepare package” button
- DSS asks you for a package version number. This version number will be the identifier of this generation for all interaction with API node. It is recommended that you use a meaningful short name like
v4-new-customer-features. You want to be able to remember what was new in that generation (think of it as a Git tag)
- Go to the packages tab.
- Click on the Download button
The package file (a .zip file) is downloaded to your computer. Upload the zip file to all hosts running the API node software.
We are now going to actually activate the package in the API node
- Go to the API node directory
- Create the service: run the following command
./bin/apinode-admin service-create <SERVICE_ID>
- Then, we need to import the package zip file:
./bin/apinode-admin service-import-generation <SERVICE_ID> <PATH TO ZIP FILE>
Now, the API node has unzipped the package in its own folders, and is ready to start using it. At that point, however, the new generation is only available, it’s not active. In other words, if we were to perform an API query, it would fail because no generation is currently active.
./bin/apinode-admin service-switch-to-newest <SERVICE_ID>
When this command returns, the API node service is now active, running on the latest (currently the only) generation of the package.
We can now actually perform a prediction. Query the following URL (using your browser for example):
feat2 are the names of features (a.k.a. columns) in your train set.
You should receive a JSON reply with a
result section containing your prediction (and probabilities in case of a classification model)
Once you have confirmed that your service endpoint works, you can actually use the API to integrate in your application.