Intermediate
Set Up the SDK for Data Attribute Recommendation
November 5, 2021
Created by
August 5, 2020
15 min.
Create a virtual environment and install the Python SDK (Software Development Kit) for Data Attribute Recommendation.
You will learn
- How to set up a virtual environment
- How to install the SDK for Data Attribute Recommendation
Use Data Attribute Recommendation (one of the SAP AI Business Services in SAP Business Technology Platform) to classify entities such as products, stores and users into multiple classes, using free text, numbers and categories. For more information, see Data Attribute Recommendation help portal documentation. For the definition of specific terms, see Concepts.
The Data Attribute Recommendation service provides a REST API to communicate and interact with your service instance. The Python SDK for Data Attribute Recommendation provides an implementation that allows you to easily consume the APIs of the service without having to write your own code. It also serves as a reference implementation and can be easily transferred into other programming environments and languages.
The SDK is available in GitHub.
Step 1: Install Python
First, you need to make sure that you have Python installed. Ideally, you have a Python version from 3.5 up to 3.8 as those are actively supported by the SDK.
Open up a command prompt and enter python --version to check the version that you have installed.
Open up a terminal and enter python3 --version to check the version that you have installed.
In case you have a Python installation, it will output the version. If you have a version lower than 3.5 head over here and install an up-to-date version that is supported by the SDK.
In case command not found is printed, you do not have Python installed. Head over here and install an up-to-date version that is supported by the SDK. Then, close down and open up a new command prompt or terminal and run the command python --version again to check if the installation was successful.
Step 2: Set up virtual environment
Next, you will set up a virtual environment using the Python package virtualenv. With this package you avoid installing Python packages globally which could break system tools or other projects.
In a command prompt, enter python -m pip install --user virtualenv to install the package.
Once the package is installed, you receive a similar output as above.
Next, you will create a new directory in which you work throughout the tutorial.
In a command prompt or terminal, enter mkdir data-attribute-recommendation-python-sdk to create this new directory. Next, navigate into it by using the command cd data-attribute-recommendation-python-sdk.
Within this directory, you will create a new virtual environment.
In a command prompt, enter python -m virtualenv dar-sdk-venv to create a new virtual environment and run .\dar-sdk-venv\Scripts\activate right afterwards to activate the environment.
In a terminal, enter python3 -m pip install --user virtualenv to install the package.
Once the package is installed, you receive a similar output as above.
Next, you will create a new directory in which you work throughout the tutorial.
In a command prompt or terminal, enter mkdir data-attribute-recommendation-python-sdk to create this new directory. Next, navigate into it by using the command cd data-attribute-recommendation-python-sdk.
Within this directory, you will create a new virtual environment.
In a terminal, enter python3 -m virtualenv dar-sdk-venv to create a new virtual environment and run source dar-sdk-venv/bin/activate right afterwards to activate the environment.
You can tell that the environment is activated as it is added to your current path.
Step 3: Install SDK
Finally, you can install the SDK of Data Attribute Recommendation.
Within your virtual environment, run the command pip install data-attribute-recommendation-sdk to install the SDK.
You will then receive a similar output as above.
As a first test, enter the command python -c "import sap.aibus.dar.client" to import the SDK. If there is no error the installation was successful and you can move onto the next tutorial.
Step 4: Test yourself
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