Prepare Environment - Kinetica Docs

This example runs inside a Conda environment. The environment can be automatically configured using the conda_env_py3.yml file found in the Python UDF API repository.

In the same directory you cloned the API, change directory into the root folder of the Python UDF API repository:
cd kinetica-udf-api-python/
Create the Conda environment, replacing <environment name> with the desired name:
conda env create --name <environment name> --file conda_env_py3.yml
It may take a few minutes to create the environment.
Verify the environment was created properly:
conda info --envs
Activate the new environment:
conda activate <environment name>

Install PyGDF:

conda install -c numba -c conda-forge -c gpuopenanalytics/label/dev -c defaults pygdf=0.1.0a2

Install the Kinetica Python API:
pip install gpudb~=7.2.0
Add the Python UDF API repo’s root directory to the PYTHONPATH:
export PYTHONPATH=$(pwd):$PYTHONPATH

Edit the util/test_environment.py script for the correct database url, user, and password for your Kinetica instance:

URL = 'http://localhost:9191;CombinePrepareAndExecute=1;RowsPerFetch=20000'
USER = 'admin'
PASSWORD = 'admin123'