About
This section provides the API documentation for the kxi.ml
Python library provided by KX for use by customers of KX Insights, both for interactions with the microservices/platform, and independent use.
Presently this library provides users with the ability to interact with the ML Registry
for persistence, retrieval and removal of Machine Learning models written in q or Python within the following libraries:
- q functions generated using PyKX.
- Scikit-learn
- Keras
- Theano
- PyTorch
Installation
Note
Python packages should typically be installed in a virtual environment. This can be done with the venv package from the standard library.
kxi.ml
is only intended for use on Linux based systems; it is not supported presently on Windows or MacOS
Ensure you have a recent version of pip:
pip install --upgrade pip
Then to install kxi.ml
using the KX PyPI server run the following command:
pip install --extra-index-url=https://$KX_PYPI_USER:$KX_PYPI_PASS@nexus.dl.kx.com/repository/kxi/simple/ kxi.ml
Where the environment variables KX_PYPI_USER
and KX_PYPI_PASS
have been set as appropriate. Please contact kxpy@kx.com for information on getting access to the KX PyPI server.
Python Dependencies
kxi.ml
depends on the following Python packages:
kxi==1.0.2
scipy==1.7.3
scikit-learn==1.0.2
statsmodels==0.13.1
pandas==1.4.1
joblib==1.1.0
These are installed automatically by pip
when kxi.ml
is installed. The following are optional dependencies which can be installed by a user to provided additional functionality:
Library | Version | Usage |
---|---|---|
keras |
2.7.0 |
Save Keras models to the ML Registry |
torch |
1.7.1 |
Save PyTorch models to the ML Registry |
theano |
1.0.5 |
Save Theano models to the ML Registry |
Use cases
There are many ways users can make use of the machine learning functionality provided here:
- Train models on historical data and deploy these to real-time systems
- Anomaly detection within manufacturing.
- Trade surveillance.
- Machine Learning Operations development cycle
- Storage of frequently updated models for future deployment (trading models/recommendation engines).
- Deployment of stored models to production settings.
- Use of versioning to maintain control over model accountability.