This is GridGain ML client library, written in Python 3, abbreviated as ggml.
GridGain is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale.
GridGain ML client library provides user applications the ability to work with GridGain ML functionality using Py4J as an integration mechanism.
- Python 3.4 or above (3.6 is tested),
- IGNITE_HOME environment variable with path to Apache Ignite.
- Apache Ignite should includea ml-python-api.jar in libs folder. The ml-python-api.jar could be built using this repository.
If you want to use ggml in your project, you may install it from PyPI:
$ pip install ggml
If you want to run tests, examples or build documentation, clone the whole repository:
$ git clone [email protected]:gridgain/ml-python-api.git $ cd python $ pip install -e .
This will install the repository version of ggml into your environment in so-called “develop” or “editable” mode. You may read more about editable installs in the pip manual.