redisai 1.3.0

Installation

1. Install Redis 5.0 or above

2. Install RedisAI

3. Install the Python client

$ pip install redisai

4. Install serialization-deserialization utility (optional)

$ pip install ml2rt

Development

1. Assuming you have virtualenv installed, create a virtualenv to manage your python dependencies, and activate it. `virtualenv -v venv; source venv/bin/activate`

2. Install [pypoetry](https://python-poetry.org/) to manage your dependencies. `pip install poetry`

3. Install dependencies. `poetry install --no-root`

[tox](https://tox.readthedocs.io/en/latest/) runs all tests as its default target. Running tox by itself will run unit tests. Ensure you have a running redis, with the module loaded.

Contributing

Prior to submitting a pull request, please ensure you’ve built and installed poetry as above. Then:

1. Run the linter. `tox -e linters.`

2. Run the unit tests. This assumes you have a redis server running, with the [RedisAI module](https://redisai.io) already loaded. If you don’t, you may want to install a [docker build](https://hub.docker.com/r/redislabs/redisai/tags). `tox -e tests`

RedisAI example repo shows few examples made using redisai-py under python_client folder. Also, checkout ml2rt for convenient functions those might help in converting models (sparkml, sklearn, xgboost to ONNX), serializing models to disk, loading it back to redisai-py etc.