semvec/pypi-packages huggingface dataset:
sparklightautoml-dev 0.3.2
Spark-based distribution version of fast and customizable framework for automatic ML model creation (AutoML)
License: Apache-2.0
Requires Python: >=3.8,<3.10

# SLAMA: LightAutoML on Spark

SLAMA is a version of [LightAutoML library](https://github.com/AILab-MLTools/LightAutoML) modified to run in distributed mode with Apache Spark framework.

It requires:
1. Python 3.9
2. PySpark 3.2+ (installed as a dependency)
3. [Synapse ML library](https://microsoft.github.io/SynapseML/)
   (It will be downloaded by Spark automatically)
   
Currently, only tabular Preset is supported. See demo with spark-based tabular automl 
preset in [examples/spark/tabular-preset-automl.py](https://github.com/fonhorst/LightAutoML_Spark/blob/distributed/master/examples/spark/tabular-preset-automl.py). 
For further information check docs in the root of the project containing dedicated SLAMA section. 

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# License
This project is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/fonhorst/LightAutoML_Spark/blob/distributed/master/LICENSE) file for more details.


# Installation
First of all you need to install [git](https://git-scm.com/downloads) and [poetry](https://python-poetry.org/docs/#installation).

```bash

# Load LAMA source code
git clone https://github.com/fonhorst/LightAutoML_Spark.git

cd LightAutoML/

# !!!Choose only one item!!!

# 1. Global installation: Don't create virtual environment
poetry config virtualenvs.create false --local

# 2. Recommended: Create virtual environment inside your project directory
poetry config virtualenvs.in-project true

# For more information read poetry docs

# Install LAMA
poetry lock
poetry install
```