semvec/pypi-packages huggingface dataset:
lightautoml-gpu 0.2.15
Fast and customizable framework for automatic ML model creation (AutoML)
License: Apache-2.0
Requires Python: >=3.6.1,<4.0.0

## Developing LightAutoML on GPU

To develop LightAutoML on GPUs using RAPIDS some prerequisites need to be met:
1. NVIDIA GPU: Pascal or higher
2. CUDA 11.0 (drivers v460.32+) or higher need to be installed
3. Python version 3.8 or higher
4. OS: Ubuntu 16.04/18.04/20.04 or CentOS 7/8 with gcc/++ 9.0+

### Installation

[Anaconda](https://www.anaconda.com/products/individual#download-section) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) is necessary to install RAPIDS and work with environments.

1. Once you install Anaconda/Miniconda, you need to set up your own environment. For example:
```bash
conda create -n lama_venv python=3.8
conda activate lama_venv
```

2. To install RAPIDS for Python 3.8 and CUDA 11.0 use the following command:
```bash
conda install -c rapidsai -c nvidia -c conda-forge rapids=22.10 cudatoolkit=11.0
pip install dask-ml
```

3. To clone the project on your own local machine:
```bash
git clone https://github.com/ekonyagin/LightAutoML-1.git
cd LightAutoML-1
```

4. Install LightAutoML in develop mode and other necessary libraries:
```bash
pip install .
pip install catboost
pip install py-boost
```

After you change the library code, you need to re-install the library: go to LightAutoML directory and call ```pip install ./ -U```

Please note, if you use NVIDIA GPU Ampere architecture (i.e. Tesla A100 or RTX3000 series), you may need to uninstall pytorch and install it manually 
due to compatibility issues. To do so, run following commands:
```bash
pip uninstall torch torchvision
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html
```

Once the RAPIDS is installed, the environment is fully ready. You can activate it using the `source` command and test and implement your own code.