Metadata-Version: 2.1
Name: lightautoml-gpu
Version: 0.2.15
Summary: Fast and customizable framework for automatic ML model creation (AutoML)
Home-page: https://lightautoml.readthedocs.io/en/latest/
License: Apache-2.0
Author: Alexander Ryzhkov
Author-email: AMRyzhkov@sberbank.ru
Requires-Python: >=3.6.1,<4.0.0
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: Russian
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Provides-Extra: pdf
Requires-Dist: albumentations (>=0.4.6)
Requires-Dist: autowoe (>=1.2)
Requires-Dist: cffi (>=1.14.5,<2.0.0); extra == "pdf"
Requires-Dist: cmaes
Requires-Dist: efficientnet-pytorch
Requires-Dist: gensim
Requires-Dist: holidays
Requires-Dist: importlib-metadata (>=1.0,<2.0); python_version < "3.8"
Requires-Dist: jinja2
Requires-Dist: joblib
Requires-Dist: json2html
Requires-Dist: lightgbm (>=2.3,<3.0)
Requires-Dist: log-calls
Requires-Dist: networkx
Requires-Dist: nltk
Requires-Dist: numpy
Requires-Dist: opencv-python
Requires-Dist: optuna
Requires-Dist: pandas (>=1)
Requires-Dist: poetry-core (>=1.0.0,<2.0.0)
Requires-Dist: pywavelets
Requires-Dist: pyyaml
Requires-Dist: scikit-image
Requires-Dist: scikit-learn (>=0.22)
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: torch (>=1.6)
Requires-Dist: torchvision
Requires-Dist: tqdm
Requires-Dist: transformers (>=4)
Requires-Dist: weasyprint (>=52.5,<53.0); extra == "pdf"
Requires-Dist: webencodings
Project-URL: Repository, https://github.com/sberbank-ai-lab/LightAutoML
Description-Content-Type: text/markdown

## 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.


