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Repo sync #3

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Aug 10, 2023
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commit from https://github.com/ekonyagin/LightAutoML-1/tree/master
Rishat-Zagidullin Mar 13, 2023
1325ef8
Merge pull request #2 from RishatZagidullin/master
btbpanda Mar 17, 2023
49dfd20
Fix tutorial
btbpanda Aug 10, 2023
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28 changes: 14 additions & 14 deletions README.md
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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+) need to be installed
3. Python version 3.8
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 your own environment. For example:
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 clone the project to your own local machine:
2. To install RAPIDS for Python 3.8 and CUDA 11.0 use the following command:
```bash
git clone https://github.com/sb-ai-lab/LightAutoML_GPU.git
cd LightAutoML_GPU
conda install -c rapidsai -c nvidia -c conda-forge rapids=22.10 cudatoolkit=11.0
pip install dask-ml
```

3. Install LightAutoML in develop mode:
3. To clone the project on your own local machine:
```bash
pip install .
pip install catboost==1.0.4
git clone https://github.com/ekonyagin/LightAutoML-1.git
cd LightAutoML-1
```


5. To install RAPIDS for Python 3.8 and CUDA 11.0 use the following command:
4. Install LightAutoML in develop mode and other necessary libraries:
```bash
conda install -c rapidsai -c nvidia -c conda-forge rapids=22.02 cudatoolkit=11.0
pip install dask-ml
pip install .
pip install catboost
pip install py-boost
```

After you change the library code, you need to re-install the library (```pip uninstall lightautoml```). You need to go to LightAutoML directory and then install it once again, calling ```pip install .```
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:
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