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Merge pull request #514 from vue1999/simplify-install-steps
Simplify installation steps
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README.md

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@@ -50,64 +50,33 @@ A partial documentation is available at: https://mace-docs.readthedocs.io
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## Installation
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Requirements:
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### 1. Requirements:
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- Python >= 3.7
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- [PyTorch](https://pytorch.org/) >= 1.12 **(training with float64 is not supported with PyTorch 2.1 but is supported with 2.2 and later.)**.
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- Python >= 3.7 (for openMM, use Python = 3.9)
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- [PyTorch](https://pytorch.org/) >= 1.12 **(training with float64 is not supported with PyTorch 2.1 but is supported with 2.2 and later)**
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(for openMM, use Python = 3.9)
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**Make sure to install PyTorch.** Please refer to the [official PyTorch installation](https://pytorch.org/get-started/locally/) for the installation instructions. Select the appropriate options for your system.
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### pip installation
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### 2a. Installation from PyPI
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This is the recommended way to install MACE.
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**First, make sure to install PyTorch.** Please refer to the [official PyTorch installation](https://pytorch.org/get-started/locally/) for the installation instructions. Select the appropriate options for your system. For GPU installation, make sure to select pip + the appropriate CUDA version for your system. For recent GPUs, the latest cuda version is usually the best choice.
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To install via `pip`, follow the steps below:
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```sh
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pip install --upgrade pip
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pip install mace-torch
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```
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**Note:** The homonymous package on [PyPI](https://pypi.org/project/MACE/) has nothing to do with this one.
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For CPU or MPS (Apple Silicon) installation, use `pip install torch torchvision torchaudio` instead.
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### conda installation from source
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To install from source using `conda`, follow the steps below:
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```sh
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# Create a virtual environment and activate it
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conda create --name mace_env
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conda activate mace_env
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# Install PyTorch
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conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
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### 2b. Installation from source
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# (optional) Install MACE's dependencies from Conda as well
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conda install numpy scipy matplotlib ase opt_einsum prettytable pandas e3nn
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# Clone and install MACE (and all required packages)
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```sh
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git clone https://github.com/ACEsuit/mace.git
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pip install ./mace
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```
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For the Pytorch version, use the appropriate version for your CUDA version.
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### pip installation from source
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To install via `pip`, follow the steps below:
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```sh
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# Create a virtual environment and activate it
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python -m venv mace-venv
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source mace-venv/bin/activate
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# Install PyTorch (for example, for CUDA 11.6 [cu116])
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pip3 install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118
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# Clone and install MACE (and all required packages)
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git clone https://github.com/ACEsuit/mace.git
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pip install ./mace
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```
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**Note:** The homonymous package on [PyPI](https://pypi.org/project/MACE/) has nothing to do with this one.
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## Usage
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