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YutackPark merged 1 commit into from
Apr 21, 2025
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Update README.md #206

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18 changes: 7 additions & 11 deletions README.md
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Expand Up @@ -107,13 +107,9 @@ You can find our legacy models in [pretrained_potentials](./sevenn/pretrained_po
## Installation<a name="installation"></a>
### Requirements
- Python >= 3.8
- PyTorch >= 1.12.0
- PyTorch >= 2.0.0, PyTorch =< 2.5.2

Here are the recommended versions we have been using internally without any issues.
- PyTorch/2.2.2 + CUDA/12.1.0
- PyTorch/1.13.1 + CUDA/12.1.0
- PyTorch/1.12.0 + CUDA/11.6.2
Using newer versions of CUDA with PyTorch is typically compatible. For example, you can compile and use `PyTorch/1.13.1+cu117` with `CUDA/12.1.0`.
For CUDA version, refer to PyTorch's compatibility matrix: https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix

> [!IMPORTANT]
> Please install PyTorch manually based on your hardware before installing SevenNet.
Expand All @@ -128,10 +124,10 @@ We strongly recommend checking `CHANGELOG.md` for new features and changes, as S
## Usage<a name="usage"></a>
### ASE calculator<a name="ase_calculator"></a>

For broader applications in atomistic simulations, SevenNet provides an ASE interface via the ASE calculator. Models can be loaded using the following Python code:
SevenNet provides an ASE interface via the ASE calculator. Models can be loaded using the following Python code:
```python
from sevenn.calculator import SevenNetCalculator
# The 'modal' argument can be omitted if the model it is not multi-fidelity trained.
# The 'modal' argument is required if the model is trained with multi-fidelity learning enabled.
calc_mf_ompa = SevenNetCalculator(model='7net-mf-ompa', modal='mpa')
```
SevenNet also supports CUDA-accelerated D3 calculations.
Expand Down Expand Up @@ -191,7 +187,7 @@ We support multi-GPU training using PyTorch DDP (distributed data parallel) with
torchrun --standalone --nnodes {number of nodes} --nproc_per_node {number of GPUs} --no_python sevenn input.yaml -d
```

Please note that `batch_size` in input.yaml is per GPU.
Please note that `batch_size` in `input.yaml` refers to the per-GPU batch size.

#### 4. Inference

Expand All @@ -215,7 +211,7 @@ sevenn_get_model {checkpoint path}

This will create `deployed_serial.pt`, which can be used as a LAMMPS potential with the `e3gnn` pair_style in LAMMPS.

The potential for parallel MD simulation can be obtained in a similar way.
The potential for parallel MD simulation can be obtained similarly.

```bash
sevenn_get_model 7net-0 -p
Expand Down Expand Up @@ -246,7 +242,7 @@ git clone https://github.com/MDIL-SNU/sevennet_tutorial.git
#### Installation

##### Requirements
- PyTorch < 2.5.0 (same version as used for training)
- PyTorch (it is recommended to use the same version as used during training)
- LAMMPS version of `stable_2Aug2023_update3`
- MKL library
- [`CUDA-aware OpenMPI`](https://www.open-mpi.org/faq/?category=buildcuda) for parallel MD (optional)
Expand Down