# NepTrainKit **Repository Path**: pfsuo/NepTrainKit ## Basic Information - **Project Name**: NepTrainKit - **Description**: NepTrainKit from github - **Primary Language**: Unknown - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-01-17 - **Last Updated**: 2026-06-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

--- # NepTrainKit **NepTrainKit** is a toolkit focused on the operation and visualization of **neuroevolution potential** (NEP) training datasets. It is mainly used to simplify and optimize the NEP model training process, providing an intuitive graphical interface and analysis tools to help users adjust train dataset. --- ## Community Support - Join the community chat: [https://qm.qq.com/q/wPDQYHMhyg](https://qm.qq.com/q/wPDQYHMhyg) - Report issues or contribute via [GitHub Issues](https://github.com/aboys-cb/NepTrainKit/issues) --- ## Installation > **It is strongly recommended to use pip for installation.** ### Method 1: Install via pip If you are using Python 3.10 or a later version, you can install `NepTrainKit` using an environment manager like `conda`: 1. Create a new environment: ```bash conda create -n nepkit python=3.10 ``` 2. Activate the environment: ```bash conda activate nepkit ``` 3. For CentOS users, install PySide6 (required for GUI functionality): ```bash conda install -c conda-forge pyside6 ``` - Install directly using the `pip install` command: ```bash pip install NepTrainKit ``` > **GPU build note (Linux/WSL2):** The build auto‑detects CUDA. If a compatible CUDA > toolkit is present, the NEP backend is compiled with GPU acceleration; otherwise, > it falls back to a CPU‑only build. If CUDA is not detected automatically, export > one of `CUDA_HOME` or `CUDA_PATH` and ensure the `lib64` directory is on your loader path > before running `pip install`: ```bash # choose your installed CUDA version/path export CUDA_HOME=/usr/local/cuda-12.4 export PATH="$CUDA_HOME/bin:$PATH" export LD_LIBRARY_PATH="$CUDA_HOME/lib64:${LD_LIBRARY_PATH}" # (optional) if you need to explicitly target your GPU compute capability (SM), # set NEP_GPU_GENCODE before pip install, e.g. for Turing (7.5): export NEP_GPU_GENCODE="arch=compute_75,code=sm_75" # or multiple targets: # export NEP_GPU_GENCODE="-gencode arch=compute_75,code=sm_75 -gencode arch=compute_86,code=sm_86" # now install pip install NepTrainKit ``` > **GPU build note (Windows PowerShell):** Set `CUDA_PATH` (or `CUDA_HOME`) and add > `bin` to `Path` before `pip install`: ```powershell $env:CUDA_PATH = "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.4" $env:Path = "$env:CUDA_PATH\\bin;" + $env:Path pip install NepTrainKit ``` After installation, you can call the program using either `NepTrainKit` or `nepkit`. - For the **latest version** (from GitHub): ```bash pip install git+https://github.com/aboys-cb/NepTrainKit.git ``` --- ### Method 2: Windows Executable A standalone executable is available for Windows users. - Visit the [Releases](https://github.com/aboys-cb/NepTrainKit/releases) page - Download `NepTrainKit.win32.zip` > Note: Only supported on Windows platforms. ### GPU Acceleration (optional) - NepTrainKit includes an optional GPU‑accelerated NEP backend. - Requirements: NVIDIA GPU/driver compatible with CUDA 12.4 runtime. - Selection: In the app, go to Settings → NEP Backend and choose Auto/CPU/GPU. - Auto tries GPU first and falls back to CPU if unavailable. - Adjust GPU Batch Size to balance speed and memory. - If you see “CUDA driver version is insufficient for CUDA runtime version”, switch to CPU. --- ## Documentation For detailed usage documentation and examples, please refer to the official documentation: [https://neptrainkit.readthedocs.io/en/latest/index.html](https://neptrainkit.readthedocs.io/en/latest/index.html) - What's new: see [GitHub Releases](https://github.com/aboys-cb/NepTrainKit/releases). --- ## Citation If you use NepTrainKit in academic work, please cite the following publication and acknowledge the upstream projects where appropriate: ```bibtex @article{CHEN2025109859, title = {NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials}, journal = {Computer Physics Communications}, volume = {317}, pages = {109859}, year = {2025}, issn = {0010-4655}, doi = {https://doi.org/10.1016/j.cpc.2025.109859}, url = {https://www.sciencedirect.com/science/article/pii/S0010465525003613}, author = {Chengbing Chen and Yutong Li and Rui Zhao and Zhoulin Liu and Zheyong Fan and Gang Tang and Zhiyong Wang}, } ``` ## Licensing and Attribution - License: This repository is licensed under the GNU General Public License v3.0 (or, at your option, any later version). See `LICENSE` at the repository root. - Third‑party code: NepTrainKit incorporates source files and adapted logic from: - NEP_CPU (by Zheyong Fan, Junjie Wang, Eric Lindgren, and contributors): https://github.com/brucefan1983/NEP_CPU (GPL‑3.0‑or‑later) - GPUMD (by Zheyong Fan and the GPUMD development team): https://github.com/brucefan1983/GPUMD (GPL‑3.0‑or‑later) - Directory‑level notes: See `src/nep_cpu/README.md` and `src/nep_gpu/README.md` for file‑level provenance, what was modified or added, and links to the upstream projects. A consolidated overview is also available in `THIRD_PARTY_NOTICES.md`. - Redistribution: Any modifications and redistributions must remain under the GPL and preserve copyright and license notices, per the GPL requirements. For academic use, cite NepTrainKit as shown above and acknowledge NEP_CPU and/or GPUMD as appropriate.