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https://github.com/zhangzhengde0225/hai)
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https://github.com/zhangzhengde0225/hai/issues)
<!-- [](
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#### English | [简体中文](https://github.com/zhangzhengde0225/hai/blob/main/docs/readme_zh_cn.md)
# HepAI Library
This is [HepAI](https://ai.ihep.ac.cn) python library, the AI platform can accelerate scientific research in multidisciplinary scenarios, simplify model iteration and flow, and is a common infrastructure for the development of AI algorithms and applications.
The HepAI platform itself is a software system that carries AI algorithm models, provides AI computing power, connects data channels, and conducts AI training.
The HepAI framework integrates classic and state-of-the-art (SOTA) artificial intelligence algorithms in the field of high-energy physics. One can access related algorithm models, datasets, and computational resources through a unified interface, making the application of AI simple and efficient.
<details open>
<summary><b>News</b></summary>
+ [2024.05.16] v1.1.9 HepAI Client支持GPT-4o, 调用方法:
+ [2024.03.26] v1.0.21 Make LLM request like OpenAI via HepAI object.
+ [2023.10.24] v1.0.18 接入dalle文生图模型，调用方法教程见[此处](https://note.ihep.ac.cn/s/EG60U1Rtf)。
+ [2023.04.21] v1.0.7通过hepai使用GPT-3.5，[hepai_api.md](docs/hepai_api.md).
+ [2023.02.09] 基于ChatGPT的**HaiChatGPT**已上线，使用简单，无需梯子！详情查看：[HaiChatGPT](https://code.ihep.ac.cn/zdzhang/haichatgpt).
+ [2023.01.16] 华为NPU服务器上架，如有算法国产化需求，请查阅[NPU文档](docs/computing_power/npu_power_doc.md)。
+ [2022.10.20] HAI v1.0.6-Beta 第一个测试版本发布，4个算法和3个数据集
+ [2022.08.23] HAI v1.0.0
</details>
<details open>
<summary><b>Tutorials</b></summary>
[Quick Start to Using HepAI on Computing Clusters](docs/quickstart_hpc.md)
[Reconstruction and identification of atmospheric neutrinos in JUNO experiments using PointNet](https://code.ihep.ac.cn/zhangyiyu/pointnet)
</details>
<details open>
<summary><b>Algorithm Zoo</b></summary>
<a href="https://code.ihep.ac.cn/zdzhang/hai/-/blob/main/docs/model_zoo.md">
<ul>
<li>
<img src="https://img.shields.io/static/v1?style=social&label=粒子物理&message=4 online, 3 TODO">
<li>
<img src="https://img.shields.io/static/v1?style=social&label=天体物理&message=1 TODO">
<li>
<img src="https://img.shields.io/static/v1?style=social&label=同步辐射&message=2 TODO">
<li>
<img src="https://img.shields.io/static/v1?style=social&label=中子物理&message=0">
<li>
<img src="https://img.shields.io/static/v1?style=social&label=通用神经网络&message=2 online, 5 TODO">
<li>
<img src="https://img.shields.io/static/v1?style=social&label=经典机器学习&message=TODO">
</ul>
</a>
</details>
<details open>
<summary><b>Dataset Zoo</b></summary>
<a href="https://code.ihep.ac.cn/zdzhang/hai/-/blob/main/docs/datasets.md">
<ul>
<li>
<img src="https://img.shields.io/static/v1?style=social&label=粒子物理&message=3 available, 10+ TODO">
<li>
<img src="https://img.shields.io/static/v1?style=social&label=CV&message=1 available">
</a>
</details>
### Quick start
```
pip install hepai --upgrade
hai -V # 查看版本
```
1. 命令行使用
```bash
hai train <model_name> # 训练模型, 例如: hai train particle_transformer
hai eval <model_name>
```
2. python库使用
python库统一接口：
```python
import hai
model = hai.hub.load('<model_name>') # 加载模型
config = model.config # 获取模型配置
config.batch_size = 32 # 修改配置
model.trian() # 训练模型
model.eval() # 评估模型
model.infer('<data>') # 模型推理
hai.train('particle_transformer')
```
3. 部署和远程调用
跨语言、跨平台的模型部署和远程调用
服务端：
```bash
hai start server # 启动服务
```
客户端
```bash
pip install hai-client
```
```python
import hai_client
hai = hai_client.HAI()
```
或其他支持gRPC的语言，详见[deploy](docs/deploy.md)
note
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"description": "\n[](\nhttps://github.com/zhangzhengde0225/hai)\n[](\nhttps://github.com/zhangzhengde0225/hai/issues)\n<!-- [](\nhttps://github.com/zhangzhengde0225/CDNet/blob/master/docs/DATASETS.md)\n[](\nhttps://github.com/zhangzhengde0225/CDNet/archive/refs/heads/master.zip) -->\n\n#### English | [\u7b80\u4f53\u4e2d\u6587](https://github.com/zhangzhengde0225/hai/blob/main/docs/readme_zh_cn.md)\n\n# HepAI Library\nThis is [HepAI](https://ai.ihep.ac.cn) python library, the AI platform can accelerate scientific research in multidisciplinary scenarios, simplify model iteration and flow, and is a common infrastructure for the development of AI algorithms and applications.\n\nThe HepAI platform itself is a software system that carries AI algorithm models, provides AI computing power, connects data channels, and conducts AI training.\n\nThe HepAI framework integrates classic and state-of-the-art (SOTA) artificial intelligence algorithms in the field of high-energy physics. One can access related algorithm models, datasets, and computational resources through a unified interface, making the application of AI simple and efficient.\n\n<details open>\n<summary><b>News</b></summary>\n\n+ [2024.05.16] v1.1.9 HepAI Client\u652f\u6301GPT-4o, \u8c03\u7528\u65b9\u6cd5:\n+ [2024.03.26] v1.0.21 Make LLM request like OpenAI via HepAI object.\n+ [2023.10.24] v1.0.18 \u63a5\u5165dalle\u6587\u751f\u56fe\u6a21\u578b\uff0c\u8c03\u7528\u65b9\u6cd5\u6559\u7a0b\u89c1[\u6b64\u5904](https://note.ihep.ac.cn/s/EG60U1Rtf)\u3002\n+ [2023.04.21] v1.0.7\u901a\u8fc7hepai\u4f7f\u7528GPT-3.5\uff0c[hepai_api.md](docs/hepai_api.md).\n+ [2023.02.09] \u57fa\u4e8eChatGPT\u7684**HaiChatGPT**\u5df2\u4e0a\u7ebf\uff0c\u4f7f\u7528\u7b80\u5355\uff0c\u65e0\u9700\u68af\u5b50\uff01\u8be6\u60c5\u67e5\u770b\uff1a[HaiChatGPT](https://code.ihep.ac.cn/zdzhang/haichatgpt).\n+ [2023.01.16] \u534e\u4e3aNPU\u670d\u52a1\u5668\u4e0a\u67b6\uff0c\u5982\u6709\u7b97\u6cd5\u56fd\u4ea7\u5316\u9700\u6c42\uff0c\u8bf7\u67e5\u9605[NPU\u6587\u6863](docs/computing_power/npu_power_doc.md)\u3002\n+ [2022.10.20] HAI v1.0.6-Beta \u7b2c\u4e00\u4e2a\u6d4b\u8bd5\u7248\u672c\u53d1\u5e03\uff0c4\u4e2a\u7b97\u6cd5\u548c3\u4e2a\u6570\u636e\u96c6\n+ [2022.08.23] HAI v1.0.0\n</details>\n\n<details open>\n<summary><b>Tutorials</b></summary>\n\n[Quick Start to Using HepAI on Computing Clusters](docs/quickstart_hpc.md)\n\n[Reconstruction and identification of atmospheric neutrinos in JUNO experiments using PointNet](https://code.ihep.ac.cn/zhangyiyu/pointnet)\n\n</details>\n\n<details open>\n<summary><b>Algorithm Zoo</b></summary>\n<a href=\"https://code.ihep.ac.cn/zdzhang/hai/-/blob/main/docs/model_zoo.md\">\n <ul>\n <li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=\u7c92\u5b50\u7269\u7406&message=4 online, 3 TODO\">\n <li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=\u5929\u4f53\u7269\u7406&message=1 TODO\">\n <li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=\u540c\u6b65\u8f90\u5c04&message=2 TODO\">\n <li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=\u4e2d\u5b50\u7269\u7406&message=0\">\n <li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=\u901a\u7528\u795e\u7ecf\u7f51\u7edc&message=2 online, 5 TODO\">\n <li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=\u7ecf\u5178\u673a\u5668\u5b66\u4e60&message=TODO\">\n </ul>\n </a>\n \n</details>\n\n<details open>\n<summary><b>Dataset Zoo</b></summary>\n<a href=\"https://code.ihep.ac.cn/zdzhang/hai/-/blob/main/docs/datasets.md\">\n<ul>\n<li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=\u7c92\u5b50\u7269\u7406&message=3 available, 10+ TODO\">\n <li>\n <img src=\"https://img.shields.io/static/v1?style=social&label=CV&message=1 available\">\n </a>\n</details>\n\n\n### Quick start\n```\npip install hepai --upgrade\nhai -V # \u67e5\u770b\u7248\u672c\n```\n\n1. \u547d\u4ee4\u884c\u4f7f\u7528\n\n ```bash\n hai train <model_name> # \u8bad\u7ec3\u6a21\u578b, \u4f8b\u5982: hai train particle_transformer\n hai eval <model_name>\n ```\n\n2. python\u5e93\u4f7f\u7528\n\n python\u5e93\u7edf\u4e00\u63a5\u53e3\uff1a\n ```python\n import hai\n \n model = hai.hub.load('<model_name>') # \u52a0\u8f7d\u6a21\u578b\n config = model.config # \u83b7\u53d6\u6a21\u578b\u914d\u7f6e\n config.batch_size = 32 # \u4fee\u6539\u914d\u7f6e\n model.trian() # \u8bad\u7ec3\u6a21\u578b\n model.eval() # \u8bc4\u4f30\u6a21\u578b\n model.infer('<data>') # \u6a21\u578b\u63a8\u7406\n hai.train('particle_transformer')\n ```\n\n3. \u90e8\u7f72\u548c\u8fdc\u7a0b\u8c03\u7528\n\n \u8de8\u8bed\u8a00\u3001\u8de8\u5e73\u53f0\u7684\u6a21\u578b\u90e8\u7f72\u548c\u8fdc\u7a0b\u8c03\u7528\n\n \u670d\u52a1\u7aef\uff1a\n ```bash\n hai start server # \u542f\u52a8\u670d\u52a1\n ```\n \u5ba2\u6237\u7aef\n ```bash\n pip install hai-client\n ```\n ```python\n import hai_client\n hai = hai_client.HAI()\n ```\n \u6216\u5176\u4ed6\u652f\u6301gRPC\u7684\u8bed\u8a00\uff0c\u8be6\u89c1[deploy](docs/deploy.md)\n\n\nnote\n",
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