提交 d4516027 编写于 作者: F feilong

初始化

上级 bcc7fdb8
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__pycache__
*.pyc
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# skill_tree_cuda
CUDA 技能树
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`CUAD入门技能树`[技能森林](https://gitcode.net/csdn/skill_tree)的一部分。
## 编辑环境初始化
```
pip install -r requirements.txt
```
## 目录结构说明
技能树编辑仓库的 data 目录是主要的编辑目录,目录的结构是固定的
* 技能树`骨架文件`
* 位置:`data/tree.json`
* 说明:该文件是执行 `python main.py` 生成的,请勿人工编辑
* 技能树`根节点`配置文件:
* 位置:`data/config.json`
* 说明:可编辑配置关键词等字段,其中 `node_id` 字段是生成的,请勿编辑
* 技能树`难度节点`
* 位置:`data/xxx`,例如: `data/1.CUAD入门初阶`
* 说明:
* 每个技能树有 3 个等级,目录前的序号是必要的,用来保持文件夹目录的顺序
* 每个目录下有一个 `config.json` 可配置关键词信息,其中 `node_id` 字段是生成的,请勿编辑
* 技能树`章节点`
* 位置:`data/xxx/xxx`,例如:`data/1.CUAD入门初阶/1.1.GPU架构及异构计算`
* 说明:
* 每个技能树的每个难度等级有 n 个章节,目录前的序号是必要的,用来保持文件夹目录的顺序
* 每个目录下有一个 `config.json` 可配置关键词信息,其中 `node_id` 字段是生成的,请勿编辑
* 技能树`知识节点`
* 位置:`data/xxx/xxx`,例如:`data/1.CUAD入门初阶/1.1.GPU架构及异构计算`
* 说明:
* 每个技能树的每章有 n 个知识节点,目录前的序号是必要的,用来保持文件夹目录的顺序
* 每个目录下有一个 `config.json`
* 其中 `node_id` 字段是生成的,请勿编辑
* 其中 `keywords` 可配置关键字字段
* 其中 `children` 可配置该`知识节点`下的子树结构信息,参考后面描述
* 其中 `export` 可配置该`知识节点`下的导出习题信息,参考后面描述
## `知识节点` 子树信息结构
例如 `data/1.CUAD入门初阶/1.1.GPU架构及异构计算/介绍GPU架构以及异构计算的基本原理/config.json` 里配置对该知识节点子树信息结构,这个配置是可选的:
```json
{
// ...
"children": [
{
"XX开发入门": {
"keywords": [
"XX开发",
],
"children": [],
"keywords_must": [
"XX"
],
"keywords_forbid": []
}
}
],
}
```
## `知识节点` 的导出习题编辑
例如 `data/1.CUAD入门初阶/1.1.GPU架构及异构计算/介绍GPU架构以及异构计算的基本原理/config.json` 里配置对该知识节点导出的习题
```json
{
// ...
"export": [
"helloworld.json"
]
}
```
helloworld.json 的格式如下:
```bash
{
"type": "code_options",
"author": "xxx",
"source": "helloworld.md",
"notebook_enable": false,
"exercise_id": "xxx"
}
```
其中
* "type": "code_options" 表示是一个选择题
* "author" 可以放作者的 CSDN id,
* "source" 指向了习题 MarkDown文件
* "notebook_enable" 目前都是false
* "exercise_id" 是工具生成的,不填
习题格式模版如下:
````mardown
# {标题}
{习题描述}
以下关于上述游戏代码说法[正确/错误]的是?
## 答案
{目标选项}
## 选项
### A
{混淆选项1}
### B
{混淆选项2}
### C
{混淆选项3}
````
## 技能树合成
在根目录下执行 `python main.py` 会合成技能树文件,合成的技能树文件: `data/tree.json`
* 合成过程中,会自动检查每个目录下 `config.json` 里的 `node_id` 是否存在,不存在则生成
* 合成过程中,会自动检查每个知识点目录下 `config.json` 里的 `export` 里导出的习题配置,检查是否存在`exercise_id` 字段,如果不存在则生成
* 在 节 目录下根据需要,可以添加一些子目录用来测试代码。
* 开始游戏入门技能树构建之旅,GoodLuck!
## FAQ
**难度目录是固定的么?**
1. data/xxx 目录下的子目录是固定的初/中/高三个难度等级目录
**如何增加章目录?**
1. 在VSCode里打开项目仓库
2. 在对应的难度等级目录新建章目录,例如在 data/1.xxx初阶/ 下新建章文件夹,data/1.xxx初阶/1.yyy
3. 在项目根目录下执行 python main.py 脚本,会自动生成章的配置文件 data/1.xxx初阶/1.yyy/config.json
**如何增加节目录?**:
1. 直接在VSCode里创建文件夹,例如 "data/1.xxx初阶/1.yyy/2.zzz"
2. 项目根目录下执行 python main.py 会自动为新增节创建配置文件 data/1.xxx初阶/1.yyy/2.zzz/config.json
**如何在节下新增一个习题**:
3. 在"data/1.xxx初阶/1.yyy/2.zzz" 目录下添加一个 markdown 文件编辑,例如 yyy.md,按照习题markdown格式编辑习题。
4. md编辑完后,可以再次执行 python main.py 会自动生成同名的 yyy.json,并将 yyy.json 添加到config.json 的export数组里。
5. yyy.json里的author信息放作者 CSDN ID。
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{
"node_id": "cuda-bdd8df6c59d0460bbf30d3a4a6203b06",
"keywords": [],
"children": [],
"export": [
"helloworld.json"
],
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"keywords_forbid": []
}
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{
"type": "code_options",
"author": null,
"source": "helloworld.md",
"notebook_enable": false,
"exercise_id": "02176393bfff4c7ea60d9f85fd095224"
}
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# {标题}
{习题描述}
以下关于上述游戏代码说法[正确/错误]的是?
## 答案
{目标选项}
## 选项
### A
{混淆选项1}
### B
{混淆选项2}
### C
{混淆选项3}
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}
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}
],
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}
}
\ No newline at end of file
{
"level":{
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"GPU架构及异构计算",
"初识CUDA",
"CUDA存储单元的使用",
"多种CUDA存储单元详解",
"利用共享存储单元优化应用",
"统一内存"
],
"level_1":[
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"CUDA stream"
],
"level_2": [
"CUDA 调试分析",
"CUDA 优化",
"CUDA 加速库"
]
},
"tree": {
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"介绍GPU硬件平台",
"环境安装配置"
],
"初识CUDA": [
"CUDA程序的编译",
"GPU线程的调用",
"GPU和CPU的通讯",
"使用多个线程的核函数",
"使用线程索引",
"多维网络",
"网格与线程块"
],
"CUDA存储单元的使用": [
"设备初始化",
"GPU的存储单元",
"GPU存储单元的分配与释放",
"数据的传输",
"数据与线程之间的对应关系"
],
"多种CUDA存储单元详解": [
"CUDA中的存储单元种类",
"CUDA中的各种存储单元的使用方法",
"CUDA中的各种存储单元的适用条件"
],
"利用共享存储单元优化应用": [
"共享存储单元详解",
"共享内存的Bank conflict",
"利用共享存储单元进行矩阵转置和矩阵乘积"
],
"统一内存": [
"统一内存的基本概念和使用"
],
"CUA错误检测与事件": [
"CUDA应用程序运行时的错误检测",
"CUDA中的事件"
],
"原子操作": [
"CUDA中的原子操作",
"原子操作的适用场景"
],
"CUDA stream": [
"CUDA流的基本概念",
"默认流与非默认流",
"利用CUDA流重叠计算和数据传输"
],
"CUDA 调试分析": [
"利用Nsight等分析工具对程序性能进行分析",
"根据实际硬件调整程序"
],
"CUDA 优化": [
"存储优化",
"执行设置优化",
"指令级优化",
"控制流优化"
],
"CUDA 加速库": [
"cuBLAS",
"cuFFT",
"cuRAND",
"cuSPARSE",
"cuDNN"
]
}
}
\ No newline at end of file
from skill_tree.tree import TreeWalker, load_json, dump_json
if __name__ == '__main__':
walker = TreeWalker("data", "cuda", "CUDA入门", ignore_keywords=True)
walker.walk()
.pre_commit
skill-tree-parser
\ No newline at end of file
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