feat: 更新README

README.md

 # gpu-base-image-build
-## 构建命令
+## 流程
+包括准备工作、镜像构建、镜像验证、打包镜像四个模块
+## 准备工作
+1. 准备一台裸机器，安装[nvidia-docker2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)、git
+2. 下载镜像验证中需要的代码和模型（或从陈宜航处拷贝），放在项目根目录下
+   1. 下载代码：`git clone http://developer.hpccube.com/codes/chenpangpang/gpu-base-image-test.git`
+   2. 下载模型: `cd gpu-base-image-test && python hf_down.py`
+3. 确认要构建的镜像
+  -  镜像制作进度：https://bvjoh3z2qoz.feishu.cn/base/BKl6birVbarmzJsnznkcEDFTnV9?table=tbl3bCdS7qfjPn6j&view=vewww0URg8
+## 镜像构建
 - 基于[pytorch官方镜像](https://hub.docker.com/r/pytorch/pytorch)构建镜像
   ```bash
+  cd build_space && \
   ./build_ubuntu.sh jupyterlab \
-                    juypterlab-pytorch:2.3.1-py3.10-cuda12.1-ubuntu22.04 \
-                    pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime
+                    juypterlab-pytorch:2.3.1-py3.10-cuda12.1-ubuntu22.04-devel \
+                    pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel
   ```
   - 参数1: framework，不需要改动
   - 参数2: 输出镜像名称
   - 参数3: 基础镜像
 - 基于[nvidia官方镜像](https://hub.docker.com/r/nvidia/cuda)构建镜像
     ```bash
+  cd build_space && \
   ./build_ubuntu.sh jupyterlab \
-                    juypterlab-pytorch:2.3.1-py3.8-cuda12.1-ubuntu22.04 \
-                    nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04 \
+                    juypterlab-pytorch:2.3.1-py3.8-cuda12.1-ubuntu22.04-devel \
+                    nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 \
                     TORCH_VERSION="2.3.1" \
                     TORCHVISION_VERSION="0.18.1" \
                     TORCHAUDIO_VERSION="2.3.1" \
@@ -22,5 +33,87 @@
   - 参数1: framework，不需要改动
   - 参数2: 输出镜像名称
   - 参数3: 基础镜像
-    ## 验证镜像
-- 
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+  - TORCH_VERSION：torch版本
+  - TORCHVISION_VERSION：torchvision版本
+  - TORCHAUDIO_VERSION：torchaudio版本
+  - CONDA_URL：安装conda的url
+
+### 相关链接
+- pytorch镜像：https://hub.docker.com/r/pytorch/pytorch/tags
+- nvidia镜像：https://hub.docker.com/r/nvidia/cuda/tags
+- torch、torchvision、torchaudio、cuda版本对应：https://pytorch.org/get-started/previous-versions/
+- conda安装：https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/
+
+## 镜像验证
+1. 版本验证：运行：`sh script/1_base_test.sh $IMAGE_NAME`，输出：
+  ```
+  
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.1.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+Ubuntu 22.04.3 LTS \n \l
+
+python version:  3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0]
+torch version:  2.3.1
+torch cuda available:  True
+torch cuda version:  12.1
+torch cudnn version:  8902
+torchvision version:  0.18.1
+torchaudio version:  2.3.1
+  ```
+确认`输出的版本信息`和`镜像名称`是否匹配，确认`torch cuda`是否可用。
+2. 文本生成验证：运行：`sh script/2_text_generate_test.sh $IMAGE_NAME`，输出：
+  ```
+  Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
+  Hello, I'm a language model, to be honest." (Hooker)
+  
+  "Let's start an internal test now, and then
+  ```
+确认`输出信息`是否符合预期。
+3. 图像生成验证：运行`sh script/3_image_generate_test.sh $IMAGE_NAME`，输出：
+```
+
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.1.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
+0it [00:00, ?it/s]
+Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
+  warnings.warn(
+Loading pipeline components...: 100%|██████████| 7/7 [00:02<00:00,  3.14it/s]
+100%|██████████| 50/50 [00:03<00:00, 13.03it/s]
+The output image has been saved as output.png
+```
+![output.png](assert/output.png)<br>
+确认`输出图片`是否符合预期。
+## 打包镜像
+运行`docker save -o $FILENAME $IMAGE_NAME`打包镜像。
+
+## TODO：
+- 从docker hub获取镜像tag
+- 根据计划表和镜像tag表，获取base镜像名称、输出镜像名称
+- 飞书机器人-自动统计镜像制作情况
+- 飞书机器人-待推送镜像提醒
+- CI/CD流程
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