explain the change about pdmodel to json

docs/module_usage/tutorials/cv_modules/3d_bev_detection.en.md

@@ -409,7 +409,8 @@ python main.py -c paddlex/configs/modules/3d_bev_detection/BEVFusion.yaml \
 </li>
 <li><code>train.log</code>: The training log file, which records the changes in model metrics and loss during the training process.</li>
 <li><code>config.yaml</code>: The training configuration file, which records the hyperparameter settings for this training session.</li>
-<li><code>.pdparams</code>, <code>.pdopt</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, static graph network parameters, and static graph network structure, etc.</li>
+<li><code>.pdparams</code>, <code>.pdopt</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, static graph network parameters, and static graph network structure, etc.</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/3d_bev_detection.md

@@ -422,7 +422,8 @@ python main.py -c paddlex/configs/modules/3d_bev_detection/BEVFusion.yaml \
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdopt</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdopt</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md

@@ -360,7 +360,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/anomaly_detection.md

@@ -363,7 +363,8 @@ python main.py -c paddlex/configs/modules/image_anomaly_detection/STFPM.yaml \
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/face_detection.en.md

@@ -468,7 +468,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/face_detection.md

@@ -458,7 +458,8 @@ python main.py -c paddlex/configs/modules/face_detection/PicoDet_LCNet_x2_5_face
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/face_feature.en.md

@@ -414,7 +414,8 @@ After completing model training, all outputs are saved in the specified output d
 <li><code>train_result.json</code>: A file that records the training results, indicating whether the training task was successfully completed, and includes metrics, paths to related files, etc.</li>
 <li><code>train.log</code>: A log file that records changes in model metrics, loss variations, and other details during the training process.</li>
 <li><code>config.yaml</code>: A configuration file that logs the hyperparameter settings for the current training session.</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Files related to model weights, including network parameters, optimizer, EMA (Exponential Moving Average), static graph network parameters, and static graph network structure.</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Files related to model weights, including network parameters, optimizer, EMA (Exponential Moving Average), static graph network parameters, and static graph network structure.</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul>
 <details>
 

docs/module_usage/tutorials/cv_modules/face_feature.md

@@ -441,7 +441,8 @@ python main.py -c paddlex/configs/modules/face_feature/MobileFaceNet.yaml \
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/human_detection.en.md

@@ -439,7 +439,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/human_detection.md

@@ -437,7 +437,8 @@ python main.py -c paddlex/configs/modules/human_detection/PP-YOLOE-S_human.yaml
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/human_keypoint_detection.md

@@ -457,19 +457,23 @@ python main.py -c paddlex/configs/modules/keypoint_detection/PP-TinyPose_128x96.
 * 其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置，也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练：`-o Global.device=gpu:0,1`；设置训练轮次数为 10：`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释，可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)
 * 新特性：Paddle 3.0 版本支持了 CINN 神经网络编译器，在使用 GPU 设备训练时，不同模型有不同程度的训练加速效果。在 PaddleX 中训练模型时，可通过指定参数 `-o Train.dy2st=True` 开启。
 
-<details>
-  <summary>👉 <b>更多说明（点击展开）</b></summary>
-
 
-* 模型训练过程中，PaddleX 会自动保存模型权重文件，默认为`output`，如需指定保存路径，可通过配置文件中 `-o Global.output` 字段进行设置。
-* PaddleX 对您屏蔽了动态图权重和静态图权重的概念。在模型训练的过程中，会同时产出动态图和静态图的权重，在模型推理时，默认选择静态图权重推理。
-* 在完成模型训练后，所有产出保存在指定的输出目录（默认为`./output/`）下，通常有以下产出：
+<details><summary>👉 <b>更多说明（点击展开）</b></summary>
 
-* `train_result.json`：训练结果记录文件，记录了训练任务是否正常完成，以及产出的权重指标、相关文件路径等；
-* `train.log`：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；
-* `config.yaml`：训练配置文件，记录了本次训练的超参数的配置；
-* `.pdparams`、`.pdema`、`.pdopt.pdstate`、`.pdiparams`、`.pdmodel`：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；
-</details>
+<ul>
+<li>模型训练过程中，PaddleX 会自动保存模型权重文件，默认为<code>output</code>，如需指定保存路径，可通过配置文件中 <code>-o Global.output</code> 字段进行设置。</li>
+<li>PaddleX 对您屏蔽了动态图权重和静态图权重的概念。在模型训练的过程中，会同时产出动态图和静态图的权重，在模型推理时，默认选择静态图权重推理。</li>
+<li>
+<p>在完成模型训练后，所有产出保存在指定的输出目录（默认为<code>./output/</code>）下，通常有以下产出：</p>
+</li>
+<li>
+<p><code>train_result.json</code>：训练结果记录文件，记录了训练任务是否正常完成，以及产出的权重指标、相关文件路径等；</p>
+</li>
+<li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
+<li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
+<li><code>.pdparams</code>、<code>.pdopt</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
+</ul></details>
 
 ## **4.3 模型评估**
 在完成模型训练后，可以对指定的模型权重文件在验证集上进行评估，验证模型精度。使用 PaddleX 进行模型评估，一条命令即可完成模型的评估：

docs/module_usage/tutorials/cv_modules/image_classification.en.md

@@ -1048,7 +1048,8 @@ the following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/image_classification.md

@@ -1066,7 +1066,8 @@ python main.py -c paddlex/configs/modules/image_classification/PP-LCNet_x1_0.yam
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/image_feature.en.md

@@ -455,7 +455,8 @@ The following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/image_feature.md

@@ -455,7 +455,8 @@ python main.py -c paddlex/configs/modules/image_feature/PP-ShiTuV2_rec.yaml \
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/image_multilabel_classification.en.md

@@ -485,11 +485,11 @@ the following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 
-
 ## <b>4.3 Model Evaluation</b>
 After completing model training, you can evaluate the specified model weights file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation can be done with a single command:
 

docs/module_usage/tutorials/cv_modules/image_multilabel_classification.md

@@ -508,7 +508,8 @@ python main.py -c paddlex/configs/modules/image_multilabel_classification/PP-LCN
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/instance_segmentation.en.md

@@ -588,7 +588,8 @@ The following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/instance_segmentation.md

@@ -592,7 +592,8 @@ python main.py -c paddlex/configs/modules/instance_segmentation/Mask-RT-DETR-L.y
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/mainbody_detection.en.md

@@ -430,7 +430,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/mainbody_detection.md

@@ -425,7 +425,8 @@ python main.py -c paddlex/configs/modules/mainbody_detection/PP-ShiTuV2_det.yaml
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/object_detection.en.md

@@ -806,7 +806,8 @@ The following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/object_detection.md

@@ -824,7 +824,8 @@ python main.py -c paddlex/configs/modules/object_detection/PicoDet-S.yaml \
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.en.md

@@ -464,7 +464,8 @@ the following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md

@@ -463,7 +463,8 @@ python main.py -c paddlex/configs/modules/pedestrian_attribute_recognition/PP-LC
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/rotated_object_detection.en.md

@@ -467,7 +467,8 @@ The following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/rotated_object_detection.md

@@ -462,7 +462,8 @@ python main.py -c paddlex/configs/modules/rotated_object_detection/PP-YOLOE-R-L.
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/semantic_segmentation.en.md

@@ -639,7 +639,8 @@ You need to follow these steps:
 </li>
 <li><code>train.log</code>: Training log file, recording model metric changes, loss changes, etc.</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameters used for this training session.</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure.</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure.</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### 4.3 Model Evaluation

docs/module_usage/tutorials/cv_modules/semantic_segmentation.md

@@ -631,7 +631,8 @@ python main.py -c paddlex/configs/modules/semantic_segmentation/PP-LiteSeg-T.yam
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## 4.3 模型评估

docs/module_usage/tutorials/cv_modules/small_object_detection.en.md

@@ -478,7 +478,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/small_object_detection.md

@@ -472,7 +472,8 @@ python main.py -c paddlex/configs/modules/small_object_detection/PP-YOLOE_plus_S
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.en.md

@@ -446,7 +446,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md

@@ -443,7 +443,8 @@ python main.py -c paddlex/configs/modules/vehicle_attribute_recognition/PP-LCNet
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/cv_modules/vehicle_detection.en.md

@@ -434,7 +434,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/cv_modules/vehicle_detection.md

@@ -431,7 +431,8 @@ python main.py -c paddlex/configs/modules/vehicle_detection/PP-YOLOE-S_vehicle.y
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md

@@ -424,7 +424,8 @@ Other relevant parameters can be set by modifying fields under `Global` and `Tra
 </li>
 <li><code>train.log</code>: Training log file, which records changes in model metrics and loss during training.</li>
 <li><code>config.yaml</code>: Training configuration file, which records the hyperparameter configuration for this training.</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md

@@ -421,7 +421,8 @@ python main.py -c paddlex/configs/modules/doc_text_orientation/PP-LCNet_x1_0_doc
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/formula_recognition.en.md

@@ -480,7 +480,8 @@ python -m pip install Wand
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 

docs/module_usage/tutorials/ocr_modules/formula_recognition.md

@@ -474,7 +474,8 @@ python -m pip install Wand
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/layout_detection.en.md

@@ -622,7 +622,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/ocr_modules/layout_detection.md

@@ -683,7 +683,8 @@ python main.py -c paddlex/configs/modules/layout_detection/PP-DocLayout-L.yaml \
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/seal_text_detection.en.md

@@ -578,7 +578,8 @@ You need to follow these steps:
 </li>
 <li><code>train.log</code>: Training log file, recording model metric changes, loss changes, etc.</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameters used for this training session.</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure.</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure.</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### 4.3 Model Evaluation

docs/module_usage/tutorials/ocr_modules/seal_text_detection.md

@@ -570,7 +570,8 @@ python main.py -c paddlex/configs/modules/seal_text_detection/PP-OCRv4_server_se
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/table_cells_detection.en.md

@@ -497,7 +497,8 @@ The following steps are required:
 </li>
 <li><code>train.log</code>: The training log file, which records changes in model metrics and loss during the training process;</li>
 <li><code>config.yaml</code>: The training configuration file, which logs the hyperparameter settings for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: These are model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure, etc.</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: These are model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure, etc.</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/ocr_modules/table_cells_detection.md

@@ -496,7 +496,8 @@ python main.py -c paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/table_classification.en.md

@@ -403,7 +403,8 @@ python main.py -c paddlex/configs/modules/table_classification/PP-LCNet_x1_0_tab
 </li>
 <li><code>train.log</code>: The training log file, which records changes in model metrics and loss during the training process;</li>
 <li><code>config.yaml</code>: The training configuration file, which logs the hyperparameter settings for this training session;</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure, etc.</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, and static graph network structure, etc.</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/ocr_modules/table_classification.md

@@ -405,7 +405,8 @@ python main.py -c paddlex/configs/modules/table_classification/PP-LCNet_x1_0_tab
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/table_structure_recognition.en.md

@@ -408,7 +408,8 @@ the following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md

@@ -402,7 +402,8 @@ python main.py -c paddlex/configs/modules/table_structure_recognition/SLANet.yam
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 <li>特别地，SLANeXt 系列模型默认仅对表格结构识别进行训练，并不会同时对单元格定位进行训练。</li>
 </ul></details>
 

docs/module_usage/tutorials/ocr_modules/text_detection.en.md

@@ -523,7 +523,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/ocr_modules/text_detection.md

@@ -538,7 +538,8 @@ python main.py -c paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yam
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/text_recognition.en.md

@@ -659,7 +659,8 @@ The steps required are:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/ocr_modules/text_recognition.md

@@ -678,7 +678,8 @@ python main.py -c paddlex/configs/modules/text_recognition/PP-OCRv4_mobile_rec.y
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.en.md

@@ -420,7 +420,8 @@ The following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics, loss, etc., during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configurations for this training;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ### **4.3 Model Evaluation**

docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.md

@@ -423,7 +423,8 @@ python main.py -c paddlex/configs/modules/textline_orientation/PP-LCNet_x0_25_te
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ### <b>4.3 模型评估</b>

docs/module_usage/tutorials/video_modules/video_classification.en.md

@@ -441,7 +441,8 @@ the following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/video_modules/video_classification.md

@@ -441,7 +441,8 @@ python main.py -c paddlex/configs/modules/video_classification/PP-TSMv2-LCNetV2_
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>

docs/module_usage/tutorials/video_modules/video_detection.en.md

@@ -424,7 +424,8 @@ the following steps are required:
 </li>
 <li><code>train.log</code>: Training log file, recording changes in model metrics and loss during training;</li>
 <li><code>config.yaml</code>: Training configuration file, recording the hyperparameter configuration for this training session;</li>
-<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.pdmodel</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li><code>.pdparams</code>, <code>.pdema</code>, <code>.pdopt.pdstate</code>, <code>.pdiparams</code>, <code>.json</code>: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;</li>
+<li>Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current<code>.json</code> file) from protobuf(the former<code>.pdmodel</code> file) to be compatible with PIR and more flexible and scalable.</li>
 </ul></details>
 
 ## <b>4.3 Model Evaluation</b>

docs/module_usage/tutorials/video_modules/video_detection.md

@@ -432,7 +432,8 @@ python main.py -c paddlex/configs/modules/video_detection/YOWO.yaml  \
 </li>
 <li><code>train.log</code>：训练日志文件，记录了训练过程中的模型指标变化、loss 变化等；</li>
 <li><code>config.yaml</code>：训练配置文件，记录了本次训练的超参数的配置；</li>
-<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.pdmodel</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等；</li>
+<li><code>.pdparams</code>、<code>.pdema</code>、<code>.pdopt.pdstate</code>、<code>.pdiparams</code>、<code>.json</code>：模型权重相关文件，包括网络参数、优化器、EMA、静态图网络参数、静态图网络结构等； </li>
+<li>【注意】：Paddle 3.0.0 对于静态图网络结构信息的存储格式，由protobuf（原<code>.pdmodel</code>后缀文件）升级为json（现<code>.json</code>后缀文件），以兼容PIR体系，并获得更好的灵活性与扩展性。</li>
 </ul></details>
 
 ## <b>4.3 模型评估</b>