Fix docs (#2990)

* add 3 pipelines serve

* fix sth

* add docs for 6 pipelines

* fix docs

docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.en.md

@@ -209,7 +209,7 @@ The visualization results are saved under `save_path`, and the visualization res
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="instance_segmentation")
+pipeline = create_pipeline(pipeline="instance_segmentation")
 output = pipeline.predict(input="general_instance_segmentation_004.png", threshold=0.5)
 for res in output:
     res.print()
@@ -232,15 +232,16 @@ In the above Python script, the following steps are performed:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>Pipeline name, must be a pipeline supported by PaddleX.</td>
+<td><code>pipeline</code></td>
+<td>Pipeline name or path to pipeline config file, if it's set as a pipeline name, it must be a pipeline supported by PaddleX.</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>Path to the pipeline configuration file</td>
-<td><code>str</code></td>
+<td>Specific configuration information for the pipeline (if set simultaneously with the <code>pipeline</code>, it takes precedence over the <code>pipeline</code>, and the pipeline name must match the <code>pipeline</code>).
+</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.md

@@ -209,7 +209,7 @@ paddlex --pipeline instance_segmentation \
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="instance_segmentation")
+pipeline = create_pipeline(pipeline="instance_segmentation")
 output = pipeline.predict(input="general_instance_segmentation_004.png", threshold=0.5)
 for res in output:
     res.print()
@@ -232,15 +232,15 @@ for res in output:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>产线名称, 必须为 PaddleX 所支持的产线。</td>
+<td><code>pipeline</code></td>
+<td>产线名称或是产线配置文件路径。如为产线名称，则必须为 PaddleX 所支持的产线。</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>产线配置文件路径</td>
-<td><code>str</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.md

@@ -71,7 +71,7 @@ paddlex --pipeline open_vocabulary_detection \
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="open_vocabulary_detection")
+pipeline = create_pipeline(pipeline="open_vocabulary_detection")
 output = pipeline.predict(input="open_vocabulary_detection.jpg", prompt="bus . walking man . rearview mirror .")
 for res in output:
     res.print()
@@ -94,15 +94,15 @@ for res in output:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>产线名称, 必须为 PaddleX 所支持的产线。</td>
+<td><code>pipeline</code></td>
+<td>产线名称或是产线配置文件路径。如为产线名称，则必须为 PaddleX 所支持的产线。</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>产线配置文件路径</td>
-<td><code>str</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.md

@@ -73,7 +73,7 @@ paddlex --pipeline open_vocabulary_segmentation \
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="open_vocabulary_segmentation")
+pipeline = create_pipeline(pipeline="open_vocabulary_segmentation")
 output = pipeline.predict(input="open_vocabulary_segmentation.jpg", prompt_type="box", prompt=[[112.9,118.4,513.8,382.1],[4.6,263.6,92.2,336.6],[592.4,260.9,607.2,294.2]])
 for res in output:
     res.print()
@@ -96,15 +96,15 @@ for res in output:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>产线名称, 必须为 PaddleX 所支持的产线。</td>
+<td><code>pipeline</code></td>
+<td>产线名称或是产线配置文件路径。如为产线名称，则必须为 PaddleX 所支持的产线。</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>产线配置文件路径</td>
-<td><code>str</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.md

@@ -68,7 +68,7 @@ paddlex --pipeline rotated_object_detection \
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="rotated_object_detection")
+pipeline = create_pipeline(pipeline="rotated_object_detection")
 output = pipeline.predict(input="rotated_object_detection_001.png", threshold=0.5)
 for res in output:
     res.print()
@@ -91,15 +91,15 @@ for res in output:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>产线名称, 必须为 PaddleX 所支持的产线。</td>
+<td><code>pipeline</code></td>
+<td>产线名称或是产线配置文件路径。如为产线名称，则必须为 PaddleX 所支持的产线。</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>产线配置文件路径</td>
-<td><code>str</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.en.md

@@ -237,7 +237,7 @@ The visualization results are saved under `save_path`, and the visualization res
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="semantic_segmentation")
+pipeline = create_pipeline(pipeline="semantic_segmentation")
 output = pipeline.predict(input="general_semantic_segmentation_002.png", target_size = -1)
 for res in output:
     res.print()
@@ -255,31 +255,32 @@ In the above Python script, the following steps are executed:
 <th>Parameter</th>
 <th>Description</th>
 <th>Type</th>
-<th>Default Value</th>
+<th>Default</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>The name of the pipeline, which must be supported by PaddleX.</td>
+<td><code>pipeline</code></td>
+<td>Pipeline name or path to pipeline config file, if it's set as a pipeline name, it must be a pipeline supported by PaddleX.</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>The path to the pipeline configuration file.</td>
-<td><code>str</code></td>
+<td>Specific configuration information for the pipeline (if set simultaneously with the <code>pipeline</code>, it takes precedence over the <code>pipeline</code>, and the pipeline name must match the <code>pipeline</code>).
+</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>device</code></td>
-<td>The device for pipeline inference. It supports specifying specific GPU card numbers (e.g., "gpu:0"), specific card numbers for other hardware (e.g., "npu:0"), and CPU (e.g., "cpu").</td>
+<td>Pipeline inference device. Supports specifying the specific GPU card number, such as "gpu:0", other hardware specific card numbers, such as "npu:0", CPU such as "cpu".</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>use_hpip</code></td>
-<td>Whether to enable high-performance inference, which is only available if the pipeline supports it.</td>
+<td>Whether to enable high-performance inference, only available when the pipeline supports high-performance inference.</td>
 <td><code>bool</code></td>
 <td><code>False</code></td>
 </tr>

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.md

@@ -241,7 +241,7 @@ paddlex --pipeline semantic_segmentation \
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="semantic_segmentation")
+pipeline = create_pipeline(pipeline="semantic_segmentation")
 output = pipeline.predict(input="general_semantic_segmentation_002.png", target_size = -1)
 for res in output:
     res.print()
@@ -264,15 +264,15 @@ for res in output:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>产线名称, 必须为 PaddleX 所支持的产线。</td>
+<td><code>pipeline</code></td>
+<td>产线名称或是产线配置文件路径。如为产线名称，则必须为 PaddleX 所支持的产线。</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>产线配置文件路径</td>
-<td><code>str</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/cv_pipelines/small_object_detection.en.md

@@ -84,7 +84,7 @@ The visualization results are saved under `save_path`, and the visualization res
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="small_object_detection")
+pipeline = create_pipeline(pipeline="small_object_detection")
 output = pipeline.predict(input="small_object_detection.jpg", threshold=0.5)
 for res in output:
     res.print()
@@ -107,15 +107,16 @@ In the above Python script, the following steps are performed:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>Pipeline name, must be a pipeline supported by PaddleX.</td>
+<td><code>pipeline</code></td>
+<td>Pipeline name or path to pipeline config file, if it's set as a pipeline name, it must be a pipeline supported by PaddleX.</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>Path to the pipeline configuration file</td>
-<td><code>str</code></td>
+<td>Specific configuration information for the pipeline (if set simultaneously with the <code>pipeline</code>, it takes precedence over the <code>pipeline</code>, and the pipeline name must match the <code>pipeline</code>).
+</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/cv_pipelines/small_object_detection.md

@@ -82,7 +82,7 @@ paddlex --pipeline small_object_detection \
 
 ```python
 from paddlex import create_pipeline
-pipeline = create_pipeline(pipeline_name="small_object_detection")
+pipeline = create_pipeline(pipeline="small_object_detection")
 output = pipeline.predict(input="small_object_detection.jpg", threshold=0.5)
 for res in output:
     res.print()
@@ -105,15 +105,15 @@ for res in output:
 </thead>
 <tbody>
 <tr>
-<td><code>pipeline_name</code></td>
-<td>产线名称, 必须为 PaddleX 所支持的产线。</td>
+<td><code>pipeline</code></td>
+<td>产线名称或是产线配置文件路径。如为产线名称，则必须为 PaddleX 所支持的产线。</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
 <td><code>config</code></td>
-<td>产线配置文件路径</td>
-<td><code>str</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>

docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.en.md

@@ -146,25 +146,32 @@ In the above Python script, the following steps are executed:
 <th>Parameter</th>
 <th>Description</th>
 <th>Type</th>
-<th>Default Value</th>
+<th>Default</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td><code>pipeline</code></td>
-<td>The name of the pipeline or the path to the pipeline configuration file. If it is a pipeline name, it must be a pipeline supported by PaddleX.</td>
+<td>Pipeline name or path to pipeline config file, if it's set as a pipeline name, it must be a pipeline supported by PaddleX.</td>
 <td><code>str</code></td>
-<td>None</td>
+<td><code>None</code></td>
+</tr>
+<tr>
+<td><code>config</code></td>
+<td>Specific configuration information for the pipeline (if set simultaneously with the <code>pipeline</code>, it takes precedence over the <code>pipeline</code>, and the pipeline name must match the <code>pipeline</code>).
+</td>
+<td><code>dict[str, Any]</code></td>
+<td><code>None</code></td>
 </tr>
 <tr>
 <td><code>device</code></td>
-<td>The inference device for the pipeline. It supports specifying the specific GPU card number, such as "gpu:0", other hardware card numbers, such as "npu:0", and CPU as "cpu".</td>
+<td>Pipeline inference device. Supports specifying the specific GPU card number, such as "gpu:0", other hardware specific card numbers, such as "npu:0", CPU such as "cpu".</td>
 <td><code>str</code></td>
-<td><code>gpu:0</code></td>
+<td><code>None</code></td>
 </tr>
 <tr>
 <td><code>use_hpip</code></td>
-<td>Whether to enable high-performance inference, available only if the pipeline supports high-performance inference.</td>
+<td>Whether to enable high-performance inference, only available when the pipeline supports high-performance inference.</td>
 <td><code>bool</code></td>
 <td><code>False</code></td>
 </tr>
@@ -285,7 +292,7 @@ In the above Python script, the following steps are executed:
   <li><b>None</b>: If not specified, the default PaddleX official model configuration will be used</li>
 </ul>
 </td>
-</tr>
+<td><code>None</code></td>
 <tr>
 <td><code>layout_merge_bboxes_mode</code></td>
 <td>Merging mode for the bounding boxes output by the model; if not specified, the default PaddleX official model configuration will be used</td>

docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.md

@@ -381,13 +381,19 @@ for res in output:
 <td><code>pipeline</code></td>
 <td>产线名称或是产线配置文件路径。如为产线名称，则必须为 PaddleX 所支持的产线。</td>
 <td><code>str</code></td>
-<td>None</td>
+<td><code>None</code></td>
+</tr>
+<tr>
+<td><code>config</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
+<td><code>None</code></td>
 </tr>
 <tr>
 <td><code>device</code></td>
 <td>产线推理设备。支持指定GPU具体卡号，如“gpu:0”，其他硬件具体卡号，如“npu:0”，CPU如“cpu”。</td>
 <td><code>str</code></td>
-<td><code>gpu:0</code></td>
+<td><code>None</code></td>
 </tr>
 <tr>
 <td><code>use_hpip</code></td>
@@ -517,6 +523,7 @@ for res in output:
   <li><b>None</b>, 不指定，将默认使用PaddleX官方模型配置</li>
 </ul>
 </td>
+<td><code>None</code></td>
 <tr>
 <td><code>layout_merge_bboxes_mode</code></td>
 <td>模型输出的检测框的合并处理模式；如果不指定，将默认使用PaddleX官方模型配置</td>

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.en.md

@@ -145,25 +145,32 @@ In the above Python script, the following steps are executed:
 <th>Parameter</th>
 <th>Description</th>
 <th>Type</th>
-<th>Default Value</th>
+<th>Default</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td><code>pipeline</code></td>
-<td>The name of the pipeline or the path to the pipeline configuration file. If it is a pipeline name, it must be a pipeline supported by PaddleX.</td>
+<td>Pipeline name or path to pipeline config file, if it's set as a pipeline name, it must be a pipeline supported by PaddleX.</td>
 <td><code>str</code></td>
 <td><code>None</code></td>
 </tr>
 <tr>
+<td><code>config</code></td>
+<td>Specific configuration information for the pipeline (if set simultaneously with the <code>pipeline</code>, it takes precedence over the <code>pipeline</code>, and the pipeline name must match the <code>pipeline</code>).
+</td>
+<td><code>dict[str, Any]</code></td>
+<td><code>None</code></td>
+</tr>
+<tr>
 <td><code>device</code></td>
-<td>The device for pipeline inference. It supports specifying specific card numbers for GPU (e.g., "gpu:0"), other hardware (e.g., "npu:0"), or CPU (e.g., "cpu").</td>
+<td>Pipeline inference device. Supports specifying the specific GPU card number, such as "gpu:0", other hardware specific card numbers, such as "npu:0", CPU such as "cpu".</td>
 <td><code>str</code></td>
-<td><code>gpu:0</code></td>
+<td><code>None</code></td>
 </tr>
 <tr>
 <td><code>use_hpip</code></td>
-<td>Whether to enable high-performance inference. This is only available if the pipeline supports high-performance inference.</td>
+<td>Whether to enable high-performance inference, only available when the pipeline supports high-performance inference.</td>
 <td><code>bool</code></td>
 <td><code>False</code></td>
 </tr>

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.md

@@ -107,7 +107,7 @@ paddlex --pipeline ts_forecast --input ts_fc.csv --device gpu:0 --save_path ./ou
 
 <details><summary> 👉点击展开</summary>
 
-```
+```bash
 {'input_path': 'ts_fc.csv', 'forecast':                            OT
 date
 2018-06-26 20:00:00  9.586131
@@ -167,10 +167,16 @@ for res in output:
 <td><code>None</code></td>
 </tr>
 <tr>
+<td><code>config</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
+<td><code>None</code></td>
+</tr>
+<tr>
 <td><code>device</code></td>
 <td>产线推理设备。支持指定GPU具体卡号，如“gpu:0”，其他硬件具体卡号，如“npu:0”，CPU如“cpu”。</td>
 <td><code>str</code></td>
-<td><code>gpu:0</code></td>
+<td><code>None</code></td>
 </tr>
 <tr>
 <td><code>use_hpip</code></td>