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+- [Implement Sedna Python SDK support TinyMS](#mplement Sedna Python SDK support TinyMS)
+  - [Motivation](#Motivation)
+    - [Goals](#Goals)
+- [Proposal](#Proposal)
+    - [Sedna Architecture analysis](#Sedna Architecture analysis)
+    - [Feasibility](#Feasibility)
+- [Design Details](#Design Details)
+    - [set_backend()](#set_backend()  )
+    - [TInyMS API](#TInyMS API)
+    - [Demo Based on MSBackend](#Based on MSBackend)
+    - [Demo Based on TinyMSBackend](#Based on MSBackend)
+- [Road Map](#Road Map)
+
+#  Implement Sedna Python SDK support TinyMS
+
+## Motivation
+
+Currently, Sedna only supports sklearn's xgboost, Tensorflow, Keras, PyTorch and MindSpore, but doesn’t support MindSpore’s high-level API tool TinyMS.
+
+This proposal aims to implement Senda Python SDK ‘s support for TinyMS based on  Sedna's lib development interface and implements an Incremental Learning demo.
+
+### Goals
+
+- Implement Sedna Python SDK support of TinyMS
+- Write user guide document of TinyMS
+
+## Proposal
+
+### Sedna Architecture analysis
+
+![](./images/tinyms-support-architecture.png)
+
+Taking the case yolo3 incremental learning in Sedna as an example, the user wraps the estimator for the specific model. when the incremental learning interface `IncrementalLearning()` is called, the following process takes place:
+
+- IncrementalLearning instantiates its parent class JobBase.
+- Call `set_backend()` in the constructor of JobBase to resolve the framework used by the model to TensorFlow and instantiate the corresponding TFBackend.
+
+- TFBackend inherits BackendBase, which is initialized with OS-related configuration when instantiated.
+
+- TFBackend implements the specific framework-related configuration.
+
+### Feasibility
+
+Sedna parses the underlying framework of the model and runs the model using a different machine learning framework through a backend pair. So we need to :
+
+- implement Sedna's parsing of the underlying machining learning framework 
+
+![](./images/tinyms-support-set_bakcend.png)
+
+- implement TinyMSBackend
+
+# Design Details
+
+## set_backend()  
+
+Improve function `set_backend()` in `sedna/backend/__init__.py`
+
+```python
+if backend_type == "TINYMS":
+	from sedna.backend.tinyms import TinyMSBackend as REGISTER
+```
+
+## TInyMS API
+
+TinyMS is MindSpore's high-level API, but the TinyMS’s methods are not the same as the MindSpore framework’s , so we can not use the idea of inheritance for encapsulation.
+
+`TinyMSBackend`class
+
+-  `__init__(self, estimator, fine_tune=True, **kwargs) `  ：initialize
+
+-  `train(self, train_data, valid_data=None, **kwargs)`: train the model
+
+-  `predict(self, data, **kwargs) `: use the model to preidct result
+
+-  `evaluate(self, data, **kwargs)`: evaluate the accuracy
+
+-  `load_weights(self)`: load model weight
+
+-  `get_weights(self) `：return weight
+
+-  `set_weights(self, weights) `：set weight
+
+```python
+class TinyMSBackend(MSBackend):
+    def __init__(self, estimator, fine_tune=True, **kwargs):
+        ...
+	
+    def train(self, train_data, valid_data=None, **kwargs):
+        ...
+    
+    def predict(self, data, **kwargs):
+        ...
+    
+    def evaluate(self, valid_data, **kwargs):
+        ...
+  
+    def save(self, model_path=None):
+        ...
+  
+    def load(self, model_url=""):
+        ...
+  
+    def load_weights(self):
+	    ...
+    
+    def get_weights(self):
+	    ...
+	
+    def set_weights(self, weights):
+	    ...
+```
+
+## Demo Based on MSBackend
+
+- [implement MobileNetV2 with MindSpore](https://www.mindspore.cn/tutorial/zh-CN/r0.7/advanced_use/mobilenetv2_incremental_learning.html)
+
+- Build an estimator class based on MobileNetV2, encapsulating a series of methods such as `train(),evaluate(),predict()`, etc.
+- Call incremental learning API to accomplish model training, evaluation, and inference
+
+## Demo Based on TinyMSBackend
+
+- [implement MobileNetV2 with TinyMS](https://tinyms.readthedocs.io/en/latest/tutorials/ipynb/TinyMS_MobileNetV2_tutorial.html)
+
+- Build an estimator class based on MobileNetV2, encapsulating a series of methods such as `train(),evaluate(),predict()`, etc.
+
+- Call incremental learning API to accomplish model training, evaluation and inference
+
+# Road Map
+
+- Implementing an incremental learning demo based on MSBackend
+- [ ] week1 : implement `estimator`
+- [ ] week2 : debug model
+
+- TinyMS（08/8-09/16）
+
+- [ ] week3 : learn `TinyMS`documentation
+- [ ] week4 : implement `TinyMSBackend` class
+- [ ] week5 : implement `estimator`
+- [ ] week6 : debug model
+
+- Write documentation（09/17-09/30）
+
+- [ ] week7 : write API documentation
+- [ ] week8 : write demo documentation and submit PR
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