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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_en/architecture.md)
The MindSpore framework consists of the Frontend Expression layer, Graph Engine layer, and Backend Runtime layer.
![architecture](./images/architecture.png)
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# Benchmarks
[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_en/benchmark.md)
This document describes the MindSpore benchmarks.
For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo).
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_en/constraints_on_network_construction.md)
## Overview
MindSpore can compile user source code based on the Python syntax into computational graphs, and can convert common functions or instances inherited from nn.Cell into computational graphs. Currently, MindSpore does not support conversion of any Python source code into computational graphs. Therefore, there are constraints on source code compilation, including syntax constraints and network definition constraints. As MindSpore evolves, the constraints may change.
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_en/glossary.md)
| Acronym and Abbreviation | Description |
| ----- | ----- |
| Ascend | Name of Huawei Ascend series chips. |
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_en/operator_list.md)
## mindspore.nn
| Operation | Ascend | GPU | CPU |Operator Type
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_en/roadmap.md)
In general, we will make continuous improvements in the following aspects:
1. Support more preset models.
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/architecture.md)
MindSpore框架架构总体分为MindSpore前端表示层、MindSpore计算图引擎和MindSpore后端运行时三层。
![architecture](./images/architecture.png)
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# 基准性能
[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/benchmark.md)
本文介绍MindSpore的基准性能。MindSpore预训练模型可参考[Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
## 训练性能
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/constraints_on_network_construction.md)
## 概述
MindSpore完成从用户源码到计算图的编译,用户源码基于Python语法编写,当前MindSpore支持将普通函数或者继承自nn.Cell的实例转换生成计算图,暂不支持将任意Python源码转换成计算图,所以对于用户源码支持的写法有所限制,主要包括语法约束和网络定义约束两方面。随着MindSpore的演进,这些约束可能会发生变化。
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/glossary.md)
| 术语/缩略语 | 说明 |
| ----- | ----- |
| Ascend | 华为昇腾系列芯片的系列名称。 |
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/operator_list.md)
## mindspore.nn
| 操作名 | Ascend | GPU | CPU |算子类别
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[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/roadmap.md)
## 预置模型
* CV:目标检测、GAN、图像分割、姿态识别等场景经典模型。
* NLP:RNN、Transformer类型神经网络,拓展基于Bert预训练模型的应用。
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