-[Verifying the Installation](#verifying-the-installation)
-[Supported Operators](#supported-operators)
-[Network Models](#network-models)
-[Platform and System](#platform-and-system)
-[Backend Running](#backend-running)
-[Programming Language Extensions](#programming-language-extensions)
-[Supported Features](#supported-features)
<!-- /TOC -->
## Installation
### Pip Installation
### Installing Using pip
Q: What should I do if an error message `SSL:CERTIFICATE_VERIFY_FATLED` is displayed when I use pip to install MindSpore?
Q: Any specific requirements for Python version when pip install MindSpore?
A: MindSpore utilizes many of the new features in Python3.7+，therefore we recommend you add Python3.7.5 develop environment via `conda`.
A: Add the `--trusted-host=ms-release.obs.cn-north-4.myhuaweicloud.com` parameter to the pip installation command and try again.
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Q: What should I do when error `SSL: CERTIFICATE_VERIFY_FAILED` occurs during pip install?
Q: Any specific requirements for Python version when pip install MindSpore?
A: Please append parameter `--trusted-host=ms-release.obs.cn-north-4.myhuaweicloud.com` after pip install command and try again.
A: MindSpore utilizes many of the new features in Python3.7+，therefore we recommend you add Python3.7.5 develop environment via `conda`.
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Q：Some frequently-used environment settings need to be reset in the newly started terminal window, which is easy to be forgotten, What should I do?
A：You can write the frequently-used environment settings to `~/.bash_profile` or `~/.bashrc` so that the settings can take effect immediately when you start a new terminal window.
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Q: What should I do when error prompts during pip install?
A: Please execute `pip -V` to check if pip is linked to Python3.7+. If not, we recommend you
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A: Please install the software manually if there is any suggestion of certain `software not found`.
### Environment Variables
Q：Some frequently-used environment settings need to be reset in the newly started terminal window, which is easy to be forgotten, What should I do?
A：You can write the frequently-used environment settings to `~/.bash_profile` or `~/.bashrc` so that the settings can take effect immediately when you start a new terminal window.
### Verifying the Installation
Q: After MindSpore is installed on a CPU of a PC, an error message `the pointer[session] is null` is displayed during code verification. The specific code is as follows. How do I verify whether MindSpore is successfully installed?
```python
importnumpyasnp
frommindsporeimportTensor
frommindspore.opsimportfunctionalasF
importmindspore.contextascontext
context.set_context(device_target="Ascend")
x=Tensor(np.ones([1,3,3,4]).astype(np.float32))
y=Tensor(np.ones([1,3,3,4]).astype(np.float32))
print(F.tensor_add(x,y))
```
A: After MindSpore is installed on a CPU hardware platform, run the `python -c'import mindspore'` command to check whether MindSpore is successfully installed. If no error message such as `No module named'mindspore'` is displayed, MindSpore is successfully installed. The verification code is used only to verify whether a Ascend platform is successfully installed.
## Supported Operators
Q: What can I do if the LSTM example on the official website cannot run on Ascend?
A: Currently, the LSTM runs only on a GPU or CPU and does not support the hardware environment. You can click [here](https://www.mindspore.cn/docs/en/master/operator_list.html) to view the supported operators.
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Q: When conv2d is set to (3,10), Tensor[2,2,10,10] and it runs on Ascend on ModelArts, the error message `FM_W+pad_left+pad_right-KW>=strideW` is displayed. However, no error message is displayed when it runs on a CPU. What should I do?
A: This is a TBE operator restriction that the width of x must be greater than that of the kernel. The CPU does not have this operator restriction. Therefore, no error is reported.
## Network Models
Q: Which framework models can be directly read by MindSpore? What formats are supported?
A: MindSpore uses protocol buffers (protobuf) to store training parameters and cannot directly read framework models. If you want to use the .ckpt file trained by a framework, read the parameters and then call the save_checkpoint API of MindSpore to save the file as a .ckpt file that can be read by MindSpore.
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Q: How do I use models trained by MindSpore on Ascend 310?
A: Ascend 310 supports the offline model (OM). Therefore, you need to export the Open Neural Network Exchange (ONNX) or Ascend intermediate representation (AIR) model and then convert it into OM supported by Ascend 310. For details, see [Multi-Platform Inference](https://www.mindspore.cn/tutorial/en/master/use/multi_platform_inference.html).
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Q: What should I do if prompt message is that the directory path has Chinese garbled code when I compile MindSpore from source code in Windows system?
Q: How do I modify parameters (such as the dropout value) on MindSpore?
A: When building a network, use `if self.training: x = dropput(x)`. During verification, set `network.set_train(mode_false)` before execution to disable the dropout function. During training, set `network.set_train(mode_false)` to True to enable the dropout function.
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A: Please change the Chinese directory to English, and make sure there are no Chinese characters in the directory.
Q: Where can I view the sample code or tutorial of MindSpore training and inference?
## Support
A: Please visit the [MindSpore official website](https://www.mindspore.cn/tutorial/en/master/index.html).
### Model Support
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Q: What types of model is currently supported by MindSpore for training?
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A: Currently, recommendation models such as Wide & Deep, DeepFM, and NCF are under development. In the natural language processing (NLP) field, Bert\_NEZHA is available and models such as MASS are under development. You can rebuild the network into a text generation network based on the scenario requirements. Please stay tuned for updates on the [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
### Backend Support
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Q: When install or run MindSpore, are there any requirements for hardwares like GPU, NPU and so forth?
Q: How simple can the MindSpore model training code be?
A: MindSpore provides Model APIs except for network definitions. In most scenarios, model training can be completed using only a few lines of code.
## Platform and System
Q: Can MindSpore be installed on Ascend 310?
A: MindSpore currently supports Ascend AI processor, CPU and GPU。For common models like lenet you can try run MindSpore on CPU alone.
A: Ascend 310 can only be used for inference. MindSpore supports training on Ascend 910. The trained model can be converted into an .om model for inference on Ascend 310.
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Q: Does MindSpore require computing units such as GPUs and NPUs? What hardware support is required?
A: MindSpore currently supports CPU, GPU, Ascend, and NPU. Currently, you can try out MindSpore through Docker images on laptops or in environments with GPUs. Some models in MindSpore Model Zoo support GPU-based training and inference, and other models are being improved. For distributed parallel training, MindSpore supports multi-GPU training. You can obtain the latest information from [Road Map](https://www.mindspore.cn/docs/en/master/roadmap.html) and [project release notes](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md).
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Q: What hardware does MindSpore require?
Q: What is the relationship between MindSpore and ModelArts? Can MindSpore be used on ModelArts?
A: Currently, you can try out MindSpore through Docker images on laptops or in environments with GPUs. Some models in MindSpore Model Zoo support GPU-based training and inference, and other models are being improved. For distributed parallel training, MindSpore supports multi-GPU training. You can obtain the latest information from [RoadMap](https://www.mindspore.cn/docs/en/master/roadmap.html) and project [Release Notes](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md).
A: ModelArts is an online training and inference platform on HUAWEI CLOUD. MindSpore is a Huawei deep learning framework. You can view the tutorials on the [MindSpore official website](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html) to learn how to train MindSpore models on ModelArts.
### System Support
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Q: Does MindSpore support Windows 10?
A: The MindSpore CPU version can be installed on Windows 10.
A: The MindSpore CPU version can be installed on Windows 10. For details about the installation procedure, please refer to the [MindSpore official website tutorial](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mindspore_cpu_win_install.html)
## Backend Running
Q: What can I do if the error message `device target [CPU] is not supported in pynative mode` is displayed for the operation operator of MindSpore?
A: Currently, the PyNative mode supports only Ascend and GPU and does not support the CPU.
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Q: What can I do if the error message `Pynative run op ExpandDims failed` is displayed when the ExpandDims operator is used? The code is as follows:
A: The problem is that the Graph mode is selected but the PyNative mode is used. As a result, an error is reported. MindSpore supports the following running modes which are optimized in terms of debugging or running:
### Programming Language
- PyNative mode: dynamic graph mode. In this mode, operators in the neural network are delivered and executed one by one, facilitating the compilation and debugging of the neural network model.
- Graph mode: static graph mode. In this mode, the neural network model is compiled into an entire graph and then delivered for execution. This mode uses technologies such as graph optimization to improve the running performance and facilitates large-scale deployment and cross-platform running.
You can select a proper mode and writing method to complete the training by referring to the official website [tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/debugging_in_pynative_mode.html).
## Programming Language Extensions
Q: The recent announced programming language such as taichi got Python extensions that could be directly used as `import taichi as ti`. Does MindSpore have similar support?
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A：MindSpore currently supports Python extensions，bindings for languages like C++、Rust、Julia are on the way.
### Others
## Supported Features
Q: Does MindSpore have a lightweight on-device inference engine?
A: MindSpore has its own on-device inference engine. In the current version, some functions of on-device inference have been open-sourced. MindSpore on-device inference engine is expected to be updated at the end of August. By then, it will be more comprehensive and powerful in terms of usability, performance, operator completeness, and third-party model support.
Q: How does MindSpore implement semantic collaboration and processing? Is the popular Formal Concept Analysis (FCA) used?
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Q: Where can I view the sample code or tutorial of MindSpore training and inference?
A: Please visit the [MindSpore official website](https://www.mindspore.cn/tutorial/en/master/index.html).
## Features
Q: Does MindSpore have any plan or consideration on the edge and device when the training and inference functions on the cloud are relatively mature?
A: MindSpore is a unified cloud-edge-device training and inference framework. Edge has been considered in its design, so MindSpore can perform inference at the edge. The open-source version will support Ascend 310-based inference. The optimizations supported in the current inference stage include quantization, operator fusion, and memory overcommitment.
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Q: What is the relationship between MindSpore and ModelArts? Can MindSpore be used on ModelArts?
A: ModelArts is an online training and inference platform on HUAWEI CLOUD. MindSpore is a Huawei deep learning framework. You can view the tutorials on the [MindSpore official website](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html) to learn how to train MindSpore models on ModelArts.
## Capabilities
Q: Does MindSpore have a module that can implement object detection algorithms as TensorFlow does?
A: The TensorFlow's object detection pipeline API belongs to the TensorFlow's Model module. After MindSpore's detection models are complete, similar pipeline APIs will be provided.
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Q: Does MindSpore provide open-source e-commerce datasets?
A: No. Please stay tuned for updates on the [MindSpore official website](https://www.mindspore.cn/en).
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Q: How simple can the MindSpore model training code be?
A: MindSpore provides Model APIs except for network definitions. In most scenarios, model training can be completed using only a few lines of code.
A: No. Please stay tuned for updates on the [MindSpore official website](https://www.mindspore.cn/en).
