What are the differences between MindSpore, TensorFlow, and Pytorch?
Most data scientists are at least familiar with how R and Python programming languages are used for machine learning, but the possibilities do not end there. Machine learning and AI tools are often software libraries, toolkits, or suites that aid in executing tasks. As with machine learning algorithms, there is not necessarily a “best” AI or machine learning tool. What you use will (and should) depend on the task you are trying to perform.
MindSpor, Tensorflow, Pytorch are three frameworks that are providing machine learning capabilities to applications. They are providing load and process data, training- reuse, and deploying models to devices and operating systems
MindSpore is supported by Huawei, TensorFlow is supported by Google, Pytorch is supported by Facebook. All of them are open-source and you can contribute and develop functionality for these frameworks.
Features and supported platforms are changing due to frameworks and using purposes.
MindSpore supported languages are Python, C++, and also Java support for the lite(mobile) model. It supports ASCEND, CUDA, and standard CPU platforms. It also gives us commands which we can use a variety of selected options.
Tensorflow supported languages are Python, C++, Java for the lite model, Swift for IOS, Go, and Haskell. Their charts are showing us which platform we can load and process data, build train reuse models, and deploying process
Pytorch supported languages Python, C++. It is similar to Mindspore, It shows only CUDA platform, language, and commands to specified system commands.
Conclusion
There are plenty of open-source frameworks that are loading processing data, building training and reusing models, and deploying them for daily use cases. However, these are for those who want to join, use, and contribute frameworks and use them with their specific platform purposes.
Framework choice can be changed which platform and what operating system developer should use and what model should be supported to their users.
Here you can find a useful article for using MindSpore Custom model on your application:
Thank you!
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