MindSpore is a deep learning framework for training and inference across cloud, edge, and mobile environments. It is designed to offer efficient execution while supporting a broad range of AI development workloads.
The framework includes automatic differentiation, distributed training capabilities, and native support for Ascend AI processors.
This is free and open source software.
Key Features
- Develop and train deep learning models.
- Supports both training and inference workloads.
- Designed for cloud, edge, and mobile deployment scenarios.
- Automatic differentiation based on source transformation.
- Supports complex control flow, higher-order functions, and closures.
- Automatic parallelisation for distributed model training.
- Combines data parallelism, model parallelism, and hybrid parallelism.
- Provides graph compilation optimisations for neural networks.
- Native support for Ascend AI processors.
- Supports CPU and GPU execution environments.
- Software and hardware co-optimisation for improved execution efficiency.
Website: https://github.com/mindspore-ai/mindspore
Support:
Developer: MindSpore Community
License: Apache License 2.0
MindSpore is written in C++ and Python. Learn C++ with our recommended free books and free tutorials. Learn Python with our recommended free books and free tutorials.
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| fastai | Simplifies training fast and accurate neural nets using modern best practices |
| PyTensor | Library for fast numerical computation |
| Elephas | Distributed deep learning with Keras and Spark |
| Chainer | Powerful, flexible, and intuitive framework for neural networks |
| Caffe | Convolutional Architecture for Fast Feature Embedding |
| TFlearn | Deep learning library featuring a higher-level API for TensorFlow |
| MXNet | Flexible and efficient library |
| CNTK | Distributed deep learning |
| Neupy | Python library for Artificial Neural Networks and Deep Learning |
Read our verdict in the software roundup.
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