Huawei has released its CloudMatrix 384 AI chip cluster, a new system for AI learning. It employs clusters of Ascend 910C processors, joined via optical links. The distributed architecture means the system can outperform traditional hardware GPU setups, particularly in terms of resource use and on-chip time, despite the individual Ascend chips being less powerful than those of competitors.
Huawei’s new framework positions the tech giant as a “formidable challenger to Nvidia’s market-leading position, despite ongoing US sanctions,” the company claims.
To use the new Huawei framework for AI, data engineers will need to adapt their workflows, using frameworks that support Huawei’s Ascend processors, such MindSpore, which are available from Huawei and its partners
Framework transition: From PyTorch/TensorFlow to MindSpore
Unlike NVIDIA’S ecosystem, which predominantly uses frameworks like PyTorch and TensorFlow (engineered to take full advantage of CUDA), Huawei’s Ascend processors perform best when used with MindSpore, a deep learning framework developed by the company.
If data engineers already have models built in PyTorch or TensorFlow, they will likely need to convert models to the MindSpore format or retrain them using the MindSpore API.
It is worth noting that MindSpore uses different syntax, training pipelines and function calls from PyTorch or TensorFlow, so a degree of re-engineering will be necessary to replicate the results from model architectures and training pipelines. For instance, individual operator behaviour varies, such as padding modes in convolution and pooling layers. There are also differences in default weight initialisation methods.
Using MindIR for model deployment
MindSpore employs MindIR (MindSpore Intermediate Representation), a close analogue to Nvidia NIM. According to MindSpore’s official documentation, once a model has been trained in MindSpore, it can be exported using the mindspore.export utility, which converts the trained network into the MindIR format.
Detailed by DeepWiki’s guide, deploying a model for inference typically involves loading the exported MindIR model and then running predictions using MindSpore’s inference APIs for Ascend chips, which handle model de-serialisation, allocation, and execution.
MindSpore separates training and inference logic more explicitly than PyTorch or TensorFlow. Therefore, all preprocessing needs to match training inputs, and static graph execution must be optimised. MindSpore Lite or Ascend Model Zoo are recommended for additional hardware-specific tuning.
Adapting to CANN (Compute Architecture for Neural Networks)
Huawei’s CANN features a set of tools and libraries tailored for Ascend software, paralleling NVIDIA’s CUDA in functionality. Huawei recommends using CANN’s profiling and debugging tools to monitor and improve model performance on Ascend hardware.
Execution Modes: GRAPH_MODE vs.PYNATIVE_MODE
MindSpore provides two execution modes:
- GRAPH_MODE –…
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