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Course Outline
Introduction to CANN and Ascend AI Processors
- What is CANN? Its role in Huawei’s AI compute stack.
- An overview of Ascend processor architecture (310, 910, etc.).
- Supported AI frameworks and a toolchain overview.
Model Conversion and Compilation
- Utilising the ATC tool for model conversion (TensorFlow, PyTorch, ONNX).
- Creating and validating OM model files.
- Managing unsupported operators and common conversion issues.
Deploying with MindSpore and Other Frameworks
- Deploying models with MindSpore Lite.
- Integrating OM models with Python APIs or C++ SDKs.
- Working with the Ascend Model Manager.
Performance Optimisation and Profiling
- Understanding AI Core, memory, and tiling optimisations.
- Profiling model execution with CANN tools.
- Best practices for improving inference speed and resource usage.
Error Handling and Debugging
- Common deployment errors and their resolution.
- Reading logs and using the error diagnosis tool.
- Unit testing and functional validation of deployed models.
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge applications.
- Integration with cloud-based APIs and microservices.
- Real-world case studies in computer vision and NLP.
Summary and Next Steps
Requirements
- Experience with Python-based deep learning frameworks such as TensorFlow or PyTorch.
- A solid understanding of neural network architectures and model training workflows.
- Basic familiarity with the Linux CLI and scripting.
Audience
- AI engineers focused on model deployment.
- Machine learning practitioners targeting hardware acceleration.
- Deep learning developers building inference solutions.
14 Hours
