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Course Outline
Introduction to CANN and Ascend AI Processors
- What is CANN? Its role within Huawei’s AI compute stack.
- Overview of the Ascend processor architecture (e.g., 310, 910).
- Supported AI frameworks and toolchain overview.
Model Conversion and Compilation
- Utilising the ATC tool for model conversion (TensorFlow, PyTorch, ONNX).
- Creating and validating OM model files.
- Handling unsupported operators and addressing common conversion issues.
Deploying with MindSpore and Other Frameworks
- Deploying models using 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 using CANN tools.
- Best practices for enhancing inference speed and resource utilisation.
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.
- Understanding of neural network architectures and model training workflows.
- Basic familiarity with the Linux CLI and scripting.
Target Audience
- AI engineers specialising in model deployment.
- Machine learning practitioners focusing on hardware acceleration.
- Deep learning developers constructing inference solutions.
14 Hours
