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Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN and its role within Huawei’s AI compute stack
- Overview of Ascend processor architectures (e.g., 310, 910)
- Survey of supported AI frameworks and toolchains
Model Conversion and Compilation
- Using the ATC tool to convert models from TensorFlow, PyTorch, and ONNX
- Creating and validating OM model files
- Managing unsupported operators and resolving common conversion issues
Deploying with MindSpore and Other Frameworks
- Deploying models using MindSpore Lite
- Integrating OM models via Python APIs or C++ SDKs
- Working with the Ascend Model Manager
Performance Optimization and Profiling
- Understanding AI Core optimizations, memory management, and tiling strategies
- Profiling model execution using CANN tools
- Best practices for enhancing inference speed and resource efficiency
Error Handling and Debugging
- Identifying common deployment errors and their resolutions
- Analyzing logs and utilizing error diagnosis tools
- Conducting unit testing and functional validation for deployed models
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 devices for edge applications
- Integrating with cloud-based APIs and microservices
- Exploring real-world case studies in computer vision and NLP
Summary and Next Steps
Requirements
- Practical experience with Python-based deep learning frameworks such as TensorFlow or PyTorch
- Knowledge of neural network architectures and model training workflows
- Basic familiarity with Linux CLI commands and scripting
Target Audience
- AI engineers focused on model deployment
- Machine learning practitioners aiming to leverage hardware acceleration
- Deep learning developers constructing inference solutions
14 Hours
