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Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN and its position within Huawei’s AI compute stack
- An overview of Ascend processor architectures, including models 310 and 910
- A summary of supported AI frameworks and the associated toolchain
Model Conversion and Compilation
- Employing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX
- Generating and validating OM model files
- Addressing unsupported operators and typical conversion hurdles
Deployment via MindSpore and Other Frameworks
- Deploying models using MindSpore Lite
- Integrating OM models with Python APIs or C++ SDKs
- Utilizing the Ascend Model Manager
Performance Optimization and Profiling
- Gaining insight into AI Core, memory, and tiling optimizations
- Profiling model execution using CANN tools
- Best practices for boosting inference speed and managing resource usage
Error Handling and Debugging
- Resolving common deployment errors
- Interpreting logs and leveraging the error diagnosis tool
- Conducting 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
- Proficiency with Python-based deep learning frameworks such as TensorFlow or PyTorch
- A solid understanding of neural network architectures and model training workflows
- Foundational knowledge of Linux CLI and scripting
Target Audience
- AI engineers focused on model deployment
- Machine learning practitioners seeking hardware acceleration solutions
- Deep learning developers constructing inference solutions
14 Hours
