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Course Outline
Introduction to CANN and Ascend AI Processors
- Definition of CANN and its role in Huawei’s AI compute stack.
- Overview of Ascend processor architecture (including models such as 310 and 910).
- Overview of supported AI frameworks and the toolchain.
Model Conversion and Compilation
- Utilizing the ATC tool for model conversion from TensorFlow, PyTorch, and ONNX.
- Creating and validating OM model files.
- Addressing unsupported operators and resolving 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 Optimization and Profiling
- Understanding AI Core, memory management, and tiling optimizations.
- Profiling model execution using CANN tools.
- Best practices for enhancing inference speed and resource utilization.
Error Handling and Debugging
- Common deployment errors and their resolutions.
- Analyzing logs and employing 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 using Python-based deep learning frameworks like TensorFlow or PyTorch.
- Understanding of neural network architectures and model training workflows.
- Basic familiarity with Linux CLI and scripting.
Target Audience
- AI engineers focused on model deployment.
- Machine learning professionals aiming for hardware acceleration.
- Deep learning developers creating inference solutions.
14 Hours
