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Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN and its function within Huawei’s AI compute stack.
- An overview of Ascend processor architectures (e.g., 310, 910).
- A survey of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Employing the ATC tool for model conversion from TensorFlow, PyTorch, and ONNX.
- Generating and validating OM model files.
- Addressing unsupported operators and resolving frequent conversion challenges.
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.
- Applying best practices to enhance inference speed and resource efficiency.
Error Handling and Debugging
- Identifying and resolving common deployment errors.
- Interpreting logs and utilizing error diagnosis tools.
- Conducting unit testing and functional validation for deployed models.
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge computing applications.
- Integrating with cloud-based APIs and microservices.
- Examining real-world case studies in computer vision and NLP.
Conclusion and Future Steps
Requirements
- Hands-on experience with Python-based deep learning frameworks, including TensorFlow or PyTorch.
- A solid understanding of neural network architectures and model training workflows.
- Basic proficiency with the Linux command-line interface (CLI) and scripting.
Target Audience
- AI engineers focused on model deployment.
- Machine learning practitioners aiming to leverage hardware acceleration.
- Deep learning developers building inference solutions.
14 Hours
