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Course Outline
Introduction to CANN and Ascend AI Processors
- Definition of CANN and its significance in Huawei's AI compute ecosystem.
- An overview of Ascend processor architectures, including models 310, 910, and others.
- A summary of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Employing the ATC tool to convert models from TensorFlow, PyTorch, and ONNX.
- Generating and verifying OM model files.
- Addressing unsupported operators and resolving typical conversion problems.
Deploying with MindSpore and Other Frameworks
- Model deployment utilizing MindSpore Lite.
- Integrating OM models via Python APIs or C++ SDKs.
- Interaction with the Ascend Model Manager.
Performance Optimization and Profiling
- Insights into AI Core, memory management, and tiling optimizations.
- Profiling model execution using CANN-specific tools.
- Best practices for enhancing inference speed and resource efficiency.
Error Handling and Debugging
- Identifying and resolving common deployment errors.
- Interpreting logs and utilizing error diagnosis utilities.
- Conducting unit tests and functional validation for deployed models.
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge-based applications.
- Integrating with cloud-based APIs and microservices.
- Examining real-world case studies in computer vision and NLP.
Summary and Next 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.
- Fundamental knowledge of Linux CLI and scripting.
Target Audience
- AI engineers focused on model deployment tasks.
- Machine learning practitioners seeking to leverage hardware acceleration.
- Deep learning developers constructing inference solutions.
14 Hours
