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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, including models 310 and 910.
- A summary of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Utilizing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX.
- Generating and validating OM model files.
- Managing unsupported operators and addressing frequent conversion challenges.
Deploying with MindSpore and Other Frameworks
- Implementing model deployment using MindSpore Lite.
- Integrating OM models via Python APIs or C++ SDKs.
Performance Optimization and Profiling
- Exploring optimizations for AI Cores, memory management, and tiling.
- Profiling model execution using dedicated CANN tools.
- Best practices for enhancing inference speed and reducing resource consumption.
Error Handling and Debugging
- Identifying common deployment errors and implementing solutions.
- Interpreting logs and utilizing error diagnostic tools.
- Conducting unit tests and functional validation of deployed models.
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge-based applications.
- Integrating with cloud-based APIs and microservice architectures.
- Examining real-world case studies in computer vision and NLP.
Summary and Next Steps
Requirements
- Proficiency with Python-based deep learning frameworks, including TensorFlow or PyTorch.
- Foundational knowledge of Linux command-line interfaces and scripting.
Target Audience
- AI engineers focused on model deployment strategies.
- Machine learning specialists seeking hardware acceleration solutions.
- Deep learning developers constructing inference solutions.
14 Hours
