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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 the 310 and 910 series.
- A summary of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Utilizing the ATC tool for model conversion from TensorFlow, PyTorch, and ONNX.
- Generating and validating OM model files.
- Addressing unsupported operators and resolving common conversion challenges.
Deploying with MindSpore and Other Frameworks
- Deploying models using MindSpore Lite.
- Integrating OM models via Python APIs or C++ SDKs.
- Utilizing the Ascend Model Manager.
Performance Optimization and Profiling
- Understanding 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 tools.
- Conducting unit testing and functional validation of deployed models.
Edge and Cloud Deployment Scenarios
- Deploying applications to Ascend 310 for edge cases.
- Integrating 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, including TensorFlow or PyTorch.
- A solid understanding of neural network architectures and model training workflows.
- Basic knowledge of Linux CLI commands and scripting.
Target Audience
- AI engineers focused on model deployment.
- Machine learning practitioners seeking hardware acceleration.
- Deep learning developers constructing inference solutions.
14 Hours
