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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 like 310 and 910.
- A review of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Utilizing the ATC tool to convert models from TensorFlow, PyTorch, and ONNX.
- The process of creating and validating OM model files.
- Strategies for handling unsupported operators and resolving common conversion issues.
Deploying with MindSpore and Other Frameworks
- Deploying models using MindSpore Lite.
- Integrating OM models via Python APIs or C++ SDKs.
- Working effectively with the Ascend Model Manager.
Performance Optimization and Profiling
- Understanding AI Core, memory management, and tiling optimizations.
- Profiling model execution using CANN diagnostic tools.
- Best practices for enhancing inference speed and resource utilization.
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 to Ascend 310 for edge computing applications.
- Integrating with cloud-based APIs and microservices.
- Real-world case studies focusing on computer vision and NLP.
Summary and Next Steps
Requirements
- Experience with Python-based deep learning frameworks, such as TensorFlow or PyTorch.
- A solid understanding of neural network architectures and model training workflows.
- Basic familiarity with Linux CLI and scripting.
Audience
- AI engineers focused on model deployment.
- Machine learning practitioners aiming for hardware acceleration.
- Deep learning developers building inference solutions.
14 Hours
