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Course Outline
Introduction to CANN and Ascend AI Processors
- Understanding CANN and its role within Huawei’s AI compute stack.
- An overview of Ascend processor architecture, including series such as 310 and 910.
- A survey of supported AI frameworks and the toolchain.
Model Conversion and Compilation
- Utilizing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX.
- Creating and validating OM model files.
- Addressing 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 with the Ascend Model Manager.
Performance Optimization and Profiling
- Understanding optimizations related to AI Cores, memory, and tiling.
- Profiling model execution using CANN tools.
- Best practices for enhancing inference speed and resource utilization.
Error Handling and Debugging
- Resolving common deployment errors.
- Interpreting logs and employing the error diagnosis tool.
- Conducting unit testing and functional validation of deployed models.
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge applications.
- Integrating with cloud-based APIs and microservices.
- Examining real-world case studies in computer vision and NLP.
Summary and Next Steps
Requirements
- Prior 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.
Target Audience
- AI engineers involved in model deployment.
- Machine learning practitioners focused on hardware acceleration.
- Deep learning developers constructing inference solutions.
14 Hours
