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Course Outline
Introduction to CANN and Ascend AI Processors
- Definition of CANN and its role within Huawei’s AI compute stack.
- Overview of the Ascend processor architecture (including series such as 310 and 910).
- Review of supported AI frameworks and toolchains.
Model Conversion and Compilation
- Utilizing the ATC tool for model conversion (supporting TensorFlow, PyTorch, 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 with Python APIs or C++ SDKs.
- Working with the Ascend Model Manager.
Performance Optimization and Profiling
- Exploring AI Core, memory, and tiling optimizations.
- Profiling model execution using CANN tools.
- Best practices for enhancing inference speed and resource efficiency.
Error Handling and Debugging
- Identifying common deployment errors and their resolutions.
- Interpreting logs and utilizing 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
- Practical experience with Python-based deep learning frameworks, such as TensorFlow or PyTorch.
- Solid understanding of neural network architectures and model training workflows.
- Basic familiarity with Linux command-line interface (CLI) and scripting.
Audience
- AI engineers focused on model deployment.
- Machine learning practitioners aiming to implement hardware acceleration.
- Deep learning developers constructing inference solutions.
14 Hours
