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Course Outline
Introduction to CANN and Ascend AI Processors
- Understanding CANN and its role within Huawei’s AI computing stack.
- Overview of Ascend processor architectures, including the 310 and 910 series.
- Examination of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Employing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX.
- Creation and validation of OM model files.
- Addressing unsupported operators and resolving common conversion issues.
Deployment 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
- Exploring optimizations for AI Cores, memory, and tiling.
- Profiling model execution using CANN tools.
- Best practices for enhancing inference speed and resource efficiency.
Error Handling and Debugging
- Resolving common deployment errors.
- Interpreting logs and utilizing the error diagnosis tool.
- Conducting unit testing and functional validation of deployed models.
Edge and Cloud Deployment Scenarios
- Deploying applications to the Ascend 310 for edge computing.
- 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.
- Solid understanding of neural network architectures and model training workflows.
- Basic proficiency with the Linux command-line interface (CLI) and scripting.
Target Audience
- AI engineers focused on model deployment.
- Machine learning professionals aiming to leverage hardware acceleration.
- Deep learning developers constructing inference solutions.
14 Hours
