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Course Outline
Introduction to CANN and Ascend AI Processors
- What is CANN? Its function within Huawei's AI compute stack.
- A look at Ascend processor architecture, including models 310, 910, and others.
- Overview of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Employing the ATC tool to convert models from TensorFlow, PyTorch, and ONNX.
- Generating and verifying OM model files.
- Managing unsupported operators and addressing common conversion pitfalls.
Deploying with MindSpore and Other Frameworks
- Implementing model deployment using MindSpore Lite.
- Integrating OM models through Python APIs or C++ SDKs.
- Utilizing the Ascend Model Manager.
Performance Optimization and Profiling
- Exploring optimizations for AI Cores, memory management, and tiling.
- Profiling model execution processes using CANN utilities.
- Best practices for enhancing inference speed and resource efficiency.
Error Handling and Debugging
- Identifying and resolving frequent deployment errors.
- Interpreting logs and utilizing error diagnosis tools.
- Conducting unit tests and functional validation for deployed models.
Edge and Cloud Deployment Scenarios
- Deploying applications to Ascend 310 for edge environments.
- Integration with cloud-based APIs and microservices.
- Real-world case studies covering computer vision and NLP.
Summary and Next Steps
Requirements
- Hands-on experience with Python-based deep learning frameworks like TensorFlow or PyTorch.
- A solid grasp 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 practitioners aiming to leverage hardware acceleration.
- Deep learning developers constructing inference solutions.
14 Hours
