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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 Ascend processor architecture, including variants such as the 310 and 910.
- Overview of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Utilizing the ATC tool for model conversion across TensorFlow, PyTorch, and ONNX.
- Creating and validating OM model files.
- Addressing unsupported operators and common conversion challenges.
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
- Understanding optimizations related to AI Core, 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.
- Reading logs and utilizing the error diagnosis tool.
- Performing 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.
- 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 the Linux command-line interface 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
