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Duration 14 hours
Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN and its role within Huawei’s AI compute stack.
- An overview of Ascend processor architectures, including models like 310 and 910.
- A summary of supported AI frameworks and the associated toolchain.
Model Conversion and Compilation
- Utilizing the ATC tool to convert models from TensorFlow, PyTorch, and ONNX.
- Generating and validating OM model files.
- Managing unsupported operators and addressing common conversion issues.
Deploying with MindSpore and Other Frameworks
- Implementing model deployment with MindSpore Lite.
- Integrating OM models using Python APIs or C++ SDKs.
- Operating with the Ascend Model Manager.
Performance Optimization and Profiling
- Gaining insight into AI Core, memory, and tiling optimizations.
- Profiling model execution using CANN tools.
- Applying best practices to enhance inference speed and resource utilization.
Error Handling and Debugging
- Resolving common deployment errors.
- Interpreting logs and leveraging the error diagnosis tool.
- Conducting unit tests 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
- Proficiency in Python-based deep learning frameworks such as TensorFlow or PyTorch.
- A solid understanding of neural network architectures and model training workflows.
- Fundamental familiarity with the Linux CLI and scripting.
Target Audience
- AI engineers focused on model deployment.
- Machine learning practitioners aiming for hardware acceleration.
- Deep learning developers constructing inference solutions.
