Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN: Its role in Huawei’s AI compute stack
- Overview of Ascend processor architectures (including 310, 910, etc.)
- Supported AI frameworks and an overview of the toolchain
Model Conversion and Compilation
- Utilizing the ATC tool for model conversion (TensorFlow, PyTorch, ONNX)
- Creating and validating OM model files
- Addressing unsupported operators and common conversion issues
Deployment 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 AI Core, memory management, and tiling optimizations
- Profiling model execution using CANN tools
- Best practices for enhancing inference speed and resource utilization
Error Handling and Debugging
- Resolving common deployment errors
- Analyzing logs and using 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
- Integration with cloud-based APIs and microservices
- Real-world case studies in computer vision and NLP
Summary and Next Steps
Requirements
- Experience using Python-based deep learning frameworks, such as TensorFlow or PyTorch
- Understanding of neural network architectures and model training workflows
- Basic proficiency with Linux CLI and scripting
Target Audience
- AI engineers specializing in model deployment
- Machine learning practitioners focused on hardware acceleration
- Deep learning developers constructing inference solutions
14 Hours
