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Duration 14 hours
Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle, from training through to deployment
- Critical performance factors for real-time CV and NLP applications
- An overview of CANN SDK tools and their function in model integration
Preparing CV and NLP Models
- Exporting models from frameworks such as PyTorch, TensorFlow, and MindSpore
- Managing model inputs and outputs for image and text-based tasks
- Leveraging ATC to convert models into OM format
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API
- Preprocessing workflows: image resizing, tokenization, and normalization
- Postprocessing steps: generating bounding boxes, classification scores, and text output
Performance Optimization Techniques
- Profiling CV and NLP models using CANN-specific tools
- Minimizing latency through mixed-precision and batch tuning strategies
- Efficient management of memory and compute resources for streaming tasks
Computer Vision Use Cases
- Case study: Implementing object detection for smart surveillance
- Case study: Visual quality inspection applications in manufacturing
- Developing live video analytics pipelines on Ascend 310
NLP Use Cases
- Case study: Performing sentiment analysis and intent detection
- Case study: Document classification and summarization workflows
- Integrating real-time NLP capabilities with REST APIs and messaging systems
Summary and Next Steps
Requirements
- Proficiency in deep learning concepts for computer vision or NLP
- Practical experience with Python and AI frameworks like TensorFlow, PyTorch, or MindSpore
- A foundational grasp of model deployment and inference workflows
Audience
- Computer vision and NLP practitioners working with Huawei’s Ascend platform
- Data scientists and AI engineers developing real-time perception models
- Developers integrating CANN pipelines into manufacturing, surveillance, or media analytics systems
