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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
- Overview of CANN SDK tools and their function in model integration
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore
- Managing model inputs and outputs for image and text tasks
- Utilizing 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 techniques: extracting bounding boxes, classification scores, and text outputs
Performance Optimization Techniques
- Profiling CV and NLP models using CANN utilities
- Minimizing latency through mixed-precision and batch tuning strategies
- Efficient memory and compute management for streaming tasks
Computer Vision Applications
- Case study: Object detection for intelligent surveillance systems
- Case study: Visual quality inspection in manufacturing environments
- Developing live video analytics pipelines on Ascend 310
NLP Applications
- Case study: Sentiment analysis and intent detection
- Case study: Document classification and summarization
- Real-time NLP integration with REST APIs and messaging systems
Summary and Future Directions
Requirements
- Proficiency in deep learning concepts for computer vision or NLP
- Practical experience with Python and AI frameworks like TensorFlow, PyTorch, or MindSpore
- Foundational understanding of model deployment and inference workflows
Target Audience
- Computer vision and NLP professionals working with Huawei’s Ascend platform
- Data scientists and AI engineers focused on developing real-time perception models
- Developers integrating CANN pipelines in sectors such as manufacturing, surveillance, or media analytics
