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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 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 pipelines: image resizing, tokenization, and normalization
- Postprocessing: extracting bounding boxes, classification scores, and text output
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools
- Minimizing latency through mixed-precision and batch tuning
- Managing memory and compute resources for streaming tasks
Computer Vision Use Cases
- Case study: object detection for smart surveillance systems
- Case study: visual quality inspection in manufacturing processes
- Creating live video analytics pipelines on Ascend 310
NLP Use Cases
- 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 Next Steps
Requirements
- Knowledge of deep learning principles for computer vision or NLP
- Proficiency with Python and AI frameworks like TensorFlow, PyTorch, or MindSpore
- Foundational understanding of model deployment or inference workflows
Target 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 in manufacturing, surveillance, or media analytics
