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Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle, spanning from training to deployment.
- Key performance considerations for real-time CV and NLP applications.
- Overview of CANN SDK tools and their role in model integration.
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore.
- Managing model inputs/outputs for image and text tasks.
- Utilizing ATC to convert models to OM format.
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference using the AscendCL API.
- Preprocessing pipelines: image resizing, tokenization, and normalization.
- Postprocessing steps: handling bounding boxes, classification scores, and text output.
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools.
- Reducing 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.
- Case study: Visual quality inspection in manufacturing.
- Building 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
- Familiarity with deep learning techniques for computer vision or NLP.
- Hands-on experience with Python and AI frameworks like TensorFlow, PyTorch, or MindSpore.
- Fundamental understanding of model deployment or inference workflows.
Target Audience
- Computer vision and NLP professionals utilizing Huawei’s Ascend platform.
- Data scientists and AI engineers developing real-time perception models.
- Developers integrating CANN pipelines within manufacturing, surveillance, or media analytics sectors.
14 Hours
