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Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle from training to deployment.
- Key performance considerations for real-time CV and NLP.
- Overview of CANN SDK tools and their role in model integration.
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore.
- Handling model inputs and outputs for image and text tasks.
- Using 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, normalization.
- Postprocessing: bounding boxes, classification scores, text output.
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools.
- Reducing latency with 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.
- Proficiency in Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore.
- Fundamental understanding of model deployment or inference workflows.
Audience
- Practitioners in computer vision and NLP utilizing Huawei’s Ascend platform.
- Data scientists and AI engineers developing real-time perception models.
- Developers integrating CANN pipelines in industries such as manufacturing, surveillance, or media analytics.
14 Hours
Testimonials (1)
I genuinely enjoyed the hands-on approach.
