CANN SDK for Computer Vision and NLP Pipelines Training Course
The CANN SDK (Compute Architecture for Neural Networks) delivers powerful deployment and optimisation tools for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.
This instructor-led, live training (online or on-site) is designed for intermediate-level AI practitioners who wish to build, deploy and optimise vision and language models using the CANN SDK for production use cases.
By the end of this training, participants will be able to:
- Deploy and optimise CV and NLP models using CANN and AscendCL.
- Use CANN tools to convert models and integrate them into live pipelines.
- Optimise inference performance for tasks such as detection, classification and sentiment analysis.
- Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.
Course Format
- Interactive lecture and demonstration.
- Hands-on lab covering model deployment and performance profiling.
- Live pipeline design using real-world CV and NLP use cases.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
Course Outline
Introduction to CV/NLP Deployment with CANN
- 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/outputs for image and text tasks
- Using ATC to convert models to OM format
Deploying Inference Pipelines with AscendCL
- Running CV/NLP inference using the AscendCL API
- Preprocessing pipelines: image resizing, tokenisation, normalisation
- Postprocessing: bounding boxes, classification scores, text output
Performance Optimisation Techniques
- Profiling CV and NLP models using CANN tools
- Reducing latency with mixed-precision and batch tuning
- Managing memory and compute 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 summarisation
- Real-time NLP integration with REST APIs and messaging systems
Summary and Next Steps
Requirements
- Familiarity with deep learning for computer vision or NLP
- Experience with Python and AI frameworks such as TensorFlow, PyTorch or MindSpore
- Basic understanding of model deployment or inference workflows
Audience
- Computer vision and NLP practitioners using Huawei's Ascend platform
- Data scientists and AI engineers developing real-time perception models
- Developers integrating CANN pipelines in manufacturing, surveillance or media analytics
Open Training Courses require 5+ participants.
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Provisional Upcoming Courses (Require 5+ participants)
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