CANN SDK for Computer Vision and NLP Pipelines Training Course
The CANN SDK (Compute Architecture for Neural Networks) equips users with robust deployment and optimization tools for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.
This instructor-led live training, available online or onsite, is designed for intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production scenarios.
Upon completion of this training, participants will be able to:
- Deploy and optimize CV and NLP models using CANN and AscendCL.
- Utilize CANN utilities to convert models and integrate them into live pipelines.
- Optimize inference performance for tasks such as detection, classification, and sentiment analysis.
- Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment.
Course Format
- Interactive lectures accompanied by demonstrations.
- Hands-on labs featuring model deployment and performance profiling.
- Live pipeline design utilizing real-world CV and NLP use cases.
Customization Options
- To arrange customized training for this course, please get in touch with us.
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, 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 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 applied to computer vision or NLP.
- Experience with Python and AI frameworks like TensorFlow, PyTorch, or MindSpore.
- Basic understanding of model deployment or inference workflows.
Audience
- Computer vision and NLP practitioners 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.
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CANN SDK for Computer Vision and NLP Pipelines Training Course - Enquiry
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