CANN SDK for Computer Vision and NLP Pipelines Training Course
The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools designed for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.
This instructor-led, live training (available online or onsite) is tailored for intermediate-level AI professionals looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.
Upon completion of this training, participants will be able to:
- Deploy and optimize CV and NLP models using CANN and AscendCL.
- Utilize CANN tools to convert models and integrate them into live pipelines.
- Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
- Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.
Course Format
- Interactive lectures and demonstrations.
- Hands-on labs focused on model deployment and performance profiling.
- Live pipeline design using real-world CV and NLP use cases.
Course Customization Options
- To request a customized training 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, 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 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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