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, with a particular focus 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 completing 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 seamlessly 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 combined with demonstrations.
- Practical labs focused on model deployment and performance profiling.
- Live pipeline design exercises using real-world CV and NLP use cases.
Customization Options
- For information on requesting customized training for this course, please get in touch with us to make arrangements.
Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle, from training to deployment.
- Key performance factors 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 and outputs for image and text tasks.
- Using ATC to convert models to OM format.
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API.
- Preprocessing pipelines: image resizing, tokenization, and normalization.
- Postprocessing: 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.
- Experience with Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore.
- A foundational understanding of model deployment and inference workflows.
Target Audience
- Practitioners working with computer vision and NLP on Huawei’s Ascend platform.
- Data scientists and AI engineers developing real-time perception models.
- Developers integrating CANN pipelines into manufacturing, surveillance, or media analytics systems.
Open Training Courses require 5+ participants.
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