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Duration 14 hours
Course Outline
Introduction to CV/NLP Deployment with CANN
- Understanding the AI model lifecycle from training through to deployment.
- Key performance factors for real-time CV and NLP applications.
- Overview of CANN SDK tools and their function in model integration.
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore.
- Managing model inputs and outputs for image and text-based tasks.
- Utilizing ATC to convert models into OM format.
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API.
- Preprocessing workflows: image resizing, tokenization, and normalization.
- Postprocessing tasks: bounding boxes, classification scores, and text generation.
Performance Optimization Techniques
- Profiling CV and NLP models using CANN-specific tools.
- Minimizing latency through mixed-precision and batch tuning strategies.
- Efficient management of memory and compute resources for streaming tasks.
Computer Vision Use Cases
- Case study: object detection in smart surveillance systems.
- Case study: visual quality inspection in manufacturing environments.
- Developing live video analytics pipelines on Ascend 310 hardware.
NLP Use Cases
- Case study: sentiment analysis and intent detection.
- Case study: document classification and summarization.
- Implementing real-time NLP integrations with REST APIs and messaging systems.
Summary and Next Steps
Requirements
- A solid understanding of deep learning principles in computer vision or NLP.
- Hands-on experience with Python and AI frameworks like TensorFlow, PyTorch, or MindSpore.
- Foundational knowledge of model deployment and inference workflows.
Audience
- Computer vision and NLP specialists working with Huawei’s Ascend platform.
- Data scientists and AI engineers developing real-time perception models.
- Developers integrating CANN pipelines in sectors such as manufacturing, surveillance, or media analytics.
