CANN SDK for Computer Vision and NLP Pipelines Training Course

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.
• An overview of CANN SDK tools and their function in model integration.

Preparing CV and NLP Models

• Exporting models from frameworks like PyTorch, TensorFlow, and MindSpore.
• Managing model inputs and outputs for image and text-based tasks.
• Leveraging ATC to convert models into OM format.

Deploying Inference Pipelines with AscendCL

• Executing CV/NLP inference via the AscendCL API.
• Implementing preprocessing pipelines: image resizing, tokenization, and normalization.
• Performing postprocessing tasks: extracting bounding boxes, classification scores, and text output.

Performance Optimization Techniques

• Profiling CV and NLP models using CANN-specific tools.
• Reducing latency through mixed-precision strategies and batch tuning.
• Efficiently managing memory and compute resources for streaming tasks.

Computer Vision Use Cases

• Case study: Implementing object detection for smart surveillance systems.
• Case study: Applying visual quality inspection in manufacturing environments.
• Constructing live video analytics pipelines on Ascend 310 hardware.

NLP Use Cases

• Case study: Enhancing sentiment analysis and intent detection capabilities.
• Case study: Automating document classification and summarization.
• Integrating real-time NLP services with REST APIs and messaging systems.

Summary and Future Steps