CANN SDK for Computer Vision and NLP Pipelines Training Course
The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.
This instructor-led live training, available online or onsite, targets intermediate AI professionals seeking 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 utilizing CANN and AscendCL.
- Leverage CANN tools to convert models and integrate them into operational pipelines.
- Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
- Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.
Course Format
- Interactive lectures combined with live demonstrations.
- Hands-on labs focused on model deployment and performance profiling.
- Practical pipeline design exercises using real-world CV and NLP use cases.
Customization Options
- For customized training arrangements, please reach out to us.
Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle from training through 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.
- Utilizing 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 outputs.
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
- Proficiency in deep learning for computer vision or NLP.
- Experience with Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore.
- Fundamental understanding of model deployment and inference workflows.
Target Audience
- Practitioners in computer vision and NLP working with Huawei’s Ascend platform.
- Data scientists and AI engineers developing real-time perception models.
- Developers integrating CANN pipelines into manufacturing, surveillance, or media analytics applications.
Open Training Courses require 5+ participants.
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