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 targeted at 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 capable of:
- Deploying and optimizing computer vision (CV) and NLP models using CANN and AscendCL.
- Utilizing CANN tools to convert models and seamlessly integrate them into active pipelines.
- Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
- Constructing real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.
Course Format
- Interactive lectures combined with live demonstrations.
- Practical hands-on labs focusing on model deployment and performance profiling.
- Live pipeline design exercises using real-world CV and NLP use cases.
Course Customization Options
- For customized training arrangements, please contact us directly.
Course Outline
Introduction to CV/NLP Deployment with CANN
- Understanding the AI model lifecycle from training to deployment.
- Key performance considerations 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-based tasks.
- Utilizing 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.
- Handling postprocessing: bounding boxes, classification scores, and text output.
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools.
- Reducing latency through mixed-precision processing and batch tuning.
- Managing memory and compute resources for streaming tasks.
Computer Vision Use Cases
- Case study: Object detection for smart surveillance systems.
- Case study: Visual quality inspection in manufacturing environments.
- Building live video analytics pipelines on Ascend 310 hardware.
Natural Language Processing (NLP) Use Cases
- Case study: Sentiment analysis and intent detection.
- Case study: Document classification and summarization.
- Integrating real-time NLP with REST APIs and messaging systems.
Summary and Next Steps
Requirements
- Familiarity with deep learning techniques for computer vision or NLP.
- Experience using Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore.
- Basic understanding of model deployment and inference workflows.
Audience
- Practitioners working with Huawei’s Ascend platform for computer vision and NLP.
- Data scientists and AI engineers developing real-time perception models.
- Developers integrating CANN pipelines within manufacturing, surveillance, or media analytics sectors.
Open Training Courses require 5+ participants.
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Provisional Upcoming Courses (Require 5+ participants)
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