Developing AI Applications with Huawei Ascend and CANN Training Course
Huawei Ascend represents a suite of AI processors engineered to deliver high-performance capabilities for both inference and training tasks.
This instructor-led live training session, available either online or onsite, is designed for AI engineers and data scientists at an intermediate level who aim to develop and refine neural network models utilizing Huawei’s Ascend platform alongside the CANN toolkit.
Upon completion of this training, participants will be equipped to:
- Establish and configure the CANN development environment.
- Create AI applications leveraging MindSpore and CloudMatrix workflows.
- Enhance performance on Ascend NPUs through the use of custom operators and tiling techniques.
- Deploy models across edge or cloud infrastructure.
Course Delivery Format
- Engaging lectures paired with interactive discussions.
- Practical application of Huawei Ascend and the CANN toolkit within sample projects.
- Guided exercises targeting model construction, training, and deployment.
Customization Opportunities
- For tailored training based on your specific infrastructure or datasets, please reach out to us to make arrangements.
Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend architecture and ecosystem
- Insights into MindSpore and CANN
- Relevant use cases and industry applications
Establishing the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project orchestration
- Verifying the environment using sample models
Model Development with MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset formatting
- Exporting models to formats compatible with Ascend
Performance Optimization on Ascend
- Operator fusion and custom kernel implementation
- Tiling strategies and AI Core scheduling
- Benchmarking and profiling utilities
Deployment Strategies
- Evaluating tradeoffs between edge and cloud deployment
- Employing the MindX SDK for deployment purposes
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Using Profiler and AiD for tracing
- Resolving runtime failures
- Tracking resource usage and throughput
Case Study and Lab Integration
- Full pipeline development using MindSpore
- Lab Exercise: Build, optimize, and deploy a model on Ascend
- Performance comparison with other platforms
Summary and Next Steps
Requirements
- A foundational understanding of neural networks and AI workflows
- Practical experience with Python programming
- Familiarity with pipelines for model training and deployment
Target Audience
- AI engineers
- Data scientists utilizing the Huawei AI stack
- ML developers working with Ascend and MindSpore
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Developing AI Applications with Huawei Ascend and CANN Training Course - Enquiry
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
Michal Maj - XL Catlin Services SE (AXA XL)
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