Developing AI Applications with Huawei Ascend and CANN Training Course
Huawei Ascend offers a family of AI processors engineered for high-performance inference and training tasks.
This instructor-led, live training session, available either online or onsite, is designed for intermediate-level AI engineers and data scientists aiming to develop and optimize neural network models using Huawei’s Ascend platform alongside the CANN toolkit.
Upon completing this training, participants will be equipped to:
- Set up 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 into edge or cloud environments.
Course Format
- Interactive lectures coupled with group discussions.
- Practical application of Huawei Ascend and the CANN toolkit within sample applications.
- Guided exercises centered on model construction, training, and deployment.
Course Customization Options
- For customized training tailored to your specific infrastructure or datasets, please reach out to us to arrange a session.
Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and ecosystem
- Introduction to MindSpore and CANN
- Real-world use cases and industry relevance
Setting Up the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project orchestration
- Validating the environment using sample models
Model Development with MindSpore
- Defining and training models in MindSpore
- Constructing data pipelines and formatting datasets
- Exporting models to an Ascend-compatible format
Performance Optimization on Ascend
- Implementing operator fusion and custom kernels
- Employing tiling strategies and AI Core scheduling
- Leveraging benchmarking and profiling tools
Deployment Strategies
- Evaluating trade-offs between edge and cloud deployment
- Utilizing the MindX SDK for deployment
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Using Profiler and AiD for tracing
- Resolving runtime failures
- Monitoring resource usage and throughput
Case Study and Lab Integration
- Executing full pipeline development using MindSpore
- Lab Activity: Build, optimize, and deploy a model on Ascend
- Comparing performance against other platforms
Summary and Next Steps
Requirements
- A solid understanding of neural networks and AI workflows
- Proficiency in Python programming
- Familiarity with model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists utilizing the Huawei AI stack
- ML developers employing Ascend and MindSpore
Need help picking the right course?
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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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