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Course Outline
Introduction to Huawei Ascend Platform for Government
- Overview of the Ascend architecture and ecosystem, highlighting its capabilities and potential applications in public sector operations.
- Detailed review of MindSpore and CANN, focusing on their roles in enhancing efficiency and scalability for government projects.
- Exploration of use cases and industry relevance, with a particular emphasis on how these technologies can support governmental missions and services.
Setting Up the Development Environment for Government
- Instructions for installing the CANN toolkit and MindSpore to establish a robust development environment tailored for government use.
- Guidance on utilizing ModelArts and CloudMatrix for effective project orchestration, ensuring seamless integration with existing governmental IT infrastructure.
- Steps for testing the environment using sample models to validate setup and performance.
Model Development with MindSpore for Government
- Procedures for defining and training models in MindSpore, optimized for government-specific datasets and requirements.
- Techniques for creating efficient data pipelines and formatting datasets to meet the stringent standards of public sector operations.
- Methods for exporting models to an Ascend-compatible format, ensuring compatibility with governmental hardware and software ecosystems.
Performance Optimization on Ascend for Government
- Strategies for operator fusion and custom kernel development to enhance performance in government applications.
- Analysis of tiling strategies and AI Core scheduling techniques to optimize resource utilization and processing speed for public sector tasks.
- Utilization of benchmarking and profiling tools to monitor and improve the efficiency of Ascend-based solutions for government use.
Deployment Strategies for Government
- Evaluation of edge versus cloud deployment tradeoffs, considering factors such as security, latency, and cost in a governmental context.
- Instructions for using the MindX SDK to facilitate deployment, ensuring that government agencies can leverage these tools effectively.
- Integration with CloudMatrix workflows to streamline deployment processes and enhance operational efficiency for government projects.
Debugging and Monitoring for Government
- Techniques for using Profiler and AiD for tracing, enabling thorough debugging of models in a governmental setting.
- Methods for diagnosing and resolving runtime failures to ensure reliable performance of government applications.
- Approaches for monitoring resource usage and throughput to maintain optimal operational conditions in public sector environments.
Case Study and Lab Integration for Government
- Comprehensive development of a full pipeline using MindSpore, tailored to address specific government needs and challenges.
- Hands-on lab: Building, optimizing, and deploying a model on Ascend, with practical exercises designed to enhance skills for government professionals.
- Performance comparison of the developed model with other platforms, providing insights into the advantages of Ascend in governmental applications.
Summary and Next Steps for Government
Requirements
- An understanding of neural networks and artificial intelligence workflows for government applications.
- Experience with Python programming in a governmental context.
- Familiarity with model training and deployment pipelines suitable for government use.
Audience
- AI engineers working on government projects.
- Data scientists utilizing the Huawei AI stack for government initiatives.
- Machine learning developers employing Ascend and MindSpore in governmental settings.
21 Hours
Testimonials (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session
