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Course Outline

Introduction to the Huawei Ascend Platform

  • Overview of Ascend ecosystem and architecture
  • MindSpore and CANN overview
  • Industry relevance and use cases

Setting Up the Development Environment

  • Installing MindSpore and the CANN toolkit
  • Utilizing CloudMatrix and ModelArts for project orchestration
  • Validating the environment with sample models

Model Development with MindSpore

  • Model definition and training in MindSpore
  • Dataset formatting and data pipelines
  • Exporting models to Ascend-compatible format

Performance Optimization on Ascend

  • Operator fusion and custom kernels
  • AI Core scheduling and tiling strategy
  • Profiling and benchmarking tools

Deployment Strategies

  • Edge vs cloud deployment tradeoffs
  • Using the MindX SDK for deployment
  • Integration with CloudMatrix workflows

Debugging and Monitoring

  • Using Profiler and AiD for tracing
  • Debugging runtime failures
  • Monitoring throughput and resource usage

Case Study and Lab Integration

  • Full pipeline development using MindSpore
  • Lab: Build, optimize, and deploy a model on Ascend
  • Performance comparison with other platforms

Summary and Next Steps

Requirements

  • Understanding of AI workflows and neural networks
  • Experience with Python programming
  • Familiarity with model deployment and training pipelines

Audience

  • Data scientists leveraging the Huawei AI stack
  • AI engineers
  • ML developers using Ascend and MindSpore
 21 Hours

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