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Course Outline

Introduction to the Huawei Ascend Platform

  • Overview of the Ascend architecture and ecosystem
  • Introduction to MindSpore and CANN
  • Industry use cases and relevance

Establishing the Development Environment

  • Installation of the CANN toolkit and MindSpore
  • Utilizing ModelArts and CloudMatrix for project orchestration
  • Environment validation using sample models

Model Development with MindSpore

  • Defining and training models in MindSpore
  • Managing data pipelines and dataset formatting
  • Converting models to Ascend-compatible formats

Performance Optimization on Ascend

  • Operator fusion and custom kernels
  • Tiling strategies and AI Core scheduling
  • Benchmarking and profiling tools

Deployment Strategies

  • Trade-offs between edge and cloud deployment
  • Employing the MindX SDK for deployment
  • Integration 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

  • End-to-end pipeline development using MindSpore
  • Lab: Constructing, optimizing, and deploying a model on Ascend
  • Performance comparison with alternative platforms

Summary and Next Steps

Requirements

  • Knowledge 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
 21 Hours

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