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Course Outline

Introduction to the Huawei Ascend Platform

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

Setting Up the Development Environment

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

Model Development with MindSpore

  • Training and model definition in MindSpore
  • Dataset formatting and data pipelines
  • Converting models to Ascend-compatible formats

Performance Optimization on Ascend

  • Custom kernels and operator fusion
  • AI Core scheduling and tiling strategies
  • Profiling and benchmarking tools

Deployment Strategies

  • Evaluating trade-offs between edge and cloud deployment
  • Deploying using the MindX SDK
  • Integrating with CloudMatrix workflows

Debugging and Monitoring

  • Tracing using AiD and Profiler
  • Diagnosing runtime failures
  • Monitoring throughput and resource usage

Lab Integration and Case Study

  • Developing a complete pipeline using MindSpore
  • Lab: Deploy, optimize, and build a model on Ascend
  • Comparing performance against other platforms

Summary and Next Steps

Requirements

  • A solid grasp of AI workflows and neural networks
  • Proficiency in Python programming
  • Familiarity with model deployment and training pipelines

Target Audience

  • AI engineers
  • Data scientists utilising the Huawei AI stack
  • ML developers working with MindSpore and Ascend
 21 Hours

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