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Course Outline

Introduction to the Huawei Ascend Platform

  • Examination of Ascend architectural components and ecosystem structure
  • Fundamentals of MindSpore and the CANN software stack
  • Applicable use cases and their significance in sectoral contexts

Establishing the Development Environment

  • Deployment of the CANN toolkit and integration of MindSpore
  • Utilizing ModelArts and CloudMatrix for the orchestration of projects
  • Verification of environment stability using standard sample models

Model Development via MindSpore

  • Defining architectures and executing training processes within MindSpore
  • Configuration of data pipelines and formatting of datasets
  • Exporting trained models to Ascend-compatible formats

Performance Optimization for Ascend

  • Implementation of operator fusion and development of custom kernels
  • Application of tiling strategies and management of AI Core scheduling
  • Utilization of benchmarking and profiling instruments

Deployment Methodologies

  • Assessment of tradeoffs between edge and cloud deployment models
  • Execution of deployment tasks using the MindX SDK
  • Integration with CloudMatrix operational workflows

Debugging and System Monitoring

  • Application of Profiler and AiD tools for trace analysis
  • Identification and resolution of runtime failures
  • Oversight of resource consumption and throughput metrics

Case Studies and Laboratory Integration

  • Development of a complete pipeline leveraging MindSpore capabilities
  • Laboratory exercise: construction, optimization, and deployment of a model on Ascend
  • Comparative performance analysis across alternative platforms

Summary and Subsequent Actions

Requirements

  • Foundational 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
  • Machine learning developers working with Ascend and MindSpore for government operations
 21 Hours

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