MindSpore

Programming Forms

  • Programming Forms Overview
  • Dynamic Graph
  • Static Graph

Data Loading and Processing

  • Data Processing Overview
  • Data Sampling
  • Converting Dataset to MindRecord
  • Lightweight Data Processing
  • Supporting Python Objects in Dataset Pipeline
  • Auto Augmentation
  • Single-Node Data Cache
  • Optimizing the Data Processing

Model Building

  • Model Building Overview
  • Tensor and Parameter
  • Functional and Cell

Training Process

  • Training Process Overview
  • Advanced Encapsulation: Model
  • Optimizing Training Performance
  • Advanced Automatic Differentiation
  • Algorithm Optimization

Distributed Parallelism

  • Distributed Parallelism Overview
  • Distributed Parallel Startup Methods
  • Data Parallel
  • Semi-automatic Parallel
  • Automatic Parallel
  • Manually Parallelism
  • Parameter Server
  • Model Saving and Loading
  • Fault Recovery
  • Optimization Techniques
  • Experimental Characteristics
  • Distributed High-Level Configuration Case

Custom High-order Programming

  • Overview of Custom Higher-Order Programming
  • Custom Operators
  • Custom Parameter Initialization
  • Custom Loss Function
  • Custom Optimizer
  • Custom Fusion
  • Custom Neural Network Layers
  • Hook Programming

Training High Availability

  • Fault Recovery
  • Training Process Exit Gracefully
  • Power-off Checkpoint Preservation
  • Interconnection with third-party storage systems

Model Debugging

  • Overview of Model Debugging
  • Using Dump in the Graph Mode
  • Running Data Recorder
  • Feature Value Detection
  • Error Reporting Analysis
  • Dynamic Graph Debugging
  • ErrorMap↗

Model Tuning

  • Overview of Model Tuning
  • Graph Kernel Fusion
  • Memory Reuse
  • Ascend Optimization Engine (AOE)
  • Ascend Performance Tuning

Models Library

  • Official Models
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