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
- Sinking Mode
- Vectorization Acceleration Interface Vmap
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

MindSpore

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Model Building and Training »
Optimizing Training Performance
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Optimizing Training Performance

Sinking Mode
Vectorization Acceleration Interface Vmap

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