Static Graph Usage Sepecifications

Process Control Statements
Calling the Custom Class
Construct Constants In the Network
Dependency Control

Distributed Parallel

Distributed Parallelism Overview
Distributed Basic Cases
Operator-level Parallelism
Pipeline Parallel
Optimizer Parallel
Recomputation
Host&Device Heterogeneous
Parameter Server Mode
Distributed Parallel Startup Methods
Distributed Inference
Distributed High-Level Configuration Case

Custom Operator

Custom Operators (Custom-based)
MindSpore Hybrid Syntax Specification
Custom Operator Registration

Performance Optimization

Profiling↗
Sinking Mode
Compiling Performance Optimization for Static Graph Network
Enabling Graph Kernel Fusion
Incremental Operator Build
Memory Reuse

Algorithm Optimization

Gradient Accumulation Algorithm
Adaptive Gradient Summation Algorithm
Dimension Reduction Training Algorithm
Second-order Optimization

High-level Functional Programming

Automatic Vectorization (Vmap)

Data Processing

Auto Augmentation
Single-Node Data Cache
Optimizing the Data Processing

Model Inference

Inference Model Overview
Model Compression

Complex Problem Debugging

Using Dump in the Graph Mode
Ascend Optimization Engine (AOE)
Running Data Recorder
Fault Recovery

MindSpore

»
Distributed Basic Cases
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Distributed Basic Cases

Distributed Parallel Training Basic Sample (Ascend)
Distributed Parallel Training Base Sample (CPU)
Distributed Parallel Training Example (GPU)

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