MindSpore
Design Concept
Model Building and Training
Programming Forms Overview
Dynamic Graph
Static Graph
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 Overview
Tensor and Parameter
Functional and Cell
Training Process Overview
Advanced Encapsulation: Model
Optimizing Training Performance
Advanced Automatic Differentiation
Algorithm Optimization
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
Overview of Custom Higher-Order Programming
Custom Operators
Custom Parameter Initialization
Custom Loss Function
Custom Optimizer
Custom Fusion Pass
Custom Neural Network Layers
Hook Programming
Fault Recovery
Training Process Exit Gracefully
Power-off Checkpoint Preservation
Interconnection with third-party storage systems
Overview of Model Debugging
Using Dump in the Graph Mode
Running Data Recorder
Feature Value Detection
Error Reporting Analysis
Dynamic Graph Debugging
ErrorMap↗
Overview of Model Tuning
Graph Kernel Fusion
Memory Reuse
Ascend Optimization Engine (AOE)
Official Models
Building
Training
Debugging and Optimization
Model Inference
Model Migration
API
Orange Pi Development
FAQ
Release Notes
MindSpore
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Model Building and Training
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Overview of Model Debugging
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Overview of Model Debugging
