Overview

Overall Architecture
MindSpore API Overview

Design

Technical White Paper
MindSpore Automatic Differentiation
Distributed Training Design
MindSpore IR (MindIR)
Design of Visualization↗
Glossary

Quickstart

Implementing Simple Linear Function Fitting↗
Implementing an Image Classification Application↗

Basic Concepts

DataType
Tensor
Parameter
Operators
Cell
Dataset

Data Pipeline

Quick Start of Dataset
Loading Dataset
Processing Data
Advanced Usage of Pipeline
Data Iteration

Build the Network

Constructing Single Operator Network and Multi-layer Network
Initializer
Network Parameters
Using the Process Control Statement
Parameter Passing
Loss Function
Gradient Operation
Construct Constants In the Network
Operation Overloading
Optimization Algorithms

Model Running

Configuring Running Information
Running Mode
Save and Load Models
Application of Model

Inference

Inference Model Overview
Online Inference with Checkpoint
Using Offline Model for Inference

Distributed Training

Distributed Parallel Overview
Distributed Parallel Advanced Features
Distributed Parallel Usage Example

PyNative

Debugging in PyNative Mode

Numpy

NumPy Interfaces in MindSpore

Function Debugging

Reading IR
Debugging in PyNative Mode↗
Using Dump in the Graph Mode
Custom Debugging Information
Incremental Operator Build

Performance Optimization

Enabling Mixed Precision
AutoTune
Dataset AutoTune for Dataset Pipeline
Enabling Offload for Dataset
Gradient Accumulation Algorithm
Debugging performance with Profiler↗

Advanced Features

Second Order Optimizer
Graph Kernel Fusion
Quantization Aware Training

Application

Computer Vision
Natural Language Processing
High Performance Computing
Using MindSpore on the Cloud

MindSpore

»
Application of Model
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Application of Model

Basic Use of Model
Callback Mechanism
Customize Metrics to Verify Model Evaluation Accuracy
Evaluating the Model during Training
On-Device Execution

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