MindSpore

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
  • Loss Function
  • Gradient Operation
  • Parameter Passing
  • 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

Advanced Features

  • Second Order Optimizer
  • Applying Quantization Aware Training

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
  • Enabling Graph Kernel Fusion
  • Enabling AutoTune
  • Applying a Gradient Accumulation Algorithm
  • Debugging performance with Profiler↗

Application

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