Overview

Overall Architecture
MindSpore API Overview

Quickstart

Implementing Simple Linear Function Fitting
Implementing an Image Classification Application
Hands-on Installation and Experience

Basic Concepts

dtype
Tensor
Operators
Cell

Numpy

NumPy Interfaces in MindSpore

Execution Management

Configuring Running Information

Data Pipeline

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

Build the Network

Initialization of Network Parameters
Updating Network Parameters
Model Layers
Loss Function
Optimization Algorithms
Building a Customized Network
Common Network Components

Train Models

Running Mode
Callback Mechanism
Saving Models
Loading a Model for Inference and Transfer Learning
Advanced Usage of Training

Inference

Inference Model Overview
Inference on the Ascend 910 AI processor
Inference on Ascend 310
Inference on a GPU
Inference on a CPU

Distributed Training

Distributed Training Overview
Parallel Distributed Training (Ascend)
Distributed Parallel Training (GPU)
Pipeline Parallelism Application
Applying Host&Device Hybrid Training
Training with Parameter Server
Saving and Loading Models in Hybrid Parallel Mode
Distributed Inference With Multi Devices
Parallel Distributed Training Interfaces

Function Debugging

Debugging in PyNative Mode
Using Dump in the Graph Mode
Custom Debugging Information
Evaluating the Model during Training
Incremental Operator Build

Performance Optimization

Optimizing the Data Processing
Enabling Mixed Precision
Enabling Graph Kernel Fusion
Applying a Gradient Accumulation Algorithm
Applying Quantization Aware Training
Applying Post Training Quantization

Application

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

MindSpore

»
MindSpore Lite Quantization
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MindSpore Lite Quantization

See More: https://www.mindspore.cn/lite/docs/en/r1.3/use/post_training_quantization.html

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