MindSpore

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
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