Design

MindSpore Design Overview
Programming Paradigm
Functional Differential Programming
MindSpore IR (MindIR)
Full-scenarios Unification
Combination of Dynamic and Static Graphs
Third-Party Hardware Interconnection
Distributed Training Design
Graph-Kernel Fusion Acceleration Engine
High Performance Data Processing Engine
Glossary

Models

Official Models

API

mindspore
mindspore.nn
mindspore.ops
mindspore.ops.primitive
mindspore.amp
mindspore.train
mindspore.communication
mindspore.common.initializer
mindspore.dataset
mindspore.dataset.transforms
mindspore.mindrecord
mindspore.nn.probability
mindspore.rewrite
mindspore.boost
mindspore.numpy
mindspore.scipy

API Mapping

PyTorch and MindSpore API Mapping Table
TensorFlow and MindSpore API Mapping Table

Migration Guide

Overview of Migration Guide
Environment Preparation and Information Acquisition
Model Analysis and Preparation
Constructing MindSpore Network
Debugging and Tuning
Network Migration Debugging Example
FAQs

Syntax Support

Static Graph Syntax Support
Tensor Index Support

FAQ

Installation
Data Processing
Implement Problem
Network Compilation
Operators Compile
Migration from a Third-party Framework
Performance Tuning
Precision Tuning
Distributed Parallel
Inference
Feature Advice

RELEASE NOTES

Release Notes

MindSpore

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mindspore.Tensor.logical_and
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mindspore.Tensor.logical_and

Tensor.logical_and(other)[source]: For details, please refer to mindspore.ops.logical_and().

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