Design

MindSpore Design Overview
TENSOR VIEWS
Functional and Object-Oriented Fusion Programming Paradigm
Combination of Dynamic and Static Graphs
Static Graph Dynamic Shape
Distributed Parallel Native
High Performance Data Processing Engine
Full-scenarios Unified Architecture
Graph-Kernel Fusion Acceleration Engine
Third-Party Hardware Interconnection
Glossary

Models

Official Models

API

mindspore
mindspore.nn
mindspore.ops
mindspore.ops.primitive
mindspore.mint
mindspore.amp
mindspore.train
mindspore.communication
mindspore.communication.comm_func
mindspore.common.initializer
mindspore.hal
mindspore.dataset
mindspore.dataset.transforms
mindspore.mindrecord
mindspore.nn.probability
mindspore.rewrite
mindspore.multiprocessing
mindspore.boost
mindspore.numpy
mindspore.scipy
mindspore.experimental

API Mapping

PyTorch and MindSpore API Mapping Table

Migration Guide

Overview of Migration Guide
Environment Preparation
Model Analysis and Preparation
Network Constructing Comparison
Debugging and Tuning
Network Migration Debugging Example
FAQs

Syntax Support

Static Graph Syntax Support
Static Graph Syntax - Operators
Static Graph Syntax - Python Statements
Static Graph Syntax - Python Built-in Functions
Tensor Index Support

Environment Variables

Environment Variables

FAQ

Installation
Data Processing
Implement Problem
Network Compilation
Operators Compile
Performance Tuning
Precision Tuning
Distributed Parallel
Inference
Feature Advice

RELEASE NOTES

Release Notes

MindSpore

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mindspore.Tensor.lerp
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mindspore.Tensor.lerp

Tensor.lerp(end, weight)[source]: For details, please refer to mindspore.ops.lerp().

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