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AutoDL
develop(Fluid)
1.5(Fluid)
1.4(Fluid)
1.3(Fluid)
1.2(Fluid)
1.1(Fluid)
1.0(Fluid)
0.15.0(Fluid)
0.14.0(Fluid)
0.13.0(Fluid)
0.12.0(v2)
0.11.0(v2)
0.10.0(v2)
中文(简)
English(En)
Beginner’s Guide
Installation Manuals
Install on Ubuntu
Install on CentOS
Install on MacOS
Installation on Windows
Compile From Source Code
Compile on Ubuntu from Source Code
Compile on CentOS from Source Code
Compile on MacOS from Source Code
Compile on Windows from Source Code
Appendix
Basic Deep Learning Models
Linear Regression
Recognize Digits
Image Classification
Word Vector
Recommender System
Sentiment Analysis
Label Semantic Roles
Machine Translation
Guide to Fluid Programming
User Guides
Basic Concepts
LoD-Tensor User Guide
Prepare Data
Take Numpy Array as Training Data
Python Reader
Use PyReader to read training and test data
Set up Simple Model
Train Neural Networks
Single-node training
Evaluate model while training
Multi-node Training
Quick Start with Distributed Training
Manual for Distributed Training with Fluid
Distributed Training on Baidu Cloud
Save, Load Models or Variables & Incremental Learning
Model Evaluation and Debugging
Model Evaluation
VisualDL Tools
Introduction to VisualDL Toolset
VisualDL user guide
Advanced User Guides
Design Principles of Fluid
Deploy Inference Model
Server-side Deployment
Install and Compile C++ Inference Library
Introduction to C++ Inference API
Use Paddle-TensorRT Library for inference
Performance Profiling for TensorRT Library
Model Inference on Windows
Mobile Deployment
Write New Operators
How to write a new operator
Notes on operator development
Performance Profiling and Optimization
Tune CPU performance
Heap Memory Profiling and Optimization
How to use timeline tool to do profile
How to contribute codes to Paddle
Guide of local development
Guide of submitting PR to Github
How to contribute documentation
Best Practice
Best practices of distributed training on CPU
API Reference
API Quick Search
Basic Concept
Neural Network Layer
Convolution
Pooling
Image Detection
Sequence
Mathematical operation
Activation Function
Loss function
Data input and output
Control Flow
Sparse update
Feed training/inference data with DataFeeder
Learning rate scheduler
Tensor
Complex Networks
Optimizer
Back Propagation
Metrics
Save and Load a Model
Inference Engine
Video Memory Optimization
Executor
Parallel Executor
CompiledProgram
Model Parameters
Distributed Training
Synchronous Distributed Training
Asynchronous Distributed Training
Training of Models with Large Scale Sparse Features
Preparing Data Reader for Distributed Training
fluid
fluid.average
fluid.backward
fluid.clip
Data Reader
dataset
fluid.data_feeder
fluid.dataset
fluid.dygraph
fluid.executor
fluid.initializer
fluid.io
fluid.layers
control_flow
detection
io
learning_rate_scheduler
metric_op
nn
ops
tensor
fluid.metrics
fluid.nets
fluid.optimizer
fluid.profiler
fluid.recordio_writer
fluid.regularizer
fluid.transpiler
fluid.unique_name
FLAGS
cudnn
data processing
debug
device management
distributed
executor
memory management
others
Mobile Deployment
»
Advanced User Guides
»
Deploy Inference Model
»
Mobile Deployment
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Mobile Deployment
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