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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
LoD-Tensor User Guide
Prepare Data
Prepare Steps
Python Reader
Asynchronous Data Reading
Take Numpy Array as Training 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
FLAGS
cudnn
data processing
debug
device management
distributed
executor
memory management
others
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
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
BuildStrategy
CompiledProgram
cpu_places
CPUPlace
create_lod_tensor
create_random_int_lodtensor
cuda_pinned_places
cuda_places
CUDAPinnedPlace
CUDAPlace
data
DataFeedDesc
DataFeeder
default_main_program
default_startup_program
DistributeTranspiler
DistributeTranspilerConfig
embedding
ExecutionStrategy
Executor
global_scope
gradients
in_dygraph_mode
is_compiled_with_cuda
load
load_op_library
LoDTensor
LoDTensorArray
memory_optimize
name_scope
one_hot
ParallelExecutor
ParamAttr
Program
program_guard
release_memory
require_version
save
scope_guard
Tensor
Variable
WeightNormParamAttr
fluid.average
WeightedAverage
fluid.backward
append_backward
gradients
fluid.clip
ErrorClipByValue
GradientClipByGlobalNorm
GradientClipByNorm
GradientClipByValue
set_gradient_clip
Data Reader
Reader
dataset
cifar
conll05
imdb
imikolov
movielens
sentiment
uci_housing
wmt14
wmt16
fluid.data_feeder
DataFeeder
fluid.dataset
DatasetFactory
InMemoryDataset
QueueDataset
fluid.dygraph
BackwardStrategy
BatchNorm
BilinearTensorProduct
Conv2D
Conv2DTranspose
Conv3D
Conv3DTranspose
CosineDecay
Embedding
enabled
ExponentialDecay
FC
GroupNorm
GRUUnit
guard
InverseTimeDecay
Layer
LayerNorm
load_dygraph
load_persistables
NaturalExpDecay
NCE
no_grad
NoamDecay
not_support
PiecewiseDecay
PolynomialDecay
Pool2D
PRelu
prepare_context
save_dygraph
save_persistables
SpectralNorm
start_gperf_profiler
stop_gperf_profiler
to_variable
Tracer
TreeConv
fluid.executor
Executor
global_scope
scope_guard
fluid.initializer
Bilinear
BilinearInitializer
Constant
ConstantInitializer
force_init_on_cpu
init_on_cpu
MSRA
MSRAInitializer
Normal
NormalInitializer
NumpyArrayInitializer
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Uniform
UniformInitializer
Xavier
XavierInitializer
fluid.io
batch
buffered
cache
chain
compose
ComposeNotAligned
DataLoader
Fake
firstn
load
load_inference_model
load_params
load_persistables
load_vars
map_readers
multiprocess_reader
PyReader
save
save_inference_model
save_params
save_persistables
save_vars
shuffle
xmap_readers
fluid.layers
abs
accuracy
acos
adaptive_pool2d
adaptive_pool3d
add_position_encoding
affine_channel
affine_grid
anchor_generator
argmax
argmin
argsort
array_length
array_read
array_write
asin
assign
atan
auc
autoincreased_step_counter
batch_norm
beam_search
beam_search_decode
BeamSearchDecoder
bilinear_tensor_product
bipartite_match
box_clip
box_coder
box_decoder_and_assign
bpr_loss
brelu
cast
Categorical
ceil
center_loss
chunk_eval
clip
clip_by_norm
collect_fpn_proposals
concat
continuous_value_model
conv2d
conv2d_transpose
conv3d
conv3d_transpose
cos
cos_sim
cosine_decay
create_array
create_global_var
create_parameter
create_py_reader_by_data
create_tensor
crf_decoding
crop
crop_tensor
cross_entropy
ctc_greedy_decoder
cumsum
data
data_norm
Decoder
deformable_conv
deformable_roi_pooling
density_prior_box
detection_map
detection_output
diag
dice_loss
distribute_fpn_proposals
double_buffer
dropout
dynamic_decode
dynamic_gru
dynamic_lstm
dynamic_lstmp
DynamicRNN
edit_distance
elementwise_add
elementwise_div
elementwise_floordiv
elementwise_max
elementwise_min
elementwise_mod
elementwise_mul
elementwise_pow
elementwise_sub
elu
embedding
equal
exp
expand
exponential_decay
eye
fc
fill_constant
fill_constant_batch_size_like
filter_by_instag
flatten
floor
fsp_matrix
gather
gather_nd
gather_tree
gaussian_random
gaussian_random_batch_size_like
generate_mask_labels
generate_proposal_labels
generate_proposals
get_tensor_from_selected_rows
greater_equal
greater_than
grid_sampler
group_norm
gru_unit
GRUCell
hard_shrink
hard_sigmoid
hard_swish
has_inf
has_nan
hash
hsigmoid
huber_loss
IfElse
im2sequence
image_resize
image_resize_short
increment
instance_norm
inverse_time_decay
iou_similarity
is_empty
isfinite
kldiv_loss
l2_normalize
label_smooth
layer_norm
leaky_relu
less_equal
less_than
linear_chain_crf
linear_lr_warmup
linspace
load
lod_append
lod_reset
log
log_loss
logical_and
logical_not
logical_or
logical_xor
logsigmoid
lrn
lstm
lstm_unit
LSTMCell
margin_rank_loss
match_matrix_tensor
matmul
maxout
mean
mean_iou
merge_selected_rows
mse_loss
mul
multi_box_head
multiclass_nms
multiclass_nms2
multiplex
MultivariateNormalDiag
natural_exp_decay
nce
noam_decay
Normal
not_equal
npair_loss
one_hot
ones
ones_like
pad
pad2d
pad_constant_like
piecewise_decay
pixel_shuffle
polygon_box_transform
polynomial_decay
pool2d
pool3d
pow
prelu
Print
prior_box
prroi_pool
psroi_pool
py_func
py_reader
random_crop
range
rank
rank_loss
read_file
reciprocal
reduce_all
reduce_any
reduce_max
reduce_mean
reduce_min
reduce_prod
reduce_sum
relu
relu6
reorder_lod_tensor_by_rank
reshape
resize_bilinear
resize_nearest
resize_trilinear
retinanet_detection_output
retinanet_target_assign
reverse
rnn
RNNCell
roi_align
roi_perspective_transform
roi_pool
round
row_conv
rpn_target_assign
rsqrt
sampled_softmax_with_cross_entropy
sampling_id
scale
scatter
scatter_nd
scatter_nd_add
selu
sequence_concat
sequence_conv
sequence_enumerate
sequence_expand
sequence_expand_as
sequence_first_step
sequence_last_step
sequence_mask
sequence_pad
sequence_pool
sequence_reshape
sequence_reverse
sequence_scatter
sequence_slice
sequence_softmax
sequence_unpad
shape
shard_index
shuffle_channel
sigmoid
sigmoid_cross_entropy_with_logits
sigmoid_focal_loss
sign
similarity_focus
sin
size
slice
smooth_l1
soft_relu
softmax
softmax_with_cross_entropy
softplus
softshrink
softsign
space_to_depth
spectral_norm
split
sqrt
square
square_error_cost
squeeze
ssd_loss
stack
stanh
StaticRNN
strided_slice
sum
sums
swish
Switch
tanh
tanh_shrink
target_assign
teacher_student_sigmoid_loss
temporal_shift
tensor_array_to_tensor
thresholded_relu
topk
transpose
tree_conv
unfold
Uniform
uniform_random
uniform_random_batch_size_like
unique
unique_with_counts
unsqueeze
unstack
var_conv_2d
warpctc
where
While
yolo_box
yolov3_loss
zeros
zeros_like
fluid.metrics
Accuracy
Auc
ChunkEvaluator
CompositeMetric
DetectionMAP
EditDistance
MetricBase
Precision
Recall
fluid.nets
glu
img_conv_group
scaled_dot_product_attention
sequence_conv_pool
simple_img_conv_pool
fluid.optimizer
Adadelta
AdadeltaOptimizer
Adagrad
AdagradOptimizer
Adam
Adamax
AdamaxOptimizer
AdamOptimizer
DecayedAdagrad
DecayedAdagradOptimizer
DGCMomentumOptimizer
Dpsgd
DpsgdOptimizer
ExponentialMovingAverage
Ftrl
FtrlOptimizer
LambOptimizer
LarsMomentum
LarsMomentumOptimizer
LookaheadOptimizer
ModelAverage
Momentum
MomentumOptimizer
PipelineOptimizer
RecomputeOptimizer
RMSPropOptimizer
SGD
SGDOptimizer
fluid.profiler
cuda_profiler
profiler
reset_profiler
start_profiler
stop_profiler
fluid.regularizer
L1Decay
L1DecayRegularizer
L2Decay
L2DecayRegularizer
fluid.transpiler
DistributeTranspiler
DistributeTranspilerConfig
HashName
memory_optimize
release_memory
RoundRobin
fluid.unique_name
generate
guard
switch
FLAGS
cudnn
data processing
debug
device management
distributed
executor
memory management
others
Server-side Deployment
»
Advanced User Guides
»
Deploy Inference Model
»
Server-side Deployment
View page source
Server-side Deployment
¶
PaddlePaddle Fluid provides C++ API to support deployment and release of trained models.
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