API Reference¶

PaddlePaddle (PArallel Distributed Deep LEarning) is an efficient, flexible, and extensible deep learning framework, commits to making the innovation and application of deep learning technology easier.

In this version, PaddlePaddle has made many optimizations to the APIs. You can refer to the following table to understand the API directory structure and description of the latest version of PaddlePaddle.In addition, you can refer to PaddlePaddle’s GitHub for details, or read Release Notes to learn about the features of the new version.

The API directory structure of PaddlePaddle is as follows:

Directory	Functions and Included APIs
paddle.*	The aliases of commonly used APIs are reserved in the paddle root directory, which currently include all the APIs in the paddle.tensor, paddle.framework and paddle.device directories.
paddle.tensor	APIs related to tensor operations such as creating zeros, matrix operation matmul, transforming concat, computing add, and finding argmax.
paddle.framework	PaddlePaddle universal APIs and dynamic graph APIs such as no_grad, save and load.
paddle.amp	Paddle automatic mixed precision strategy, including auto_cast, GradScaler, etc.
paddle.audio	Audio API, including feature extraction and dataset operations.
paddle.autograd	Auto grad APIs, including backward, PyLayer, etc.
paddle.callbacks	Paddle log callback APIs, including ModelCheckpoint, ProgBarLogger, etc.
paddle.device	Device management related APIs, such as set_device, get_device, etc.
paddle.distributed	Distributed related basic APIs.
paddle.distributed.fleet	Distributed related high-level APIs.
paddle.distribution	Probability distribution class APIs, including various common probability distribution.
paddle.fft	Fast Fourier Transform related APIs, such as fft, fft2, etc.
paddle.geometric	APIs for Graph Learning, including message passing, graph sampling, etc.
paddle.hub	Model extension API, including list, load, help, etc.
paddle.incubate	APIs for incubating new features.
paddle.io	APIs related to data input and output such as Dataset, and DataLoader.
paddle.inference	APIs related to inference for predictions.
paddle.jit	Dynamic graph to static graph APIs, including to_static, not_to_static, save, load, etc.
paddle.linalg	Linear algebra related APIs, such as det, svd, etc.
paddle.metric	APIs related to evaluation computation such as Accuracy and Auc.
paddle.nn	Networking-related APIs such as Linear, Conv2D, CrossEntropyLoss, RNN，and ReLU, etc.
paddle.onnx	APIs related to convert paddle model to ONNX，such as export
paddld.optimizer	APIs related to optimization algorithms such as SGD, Adagrad, and Adam.
paddle.optimizer.lr	APIs related to learning rate decay, such as NoamDecay, StepDecay, PiecewiseDecay, etc.
paddle.profiler	Performance profiler APIs for the Paddle framework, providing functionality to display and analyze performance for model training and inference processes.
paddle.quantization	Quantization related APIs.
paddle.regularizer	Regularization APIs, including L1Decay, L2Decay, etc.
paddle.signal	APIs for signal processing.
paddle.sparse	The Sparse domain API.
paddle.static	Basic framework related APIs under static graph, such as Variable, Program, Executor, etc.
paddle.static.nn	Special APIs for networking under static graph such as full connect layer fc and control flow while_loop/cond.
paddle.sysconfig	System configuration APIs, such as get_include, get_lib.
paddle.text	The NLP domain API currently includes data sets related to the NLP domain, such as Imdb and Movielens.
paddle.utils	Utils APIs, including CppExtension, CUDAExtension.
paddle.vision	Vision domain APIs such as datasets Cifar10, data processing ColorJitter, and commonly used models like resnet.