See Installation Guide to install from pre-built package or build from the source code. (Note: The intallation packages are still in pre-release state and your experience of installation may not be smooth.).
-
You can follow the quick start tutorial to learn how use PaddlePaddle step-by-step.
-
We provide five demos, including: image classification, sentiment analysis, sequence to sequence model, recommendation, semantic role labelling.
-
This system supports training deep learning models on multiple machines with data parallelism.
-
PaddlePaddle supports using either Python interface or C++ to build your system. We also use SWIG to wrap C++ source code to create a user friendly interface for Python. You can also use SWIG to create interface for your favorite programming language.
-
We sincerely appreciate your interest and contributions. If you would like to contribute, please read the contribution guide.