#2525th of 37 in ML frameworks and libraries
paddle
Deep learning framework with dynamic/static graphs and automatic parallel training
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- Stars
- 24.1k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- C++
Missed the latest GitHub check; these facts may be older.
Overview
PaddlePaddle is a C++ deep learning framework that covers training and inference of large models in one codebase. It supports dynamic and static graphs, derives distributed parallel strategies from single-card code with tensor partition annotations, and offers high-order differentiation for scientific computing plus a neural network compiler. A pluggable interface adapts it to multiple chip types.
Who it is for: ML engineers training large or scientific models, including on non-NVIDIA chips
Strengths
- Automatic parallelism from minimal tensor partition annotations on single-card code
- Same framework handles both large-model training and inference
- High-order autodiff, complex numbers and Fourier transforms for scientific computing
- Pluggable multi-chip adaptation layer and a neural network compiler
Weaknesses
- README gives no hardware requirements or supported Python versions
- README refers to both 3.2 and 3.3, so the current feature set is unclear
- No Docker or compose files detected in the repository
- Practice tutorials link to the Chinese docs, so English coverage may be thinner
What it needs
- GPU optional
- Models: dynamic graph, static graph, large models, generative models, scientific computing models
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