Apache SINGA vs PaddlePaddle
Apache SINGA
singa.apache.orgPaddlePaddle
paddlepaddle.org.cnSide by side
| Figure | Apache SINGA | PaddlePaddle |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.2 (#8) | 7.4 (#24) |
| Distributed training | SINGA supports data-parallel training across multiple GPUs on one node or across different nodes. | The guides include distributed training with PaddlePaddle. |
| GPU support | The installation guide documents GPU packages using CUDA and cuDNN, and Docker images for Nvidia GPUs. | The package appendix lists NVIDIA GPU architectures through Blackwell and CUDA package options through CUDA 13.0. |
| Installation | The site documents installation using pip, Docker, or from source, and also lists Conda as an installation option. | The installation guide offers pip, Docker, and source compilation methods. |
| Intended users | The project describes its focus as distributed training of deep-learning and machine-learning models and highlights large-scale data analytics. | The documentation recommends pip installation for users who only need to use PaddlePaddle and source compilation for developers who need to develop the framework. |
Plans
In their own words
Apache SINGA
Apache SINGA is a free distributed deep-learning library for training deep-learning and machine-learning models. Its listed training mode is local, with data-parallel training across multiple GPUs on one node or across multiple nodes. GPU packages use CUDA and cuDNN, and Docker images are available for Nvidia GPUs. Installation options include pip, Docker, Conda, and source builds; pip documentation covers Python 3.9, 3.10, and 3.11. SINGA supports Python and C++, loads ONNX models, and can save models defined through its APIs in ONNX format. Optimizers listed include momentum-based stochastic gradient descent, Adam, RMSProp, and AdaGrad. Models can be queried in an RDBMS. The repository and Google Colab provide domain-specific models, including healthcare and science examples. It runs on Linux, macOS, Windows, and self-hosted environments, with on-premises deployment and Apache License 2.0 licensing.
PaddlePaddle
PaddlePaddle has no write-up on Specifiction yet; its figures are above.