Apache SINGA vs PyTorch
Apache SINGA
singa.apache.orgPyTorch
pytorch.orgSide by side
| Figure | Apache SINGA | PyTorch |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.2 (#8) | 7.3 (#29) |
| Distributed training | SINGA supports data-parallel training across multiple GPUs on one node or across different nodes. | PyTorch provides asynchronous collective operations and peer-to-peer communication through Python and C++ interfaces. |
| Support | The project lists mailing lists, issue tracking, and a security page under its community resources. | The Foundation directs users with technical questions to the PyTorch discussion community. |
| What it does | Apache SINGA is a distributed deep-learning library focused on training deep-learning and machine-learning models. | PyTorch is an end-to-end machine learning framework for fast experimentation and production. |
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.