Apache SINGA vs fastai
Apache SINGA
singa.apache.orgfastai
fast.aiSide by side
| Figure | Apache SINGA | fastai |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.2 (#8) | 8.1 (#10) |
| Installation | The site documents installation using pip, Docker, or from source, and also lists Conda as an installation option. | The documentation says to install fastai on a machine with `pip install fastai` and recommends installing PyTorch first. |
| License | The SINGA history page says the project is released under Apache License Version 2.0. | The fastai GitHub repository identifies its license as Apache-2.0. |
| Support | The project lists mailing lists, issue tracking, and a security page under its community resources. | The fast.ai forums include a category for help installing and using the fastai library for users at any level. |
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.