Apache SINGA vs Keras
Apache SINGA
singa.apache.orgKeras
keras.ioSide by side
| Figure | Apache SINGA | Keras |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.2 (#8) | 7.5 (#19) |
| Installation | The site documents installation using pip, Docker, or from source, and also lists Conda as an installation option. | Keras installs from PyPI with pip install --upgrade keras; using Keras 3 also requires installing a backend framework. |
| Intended users | The project describes its focus as distributed training of deep-learning and machine-learning models and highlights large-scale data analytics. | Keras describes its audience as machine learning engineers and presents guides and examples for model development across common ML use cases. |
| Support | The project lists mailing lists, issue tracking, and a security page under its community resources. | The Keras site directs users to its Google Group for questions and development discussion, and GitHub issues for bug reports and feature requests. |
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.