Apache SINGA vs TensorFlow
Apache SINGA
singa.apache.orgTensorFlow
tensorflow.orgSide by side
| Figure | Apache SINGA | TensorFlow |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.2 (#8) | 7.5 (#22) |
| Support | The project lists mailing lists, issue tracking, and a security page under its community resources. | TensorFlow directs users to its issue tracker, release notes, Stack Overflow, community forum, and announcement mailing list. |
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.
TensorFlow
TensorFlow is a free, open-source platform for creating machine learning models and running them in different environments. Model-building options include the high-level Keras API, eager execution, and distributed training tools. Models can be deployed on servers, edge devices, and the web: TFX supports production pipelines, TensorFlow Lite supports mobile and edge inference, and TensorFlow.js covers JavaScript environments. The ecosystem also includes LiteRT, tf.data, TensorFlow Datasets, and TensorBoard. TensorFlow provides resources addressing fairness, interpretability, privacy, and security in machine learning workflows, including TF Privacy for privacy-focused training and TF Federated for federated learning. Supported 64-bit environments include Ubuntu, Windows, and macOS; WSL2 GPU support is marked experimental. TensorFlow's install guide states that macOS has no GPU support. Google Colab can run TensorFlow tutorials in browser-based Jupyter notebooks without installation or setup. The API and reference implementation were released under the Apache 2.0 license in November 2015. Listed platforms include Android, iOS, Linux, macOS, self-hosted, web, and Windows.