Apache SINGA vs JAX
Apache SINGA
singa.apache.orgJAX
docs.jax.devSide by side
| Figure | Apache SINGA | JAX |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| 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) | 8.3 (#7) |
| Installation | The site documents installation using pip, Docker, or from source, and also lists Conda as an installation option. | JAX uses the `jax` Python package and `jaxlib`, which contains compiled binaries with different builds for operating systems and accelerators. |
| Support | The project lists mailing lists, issue tracking, and a security page under its community resources. | The installation guide directs users with problems using prebuilt wheels to the JAX GitHub issue tracker. |
| What it does | Apache SINGA is a distributed deep-learning library focused on training deep-learning and machine-learning models. | JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. |
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.