Apache SINGA
singa.apache.org
What it is
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.
Spec sheet
- What it does
- Apache SINGA is a distributed deep-learning library focused on training deep-learning and machine-learning models.Read 8 Oct 2026 · singa.apache.org
- Intended users
- The project describes its focus as distributed training of deep-learning and machine-learning models and highlights large-scale data analytics.Read 8 Oct 2026 · singa.apache.org
- Support
- The project lists mailing lists, issue tracking, and a security page under its community resources.Read 8 Oct 2026 · singa.apache.org
- License
- The SINGA history page says the project is released under Apache License Version 2.0.Read 8 Oct 2026 · singa.apache.org
- Database integration
- The project says models trained with SINGA can be queried in an RDBMS.Read 8 Oct 2026 · singa.apache.org
- Distributed training
- SINGA supports data-parallel training across multiple GPUs on one node or across different nodes.Read 8 Oct 2026 · singa.apache.org
- GPU support
- The installation guide documents GPU packages using CUDA and cuDNN, and Docker images for Nvidia GPUs.Read 8 Oct 2026 · singa.apache.org
- Healthcare examples
- The project announced curated model examples for diabetic retinopathy classification, malaria detection, and thyroid eye disease detection.Read 8 Oct 2026 · singa.apache.org
- Installation
- The site documents installation using pip, Docker, or from source, and also lists Conda as an installation option.Read 8 Oct 2026 · singa.apache.org
- Model zoo
- The site says the repository and Google Colab provide domain-specific deep-learning models, including healthcare and science models.Read 8 Oct 2026 · singa.apache.org
- ONNX integration
- SINGA supports loading ONNX models and saving models defined with its APIs in ONNX format.Read 8 Oct 2026 · singa.apache.org
- Python versions
- The pip installation page says SINGA works with Python 3.9, 3.10, and 3.11.Read 8 Oct 2026 · singa.apache.org
- Security reporting
- The Apache Security Team asks that potential vulnerabilities in Apache projects be reported privately first and publishes project advisories.Read 8 Oct 2026 · apache.org
- Training optimizers
- SINGA lists support for stochastic gradient descent with momentum, Adam, RMSProp, and AdaGrad.Read 8 Oct 2026 · singa.apache.org
Plans and prices
Apache SINGA Free Apache License 2.0 · Distributed deep-learning library Read 8 Oct 2026 · singa.apache.org
Strong points and limits
Strong points
- Free plan under Apache License 2.0
- Distributed training across GPUs and nodes
- Supports Python, C++, and ONNX
- Multiple installation methods, including pip and Docker
- Runs on Linux, macOS, and Windows
Limits
- No free trial is listed
- GPU use requires CUDA and cuDNN packages
- Training mode is listed as local
- Deployment target is on-premises
Who it suits
It suits teams training models locally or across distributed GPU setups, especially those using Python, C++, ONNX, or on-premises deployment. Healthcare and science practitioners may use its listed domain-specific model examples.
Verdict
Apache SINGA specifies a free, locally trained library with distributed GPU support and clear Python, C++, and ONNX options. Its fit depends on needing on-premises deployment and the documented installation and GPU stack.
Where Specifiction places it
- Deep Learning Software#8 of 358.2