Apache SINGA
Apache-licensed distributed deep learning library focused on scalable training across GPUs and nodes.
Pick Apache SINGA if you need an open-source, distributed deep learning framework you can self-host and tune at the operator level.
Skip it if you want a hosted training platform, a no-code interface, or the breadth of integrations available around PyTorch.
Apache SINGA is a top-level Apache project that provides a distributed deep learning library for training and deploying neural networks across single or multiple GPU nodes. It builds computation graphs for automatic gradient calculation, ships memory and parameter optimizations, supports ONNX for model interoperability, and exposes a Python API documented at apache-singa.readthedocs.io. Installation is offered via Pip, Conda, Docker, or source build.
It's aimed at researchers and engineering teams who want a framework-level alternative to PyTorch or TensorFlow with first-class distributed training and database integration for model querying. Pricing isn't a factor — it's free and Apache-licensed, with a public GitHub repo and an active committer community. Reported users span universities, healthcare institutions, and enterprises including Alibaba, NetEase, and Citigroup.
Feature highlights include built-in optimizers (SGD, Adam, RMSProp), operator-level time profiling, and tooling for model serving through database integration. It's a library, not a managed service, so you'll bring your own infrastructure, MLOps glue, and GPU capacity.
SINGA is a legitimate Apache-governed deep learning framework with a real production track record at large Asian enterprises, but it sits firmly in PyTorch's shadow. Reach for it when distributed training and ONNX export matter and you're comfortable wiring up your own infrastructure.
— The AI Tool Bible editorial team
Pros
- ✅ Apache 2.0 licensed with active top-level project governance
- ✅ First-class distributed training across multi-GPU and multi-node setups
- ✅ ONNX support plus automatic gradient/computation-graph optimization
- ✅ Adopted by serious users (Alibaba, NetEase, Citigroup, universities)
Cons
- ⚠️ Smaller ecosystem and community than PyTorch or TensorFlow
- ⚠️ Library only — no managed service, hosting, or UI
- ⚠️ Requires self-managed GPU infrastructure and MLOps tooling
Use cases
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