Apache SINGA vs OpenVINO

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.

OpenVINO is a free, open-source toolkit for accelerating AI inference, shrinking model footprints and making more efficient use of hardware. It supports deep learning work in computer vision, large language models and generative AI, with models from PyTorch, TensorFlow, TensorFlow Lite, PaddlePaddle and ONNX. C++, Python, C and NodeJS APIs are available; the NodeJS API is described as early and actively developed. For compression, NNCF provides training-time and post-training methods including pruning, sparsity, quantization and weight compression. Models can be deployed across Intel hardware on-premises, on devices, in browsers or in the cloud. Serving options include OpenVINO Model Server, plus integrations with vLLM and Triton. Packages are listed for Windows, macOS and Linux, with the macOS package for macOS 12.6 and later. OpenVINO does not provide model encryption, decryption or authentication; its security guidance points to third-party tools for these protections. Help channels include an Intel community forum, Intel Support, GitHub issues and Stack Overflow.