Apache SINGA vs Apache TVM

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.

Apache TVM is a machine learning compilation framework that turns pre-trained models into deployable modules. Its optimization process can be customized in Python without rebuilding the TVM stack, and users can combine optimization passes, libraries, and code generation. It imports models from PyTorch, ONNX, and TensorFlow Lite. Target backends include CPU, GPU, Metal, ROCm, Vulkan, OpenCL, x86, ARM, and WebAssembly. A lightweight runtime can execute compiled code in JavaScript, Java, Python, and C++ on Android, iOS, Raspberry Pi, and web browsers. Users can install it from PyPI, build from source, or use Docker images; cross-compilation and RPC deployment cover ARM, x86, RISC-V, embedded systems, and accelerator devices. The default generated binary uses a minimal runtime API and limited system calls such as malloc. Apache TVM is free open-source software under Apache License 2.0. Its RPC server assumes trusted users and networks: API users can write arbitrary files and obtain full remote code execution, so it should be treated as a high-trust interface.