Apache SINGA vs Ludwig

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.

Ludwig is an open-source deep learning framework for building, fine-tuning, and deploying models across data types including tabular, text, image, audio, time series, and geospatial data. Users describe preprocessing, model components, training, and hyperparameter optimization in a validated YAML configuration instead of writing training loops. It supports multimodal and multi-task models, plus LLM fine-tuning methods including SFT, DPO, KTO, ORPO, and GRPO, with parameter-efficient options such as LoRA and QLoRA. Training can scale through Ray using DDP, FSDP, or DeepSpeed; Kubernetes and KubeRay are also listed. Hyperparameter optimization integrates Ray Tune and Optuna, with SQLite or PostgreSQL persistence. Models can be served as REST APIs or exported to SafeTensors, ONNX, or torch.export. The framework also offers experiment tracking integrations, generated reports and visualizations, and explainability features. Ludwig is free under the Apache 2.0 License, supports Python, and runs on Linux, macOS, Windows, or self-hosted infrastructure. The FAQ notes that Unsloth may be faster for users focused only on LLM fine-tuning and maximum throughput.