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MMDeploy is documented at version 1.3.1 and covers getting started, converting, running, evaluating and quantizing models, building from source or Docker image, and SDK usage. Its backend support pages describe ncnn, ONNX Runtime, OpenVINO, PPLNN, SNPE, TensorRT, TorchScript, RKNN, TVM and Core ML, and its codebase support covers several OpenMMLab projects.
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Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
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Open WebUI is a self-hosted AI interface for running AI on the user's own hardware. It connects to local or cloud models including Ollama, OpenAI, Anthropic, or compatible services, and can be extended with Python. The project offers community-shared prompts, models, tools, and functions, and lists deployment options including on-premises or air-gapped setups. Installation can be done with a pip command.
LM Studio helps build, test, automate, or manage AI agents, prompts, models, and API workflows.
MMDeploy is documented at version 1.3.1 and covers getting started, converting, running, evaluating and quantizing models, building from source or Docker image, and SDK usage. Its backend support pages describe ncnn, ONNX Runtime, OpenVINO, PPLNN, SNPE, TensorRT, TorchScript, RKNN, TVM and Core ML, and its codebase support covers several OpenMMLab projects.
Odysseus is a self-hosted AI workspace for running language models on one's own hardware. It offers chat, autonomous agents, tools and MCP connections, model serving, an email assistant, deep research, model comparison and persistent memory. The project is described as local-first and privacy-first, with no telemetry.
DeepSpeed is a deep learning optimization library intended to make distributed training easy, efficient and effective. It offers system innovations including ZeRO, 3D-Parallelism, DeepSpeed-MoE and ZeRO-Infinity, and has been integrated with several open-source deep learning frameworks.
Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
