mlcommons/inference
Reference implementations of MLPerf® inference benchmarks
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A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
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Latest capture 2026-07-13 03:08
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build.gradle
java ecosystem,
3 dependencies
settings.gradle
java ecosystem,
0 dependencies
isolation-forest-onnx/build.gradle
java ecosystem,
1 dependency
isolation-forest-onnx/pyproject.toml
python ecosystem,
0 dependencies
isolation-forest-onnx/requirements.txt
python ecosystem,
5 dependencies
isolation-forest-onnx/setup.cfg
python ecosystem,
12 dependencies
isolation-forest-onnx/setup.py
python ecosystem,
0 dependencies
isolation-forest/build.gradle
java ecosystem,
8 dependencies
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Reference implementations of MLPerf® inference benchmarks
Reference implementations of MLPerf® training benchmarks
Algorithms for outlier, adversarial and drift detection
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.6, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Gemma4, Llava, Phi4, ...) (AAAI 2025).
Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
AI-native ontology engine: a Rust MCP server with tools for building, validating, querying, and reasoning over RDF/OWL ontologies. In-memory Oxigraph triple store, native OWL2-DL tableaux reasoner, SHACL validation, SPARQL, versioning. Single binary, no JVM.