This repository contains the results and code for the MLPerf™ Storage v2.0 benchmark.
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Updated
Aug 4, 2025 - Python
This repository contains the results and code for the MLPerf™ Storage v2.0 benchmark.
Open-source PyTorch AI model profiler for latency, memory, energy (NVML/RAPL), and layer-wise bottlenecks. Measure green AI / ML efficiency no fabricated joules.
La Perf is a framework for AI performance benchmarking — covering LLMs, VLMs, embeddings, with power-metrics collection.
Optuna-optimized ML methods, with scikit-learn like API
Pushback Resistance & Epistemic Stability Score - A standardized framework for quantifying sycophancy — measuring how confidently language models hold correct beliefs under social pressure.
Reproducible benchmarking framework for signal-processing algorithms and ML inference supporting real and synthetic ECG inputs.
This repository belongs to my diploma thesis "Benchmarking Machine Learning Models on Tabular Data: A Statistically Rigorous Evaluation Pipeline". In the thesis I benchmark various ML models and assess their predictive performance differences using statistical hypothesis testing.
Production continual learning in PyTorch — three scenarios, five methods, complete benchmarks.
benchmark scaled LLM inference and training
Benchmark and explain x86 CPU AI performance with scaling sweeps, deep telemetry, normalized results, charts, and HTML reports.
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