lightweight MCP server for ClearML
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Updated
Jun 16, 2026 - Python
lightweight MCP server for ClearML
Research-first machine learning experiment tracker for comparing model metrics, scalar curves, artifacts, and experiment lineage.
A lightweight Python library for reproducible computational experiments with an ultra-simple, smart API. From idea to insight in under 5 minutes, with zero configuration.
An application of the WhizML codebase for an analysis of cardiovascular disease risk.
MediaPipe face landmarker, detector, and recognition experiments.
An application of the WhizML codebase for an analysis of Walmart weekly sales.
A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.
AutoResearch Practice Kit — Generic template for applying Karpathy's autonomous experimentation loop to any domain
YAML-driven phase-chained hyperparameter sweeps with Optuna: each phase's winner is set for all following sweeps [of other params]
Lightweight ML experiment tracking backend built with FastAPI, PostgreSQL, SQLAlchemy, and Alembic.
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