Benchmark ML experiments with structured workflows, two-table storage, and SQL/DuckDB analytics.
npx skills add https://github.com/xdssio/xetrack --skill benchmark-xdssio
Please help me install this Agent Skill. Skill: benchmark Source: https://github.com/xdssio/xetrack/tree/main/skills/benchmark Command: npx skills add https://github.com/xdssio/xetrack --skill benchmark-xdssio
Benchmark ML experiments with a structured workflow and tracking infrastructure.
Use the benchmark skill to set up an end-to-end ML benchmark on your dataset.
High-intent search queries and answers about installing and using this skill.
Run reproducible ML benchmarks by utilizing a structured workflow with two-table storage and caching to compare models. This infrastructure tracks end-to-end experiments, applying parameter unpacking and error handling to ensure consistent results.
DuckDB enables integrated SQL analytics to compare ML benchmark results directly. The tracking infrastructure stores experiment data in a structured format, allowing you to query and analyze model comparisons and hyperparameter sweeps efficiently.
Set up benchmarking pipelines for hyperparameter sweeps using an end-to-end structured workflow. It handles parameter unpacking, caches intermediate results, and provides error handling to manage data processing pipelines and model comparisons reliably.
The benchmarking workflow supports optional DVC integration and a Git tagging workflow for enhanced reproducibility checks. These features help version data pipelines and track experiment phases across development environments.
Pandas and xetrack are required dependencies to track ML experiments end-to-end. They provide the foundational data structures and tracking infrastructure needed for the two-table result storage and experiment logging.
Two-table storage separates parameters from results to structure ML experiment tracking data effectively. This design satisfies reproducibility requirements, enabling precise caching, error handling, and SQL analytics for model comparisons.
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