Harness Ml

Harness 2

Harness is an agent-first platform for building, evaluating, and iterating on tabular machine-learning systems through typed experiments.

Harness 2 replaces the monolithic v1 runner with five focused Python packages:

• harness-data — source ingestion, declarative transforms, and profiling
• harness-ml — task types, model families, cross-validation, caching, ensembles, and evaluation
• harness-app — workspaces, immutable experiment versions, and CLI
• harness-server — MCP interface with 17 tools and 5 resources
• harness-studio — FastAPI and React experiment dashboard

Harness 2 is the current release. The stable v1 code remains permanently available on `v1-maintenance` and at the `v1-final` tag; full incremental v2 development history is preserved on `harness2-development-history`.

Development quickstart

Requirements: Python 3.11+, [uv](https://docs.astral.sh/uv/), and Node.js 20+.

git clone https://github.com/msilverblatt/harness-ml.git
cd harness-ml
git switch harness2-development-history
uv sync --all-packages

uv run --package harness-app harness init my-project
cd my-project
uv run --package harness-app harness doctor
uv run --package harness-app harness serve --studio
# MCP clients launch packages/harness-server/src/harness/server/main.py via pmcp.

Successful experiments persist a fitted `model.bundle` containing full-data seed models, the provider DAG, ensemble, calibration, and optional conformal interval metadata. Use it from the CLI:

harness export ./model.bundle --version v003
harness predict ./scoring.csv ./predictions.parquet --version v003

Native feature importance is persisted with each version. Install `harness-ml[explain]` and call `ProductionBundle.explain(frame)` for on-demand SHAP attribution.

Run the test suites:

for package in harness-data harness-ml harness-app harness-server harness-studio; do
  uv run --package "$package" --with pytest pytest "packages/$package/t