Command line

Installing the package provides a bdp-model-gate console script, intended as a pre-deployment step — after training, before promotion. Not a per-PR check.

bdp-model-gate \
  --model model.joblib \
  --data validation.csv \
  --target-col label \
  --protected protected.csv \
  --model-card model_card.json \
  --task binary \
  --metric roc_auc --min-score 0.80 \
  --output gate_report.json

Exit codes

Code	Status	Pipeline should
0	PASS	deploy
2	NEEDS_REVIEW	pause for approval
1	BLOCKED	hard fail

Three codes, not two, so a pipeline can tell deploy from ask a human.

Options

Model

Flag	Notes
--model PATH	a joblib-serialised model
--model-loader "pkg.mod:factory"	a function returning a model or fn(DataFrame) -> array

Mutually exclusive. Use --model-loader for anything joblib cannot unpickle — PyTorch checkpoints, Keras SavedModel, ONNX, a remote endpoint. Your loader does the framework import, so this package needs no deep-learning dependency.

# mypkg/serving.py
def load_scorer():
    net = torch.load("model.pt")
    net.eval()
    return lambda df: net(torch.tensor(df.values).float()).detach().numpy()

Data

Flag	Notes
--data PATH	CSV of validation data — required
--target-col NAME	ground-truth column — required
--protected PATH	CSV of protected attributes, row-aligned
--expected-loss-col NAME	column holding per-row expected loss
--latencies PATH	one latency in ms per line
--cost-per-inference FLOAT
--model-card PATH	JSON model card

Task and scoring

Flag	Notes
--task {auto,binary,multiclass,regression}	default auto, which infers and logs
--class-order "a,b,c"	ascending favourability; marks the problem ordinal
--favourable-classes "accept"	defaults to the last of --class-order
--metric NAME	see Configuration
--min-score FLOAT	higher-is-better metrics
--max-error FLOAT	error metrics — required when one is selected
--decision-threshold FLOAT	default 0.5
--average {macro,micro,weighted}	multiclass f1 / precision / recall

Other

Flag	Notes
--config PATH	JSON, YAML or TOML; CLI flags win
--output PATH	default gate_report.json
-v, --verbose	debug logging — per-check timing, what ran and why

In CI

- name: Model governance gate
  id: gate
  continue-on-error: true
  run: |
    bdp-model-gate --model model.joblib --data validation.csv \
      --target-col label --protected protected.csv \
      --model-card model_card.json --output gate_report.json

- name: Block on hard failure
  if: steps.gate.outcome == 'failure'
  run: exit 1

# exit 2 -> route to an environment with required reviewers

Ready-to-adapt GitHub Actions and Azure Pipelines examples ship in ci_examples/. Both structure it as three stages: run the gate, a manual approval gated behind exit code 2, and a deploy that runs only if the gate passed outright or was approved.