Reference

SDK & CLI

Track experiments, drive compute and query your lakehouse from Python, Go, or the terminal.

Python, drop-in MLflow

Already use MLflow? Change one import. polnor.mlflow mirrors the MLflow tracking API and logs to your workspace.

from polnor import mlflow

with mlflow.start_run(experiment="churn-v3"):
    mlflow.log_param("lr", 3e-4)
    for step, loss in train():
        mlflow.log_metric("loss", loss, step=step)
    mlflow.log_artifact("model.pt")

# install
pip install polnor

Supports start_run, log_metric(s), log_param, log_artifact, set_tag, autolog and get_metric_history. Artifacts upload to your own S3 via presigned URLs.

Go SDK

A typed client for the REST API, ideal for platform automation and CI.

import "github.com/polnor/polnor/pkg/sdk"

c := sdk.New("https://api.polnor.net", token)
runs, _ := c.Runs.List(ctx, sdk.RunFilter{Status: "running"})

CLI

One binary for auth, compute, databases, git and SQL.

# install (macOS / Linux)
curl -fsSL https://polnor.net/install.sh | sh

polnor auth login --workspace acme
polnor compute list
polnor sql "SELECT * FROM acme.sales.orders LIMIT 10"
polnor jobs run training-pipeline --follow

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