MLflow Typescript SDK - Core

This is the core package of the MLflow Typescript SDK. It is a skinny package that includes the core tracing functionality and manual instrumentation.

| Package | NPM | Description | | -------------------- | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- | | mlflow-tracing | | The core tracing functionality and manual instrumentation. |

Installation

npm install mlflow-tracing

Quickstart

Start MLflow Tracking Server. If you have a local Python environment, you can run the following command:

pip install mlflow
mlflow server --backend-store-uri sqlite:///mlruns.db --port 5000

If you don't have Python environment locally, MLflow also supports Docker deployment or managed services. See Self-Hosting Guide for getting started.

Instantiate MLflow SDK in your application:

import * as mlflow from 'mlflow-tracing';

mlflow.init({
  trackingUri: 'http://localhost:5000',
  experimentId: '<experiment-id>',
});

Create a trace:

// Wrap a function with mlflow.trace to generate a span when the function is called.
// MLflow will automatically record the function name, arguments, return value,
// latency, and exception information to the span.
const getWeather = mlflow.trace(
  (city: string) => {
    return `The weather in ${city} is sunny`;
  },
  // Pass options to set span name. See https://mlflow.org/docs/latest/genai/tracing/quickstart
  // for the full list of options.
  { name: 'get-weather' },
);
getWeather('San Francisco');

// Alternatively, start and end span manually
const span = mlflow.startSpan({ name: 'my-span' });
span.end();

Documentation 📘

Official documentation for MLflow Typescript SDK can be found here.

License

This project is licensed under the Apache License 2.0.