# ML-Dash

> ML-Dash is a simple, flexible SDK for ML experiment tracking and data storage — log parameters, metrics, files, and time-series tracks locally or against a remote dash.ml server.

## Overview

- [Overview](https://docs.dash.ml/index.md): ML-Dash is a simple, flexible SDK for ML experiment tracking and data storage. Log parameters, metrics, files, and time-series tracks with one API — locally or against a remote dash.ml server.

## Core

- [Getting Started](https://docs.dash.ml/getting-started.md): Get up and running with ML-Dash in under 5 minutes.
- [Experiment](https://docs.dash.ml/experiments.md): The Experiment class is the core abstraction in ML-Dash. A single instance owns one run's logs, parameters, metrics, and files.
- [Parameters](https://docs.dash.ml/parameters.md): Hyperparameters and configuration values for an experiment. Parameters are static key-value pairs set once (or merged across calls) — for time-series data, see [Metrics](/metrics).
- [Metrics](https://docs.dash.ml/metrics.md): Time-series data that changes over the course of a run: loss, accuracy, learning rate, and any custom scalars you want to chart.
- [Logs](https://docs.dash.ml/logging.md): Structured event logging with timestamps, levels, and optional metadata. For numeric series use [Metrics](/metrics); for hyperparameters use [Parameters](/parameters).
- [Files](https://docs.dash.ml/files.md): Upload and manage experiment artifacts — checkpoints, configs, results, and arbitrary blobs. Files are automatically checksummed, organized by prefix, and addressable by path.
- [CLI](https://docs.dash.ml/cli.md): The ml-dash CLI authenticates, queries, and manages projects and experiments on a remote server. Installed with the Python package:
- [API Reference](https://docs.dash.ml/api-reference.md): Complete API reference for the ML-Dash Python SDK. For tutorials and workflow examples, see [/parameters](/parameters), [/metrics](/metrics), [/files](/files), and [/complete-examples](/complete-examples).

## Advanced

- [Buffering](https://docs.dash.ml/buffering.md): ML-Dash writes are non-blocking. Logs, [metrics](/metrics), [tracks](/tracks), and [files](/files) are queued and flushed from a background daemon thread so the hot path stays fast.
- [Tracks](https://docs.dash.ml/tracks.md): Tracks are timestamp-indexed, multi-modal streams: robot poses, sensor readings, per-step state. Each entry carries a float timestamp and an arbitrary dict payload, and entries that share a timestamp on the same topic merge into one row.
- [Images](https://docs.dash.ml/images.md): saveimage() writes a numpy array directly to PNG or JPEG — no manual conversion needed. Useful for MuJoCo/PyBullet renders, RL observations, model predictions, or any HxW / HxWxC array.
- [LLM-Readable Docs](https://docs.dash.ml/llm-readable.md): Every page is available as clean markdown, plus an llms.txt index, a full-corpus dump, and an importable agent skill.

## Examples

- [Examples](https://docs.dash.ml/complete-examples.md): End-to-end, runnable examples for distinct ML-Dash use cases. Each example is self-contained — copy, paste, and run.