ML-Dash Documentation

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.

Installation

pip install ml-dash

Quick Start

from ml_dash import Experiment

with Experiment(prefix="my-user/my-project/exp1", dash_root=".dash").run as exp:
    exp.params.set(learning_rate=0.001, batch_size=32)

    for epoch in range(10):
        loss = train_one_epoch()
        exp.metrics("train").log(loss=loss, epoch=epoch)

Swap dash_root=".dash" for dash_url="https://api.dash.ml" (after ml-dash login) to sync to a remote server. See Getting Started for both modes in detail.

Using Claude Code? Install the plugin for in-editor help: /plugin marketplace add fortyfive-labs/ml-dash then /plugin install ml-dash@ml-dash.

Documentation

Core

• Getting Started — install, auth, first experiment
• Experiments — lifecycle and configuration
• Parameters — hyperparameter logging
• Metrics — scalar metrics and batching
• Logging — text logs and console capture
• Files — artifacts and uploads
• CLI Commands — ml-dash command reference
• API Reference — full Python API
• Complete Examples — end-to-end scripts

Advanced

• Background Buffering — non-blocking I/O with auto-batching
• Tracks — time-series data for robotics and RL
• Images — numpy to PNG/JPEG conversion

Links

• GitHub: https://github.com/fortyfive-labs/ml-dash
• PyPI: https://pypi.org/project/ml-dash/
• Dashboard: https://dash.ml