ML-Dash

Getting Started

Get up and running with ML-Dash in under 5 minutes.

Installation

bash
pip install ml-dash

Your First Experiment (Local)

Local mode stores everything on your filesystem — no account, no network, perfect for trying things out.

python
from ml_dash import Experiment

# Prefix format: owner/project/experiment-name
with Experiment(
    prefix="alice/tutorial/my-first-experiment",
    dash_root=".dash",
).run as exp:
    exp.log("Training started", level="info")

    exp.params.set(
        learning_rate=0.001,
        batch_size=32,
        epochs=10,
    )

    for epoch in range(10):
        loss = 1.0 - epoch * 0.08
        exp.metrics("train").log(loss=loss, epoch=epoch)

    exp.log("Training completed", level="info")

Your data lives in .dash/alice/tutorial/my-first-experiment/:

.dash/
└── alice/                              # owner
    └── tutorial/                       # project
        └── my-first-experiment/        # experiment
            ├── logs/logs.jsonl
            ├── parameters/parameters.json
            └── metrics/train/data.jsonl

Your First Experiment (Remote)

Ready to sync to the ML-Dash server? Authenticate once, then pass a dash_url.

1. Authenticate

bash
ml-dash login

This opens your browser for OAuth2 and stores a token in your system keychain.

2. Run with dash_url

python
from ml_dash import Experiment

with Experiment(
    prefix="alice/my-project/training-run",
    dash_url="https://api.dash.ml",  # token auto-loaded from keychain
).run as exp:
    exp.log("Running on remote server", level="info")
    exp.params.set(learning_rate=0.001)

    for epoch in range(10):
        loss = 1.0 - epoch * 0.08
        exp.metrics("train").log(loss=loss, epoch=epoch)

The API is identical — only the constructor args change.

Next Steps

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

Install the docs as a skill

These docs ship as an Agent Skill so your agent can answer ML-Dash questions accurately — without you pasting context. Install it once and Claude loads it on demand.

Claude Code — this project only (drop it in the project's skills dir):

bash
curl -L https://docs.dash.ml/skills/dash-docs.zip -o dash-docs.zip
unzip dash-docs.zip -d .claude/skills/ && rm dash-docs.zip

Claude Code — every project (install under your home config):

bash
curl -L https://docs.dash.ml/skills/dash-docs.zip -o dash-docs.zip
unzip dash-docs.zip -d ~/.claude/skills/ && rm dash-docs.zip

The skill is dash-docs/ with a SKILL.md and one markdown reference file per docs page. It's regenerated on every deploy, so it never drifts from the site.

No install needed for one-off questions. Point any agent at https://docs.dash.ml/llms.txt (an index) or https://docs.dash.ml/llms-full.txt (the whole site in one file). See LLM-Readable Docs for all the ways to consume these docs as markdown.