Examples

Complete scripts you can copy and run. Each one writes to .dash/ in local mode, so you don't need an account. Add dash_url="https://api.dash.ml" to the Experiment to send the run to dash.ml as well.

Simple Training →

A minimal loop with parameters, per-epoch metrics, and logs.

PyTorch MNIST →

A full PyTorch run, from config to the best and final checkpoints.

Hyperparameter Search →

A grid sweep, one experiment per configuration under one project.

Comparing Experiments →

Train several architectures and rank their results.

Logging & Debugging →

Use log levels and metadata to find out what went wrong in a run.

Tutorial scripts

The ml-dash repository has a numbered set of runnable tutorials in examples/tutorials/:

bash

git clone https://github.com/fortyfive-labs/ml-dash
cd ml-dash/examples/tutorials

python 01_basic_experiment.py
python 02_logging_example.py
python 03_parameters_example.py
python 04_metrics_example.py
python 05_files_example.py
python 06_complete_training.py
python 07_project_root_example.py
python three_usage_styles.py

# Robotics: MuJoCo pose tracking
python robotics/mujoco_tracking.py

After a run, browse what it wrote under .dash/, or upload it with ml-dash upload (see the CLI Reference).