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ml-dash
Advanced tools
A simple and flexible SDK for ML experiment metricing and data storage.
| Using uv (recommended) | Using pip |
|
|
from ml_dash import Experiment
with Experiment(
name="my-experiment",
project="my-project",
remote="https://api.dash.ml",
api_key="your-jwt-token"
) as experiment:
print(f"Experiment ID: {experiment.id}")
from ml_dash import Experiment
with Experiment(
name="my-experiment",
project="my-project",
local_path=".ml-dash"
) as experiment:
pass # Your code here
See examples/ for more complete examples.
To contribute to ML-Dash or run tests, install the development dependencies:
| Using uv (recommended) | Using pip |
|
|
This installs:
pytest>=8.0.0 - Testing frameworkpytest-asyncio>=0.23.0 - Async test supportsphinx>=7.2.0 - Documentation buildersphinx-rtd-theme>=2.0.0 - Read the Docs themesphinx-autobuild>=2024.0.0 - Live preview for documentationmyst-parser>=2.0.0 - Markdown support for Sphinxruff>=0.3.0 - Linter and formattermypy>=1.9.0 - Type checker| Using uv | Using pytest directly |
|
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Documentation is built using Sphinx with Read the Docs theme.
| Build docs | Live preview | Clean build |
|
|
|
The live preview command starts a local server and automatically rebuilds when files change.
Alternatively, you can use the Makefile from within the docs directory:
cd docs
make html # Build HTML documentation
make clean # Clean build files
For maintainers, to build and publish a new release: uv build && uv publish
FAQs
ML experiment tracking and data storage
We found that ml-dash demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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