Sync your ML data with your favorite productivity tools!
APACHE-2.0 License
Sync your ML data seamlessly with productivity tools you love
MLSync is a Python library that acts as a bridge between your ML workflow and your project planning and management tools.
pip install mlsync
Developing ML projects is a lot of fun, but they are also hard to plan and manage. While the ML community has built several tools for developers to better track and visualize their ML workflow data, there is a disconnect between ML workflow data and the tools that are used for project management. MLSync is designed to bridge this gap.
There are four main aspects of MLSync:
We are actively building MLSync with the vision to become a one-stop standard interface to map data from ML experiments to project management tools. The above figure shows the high-level architecture of MLSync. All the functionality is not yet available; please refer to the Roadmap for the current status. If you would like to contribute to MLSync, please refer to the Contributing section.
In this example, we will sync your machine learning experiments to Notion in three simple steps!
pip install mlsync
git clone https://github.com/paletteml/mlsync.git
: Checkout the MLSync repository.cd mlsync/examples/mlflow-notion/
: Change directory to the example directorypip install -r requirements.txt
: Install the requirements for this example.
python mlflow_pytorch.py --run-name <name>
. Make sure it runs (Need not complete the run).Let us now link Notion to MLSync. This is required only for the first time you run MLSync.
+ New Integration
button.MLSync
and hit submit..env
file in your path and update the Notion token.
NOTION_TOKEN=secret_0000000000000000000000000000000000000000000
Demo
.MLSync
integration.You are now all set! Now let us sync your MLFlow runs to Notion.
mlsync --config config.yaml
{% note %}
Note: First time you run, you will be prompted to choose a page to sync to.
From the options, choose the page you created in the previous step (Demo
).
{% endnote %}
That's it! You can now view your MLFlow runs in Notion. As long as mlsync is running in the background, all your future experiments and runs in this directory should appear in the selected Notion page.
NOTION_TOKEN
not being found, you can pass the --notion-token
flag to mlsync
to specify the token.Please raise an issue, or reach out if you have any other errors.
config.yaml
file or by passing the arguments to the mlsync
command. Run mlsync --help
to see the available arguments.mlsync
allows you to customize the report much further. You can customize the report by adding your own format.yaml
file. Read documentation here to learn more.refresh_rate
field in the configuration file.Enjoy! If you have any further questions, please contact us.
We want to support different training environments and different productivity tools.
Do you have other tools/frameworks you would like to see supported? Let us know!
We welcome contributions from the community. Please feel free to open an issue or pull request. Or, if you are interested in working closely with us, please contact us directly. We will be happy to talk to you!