Splash-ML is a project intended to provide support for training and running classification ML models using a database to store information about assets and tags.
The Splash-ML project takes inspiration from the Splash project in that it uses metadata and data captured from scientific instruments and provides a service to add usefulness for the data. Like Splash, Splash-ML uses, in part, the Data Broker as a source for raw data. This data can be ingested directly from Bluesky-enabled beamlines, or ingested after the fact with custom ingestion code. Once ingested into Data Broker, Splash-ML provides a Tagging service with functions for storing and querying tag sets for tagging events that can come from a variety of sources (e.g. human taggers, tagging from trained machine learning models).
With a query-able database of tags and related assets, Splash-ML supports a variety of use cases with a central theme of making it easier to access collected data, metadata and tags in support of training and running machine learning models. See Use Cases for details.
Splash-ML currently consists of two separate projects in one repository (which might be separated in the future).
The Tag Service provides the facility for storing and query tags sets, storing information about "tagging events" and linking them to assets stored in databroker. See Tag Service Model for information on the data model. In the future, we'll add information about the tag service python API.
Included is a web server. This contains both a REST API to the Tag service and a GraphQL query interface.
There is a sample notebook that demostrates interacting with the TagService directly in splash-ml/examples/tag_notebook.py
Setup a python environment. We'll use venv for this example and install from source:
$ git clone https://github.com/als-computing/splash-ml.git
$ cd splash-ml
$ python -m venv env
$ source env/bin/activate
$ pip install -r requirements.txt
If you want to run the jupyter example in the /examples directory:
$ pip install -r requirements-examples.txt
To run the web service:
$ pip install -r requirements-webservice.txt
The simplest command for running the WebService is:
$ uvicorn tagging.api:app
By default, the service will startup look for mongo at mongodb://localhost:27107/tagging
You can change this by setting an environment variable MONGO_DB_URI, pointing to the
server and database of choice. This is probably how you would configure mongo in a container
environment.
Splash-ML Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.
NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.