This docker compose framework automatically finds the peaks within a group of 1D XRD data files and runs a dash app for more precise peak detection.
The project contains standalone version of the SplashML tagging framework in order to get a running demo.
To do this create the following directories:
- Clone this directory into any location
- Clone splash-ml at https://github.com/als-computing/splash-ml into the same directory 1D XRD exists in
Any data for automatic tagging should be under the data file.
First, let's install docker:
- https://docs.docker.com/engine/install/
- Next go into the 1D XRD dir
- type
docker-compose upinto your terminal (on more recent versions of the Docker desktop,composeis part of the docker CLI, meaning that the command is nowdocker compose up)
For AMR64 support, please type docker-compose -f docker-compose-arm64.yml up instead of docker-compose up.
Next, open up the dash app or splash-ml API:
- Dash app: http://0.0.0.0:8050/
- Splash API: http://0.0.0.0:8000/api/splash_ml/docs
Notes:
- It is assumed that splash-ml handles the path to the dataset, such that file can be read, e.g. "C:/Users/..." vs "/mnt/c/Users/..."
See the Tasks for instructions on using the application.
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