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Running AI models
On Episodes Platform
On user local machines or HPC clusters
The AI tools are available as an ordinary Python packages. We recommend for installation to use Anaconda or Mambaforge, but it should be possible to use any other Python installations or distributions.
Mambaforge installation
Mambaforge is a Python distribution based on Conda. This is a preferred way to run scripts and notebooks distributed on our Platform. It is possible to install and run on other python distributions, but we provide support only for Mambaforge/Anaconda.
The installation starts with downloading the Mambaforge binary from the official project site for your platform. Then follow the instructions in the official guide.
We prepared a Conda environment with all the AI tools installed. To create the environment please:
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Contents of this guide
This guide will introduce you to creating and managing your own AI models with the EPOS AI platform. You will learn how to store and evaluate your models in the EPOS AI Workbench as well as how to train them using computing infrastructure and, ultimately, how to publish it and use as a Custom Application of the EPISODES Platform. All these steps are further described on the subsequent pages of this documentation.
Entry-level AI Solutions
A straightforward approach to incorporating AI into your research is to employ pre-existing AI models and software to execute particular tasks:
Advanced AI Use Cases
A more advanced route involves the retraining of existing AI models or even the conception and development of customized models and software from the ground up:
Run the installation
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mamba env create -f epos-ai-tools.yml |
Then to activate the environment it is necessary to run for each new shell session:
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conda activate epos-ai-tools |
To check if you have enabled the environment correctly you should see the name of the environment in the shell prompt.
Available Tools
The installation comes with the following applications:
- gpd_tool - an application for automatic detection of the first arrival time of the P and S waves based on Generalized Seismic Phase Detection with Deep Learning by Ross, Z. E., Meier, M.-A., Hauksson, E., and T. H. Heaton (2018)
- phasenet_tool - an application for automatic detection of the first arrival time of the P and S waves based on PhaseNet: a deep-neural-network-based seismic arrival-time picking method by Zhu, W., & Beroza, G. C. (2019)
and official packages such as:
- Seisbench - an open-source python toolbox for machine learning in seismology,
- ObsPy - an open-source project dedicated to provide a Python framework for processing seismological data,
- Jupyter Notebook and JupyterLab - a web services for interactive computing,
- IPython - a powerful interactive shell,
- PyTorch - an optimized tensor library for deep learning using GPUs and CPUs
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Acknowledgements
The development of the EPOS AI platform and its integration with the EPISODES Platform was partially funded by the EPOS-PL+ project (No POIR.04.02.00-00-C005/19-00), co-financed by the European Union from the funds of the European Regional Development Fund (ERDF).
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We want to express our appreciation for the excellent efforts of the Seisbench developers, and we strongly advise citing their publication:
Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giuchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J., & Soto, H. (2022). SeisBench - A Toolbox for Machine Learning in Seismology. in Seismological Research Letters https://doi.org/10.1785/0220210324
We express our gratitude to Weights & Biases for generously granting us an academic license of their web platform service.