One aspect of using AI algorithms in research is to use already designed and trained machine learning models. For those new to the world of AI, we'll provide step-by-step guidance on how to get started with pre-trained models, including running already installed application available on the Platform as well as setting up your local environment to run your first AI-powered predictions. This guide will help you to set up your environment on an HPC cluster to run AI models on large data sets.

On Episodes Platform

The EpisodesPlatform.eu provides easy access to run AI models on data selected by the user. The models can be run as a regular application in My Workspace. For details see the following documentation:

P and S Waves Detection Tool user guide

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:

Download the epos-ai-tools.yml from our repository.
If necessary activate the Mambaforge environment.
Run the installation
```
mamba env create -f epos-ai-tools.yml
```
Then to activate the environment it is necessary to run for each new shell session:
```
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

Tools for picking P and S waves

TODO

On Episodes Platform

On user local machines or HPC clusters

Mambaforge installation

Available Tools

Tools for picking P and S waves

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Running AI Models

On Episodes Platform

On user local machines or HPC clusters

Mambaforge installation

Available Tools

Tools for picking P and S waves

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