Module `pretraining`

This module performs pretraining of the ice flow iflo_emulator on a glacier catalog to enhance its performance during glacier forward runs. Pretraining can be a computationally intensive task, taking a few hours to complete. This module should be executed independently, without involving any other IGM modules. Below is an example of a parameter file:

# @package _global_

defaults:
  - override /inputs: []
  - override /processes: [pretraining, iceflow]
  - override /outputs: []

processes:
  iceflow: 
    Nz : 10
    multiple_window_size : 8
    nb_layers : 16
    nb_out_filter : 32
    network : cnn
    new_friction_param : True
    retrain_emulator_lr : 0.0001
    solve_nbitmax : 1000
    solve_stop_if_no_decrease : False
  pretraining:
    epochs : 1000
    data_dir: data/surflib3d_shape_100
    soft_begining: 1000
    min_slidingco: 0.01
    max_slidingco: 0.4
    min_arrhenius: 5
    max_arrhenius: 400

To run this module, you first need access to a glacier catalog. A dataset of a glacier catalog (mountain glaciers) commonly used for pretraining IGM emulators is available here: .

After downloading (or generating your own dataset), organize the folder surflib3d_shape_100 into two subfolders: train and test.

Parameters

Default configuration file (pretraining.yaml):

pretraining:
  data_dir: "/path/to/tfrecords/"
  batch_size: 1
  epochs: 1000
  experiment_name: "name_of_model"
  loss_type: "huber"
  learning_rate: 0.0001
  out_dir: "/path/to/save/models/"
  resume: false

Description of the parameters:

Name	Description	Default value	Units
`data_dir`	Directory of the data of the glacier catalog.	/path/to/tfrecords/	—
`batch_size`	Batch size.	1	—
`epochs`	Number of epochs.	1000	—
`experiment_name`		name_of_model
`loss_type`		huber
`learning_rate`		0.0001
`out_dir`		/path/to/save/models/
`resume`		False

Module pretraining

Parameters

Module `pretraining`