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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -3,7 +3,7 @@ This repo trains and analyzes neural nets for predicting how a tokamak (fusion r
Generate an h5 file with [data-fetching repo](https://github.com/PlasmaControl/data-fetching)

-------- TO TRAIN A MODEL ---------
In configs/default.cfg point raw_data_filename to the generated h5 file. Then change preprocessed_data_filename_base to a "base" name for writing processed data. Run preprocess_data.py, which will generate the basename with _train.pkl, _val.pkl, and _test.pkl appended. Change output_dir in the config file to where you want to dump a model, then run python ian_train.py to train a model to go there. To train a full ensemble of models (submitting them to slurm on traverse) do python launch_ensemble.py which will train 10 with 0,...,9 appended to the end. Use modelStats.py {config_filename} to plot training losses.
In configs/default.cfg point raw_data_filename to the generated h5 file. Then change preprocessed_data_filename_base to a "base" name for writing processed data. Run preprocess_data.py, which will generate the basename with _train.pkl, _val.pkl, and _test.pkl appended. Change output_dir in the config file to where you want to dump a model, then run python ian_train.py to train a model to go there. To train a full ensemble of models (submitting them to slurm on stellar) do python launch_ensemble.py which will train 10 with 0,...,9 appended to the end. Use modelStats.py {config_filename} to plot training losses.

-------- TO CREATE AND VISUALIZE MODEL OUTPUTS ---------
Run SimpleModelRollout.py {config_filename} (where config_filename is the full path to the config file corresponding to the model) to create a pickle file with the predicted profiles. Set plot_ensemble to True or False depending on whether you're doing ensemble modeling or one model at a time. To visualize the predictions, use prediction_plotter.ipynb
Expand Down Expand Up @@ -33,4 +33,4 @@ For pytorch environment setup on PPPL/Princeton's Traverse cluster along with a
conda install -c anaconda h5py
conda activate torch

And of course reload anaconda and activate this environment every time you go to run the code.
And of course reload anaconda and activate this environment every time you go to run the code.