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WaveformML

Machine learning tools for waveform analysis.

Install

Recommended to use conda installation manager. https://docs.conda.io/projects/conda/en/latest/user-guide/install/

First, install prerequisites:

conda install numpy scipy matplotlib gitpython yaml hdf5 h5py numba pytz

Then install pytorch version 1.6 or higher, see https://pytorch.org/get-started/locally/

On linux with GPU support, cuda version 10.2, this command is

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

With no GPU support, the command is

conda install pytorch torchvision torchaudio cpuonly -c pytorch

Then, install pytorch-lightning and optuna

conda install -c conda-forge optuna

Pytorch lightning requires version 1.0 or higher:

conda install pytorch-lightning -c conda-forge

To run the sparse convolutional network code, install spconv or SparseConvNet

spconv: https://github.com/traveller59/spconv

install boost:

sudo apt-get install libboost-all-dev

make sure cmake >= 3.13.2 is installed then install spconv:

git clone https://github.com/traveller59/spconv --recursive
cd spconv
python setup.py bdist_wheel
cd ./dist
pip install <name of .whl file>
SparseConvNet

follow instructions here: https://github.com/facebookresearch/SparseConvNet

install things with pip:

Using pip (example is for linux, see pytorch install guide for your system)

pip install numpy
pip install h5py
pip install gitpython
pip install torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install optuna
pip install pytorch-lightning

Usage

python main.py <name of config.json file>

output is logged in

{model folder}/runs/<experiment name>/version_<n>

where {model folder} defaults to

./model/<model name>

and <n> is the run number of the experiment starting from 0

Selecting data

Set dataset_config.paths to the directories you would like to retrieve hdf5 data from. Set the number of events to draw from this directory with n_train, n_validate, and n_test.

Metadata of events selected will be logged to

{model folder}/datasets/<dataset name>_<dataset type>_config.json

Where <dataset name> is constructed from the directory names or can be specified via dataset_config.name. The <dataset type> is one of train, val, or test. Constructed datasets will be saved on disk to

./data/<model_name>/<dataset name>.json

To set the training, test, or validation data sets to use a specific metadata file, use dataset_config.train_config, .test_config, and .val_config - point these to the json metadata file.

Requirements for the config file are shown in config_requirements.json. This can be used as a template. A full example config file is found in config/examples

Some useful command line arguments:

--overfit_batches 0.001 // overfits on a small percentage of the data. Useful for debugging a network
--profiler true // profiles the program, showing time spent in each section. output written in log dir found in <model folder>/runs/<experiment name>/version_<n> where n is the (n-1)th run of the experiment
--limit_test_batches n // if int, limits number of batches used for testing to n batches. If float < 1, uses that fraction of the test batches.
--limit_val_batches n
--limit_train_batches n
--log_gpu_memory all | min_max // logs the gpu usage, set to min_max for only max and min usage logging
--terminate_on_nan true // terminates when nan loss is returned
--auto_lr_find true // starts the training session with a learning rate finder algorithm, prints results / saves to log folder
--row_log_interval n // records tensorboard logs every n batches (default 1)
--log_save_interval n // only writes out tensorboard logs every n batches

see https://pytorch-lightning.readthedocs.io/en/latest/trainer.html#trainer-flags for a complete list of arguments

Performance tweaking and optimization

For performance tweaking on a GPU, set

--profiler=true

then slowly increase the num_workers (under dataset_config/dataloader_params) until you find optimal performance.

Once optimal data loading performance is found, then tune your learning rate. You can set --auto_lr_find=true to find an optimal learning rate for your learning scheduler.

Hyperparameter optimization

Once a good learning rate is chosen, set up a hyperparameter optimization config and set

-oc <name of config file or path to config file>.

The search path for the optimize config is the same for model config files. Alternatively, you can add an optuna_config section in the config file.

Results are in

./studies/<experiment name>

Each trial's logs and model checkpoints are saved to the

./studies/<experiment name>/trial_<n>

folder.

Optuna Configuration

You can prune unpromising trials by adding the -p flag to the command.

You can choose your pruner by setting the pruner value in your optuna config file to one of the pruners listed here: https://optuna.readthedocs.io/en/stable/reference/pruners.html

You can pass parameters to the pruner with the pruner_params config options.

You can choose your sampler by setting the sampler value in your optuna config file to one of the samplers listed here: https://optuna.readthedocs.io/en/stable/reference/samplers.html

You can pass parameters to the pruner with the pruner_params config options.

The default sampler is TPESampler and the default pruner is MedianPruner

Viewing logs:

In the console, run the command tensorboard --logdir <log path>

This will serve the log data to http://localhost:6006

Creating your own modules

Create your own modules by extending the LitPSD class. See https://pytorch-lightning.readthedocs.io/en/latest/child_modules.html for more information.