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NEST

This is a simple utility to run a hyperparameter grid search for EGG-based games. We support two modes:

  • Local search (via nest_local.py): runs multiple jobs on a single machine, leveraging all available CUDA devices (if any);
  • Preemptable cluster-based (via nest.py).

The modes have similar behaviour, however, the latter is only implemented for the FAIR environment and uses internal libraries.

How to use

To run a hyperparameter search, we firstly need to define a json file with a list of parameters to be searched through. The syntax is simple:

{
"hyperparameter_name_1": ["value1", "value2"],
"hyperparameter_name_2": [1, 2, 3],
}

You can find an example in example.json.

Next, the module that implements the game should have a function main(params) which accepts command-line parameters and runs the game logic. Hence, the typical implementation should follow this pattern:

...
def main(params):
    egg.core.init(params=params)
    ...
    
if __name__ == '__main__':
    import sys
    main(sys.argv[1:])

An example game that meets this requirement is mnist_autoenc.

Local mode

For local search mode, we assume that (a) the game requires only a single GPU, (b) it does not assign GPU devices directly.

After the json specification is set, we can run the search as follows:

python -m egg.nest.nest_local --game egg.zoo.mnist_autoenc.train --sweep egg/nest/example.json --n_workers=1

This command will spawn n_workers processes, playing the egg.zoo.mnist_autoenc.train game over a grid of hyperparameters defined in egg/nest/example.json . If n_workers is more than the number of available GPU devices, the devices will be shared (e.g., if the number of workers is 2x the number of GPUs, each GPU will be shared by two workers).

Complex grids

For less trivial grid searches, one can use a python-based grid iterators, such as in the example example.py. Such a python file has to contain a grid() function which would return an iterable over the grid parameters (see example for the format). The path for the python grid should be specified as a classpath, e.g.

python -m egg.nest.nest_local --game egg.zoo.mnist_autoenc.train --py_sweep=egg.nest.example --n_workers=1

Parameters

nest_local has the following parameters:

  • game is a path to the game module to be run (e.g. egg.zoo.mnist_autoenc.train);
  • sweep is a path to the json file defining the grid of hyperparameters to be searched through (you can specify multiple of them);
  • py_sweep is a class-path to the python file defining the grid of hyperparameters to be searched through (you can specify multiple of them);
  • preview/dry_run suppresses running any job, simply producing the NEST-generated output and creating the output directory layout;
  • n_workers specifies the number of worker jobs;
  • name optional name of the search, which is used for naming the directory with the outputs of the runs. If not specified, nest will infer it from the game parameter (e.g., mnist_autoenc if the game parameter is set to egg.zoo.mnist_autoenc.train)
  • root_dir sets the output directory for the hyperparameter search. If not specified, nest_local will create a directory in ~/nest_local/{name}/{timestamp}/. The stdout/stderr and the checkpoints of the jobs will be stored there;
  • checkpoint_freq sets how often the checkpoints will be set (default: 0). checkpoint_dir is set automatically by nest_local to be {root_dir}/{hyperparameter_combination_id}/.