Configuring and Launching Training Runs#

Robomimic uses a centralized configuration system to specify (hyper)parameters at all levels. Below we walk through two ways to configure and launching training runs.

Best practices#

Warning! Do not modify default configs!

Do not directly modify the default configs such as config/, especially if using the codebase with version control (e.g. git). Modifying these files modifies the default settings, and it’s easy to forget that these changes were made, or unintentionally commit these changes so that they become the new defaults.

Please see the Config documentation for more information on Config objects, and the hyperparameter scan tutorial for configuring hyperparameter sweeps.

1. Using a config json (preferred)#

The preferred way to specify training parameters is to pass a config json to the main training script via the --config argument. The dataset can be specified by setting the data attribute of the train section of the config json, or specified via the --dataset argument. The example below runs a default template json for the BC algorithm. This is the preferred way to launch training runs.

$ python --config ../exps/templates/bc.json --dataset ../../tests/assets/test.hdf5

Please see the hyperparameter helper docs to see how to easily generate json configs for launching training runs.

2. Constructing a config object in code#

Another way to launch a training run is to make a default config (with a line like config = config_factory(algo_name="bc")), modify the config in python code, and then call the train function, like in the examples/ script.

import robomimic
import robomimic.utils.torch_utils as TorchUtils
from robomimic.config import config_factory
from robomimic.scripts.train import train

# make default BC config
config = config_factory(algo_name="bc")

# set config attributes here that you would like to update = "bc_rnn_example" = "/path/to/dataset.hdf5"
config.train.output_dir = "/path/to/desired/output_dir"
config.train.batch_size = 256
config.train.num_epochs = 500
config.algo.gmm.enabled = False

# get torch device
device = TorchUtils.get_torch_device(try_to_use_cuda=True)

# launch training run
train(config, device=device)