def run_by_mode(spec_file, spec_name, run_mode):
spec = spec_util.get(spec_file, spec_name)
# TODO remove when analysis can save all plotly plots
os.environ['run_mode'] = run_mode
if run_mode == 'search':
Experiment(spec).run()
elif run_mode == 'train':
Trial(spec).run()
elif run_mode == 'enjoy':
# TODO turn on save/load model mode
# Session(spec).run()
pass
elif run_mode == 'generate_benchmark':
benchmarker.generate_specs(spec, const='agent')
elif run_mode == 'benchmark':
# TODO allow changing const to env
run_benchmark(spec, const='agent')
elif run_mode == 'dev':
os.environ['PY_ENV'] = 'test' # to not save in viz
spec = util.override_dev_spec(spec)
Trial(spec).run()
else:
logger.warn(
'run_mode not recognized; must be one of `search, train, enjoy, benchmark, dev`.'
)
def run_by_mode(spec_file, spec_name, lab_mode):
logger.info(f'Running lab in mode: {lab_mode}')
spec = spec_util.get(spec_file, spec_name)
info_space = InfoSpace()
os.environ['PREPATH'] = util.get_prepath(spec, info_space)
reload(logger) # to set PREPATH properly
# expose to runtime, '@' is reserved for 'enjoy@{prepath}'
os.environ['lab_mode'] = lab_mode.split('@')[0]
if lab_mode == 'search':
info_space.tick('experiment')
Experiment(spec, info_space).run()
elif lab_mode == 'train':
info_space.tick('trial')
Trial(spec, info_space).run()
elif lab_mode.startswith('enjoy'):
prepath = lab_mode.split('@')[1]
spec, info_space = util.prepath_to_spec_info_space(prepath)
Session(spec, info_space).run()
elif lab_mode == 'generate_benchmark':
benchmarker.generate_specs(spec, const='agent')
elif lab_mode == 'benchmark':
# TODO allow changing const to env
run_benchmark(spec, const='agent')
elif lab_mode == 'dev':
spec = util.override_dev_spec(spec)
info_space.tick('trial')
Trial(spec, info_space).run()
else:
logger.warn(
'lab_mode not recognized; must be one of `search, train, enjoy, benchmark, dev`.'
)
def run_benchmark(spec, const):
benchmark_specs = benchmarker.generate_specs(spec, const)
logger.info('Running benchmark')
for spec_name, benchmark_spec in benchmark_specs.items():
# run only if not already exist; benchmark mode only
if not any(spec_name in filename for filename in os.listdir('data')):
Experiment(benchmark_spec).run()