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()