def test_set_torch_device( mock_set_default_tensor_type, device_str, expected_type, expected_index, expected_tensor_type, ): try: torch_settings = TorchSettings(device=device_str) set_torch_config(torch_settings) assert default_device().type == expected_type if expected_index is None: assert default_device().index is None else: assert default_device().index == expected_index mock_set_default_tensor_type.assert_called_once_with( expected_tensor_type) except Exception: raise finally: # restore the defaults torch_settings = TorchSettings(device=None) set_torch_config(torch_settings)
def run_training(run_seed: int, options: RunOptions) -> None: """ Launches training session. :param options: parsed command line arguments :param run_seed: Random seed used for training. :param run_options: Command line arguments for training. """ with hierarchical_timer("run_training.setup"): torch_utils.set_torch_config(options.torch_settings) checkpoint_settings = options.checkpoint_settings env_settings = options.env_settings engine_settings = options.engine_settings base_path = "results" write_path = os.path.join(base_path, checkpoint_settings.run_id) maybe_init_path = ( os.path.join(base_path, checkpoint_settings.initialize_from) if checkpoint_settings.initialize_from is not None else None ) run_logs_dir = os.path.join(write_path, "run_logs") port: Optional[int] = env_settings.base_port # Check if directory exists validate_existing_directories( write_path, checkpoint_settings.resume, checkpoint_settings.force, maybe_init_path, ) # Make run logs directory os.makedirs(run_logs_dir, exist_ok=True) # Load any needed states if checkpoint_settings.resume: GlobalTrainingStatus.load_state( os.path.join(run_logs_dir, "training_status.json") ) # Configure Tensorboard Writers and StatsReporter tb_writer = TensorboardWriter( write_path, clear_past_data=not checkpoint_settings.resume ) gauge_write = GaugeWriter() console_writer = ConsoleWriter() StatsReporter.add_writer(tb_writer) StatsReporter.add_writer(gauge_write) StatsReporter.add_writer(console_writer) if env_settings.env_path is None: port = None env_factory = create_environment_factory( env_settings.env_path, engine_settings.no_graphics, run_seed, port, env_settings.env_args, os.path.abspath(run_logs_dir), # Unity environment requires absolute path ) engine_config = EngineConfig( width=engine_settings.width, height=engine_settings.height, quality_level=engine_settings.quality_level, time_scale=engine_settings.time_scale, target_frame_rate=engine_settings.target_frame_rate, capture_frame_rate=engine_settings.capture_frame_rate, ) env_manager = SubprocessEnvManager( env_factory, engine_config, env_settings.num_envs ) env_parameter_manager = EnvironmentParameterManager( options.environment_parameters, run_seed, restore=checkpoint_settings.resume ) trainer_factory = TrainerFactory( trainer_config=options.behaviors, output_path=write_path, train_model=not checkpoint_settings.inference, load_model=checkpoint_settings.resume, seed=run_seed, param_manager=env_parameter_manager, init_path=maybe_init_path, multi_gpu=False, ) # Create controller and begin training. tc = TrainerController( trainer_factory, write_path, checkpoint_settings.run_id, env_parameter_manager, not checkpoint_settings.inference, run_seed, ) # Begin training try: tc.start_learning(env_manager) finally: env_manager.close() write_run_options(write_path, options) write_timing_tree(run_logs_dir) write_training_status(run_logs_dir)
def run_training(run_seed: int, options: RunOptions, num_areas: int) -> None: """ Launches training session. :param run_seed: Random seed used for training. :param num_areas: Number of training areas to instantiate :param options: parsed command line arguments """ with hierarchical_timer("run_training.setup"): torch_utils.set_torch_config(options.torch_settings) checkpoint_settings = options.checkpoint_settings env_settings = options.env_settings engine_settings = options.engine_settings run_logs_dir = checkpoint_settings.run_logs_dir port: Optional[int] = env_settings.base_port # Check if directory exists validate_existing_directories( checkpoint_settings.write_path, checkpoint_settings.resume, checkpoint_settings.force, checkpoint_settings.maybe_init_path, ) # Make run logs directory os.makedirs(run_logs_dir, exist_ok=True) # Load any needed states in case of resume if checkpoint_settings.resume: GlobalTrainingStatus.load_state( os.path.join(run_logs_dir, "training_status.json") ) # In case of initialization, set full init_path for all behaviors elif checkpoint_settings.maybe_init_path is not None: setup_init_path(options.behaviors, checkpoint_settings.maybe_init_path) # Configure Tensorboard Writers and StatsReporter stats_writers = register_stats_writer_plugins(options) for sw in stats_writers: StatsReporter.add_writer(sw) if env_settings.env_path is None: port = None env_factory = create_environment_factory( env_settings.env_path, engine_settings.no_graphics, run_seed, num_areas, port, env_settings.env_args, os.path.abspath(run_logs_dir), # Unity environment requires absolute path ) env_manager = SubprocessEnvManager(env_factory, options, env_settings.num_envs) env_parameter_manager = EnvironmentParameterManager( options.environment_parameters, run_seed, restore=checkpoint_settings.resume ) trainer_factory = TrainerFactory( trainer_config=options.behaviors, output_path=checkpoint_settings.write_path, train_model=not checkpoint_settings.inference, load_model=checkpoint_settings.resume, seed=run_seed, param_manager=env_parameter_manager, init_path=checkpoint_settings.maybe_init_path, multi_gpu=False, ) # Create controller and begin training. tc = TrainerController( trainer_factory, checkpoint_settings.write_path, checkpoint_settings.run_id, env_parameter_manager, not checkpoint_settings.inference, run_seed, ) # Begin training try: tc.start_learning(env_manager) finally: env_manager.close() write_run_options(checkpoint_settings.write_path, options) write_timing_tree(run_logs_dir) write_training_status(run_logs_dir)