def main(): eta, sd = com.init_ray() eta = 3 if eta == 1 else eta MyTrainable = TorchTrainer.as_trainable( data_creator=workload.data_creator, model_creator=workload.model_creator, loss_creator=workload.loss_creator, optimizer_creator=workload.optimizer_creator, training_operator_cls=workload.WLMOperator, config={ "seed": sd, "extra_fluid_trial_resources": {} }, ) params = { **com.run_options(__file__), "stop": workload.create_stopper(), **setup_tune_scheduler(), } analysis = tune.run(MyTrainable, **params) dfs = analysis.trial_dataframes for logdir, df in dfs.items(): ld = Path(logdir) df.to_csv(ld / "trail_dataframe.csv")
def main(): num_worker, sd = com.init_ray() MyTrainable_SyncBOHB = TorchTrainer.as_trainable( data_creator=workload.data_creator, model_creator=workload.model_creator, loss_creator=workload.loss_creator, optimizer_creator=workload.optimizer_creator, config={ "seed": sd, BATCH_SIZE: 64, "extra_fluid_trial_resources": {} }, ) params = { **com.run_options(__file__), "stop": workload.create_stopper(), **setup_tune_scheduler(num_worker), } analysis = tune.run(MyTrainable_SyncBOHB, **params) dfs = analysis.trial_dataframes for logdir, df in dfs.items(): ld = Path(logdir) df.to_csv(ld / "trail_dataframe.csv")
def main(): _, sd = com.init_ray() workload.init_dcgan() MyTrainable = TorchTrainer.as_trainable( data_creator=workload.data_creator, model_creator=workload.model_creator, loss_creator=workload.loss_creator, optimizer_creator=workload.optimizer_creator, training_operator_cls=workload.GANOperator, config={ "seed": sd, **workload.static_config(), }, ) params = { **com.run_options(__file__), "stop": workload.create_stopper(), **setup_tune_scheduler(), } analysis = tune.run(MyTrainable, **params) dfs = analysis.trial_dataframes for logdir, df in dfs.items(): ld = Path(logdir) df.to_csv(ld / "trail_dataframe.csv")