from polyaxon_client.tracking import Experiment experiment = Experiment() print(experiment.get_experiment_info()) experiment.log_params(activation='sigmoid', lr=0.001) experiment.log_params(dropout=0.5) experiment.log_metrics(step=123, loss=0.023, accuracy=0.91) experiment.log_data_ref(data="/data/cifar10-batches-py", data_name='my_dataset')
+ "_" + hparams.outer_model.replace(".", "_") ) print(f'This will run on polyaxon: {str(hparams.on_polyaxon)}') # hparams.device = torch.device('cuda' if hparams.on_polyaxon else 'cpu') hparams.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('torch.cuda.is_available(): ', torch.cuda.is_available()) print('device: ', hparams.device) if hparams.on_polyaxon: from polyaxon_client.tracking import Experiment, get_data_paths, get_outputs_path hparams.data_root = get_data_paths()['data1'] + polyaxon_folder hparams.output_path = get_outputs_path() poly_experiment_info = Experiment.get_experiment_info() poly_experiment_nr = poly_experiment_info['experiment_name'].split(".")[-1] hparams.name = poly_experiment_nr + "_" + exp_name print(f'get_outputs_path: {get_outputs_path()} \n ' f'experiment_info: {poly_experiment_info} \n experiment_name: {poly_experiment_nr}') else: date_str = datetime.now().strftime("%y%m%d-%H%M%S_") hparams.name = 'local_' + date_str + exp_name # hparams.output_path = Path(hparams.output_path).absolute() / hparams.name wandb_logger = WandbLogger(name=hparams.name, project=f"aortaSegm-{hparams.outer_model.split('.')[-1]}-{hparams.inner_module.split('.')[-1]}") # wandb.init(project=f"aortaSegm-{hparams.outer_model.split('.')[-1]}-{hparams.inner_module.split('.')[-1]}") argparse_summary(hparams, parser)