So for example:{'output_dir': 'shiba_ner_trainer', 'overwrite_output_dir': False, 'do_train': True, 'do_eval': True, 'do_predict': True, 'evaluation_strategy': 'epoch', 'prediction_loss_only': False, 'per_device_train_batch_size': 16, 'per_device_eval_batch_size': 16, 'per_gpu_train_batch_size': None, 'per_gpu_eval_batch_size': None, 'gradient_accumulation_steps': 1, 'eval_accumulation_steps': None, 'learning_rate': 0.0004, 'weight_decay': 0.0, 'adam_beta1': 0.9, 'adam_beta2': 0.999, 'adam_epsilon': 1e-08, 'max_grad_norm': 1.0, 'num_train_epochs': 5000, 'max_steps': -1, 'lr_scheduler_type': 'linear', 'warmup_ratio': 0.0, 'warmup_steps': 0, 'log_level': -1, 'log_level_replica': -1, 'log_on_each_node': True, 'logging_dir': 'shiba_ner_trainer', 'logging_strategy': 'steps', 'logging_first_step': False, 'logging_steps': 100, 'save_strategy': 'epoch', 'save_steps': 500, 'save_total_limit': None, 'save_on_each_node': False, 'no_cuda': False, 'seed': 42, 'fp16': False, 'fp16_opt_level': 'O1', 'fp16_backend': 'auto', 'fp16_full_eval': False, 'local_rank': -1, 'tpu_num_cores': None, 'tpu_metrics_debug': False, 'debug': ['underflow_overflow'], 'dataloader_drop_last': False, 'eval_steps': None, 'dataloader_num_workers': 0, 'past_index': -1, 'run_name': 'shiba_ner_trainer', 'disable_tqdm': False, 'remove_unused_columns': True, 'label_names': None, 'load_best_model_at_end': True, 'metric_for_best_model': 'loss', 'greater_is_better': False, 'ignore_data_skip': False, 'sharded_ddp': [], 'deepspeed': None, 'label_smoothing_factor': 0.0, 'adafactor': False, 'group_by_length': False, 'length_column_name': 'length', 'report_to': ['tensorboard'], 'ddp_find_unused_parameters': None, 'dataloader_pin_memory': True, 'skip_memory_metrics': True, 'use_legacy_prediction_loop': False, 'push_to_hub': False, 'resume_from_checkpoint': None, 'push_to_hub_model_id': 'shiba_ner_trainer', 'push_to_hub_organization': None, 'push_to_hub_token': None, '_n_gpu': 1, 'mp_parameters': ''}
OK, I guess
` training_args_dict = training_args.to_dict()
Task.current_task().set_parameters_as_dict(training_args_dict) `works, but how to change the name from "General"?
Task.current_task().connect(training_args, name='hugggingface args')And you should be able to change them when launching remotely 😉
SmallDeer34 btw: "set_parameters_as_dict" will replace all the arguments (and is one way) ...
