Hello I am running a Hyperparameter search using Optuna.
As I am using Colab, I have limited diskspace, so I was wondering how to stop saving checkpoints, I only care about the final result and don’t need all the intermediate steps saved.
I tried the following argument sin my TrainingArguments parameter, but its not working
# Define the trainig arguments training_args = TrainingArguments( output_dir='./results', # output directory seed = 0, num_train_epochs=5, # total number of training epochs per_device_train_batch_size=16, # batch size per device during training per_device_eval_batch_size=16, # batch size for evaluation warmup_steps=22, # number of warmup steps for learning rate scheduler weight_decay=0.01, # strength of weight decay learning_rate=5e-5, # initial learning rate for AdamW optimizer. load_best_model_at_end=True, # load the best model when finished training (default metric is loss) do_train=True, # Perform training do_eval=True, # Perform evaluation logging_dir='./logs', # directory for storing logs logging_steps=10, gradient_accumulation_steps=2, # total number of steps before back propagation fp16=True, # Use mixed precision fp16_opt_level="02", # mixed precision mode evaluation_strategy="epoch", # evaluate each `logging_steps` save_strategy = 'no', # The checkpoint save strategy to adopt during training. I dont want to save, probably why it did save and take up disk space in HP search save_steps = 100000, save_total_limit = 1. # Trying this to stop octuna from saving
Any help would be appreciated, thank you!