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!