# Saving CHECKPOINTS takes way too long

**URL:** <https://discuss.huggingface.co/t/saving-checkpoints-takes-way-too-long/104977>\
**Category:** Beginners\
**Created:** [September 2, 2024, 3:18pm UTC](https://discuss.huggingface.co/t/saving-checkpoints-takes-way-too-long/104977 "2024-09-02T15:18:58Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![FDM1](https://avatars.discourse-cdn.com/v4/letter/f/c57346/32.png) [@FDM1](https://discuss.huggingface.co/u/FDM1)\
**Post date:** [September 2, 2024, 3:18pm UTC](https://discuss.huggingface.co/t/saving-checkpoints-takes-way-too-long/104977/1 "2024-09-02T15:18:58Z")

</div>

Hi. I am practicing wit this awesome library and was training some object detection models on custom datasets.

I save model each epoch if it is best model + last model. But saving the model, also if save\_only\_model flag is True take so much time it makes training unpractical even for very small datasets.

Is there a way to save faster of to save stilized models to then load?

I thank you for you availability in advance. Code of trainer below and output below.  
Best regards to all.

```auto
training_args = TrainingArguments(
    output_dir="weights",
    num_train_epochs=20,
    max_grad_norm=0.01,
    learning_rate=5e-5,
    warmup_steps=300,
    per_device_train_batch_size=2,
    #gradient_accumulation_steps=4,
    dataloader_num_workers=0,
    metric_for_best_model="loss", # eval_map
    greater_is_better=False, # True if metric_for_best_model="eval_map"
    load_best_model_at_end=True,
    eval_strategy="epoch",
    save_strategy="epoch",
    save_total_limit=2,
    save_only_model=True,
    remove_unused_columns=False,
    eval_do_concat_batches=False,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=pytorch_dataset_train,
    eval_dataset=pytorch_dataset_valid,
    tokenizer=processor,
    data_collator=collate_fn,
    compute_metrics=eval_compute_metrics_fn,
)

trainer.train()

```

Output

```auto
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
 25%|██▌ | 500/1972 [02:58<07:25, 3.30it/s]{'loss': 129.9299, 'grad_norm': 232.07464599609375, 'learning_rate': 4.401913875598087e-05, 'epoch': 0.51}
 50%|█████ | 986/1972 [05:38<04:52, 3.37it/s]
  0%| | 0/28 [00:00<?, ?it/s]
  7%|▋ | 2/28 [00:00<00:05, 5.13it/s]
 11%|█ | 3/28 [00:00<00:06, 3.66it/s]
 14%|█▍ | 4/28 [00:01<00:07, 3.19it/s]
 18%|█▊ | 5/28 [00:01<00:07, 2.97it/s]
 21%|██▏ | 6/28 [00:01<00:07, 2.84it/s]
 25%|██▌ | 7/28 [00:02<00:07, 2.76it/s]
 29%|██▊ | 8/28 [00:02<00:07, 2.71it/s]
 32%|███▏ | 9/28 [00:03<00:07, 2.66it/s]
 36%|███▌ | 10/28 [00:03<00:06, 2.63it/s]
 39%|███▉ | 11/28 [00:03<00:06, 2.62it/s]
 43%|████▎ | 12/28 [00:04<00:06, 2.59it/s]
 46%|████▋ | 13/28 [00:04<00:05, 2.55it/s]
 50%|█████ | 14/28 [00:05<00:05, 2.55it/s]
 54%|█████▎ | 15/28 [00:05<00:05, 2.58it/s]
 57%|█████▋ | 16/28 [00:05<00:04, 2.59it/s]
 61%|██████ | 17/28 [00:06<00:04, 2.59it/s]
 64%|██████▍ | 18/28 [00:06<00:03, 2.61it/s]
 68%|██████▊ | 19/28 [00:06<00:03, 2.61it/s]
 71%|███████▏ | 20/28 [00:07<00:03, 2.62it/s]
 75%|███████▌ | 21/28 [00:07<00:02, 2.62it/s]
 79%|███████▊ | 22/28 [00:08<00:02, 2.61it/s]
 82%|████████▏ | 23/28 [00:08<00:01, 2.62it/s]
 86%|████████▌ | 24/28 [00:08<00:01, 2.63it/s]
 89%|████████▉ | 25/28 [00:09<00:01, 2.62it/s]
 93%|█████████▎| 26/28 [00:09<00:00, 2.62it/s]
 96%|█████████▋| 27/28 [00:09<00:00, 2.78it/s]
100%|██████████| 28/28 [00:10<00:00, 3.53it/s]

```