How to process trainer.evaluate in batch mode to deal with Out of Memory error

šŸ¤—Datasets
1

Could you please guide me how to handle to Out of memory issue while executing ā€œTrainer.evaluateā€

Here is my setup

#getting encoded datset
#preprocess_function(audio_dataset[:5])
def preprocess_function(examples):
    audio_arrays = [x["array"] for x in examples["audio"]]
    inputs = feature_extractor(
        audio_arrays, 
        sampling_rate=feature_extractor.sampling_rate, 
        max_length=int(128), 
        truncation=True, 
    )
    return inputs
audio_dict2 = {}
num_samples = 84000
audio_dict2['audio'] = ['../data/20230319_audioinput_1s_wav/test/' + str(int(y)) + '/audio_'+str(int(x))+'.wav' for x,y in zip(df_test.head(num_samples).index, df_test.head(num_samples).label)]
audio_dict2['label'] = [x for x in df_test.head(num_samples).label]
audio_dict2['split'] = len(df_test.head(num_samples))*['train']

audio_dataset2 = Dataset.from_dict(audio_dict2).cast_column("audio", Audio())
encoded_dataset2 = audio_dataset2.map(preprocess_function, remove_columns=["audio"], batched=True)

#training args

model_name = model_checkpoint.split("/")[-1]
batch_size = 4
metric = load_metric("accuracy")
args = TrainingArguments(
    f"{model_name}-finetuned-ks1",
    evaluation_strategy = "epoch",
    save_strategy = "epoch",
    learning_rate=3e-5,
    per_device_train_batch_size=batch_size,
    gradient_accumulation_steps=batch_size,
    per_device_eval_batch_size=batch_size,
    num_train_epochs=25,
    warmup_ratio=0.1,
    logging_steps=1,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy",
    push_to_hub=False,
)

trainer = Trainer(
    model,
    args,
    train_dataset=encoded_dataset2,
    eval_dataset=encoded_dataset2,
    tokenizer=feature_extractor,
    compute_metrics=compute_metrics
)

## I ge the OOM error here!

outputs = trainer.evaluate(encoded_dataset2)

In my case the dataset is available locally, Is there an option to batch process this evaluate process?