Gradient checkpointing with Trainer
You want to use gradient checkpointing to reduce the memory footprint of your model. You've seen how to write the explicit training loop with Accelerator, and now you'd like to use a simplified interface without training loops with Trainer. The exercise will take some time to run with the call to trainer.train().
Set up the arguments for Trainer to use gradient checkpointing.
本练习是课程的一部分
Efficient AI Model Training with PyTorch
练习说明
- Use four gradient accumulation steps in
TrainingArguments. - Enable gradient checkpointing in
TrainingArguments. - Pass in the training arguments to
Trainer.
交互式实操练习
通过完成这段示例代码来试试这个练习。
training_args = TrainingArguments(output_dir="./results",
evaluation_strategy="epoch",
# Use four gradient accumulation steps
gradient_accumulation_steps=____,
# Enable gradient checkpointing
gradient_checkpointing=____)
trainer = Trainer(model=model,
# Pass in the training arguments
args=____,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
compute_metrics=compute_metrics)
trainer.train()