optimum-neuron/pr_/en/quickstart.html

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	<span>Quickstart
	</span></h1>

<p>🤗 Optimum Neuron was designed with one goal in mind: <strong>to make training and inference straightforward for any 🤗 Transformers user while leveraging the complete power of AWS Accelerators</strong>.
There are two main classes one needs to know:</p>
<ul><li>TrainiumArgumentParser: inherits the original <a href="https://huggingface.co/docs/transformers/main/en/internal/trainer_utils#transformers.HfArgumentParser" rel="nofollow">HfArgumentParser</a> in Transformers with additional checks on the argument values to make sure that they will work well with AWS Trainium instances.</li>
<li><a href="https://huggingface.co/docs/optimum/neuron/package_reference/trainer" rel="nofollow">TrainiumTrainer</a>: the trainer class that takes care of compiling and distributing the model to run on Trainium Chips, and performing training and evaluation.</li></ul>
<p>The <a href="https://huggingface.co/docs/optimum/neuron/package_reference/trainer" rel="nofollow">TrainiumTrainer</a> is very similar to the <a href="https://huggingface.co/docs/transformers/main_classes/trainer" rel="nofollow">🤗 Transformers Trainer</a>, and adapting a script using the Trainer to make it work with Trainium will mostly consist in simply swapping the <code>Trainer</code> class for the <code>TrainiumTrainer</code> one.
That’s how most of the <a href="https://github.com/huggingface/optimum-neuron/tree/main/examples" rel="nofollow">example scripts</a> were adapted from their <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch" rel="nofollow">original counterparts</a>.</p>
<p>modifications:</p>

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	<pre><!-- HTML_TAG_START -->from transformers import TrainingArguments
<span class="hljs-deletion">-from transformers import Trainer</span>
<span class="hljs-addition">+from optimum.neuron import TrainiumTrainer as Trainer</span>
training_args = TrainingArguments(
  # training arguments...
)
# A lot of code here
# Initialize our Trainer
trainer = Trainer(
    model=model,
    args=training_args,  # Original training arguments.
    train_dataset=train_dataset if training_args.do_train else None,
    eval_dataset=eval_dataset if training_args.do_eval else None,
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
    data_collator=data_collator,
)<!-- HTML_TAG_END --></pre></div>


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