48 lines
2.6 KiB
ReStructuredText
48 lines
2.6 KiB
ReStructuredText
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Lightning Transformers
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======================
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`Lightning Transformers <https://lightning-transformers.readthedocs.io/en/latest/>`_ offers a flexible interface for training and fine-tuning SOTA Transformer models
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using the :doc:`PyTorch Lightning Trainer <../common/trainer>`.
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.. code-block:: bash
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pip install lightning-transformers
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In Lightning Transformers, we offer the following benefits:
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- Powered by `PyTorch Lightning <https://www.pytorchlightning.ai/>`_ - Accelerators, custom Callbacks, Loggers, and high performance scaling with minimal changes.
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- Backed by `HuggingFace Transformers <https://huggingface.co/transformers/>`_ models and datasets, spanning multiple modalities and tasks within NLP/Audio and Vision.
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- Task Abstraction for Rapid Research & Experimentation - Build your own custom transformer tasks across all modalities with little friction.
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- Powerful config composition backed by `Hydra <https://hydra.cc/>`_ - simply swap out models, optimizers, schedulers task, and many more configurations without touching the code.
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- Seamless Memory and Speed Optimizations - Out-of-the-box training optimizations such as `DeepSpeed ZeRO <https://pytorch-lightning.readthedocs.io/en/latest/multi_gpu.html#deepspeed>`_ or `FairScale Sharded Training <https://pytorch-lightning.readthedocs.io/en/latest/multi_gpu.html#sharded-training>`_ with no code changes.
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-----------------
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Using Lightning-Transformers
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----------------------------
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Lightning Transformers has a collection of tasks for common NLP problems such as `language_modeling <https://lightning-transformers.readthedocs.io/en/latest/tasks/nlp/language_modeling.html#language-modeling>`_,
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`translation <https://lightning-transformers.readthedocs.io/en/latest/tasks/nlp/translation.html#translation>`_ and more. To use, simply:
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1. Pick a task to train (passed to ``train.py`` as ``task=``)
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2. Pick a dataset (passed to ``train.py`` as ``dataset=``)
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3. Customize the backbone, optimizer, or any component within the config
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4. Add any :doc:`Lightning supported parameters and optimizations <../common/trainer>`
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.. code-block:: bash
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python train.py \
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task=<TASK> \
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dataset=<DATASET>
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backbone.pretrained_model_name_or_path=<BACKBONE> # Optionally change the HF backbone
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optimizer=<OPTIMIZER> # Optionally specify optimizer (Default AdamW)
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trainer.<ANY_TRAINER_FLAGS> # Optionally specify Lightning trainer arguments
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To learn more about Lightning Transformers, please refer to the `Lightning Transformers documentation <https://lightning-transformers.readthedocs.io/en/latest/>`_.
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