Training a language model with ๐คย Transformers using TensorFlow and TPUs
๐ฐ Hugging Face Blog
Train a language model with Hugging Face Transformers using TensorFlow and TPUs
Action Steps
- Get the data and train a tokenizer
- Tokenize the data and create TFRecords
- Train a model on data in GCS using TPU
- Upload the final model
Who Needs to Know This
AI engineers and researchers can benefit from this tutorial to train large-scale language models efficiently using TPUs and TensorFlow
Key Insight
๐ก TPU training allows for high-performance and scalable model training, making it ideal for large models
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๐ Train large-scale language models with Hugging Face Transformers and TensorFlow on TPUs!
Key Takeaways
Train a language model with Hugging Face Transformers using TensorFlow and TPUs
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Published Time: 2023-04-27T00:00:00.209Z
# Training a language model with ๐คTransformers using TensorFlow and TPUs
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# [](https://huggingface.co/blog/tf_tpu#training-a-language-model-with-%F0%9F%A4%97-transformers-using-tensorflow-and-tpus) Training a language model with ๐คTransformers using TensorFlow and TPUs
Published April 27, 2023
[Update on GitHub](https://github.com/huggingface/blog/blob/main/tf_tpu.md)
[- [x] Upvote 1](https://huggingface.co/login?next=%2Fblog%2Ftf_tpu)
* [](https://huggingface.co/Aanuoluwapo65 "Aanuoluwapo65")
[](https://huggingface.co/Rocketknight1)
[Matthew Carrigan Rocketknight1 Follow](https://huggingface.co/Rocketknight1)
[](https://huggingface.co/sayakpaul)
[Sayak Paul sayakpaul Follow](https://huggingface.co/sayakpaul)
## * [Introduction](https://huggingface.co/blog/tf_tpu#introduction "Introduction")
* [Motivation](https://huggingface.co/blog/tf_tpu#motivation "Motivation")
* [What to expect](https://huggingface.co/blog/tf_tpu#what-to-expect "What to expect")
* [Getting the data and training a tokenizer](https://huggingface.co/blog/tf_tpu#getting-the-data-and-training-a-tokenizer "Getting the data and training a tokenizer")
* [Tokenizing the data and creating TFRecords](https://huggingface.co/blog/tf_tpu#tokenizing-the-data-and-creating-tfrecords "Tokenizing the data and creating TFRecords")
* [Training a model on data in GCS](https://huggingface.co/blog/tf_tpu#training-a-model-on-data-in-gcs "Training a model on data in GCS")
* [Conclusion](https://huggingface.co/blog/tf_tpu#conclusion "Conclusion")
[](https://huggingface.co/blog/tf_tpu#introduction) Introduction
TPU training is a useful skill to have: TPU pods are high-performance and extremely scalable, making it easy to train models at any scale from a few tens of millions of parameters up to truly enormous sizes: Googleโs PaLM model (over 500 billion parameters!) was trained entirely on TPU pods.
Weโve previously written a [tutorial](https://huggingface.co/docs/transformers/main/perf_train_tpu_tf) and a [Colab example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) showing small-scale TPU training with TensorFlow and introducing the core concepts you need to understand to get your model working on TPU. This time, weโre going to step that up another level and train a masked language model from scratch using TensorFlow and TPU, including every step from training your tokenizer and preparing your dataset through to the final model training and uploading. This is the kind of task that youโll probably want a dedicated TPU node (or VM) for, rather than just Colab, and so thatโs where weโll focus.
As in our Colab example, weโre taking advantage of TensorFlow's very clean TPU support via XLA and `TPUStrategy`. Weโll also be benefiting from the fact that the majority of the TensorFlow models in ๐ค Transformers are fully [XLA-compatible](https://huggingface.co/blog/tf-xla-generate). So surprisingly, little wor
# Training a language model with ๐คTransformers using TensorFlow and TPUs
[Hugging Face](https://huggingface.co/)
* [Models](https://huggingface.co/models)
* [Datasets](https://huggingface.co/datasets)
* [Spaces](https://huggingface.co/spaces)
* [Buckets new](https://huggingface.co/storage)
* [Docs](https://huggingface.co/docs)
* [Enterprise](https://huggingface.co/enterprise)
* [Pricing](https://huggingface.co/pricing)
*
*
* * *
* [Log In](https://huggingface.co/login)
* [Sign Up](https://huggingface.co/join)
[Back to Articles](https://huggingface.co/blog)
# [](https://huggingface.co/blog/tf_tpu#training-a-language-model-with-%F0%9F%A4%97-transformers-using-tensorflow-and-tpus) Training a language model with ๐คTransformers using TensorFlow and TPUs
Published April 27, 2023
[Update on GitHub](https://github.com/huggingface/blog/blob/main/tf_tpu.md)
[- [x] Upvote 1](https://huggingface.co/login?next=%2Fblog%2Ftf_tpu)
* [](https://huggingface.co/Aanuoluwapo65 "Aanuoluwapo65")
[](https://huggingface.co/Rocketknight1)
[Matthew Carrigan Rocketknight1 Follow](https://huggingface.co/Rocketknight1)
[](https://huggingface.co/sayakpaul)
[Sayak Paul sayakpaul Follow](https://huggingface.co/sayakpaul)
## * [Introduction](https://huggingface.co/blog/tf_tpu#introduction "Introduction")
* [Motivation](https://huggingface.co/blog/tf_tpu#motivation "Motivation")
* [What to expect](https://huggingface.co/blog/tf_tpu#what-to-expect "What to expect")
* [Getting the data and training a tokenizer](https://huggingface.co/blog/tf_tpu#getting-the-data-and-training-a-tokenizer "Getting the data and training a tokenizer")
* [Tokenizing the data and creating TFRecords](https://huggingface.co/blog/tf_tpu#tokenizing-the-data-and-creating-tfrecords "Tokenizing the data and creating TFRecords")
* [Training a model on data in GCS](https://huggingface.co/blog/tf_tpu#training-a-model-on-data-in-gcs "Training a model on data in GCS")
* [Conclusion](https://huggingface.co/blog/tf_tpu#conclusion "Conclusion")
[](https://huggingface.co/blog/tf_tpu#introduction) Introduction
TPU training is a useful skill to have: TPU pods are high-performance and extremely scalable, making it easy to train models at any scale from a few tens of millions of parameters up to truly enormous sizes: Googleโs PaLM model (over 500 billion parameters!) was trained entirely on TPU pods.
Weโve previously written a [tutorial](https://huggingface.co/docs/transformers/main/perf_train_tpu_tf) and a [Colab example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) showing small-scale TPU training with TensorFlow and introducing the core concepts you need to understand to get your model working on TPU. This time, weโre going to step that up another level and train a masked language model from scratch using TensorFlow and TPU, including every step from training your tokenizer and preparing your dataset through to the final model training and uploading. This is the kind of task that youโll probably want a dedicated TPU node (or VM) for, rather than just Colab, and so thatโs where weโll focus.
As in our Colab example, weโre taking advantage of TensorFlow's very clean TPU support via XLA and `TPUStrategy`. Weโll also be benefiting from the fact that the majority of the TensorFlow models in ๐ค Transformers are fully [XLA-compatible](https://huggingface.co/blog/tf-xla-generate). So surprisingly, little wor
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