Supported reference models

Cloud TPU provides the following set of reference models that are optimized for fast and accurate training.

The following table displays the major and minor releases of TensorFlow and PyTorch that are currently supported by Cloud TPU. In order to run the latest supported version, check to see if there are any patch releases to the major release you plan on running. If so, you can run the latest supported patch release rather than the major release, for example, TF 2.4.1 rather than TF 2.4.

Framework	Major version	Model category	Reference models	Supported versions
TensorFlow	2.x	Image classification	ResNet-2.x, MNIST-2.x, EfficientNet-2.x	2.1, 2.2, 2.3, 2.4
		Language modeling	Transformer-2.x, BERT-2.x	2.1, 2.2, 2.3, 2.4
		Language modeling	XlNet-2.x	2.4
		Object detection	RetinaNet-2.x	2.1, 2.2, 2.3, 2.4
		Image segmentation	Mask-RCNN-2.x	2.2, 2.3, 2.4
		Image segmentation	ShapeMask-2.x	2.3, 2.4
		Recommendation systems	NCF-2.x	2.3, 2.4
	1.x	Image classification	ResNet, AmoebaNet, MNasNet, MNIST, EfficientNet	1.15
		Language modeling	Transformer, BERT	1.15
		Object detection	RetinaNet	1.15
PyTorch	1.x	Image classification	ResNet-PyTorch	1.7
		Language modeling	FairSeq Transformer, FairSeq RoBERTa	1.7
		Recommendation systems	DLRM	1.7