TPU VM images

When you create TPU resources, you pass the --version or --runtime-version parameter which specifies a TPU VM image. TPU VM images contain the operating system (Ubuntu) and optionally other software required to run your code on TPUs. This document provides guidance on selecting the appropriate TPU VM image when you create Cloud TPUs.

PyTorch and JAX

Use the following common TPU VM base images for PyTorch and JAX, then install the framework you want to use.

  • tpu-ubuntu2204-base (default)
  • v2-alpha-tpuv5 (TPU v5p)

Refer to the quickstart documents for PyTorch/XLA and JAX for installation instructions.


There are TPU VM images specific to each version of TensorFlow. The following TensorFlow versions are supported on Cloud TPUs:

  • 2.16.1
  • 2.15.1
  • 2.15.0
  • 2.14.1
  • 2.14.0
  • 2.13.1
  • 2.13.0
  • 2.12.1
  • 2.12.0
  • 2.11.1
  • 2.11.0
  • 2.10.1
  • 2.10.0
  • 2.9.3
  • 2.9.1
  • 2.8.4
  • 2.8.3
  • 2.8.0
  • 2.7.4
  • 2.7.3

For more information on TensorFlow patch versions, see Supported TensorFlow patch versions.

For TensorFlow versions 2.15.0 and newer there are TPU VM image variants based on the device API (PJRT or stream executor) you are using.

Training on v5p and v5e

TPU v5e and v5p support TensorFlow 2.15.0 and newer. You specify the TPU VM image using the form: tpu-vm-tf-x.y.z-{pod}-pjrt where x is the major TensorFlow version, y is the minor version, and z is the TensorFlow patch version. Add pod after the TensorFlow version if you are using a multi-host TPU. For example, if you are using TensorFlow 2.16.0 on a multi-host TPU, use the tpu-vm-tf-2.16.0-pod-pjrt TPU VM image. For other versions of TensorFlow, replace 2.16.0 with the major and patch versions of TensorFlow you are using. If you are using a single host TPU, omit pod.

Serving on v5e

There are serving Docker images that contain all needed software requirements for serving with TensorFlow, PyTorch, and JAX. For more information, see Cloud TPU v5e inference introduction.

TPU v4

If you are using TPU v4 and TensorFlow 2.15.0 or newer, follow the instructions for training on v5p and v5e. If you are using TensorFlow 2.10.0 or earlier, use a v4-specific TPU VM image:

TensorFlow version TPU VM image version
2.10.0 tpu-vm-tf-2.10.0-v4
2.9.3 tpu-vm-tf-2.9.3-v4
2.9.2 tpu-vm-tf-2.9.2-v4
2.9.1 tpu-vm-tf-2.9.1-v4

TPU v2 and v3

If you are using TPU v2 or v3, use the TPU VM image that matches the version of TensorFlow you are using. For example if you are using TensorFlow 2.14.1, use the tpu-vm-tf-2.14.1 TPU image. For other versions of TensorFlow, replace 2.14.1 with the TensorFlow version you are using. If you are using a multi-host TPU append pod to the end of the TPU image, for example tpu-vm-tf-2.14.1-pod.

Beginning with TensorFlow 2.15.0, you must also specify a device API. For example, if you are using TensorFlow 2.16.1 with the PJRT API, use the TPU image tpu-vm-tf-2.16.1-pjrt. If you are using the stream executor API with the same version of TensorFlow, use the tpu-vm-tf-2.16.1-se TPU image. TensorFlow versions older than 2.15.0 only support stream executor.

TensorFlow PJRT support

Beginning with TensorFlow 2.15.0, you can use the PJRT interface for TensorFlow on TPU. PJRT features automatic device memory defragmentation and simplifies the integration of hardware with frameworks. For more information about PJRT, see PJRT: Simplifying ML Hardware and Framework Integration.

Accelerator Feature PJRT support Stream executor support
TPU v2 - v4 Dense compute (no TPU embedding API) Yes Yes
TPU v2 - v4 Dense compute API + TPU embedding API No Yes
TPU v2 - v4 tf.summary/tf.print with soft device placement No Yes
TPU v5e Dense compute (no TPU embedding API) Yes No
TPU v5e TPU embedding API N/A No
TPU v5p Dense compute (no TPU embedding API) Yes No
TPU v5p TPU embedding API Yes No

Libtpu versions

TPU VMs TensorFlow images contain a specific TensorFlow version and the corresponding libtpu library. If you are creating your own VM image, use the following TensorFlow TPU software versions and corresponding libtpu versions:

TensorFlow version version
2.16.1 1.10.1
2.15.1 1.9.0
2.15.0 1.9.0
2.14.1 1.8.1
2.14.0 1.8.0
2.13.1 1.7.1
2.13.0 1.7.0
2.12.1 1.6.1
2.12.0 1.6.0
2.11.1 1.5.1
2.11.0 1.5.0
2.10.1 1.4.1
2.10.0 1.4.0
2.9.3 1.3.2
2.9.1 1.3.0
2.8.3 1.2.3
2.8.0 1.2.0
2.7.3 1.1.2

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