TPU management

pip install tpudiepie==0.1.7



tpunicorn (or pu for short) is a Python library and command-line program for managing TPUs. For example, if you have a preemptible TPU named foo, then pu babysit foo will recreate it automatically whenever it preempts.

See examples.



# Install pu
sudo pip3 install -U tpunicorn

(Use sudo pip3 at your own risk. It's potentially easier, since pu is guaranteed to end up on your PATH regardless of your platform, but see installation caveats for a discussion of the tradeoffs.)

# View your TPUs
pu list

# Recreate a TPU named foo
pu recreate foo

# Watch a TPU named foo. If it preempts, recreate it automatically
pu babysit foo

Skip ahead to examples to see what else pu can do.

Installation Caveats

  • pu assumes you can successfully run gcloud compute tpus list. If so, then you're done! Otherwise, see the Troubleshooting section.

  • Rather than sudo pip3 install -U tpunicorn, you can install via a more "recommended" approach. (For example, the magic wormhole project lists some reasons you might want to avoid sudo pip3.)

Option 1: a local install

pip3 install --user -U tpunicorn
# add python's user directory to your PATH
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
# restart your shell
exec -l $SHELL
# does pu work?
pu list

Option 2: a virtualenv

virtualenv venv
source venv/bin/activate
pip3 install tpunicorn
pu list

Unfortunately, you may experience a serious slowdown when using venv. pu is implemented by shelling out to gcloud compute tpus ..., and gcloud seems to be very unhappy when it's run inside of a virtualenv. gcloud compute tpus list takes ~8 seconds for me, which is a noticeable delay, and makes pu list quite uncomfortable to use. I've attempted to debug this, but as far as I can tell, the slowdown is somewhere deep inside of gcloud internals related to reconfiguring paths. I assume it's detecting the venv and doing some sort of reconfiguration to account for it.


Seeing your TPUs

pu list shows your TPUs.


The INDEX is determined by checking whether your TPU name ends with a number. It's common to create TPUs named like tpu1, tpu2, etc. If you use such a naming scheme, the number becomes its INDEX and you can refer to the TPU by number via the command line, which is far easier than typing out the whole name.

(If two TPUs have the same index, an error is thrown if you attempt to refer to either of them by number, since that would be ambiguous.)

Seeing your TPUs continuously

pu top is like htop for TPUs. Every few seconds, it clears the screen and runs pu list, i.e. it shows you the current status of all your TPUs. Use Ctrl-C to quit.

Recreating a TPU

pu recreate <TPU> recreates an existing TPU, waits for the TPU's health to become HEALTHY, then runs the commands specified via -c <command>. To run multiple commands, pass multiple -c <command> options.

# Recreate a TPU named foo
pu recreate foo
# Recreate a TPU named foo, but only if it's PREEMPTED. Don't prompt
# for confirmation. After the TPU recreates and is HEALTHY, run a
# command.
pu recreate foo --preempted --yes -c 'echo This only runs after the TPU is HEALTHY'
# `pu babysit foo` is roughly equivalent to the following. (The -c
# options are provided here for illustration purposes; you can pass
# those to `pu babysit` as well.)
while true
  pu recreate foo --preempted --yes \
   -c "echo TPU recreated. >> logs.txt" \
   -c "pkill -9 -f"
  sleep 30

Babysitting a preemptible TPU

pu babysit <TPU> will watch the specified TPU, recreating it whenever it preempts. You can specify commands to run afterwards via -c <command>. (For example, a command to kill your current training session, or send you a message.) To run multiple commands, pass multiple -c <command> options.

In a terminal, simulate a training session:

while true
  bash -c 'echo My Training Session; sleep 10000'
  echo restarting
  sleep 1

In a separate terminal, babysit a TPU named my-tpu:

pu babysit my-tpu -c 'pkill -9 -f "My Training Session"'

Whenever the TPU preempts, that command will:

  • recreate the TPU named my-tpu
  • wait for the TPU's health to become HEALTHY
  • kill our simulated training session

The simulated training session will echo "restarting", indicating that it was successfully killed and the training process restarted itself.

In a real-world scenario, be sure that the pkill command only kills one specific instance of your training script. For example, if you run multiple training sessions with a script named using different TPU_NAME environment vars, a naive pkill command like pkill -f would kill all of your training sessions, rather than the one associated with the TPU.

(To solve that, I normally pass the TPU name as a command-line argument, then run pkill -9 -f <TPU>.)

Also, be sure to pass pkill -9 rather than pkill. That way, your training session will be restarted even if it's frozen.

Lastly, consider running your actual training script like so:

while true
  timeout --signal=SIGKILL 11h <your training command>
  echo restarting
  sleep 30

This will force-kill your training command after a maximum of 11 hours of training (rather than waiting the theoretical maximum of 24 hours before your TPU preempts). This way, if your training session freezes for some reason (e.g. the TPUEstimator API stops making progress) then you'll lose no more than a few hours of training time.

Without this, we kept running into situations like "wake up the next day and discover that the training session has been frozen for the last 12 hours." We're still not entirely sure why. Suffice to say, if your training session takes an hour to get into a stable state, you'll lose only ~2 hours in the usual case (no freezes; everything normal) and gain several hours in the worst case (the training loop froze and no one noticed).

You might feel tempted to put a pu recreate $TPU_NAME -y command inside that while loop. After all, if your training session terminates, shouldn't it recreate the TPU? Perhaps; feel free to try it out and see if you like it. In our experience, we've found it's more effective to manage our TPUs separately rather than try to solve both concerns in the same script.

Listing TPUs

pu list shows the current status of all your TPUs. You can use -t/--tpu <TPU> to print the status of one specific TPU. To print the status of multiple TPUs, pass multiple -t <TPU> options.

# List TPU named foo. If it doesn't exist, throw an error.
pu list -t foo
# Dump the TPU in json format. If it doesn't exist, throw an error.
pu list -t foo --format json
# List TPUs named foo or bar. If foo or bar don't exist, don't throw an
# error. For each TPU, print a line of JSON. Then use `jq` to extract
# some interesting subfields, and format the result using `column`.
pu list -t foo -t bar -s --format json | \
     jq '.name+" "+.state+" "+(.health//"UNKNOWN")' -c -r | column -t


pu babysit

Usage: tpunicorn babysit [OPTIONS] TPU

  Checks TPU every INTERVAL seconds. Recreates the TPU if (and only if) the
  tpu has preempted.

  --zone [asia-east1-c|europe-west4-a|us-central1-a|us-central1-b|us-central1-c|us-central1-f]
  -i, --interval <seconds>        How often to check the TPU. (default: 30

  -c, --command TEXT              After the TPU has been recreated and is
                                  HEALTHY, run this command. (Useful for
                                  killing a training session after the TPU has
                                  been recreated.)

  --help                          Show this message and exit.

pu recreate

Usage: tpunicorn recreate [OPTIONS] TPU

  Recreates a TPU, optionally switching the system software to the specified

  --zone [asia-east1-c|europe-west4-a|us-central1-a|us-central1-b|us-central1-c|us-central1-f]
  --version <TF_VERSION>          By default, the TPU is recreated with the
                                  same system software version. You can set
                                  this to use a specific version, e.g.

  -y, --yes
  -p, --preempted                 Only recreate TPU if it has
                                  preempted. (Specifically, if the tpu's STATE
                                  is "PREEMPTED",proceed; otherwise do

  -c, --command TEXT              After the TPU is HEALTHY, run this
                                  command. (Useful for killing a training
                                  session after the TPU has been recreated.)

  --help                          Show this message and exit.

pu list

Usage: tpunicorn list [OPTIONS]

  List TPUs.

  --zone [asia-east1-c|europe-west4-a|us-central1-a|us-central1-b|us-central1-c|us-central1-f]
  --format [text|json]
  -c, --color / -nc, --no-color
  -t, --tpu TEXT                  List a specific TPU by id.
  -s, --silent                    If listing a specific TPU by ID, and there
                                  is no such TPU, don't throw an error.

  --help                          Show this message and exit.

pu reimage

Usage: tpunicorn reimage [OPTIONS] TPU

  Reimages the OS on a TPU.

  --zone [asia-east1-c|europe-west4-a|us-central1-a|us-central1-b|us-central1-c|us-central1-f]
  --version <TF_VERSION>          By default, the TPU is reimaged with the
                                  same system software version. (This is handy
                                  as a quick way to reboot a TPU, freeing up
                                  all memory.) You can set this to use a
                                  specific version, e.g. `nightly`.

  -y, --yes
  --help                          Show this message and exit.


  1. Ensure your project is set
gcloud config set project <your-project-id>

Note that the project ID isn't necessarily the same as the project name. You can get it via the GCE console:


While you're there, go the Cloud TPU page:


If it asks you to enable the Cloud TPU API, then do so. Afterwards you should see the GCE TPU dashboard:


Create a TPU using "Create TPU node" to verify that your project has TPU quota in the desired region.

  1. Ensure your command-line tools are properly authenticated

Use gcloud auth list to see your current account.

If security isn't a concern, you can use gcloud auth login followed by gcloud auth application-default login to log in as your primary Google identity. Usually, this means that your terminal now has "root access" to all GCE resources.

If you're on a server, you might want to use a service account instead.

  • create a service account, granting it the "TPU Admin" role for TPU management, or "TPU Viewer" role for read-only viewing.

  • create a keyfile

  • Upload the keyfile to your server. (I use wormhole send ~/keys.json for that. You can install it with pip install magic-wormhole.)

  • activate your service account

For example, I created a tpu-test service account, then created a ~/tpu_key.json keyfile:

$ gcloud iam service-accounts keys create ~/tpu_key.json --iam-account
created key [03db745322b4e7c4e9e2036386d1e908eb2e1a52] of type [json] as [/Users/bb/tpu_key.json] for []

Then I sent that ~/tpu_key.json file to my server, and activated the service account:

$ gcloud auth activate-service-account --key-file ~/tpu_key.json
Activated service account credentials for: []

I checked gcloud auth list to verify I'm now using that service account:

$ gcloud auth list
                  Credentialed Accounts

To set the active account, run:
    $ gcloud config set account `ACCOUNT`

At that point, as long as you've run gcloud config set project <your-project-id>, then gcloud compute tpus list --zone europe-west4-a should be successful.

(To avoid having to pass --zone europe-west4-a to all your gcloud commands, you can make it the default zone:

gcloud config set compute/zone europe-west4-a

As far as I know, it's completely safe to make a "TPU Viewer" service account world-readable. For example, if you want to let everyone view your TPUs for some reason, you can simply stick the ~/tpu_key.json file somewhere that anyone can download.

(If this is mistaken, please DM me on twitter.)

ML Community

If you're an ML enthusiast, join our TPU Podcast Discord Server. There are now ~400 members, with ~60 online at any given time:


There are a variety of interesting channels:

  • #papers for pointing out interesting research papers
  • #research for discussing ML research
  • #show and #samples for showing off your work
  • #hardware for hardware enthusiasts
  • #ideas for brainstorming
  • #tensorflow and #pytorch
  • #cats, #doggos, and of course #memes
  • A "bot zone" for interacting with our discord bots, such as using !waifu red_hair blue_eyes to generate an anime character using stylegan: image
  • Quite a few more.


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