TPU Initialization Fails (OpKernel Missing) Despite Active TPU Runtime in Kaggle

Question

I am facing a persistent issue when trying to initialize the TPU in my notebook. I have already confirmed that:

My account is Verified.
The Notebook Accelerator is set to TPU.
My TPU quota is currently available.

However, the standard initialization code consistently throws a NotFoundError because the required OpKernel is missing. I suspect this is an environment configuration issue on the platform itself.

Has anyone encountered this specific OpKernel not registered error recently while using the TPU runtime and found a workaround?

Code and Error Details

Code Used:

import tensorflow as tf
# Detect and initialize TPU
tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='local')
tf.tpu.experimental.initialize_tpu_system(tpu)
# Create TPU distribution strategy
strategy = tf.distribute.TPUStrategy(tpu)
print("TPU initialized successfully.")

Traceback Snippet:

InvalidArgumentError: No OpKernel was registered to support Op 'ConfigureDistributedTPU' used by {{node ConfigureDistributedTPU}}
...
Registered devices: [CPU]
Registered kernels:
  <no registered kernels>

During handling of the above exception, another exception occurred:
NotFoundError: TPUs not found in the cluster. Failed in initialization: No OpKernel was registered to support Op 'ConfigureDistributedTPU'...

Key Observation:

The output shows Registered devices: [CPU], confirming that the environment is not detecting the active TPU accelerator at the TensorFlow software level.

Any assistance or known workarounds would be greatly appreciated\! Thank you.

You are using the wrong initialization for kaggle, instead of TPUClusterResolver(tpu='local')just use TPUClusterResolver.connect() withou argument. — Jared McCarthy
– Jared McCarthy, Commented Nov 19 at 0:00

Sagar · Accepted Answer · 2025-11-25 09:50:05Z

0

The No OpKernel error happens because setting tpu='local' incorrectly tries to force a local connection, whereas modern TPUs operate as remote resources. To fix this, let TensorFlow automatically detect the cluster address from the environment. You should perform a Factory Reset to clear the current state, then run the initialization code again without the 'local' argument.

import tensorflow as tf
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.TPUStrategy(tpu)

edited Nov 25 at 9:50

answered Nov 24 at 8:23

Sagar

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