NotFoundError: Graph execution error: TPU

Comience reproduciendo el fallo notificado de model_.fit con Python 3.10, TensorFlow 2.15, Keras 3.0.5 y una TPU VM local utilizando el ejemplo de MNIST proporcionado. Inspeccione el error completo de ejecución del grafo de TPU y determine si el fallo se encuentra en la interacción entre TensorFlow, Keras y TPU; se considera completado cuando se hayan identificado una causa reproducible y una resolución verificada.

While trying to run the following code on tpu-vm, it didn't work.

tf: 2.15
keras: 3.0.5

tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu="local")
strategy = tf.distribute.TPUStrategy(tpu)

def get_compiled_model():
    # Make a simple 2-layer densely-connected neural network.
    inputs = keras.Input(shape=(784,))
    x = keras.layers.Dense(256, activation="relu")(inputs)
    x = keras.layers.Dense(256, activation="relu")(x)
    outputs = keras.layers.Dense(10)(x)
    model = keras.Model(inputs, outputs)
    model.compile(
        optimizer=keras.optimizers.Adam(),
        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        metrics=[keras.metrics.SparseCategoricalAccuracy()],
    )
    return model


def get_dataset():
    batch_size = 32
    num_val_samples = 10000

    # Return the MNIST dataset in the form of a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

    # Preprocess the data (these are Numpy arrays)
    x_train = x_train.reshape(-1, 784).astype("float32") / 255
    x_test = x_test.reshape(-1, 784).astype("float32") / 255
    y_train = y_train.astype("float32")
    y_test = y_test.astype("float32")

    # Reserve num_val_samples samples for validation
    x_val = x_train[-num_val_samples:]
    y_val = y_train[-num_val_samples:]
    x_train = x_train[:-num_val_samples]
    y_train = y_train[:-num_val_samples]
    return (
        tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size),
        tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(batch_size),
        tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size),
    )

with strategy.scope():
    model_ = get_compiled_model()
    train_dataset, val_dataset, test_dataset = get_dataset()

model_.fit(train_dataset, epochs=2, validation_data=val_dataset)

---------------------------------------------------------------------------
NotFoundError                             Traceback (most recent call last)
Cell In[5], line 1
----> 1 model_.fit(train_dataset, epochs=2, validation_data=val_dataset)

File /usr/local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:123, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    120     filtered_tb = _process_traceback_frames(e.__traceback__)
    121     # To get the full stack trace, call:
    122     # `keras.config.disable_traceback_filtering()`
--> 123     raise e.with_traceback(filtered_tb) from None
    124 finally:
    125     del filtered_tb

File /usr/local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     51 try:
     52   ctx.ensure_initialized()
---> 53   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     54                                       inputs, attrs, num_outputs)
     55 except core._NotOkStatusException as e:
     56   if name is not None:

NotFoundError: Graph execution error:

Detected at node TPUReplicate/_compile/_9074053372847989778/_4 defined at (most recent call last):
<stack traces unavailable>