Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence warning when iterating over a dataset
@p-s-p-s I tried to replicate the issue on colab and didn't face the error reported. Could you check this [gist](https://colab.research.google.com/gist/sushreebarsa/243351ba215c7da47efe5f712de96d81/62963.ipynb) and let us know?
Thank you! @sushreebarsa tf 2.15 is not affected, but 2.16 and 2.17 are. @sushreebarsa The reason you couldn't reproduce the error in colab is because the warnings are suppressed by default. Could you please check this colab https://colab.research.google.com/drive/1JuQriKXe-aJBAbValQK-8BFGtktzw4IW?usp=sharing ? @p-s-p-s TF v2.15 is the latest stable version so error is not appearing there.
We recommend you to use the stable TF version. 
Thank you! @sushreebarsa I reported this issue in order to make it fixed before 2.16 release. Moreover, tf 2.15 with https://github.com/tensorflow/tensorflow/commit/04fb826f98b92dd172ad665d8a5522a2f8201867 applied is also internally affected by this issue. 
```
>>> import tensorflow as tf
2024-02-21 10:47:33.109381: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-02-21 10:47:33.109409: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-02-21 10:47:33.110044: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-02-21 10:47:33.113595: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
>>> range_ds = tf.data.Dataset.range(10)
2024-02-21 10:47:54.344759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22462 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:01:00.0, compute capability: 8.6
>>> 
>>> for d in range_ds:
...    print(d)
... 
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)
tf.Tensor(2, shape=(), dtype=int64)
tf.Tensor(3, shape=(), dtype=int64)
tf.Tensor(4, shape=(), dtype=int64)
tf.Tensor(5, shape=(), dtype=int64)
tf.Tensor(6, shape=(), dtype=int64)
tf.Tensor(7, shape=(), dtype=int64)
tf.Tensor(8, shape=(), dtype=int64)
tf.Tensor(9, shape=(), dtype=int64)
2024-02-21 10:47:56.048435: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
>>> tf.__version__
'2.15.0'
```
I am not sure what causes the problem, but as a symptomatic solution it is possible to disable some warnings like this:
```
  if (!absl::StrContains(status.message(), "End of sequence")) {
    LOG(WARNING) << "Local rendezvous is aborting with status: " << status;
  }
```

 @sachinprasadhs I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/b2b9055007dfbb8eced10c86fab08815/copy-of-62963.ipynb), please have a look. Thank you! Can confirm this issue with tf-nightly `'2.17.0-dev20240210'`

```
2024-02-26 04:03:26.379054: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence  
	 [[{{node IteratorGetNext}}]]   
	 [[IteratorGetNext/_4]]  
2024-02-26 04:03:26.379063: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 381694510697024129
2024-02-26 04:03:26.379073: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 6451170228096927380
``` Hello, I'd like to look into this issue and try to fix it, if that is possible. Issue running default tensorflow training job after docker rebuild only on RTX-A4500

```
from tensorflow/tensorflow:latest-gpu


[+] Building 137.8s (9/9) FINISHED                                                                           docker:default
 => [internal] load build definition from Dockerfile                                                                   0.0s
 => => transferring dockerfile: 285B                                                                                   0.0s
 => [internal] load .dockerignore                                                                                      0.0s
 => => transferring context: 2B                                                                                        0.0s
 => [internal] load metadata for docker.io/tensorflow/tensorflow:latest-gpu                                            1.2s
 => [auth] tensorflow/tensorflow:pull token for registry-1.docker.io                                                   0.0s
 => [1/3] FROM docker.io/tensorflow/tensorflow:latest-gpu@sha256:4ab9ffddd6ffacc9251ac6439f431eb38d66200d3f52397b5d  135.7s


         [[RemoteCall]]
25/25 ━━━━━━━━━━━━━━━━━━━━ 8s 316ms/step - accuracy: 0.1601 - loss: 8.1802
Epoch 3/100
24/25 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step - accuracy: 0.2835 - loss: 7.41432024-03-10 04:24:44.885256: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
         [[{{node MultiDeviceIteratorGetNextFromShard}}]]
2024-03-10 04:24:44.885302: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
         [[{{node MultiDeviceIteratorGetNextFromShard}}]]
         [[RemoteCall]]
2024-03-10 04:24:44.896965: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
         [[{{node MultiDeviceIteratorGetNextFromShard}}]]
2024-03-10 04:24:44.897036: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
         [[{{node MultiDeviceIteratorGetNextFromShard}}]]
         [[RemoteCall]]
25/25 ━━━━━━━━━━━━━━━━━━━━ 8s 307ms/step - accuracy: 0.2795 - loss: 7.2638

```
https://github.com/ObrienlabsDev/machine-learning/issues/16

 My understanding is the error will not affect the execution but the iterator was not usable after the error? https://stackoverflow.com/questions/53930242/how-to-fix-a-outofrangeerror-end-of-sequence-error-when-training-a-cnn-with-t @salaki 
I didn't observe any negative impact, except it is quite annoying to receive this warning every time you iterate over a dataset. As a temporary fix for 2.16.1 I just commented out this line in [tensorflow/core/framework/local_rendezvous.cc](https://github.com/tensorflow/tensorflow/commit/04fb826f98b92dd172ad665d8a5522a2f8201867#diff-03234cff994e694d13b64ea69e4920cb17f1e07fb9a3aab1f1783ef0f9635741)
`//  LOG(WARNING) << "Local rendezvous is aborting with status: " << status;`
and recompiled TF from source.  Another example to reproduce this issue with Python 3.12.2 and TensorFlow 2.16.1 is the fourth installment of the introductory videos, "TensorFlow ML Zero to Hero". The fourth part uses this notebook. When training every second epoch falls over with the issue reported here.

When I switch to TensorFlow 2.15.1, I also have to downgrade to Python version 3.11.8 which is something I'd like to avoid. Ideally TensorFlow 2.15.1 should be made available to the most recent stable release of Python at least until a newer stable version of TensorFlow becomes available. Combo Python 3.11.8 and TensorFlow 2.15.1 works for the given notebook.

Here is the link to that notebook that I mentioned. It runs fine online but not locally if using Python 3.12.2 and TensorFlow 2.16.1.

https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Course%202%20-%20Part%208%20-%20Lesson%202%20-%20Notebook%20(RockPaperScissors).ipynb

This link is also accessible form the description in the video at https://www.youtube.com/watch?v=u2TjZzNuly8

I hope having another example to reproduce the problem helps with resolving this issue. Keep up the good work! @google-admin @goolge Just please fire all these "issue triagers". They are a waste of our time, and a waste of your money. All they do is copy paste the code in collab with blindfolds, fuck it up with a 90% chance, and tell you you're wrong. They are a disgrace to our intellect. Same issue on a larger training project :
![screen215](https://github.com/tensorflow/tensorflow/assets/97630/78745aa4-883f-4d10-840a-02307bcd2f50)

I don't know if it helps or if it is related think one of the recent additions to the code was to use strategies and scopes : 
strategy = tf.distribute.OneDeviceStrategy(device='/gpu:0')
with strategy.scope(): Hi all, this didn't make it our (tf.data team's) way until just now, when an internal user flagged it.  This should be fixed with https://github.com/tensorflow/tensorflow/commit/4924ec6c0b68ba3fb8f73a6383881cd4194ed802. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62963">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62963">No</a>
 The error is even on the official tf website, so hopefully it will soon be fixed. 
[https://www.tensorflow.org/tutorials/quickstart/advanced](https://www.tensorflow.org/tutorials/quickstart/advanced ) > Same issue on a larger training project : ![screen215](https://private-user-images.githubusercontent.com/97630/322061850-78745aa4-883f-4d10-840a-02307bcd2f50.jpg?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.X2OGLGblpjodS0bsvvKffB0sestvdhCxC-_X9B-38R4)
> 
> I don't know if it helps or if it is related think one of the recent additions to the code was to use strategies and scopes : strategy = tf.distribute.OneDeviceStrategy(device='/gpu:0') with strategy.scope():

In my circumstance, the distribution strategy is not related to this problem after my double check. Similar error. Fixed it by removing the steps_per_epoch argument from model.fit() and model.evaluate()

import sys
from matplotlib import pyplot
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import numpy as np

physical_devices = tf.config.list_physical_devices('GPU')
try:
  tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
  # Invalid device or cannot modify virtual devices once initialized.
  pass

 # define cnn model
def define_model():
 model = Sequential()
 model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))
 model.add(MaxPooling2D((2, 2)))
 model.add(Flatten())
 model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
 model.add(Dense(1, activation='sigmoid'))
 # compile model
 opt = SGD(learning_rate=0.001, momentum=0.9)
 model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
 return model

# create data generator
datagen = ImageDataGenerator(rescale=1.0/255.0)
model = define_model()

# prepare iterators
train_it = datagen.flow_from_directory('/workspace/workspace/cats_and_dogs_data/dogs-vs-cats/train/',
 class_mode='binary', batch_size=64, target_size=(200, 200))
test_it = datagen.flow_from_directory('/workspace/workspace/cats_and_dogs_data/dogs-vs-cats/test1/',
 class_mode='binary', batch_size=64, target_size=(200, 200))

# fit model
history = model.fit(train_it, validation_data=test_it, epochs=20, verbose=1)


# evaluate model
_, acc = model.evaluate(test_it, verbose=1)
print('> %.3f' % (acc * 100.0))


 I can reproduce the warning on Python 3.12 and TF 2.16. In addition, when my (custom) dataset has this 'issue' then I also get messages when calling `model.evaluate(ds)`. That just doesn't look like it's safe to ignore it. Example:

```
    919/Unknown 1s 2ms/step - loss: 1.05462024-05-23 10:15:02.410200: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
	 [[{{node IteratorGetNext}}]]
/usr/lib/python3.12/contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
  self.gen.throw(value)
927/927 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 1.0546
```

**PS:** I DO have enough data. **I made this disappear by simply using `.repeat()` and **not using** `.cache()`**  on my training and validation data batches.

My script has a very generic input pipeline based on the [ tensorflow semantic segmentation tutorial](https://www.google.com/search?client=firefox-b-d&q=tensorflow+semenatic+segmentation) with tf 2.16 and python 3.10
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

binary

### TensorFlow version

tf 2.16

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

There is a warning which appears after the last iteration over a dataset:
W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence

This warning report was introduced by this commit: https://github.com/tensorflow/tensorflow/commit/04fb826f98b92dd172ad665d8a5522a2f8201867
I believe that simple iteration over a dataset shouldn't cause such behavior. 

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

range_ds = tf.data.Dataset.range(10)

for d in range_ds:
   print(d)
```


### Relevant log output

```shell
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)
tf.Tensor(2, shape=(), dtype=int64)
tf.Tensor(3, shape=(), dtype=int64)
tf.Tensor(4, shape=(), dtype=int64)
tf.Tensor(5, shape=(), dtype=int64)
tf.Tensor(6, shape=(), dtype=int64)
tf.Tensor(7, shape=(), dtype=int64)
tf.Tensor(8, shape=(), dtype=int64)
tf.Tensor(9, shape=(), dtype=int64)
2024-02-15 08:27:36.782604: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
```
