Dataset is never fully read when caching to disk
The behavior you are observing is expected, and it is not a logging issue. When we cache a dataset to disk with **dataset.cache("some_folder")**, TensorFlow only partially caches the dataset to disk. The caching mechanism works by storing the elements of the dataset in memory as they are consumed, and then periodically flushing these elements to disk to avoid consuming too much memory.

If you want to ensure that the dataset is fully cached to disk, you can add a call to **dataset.prefetch()** after the call to **dataset.cache()**. This will cause TensorFlow to fully cache the dataset to disk before starting to consume it.

```
import tensorflow as tf

dataset = tf.data.Dataset.range(1_000_000, dtype=tf.float32)
dataset = dataset.cache("cached")
dataset = dataset.prefetch(tf.data.AUTOTUNE)
for _ in dataset:
    pass # Exhaust the dataset to force it to cache

```

 Sorry, even with the addition of `prefetch` I am still seeing this log message. Try `dataset = dataset.take(5000).cache("cached")`  #Use take() before cache(). the take() function is used to create a new dataset that contains only the first 500 elements of the original dataset. The cache() function is then called on this new dataset to ensure that only the first 500 elements are cached. Finally, the dataset is iterated over to force it to cache.

By using take() before cache(), the warning message should be resolved and the dataset should be properly cached. https://github.com/tensorflow/tensorflow/issues/42773
 I have not tested this solution, however I will not use it because I want to iterate over the entire dataset and not just part of it. Additionally, I do not use `take` or any other function that limits the number of elements used from the dataset. @Thf772 , I would second to the suggestion given by @bhagirath20. 
`Cache` should be used only when the dataset is small and if the dataset fits into your memory. 
The existing behavior is expected.
In general, cache should be used after loading and preprocessing the data but before `shuffling, repeating, batching, and prefetching` so that each instance will only be read and preprocessed once instead of every epoch in your model. This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

No

### Source

binary

### Tensorflow Version

v2.12.0-rc1-12-g0db597d0d75 2.12.0

### Custom Code

No

### OS Platform and Distribution

Windows 11

### Mobile device

_No response_

### Python version

Python 3.10.7

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current Behaviour?

When caching a dataset to disk with `dataset.cache("some_folder")`, the dataset is never fully cached according to the log message. I have reproduced this consistently in my application code and in the MWE below.

When testing this MWE, the message does not appear on subsequent runs unless I delete the cache files, which makes me think Tensorflow is actually able to them, and this might be a logging issue.

When caching to memory (`dataset.cache()`), no log message is generated.



### Standalone code to reproduce the issue

```python
import tensorflow as tf

dataset = tf.data.Dataset.range(1_000_000, dtype=tf.float32)
dataset=dataset.cache("cached")
for _ in dataset:
    pass # Exhaust the dataset to force it to cache
```


### Relevant log output

```text
2023-03-30 12:15:57.441876: W tensorflow/core/kernels/data/cache_dataset_ops.cc:296] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
```
</details>