tensorflow keras model.predict() is not thread safe
@henghamao I have tried to replicate the code in both TF v[2.12](https://colab.research.google.com/gist/sushreebarsa/e16d74cbeafeed276592a84d5034591c/61298.ipynb) and [2.13](https://colab.research.google.com/gist/sushreebarsa/c469631cfe2252acf1a871f92496cf8a/61298.ipynb#scrollTo=7oZEGiY_t-0o). I haven't faced the issue reported here. Could you find the attached gists and confirm the same? Please let me know if i am missing something to replicate the issue.
Thank you! Thanks for looking at the issue.
The issue happend with Python 3.11, and we seldom observed the issue with Python 3.8.
In addition, the model prediction were execueted with multi-thread.
Here is an example to show the major process:
```
import threading
import numpy as np
import tensorflow as tf
from models.model_attention import AttentionModel
from models.model_attention_three_category import AttentionThreeCategoryModel


class PredictTask(object):
    def __init__(self, thread_num):
        self.thread_num = thread_num
        self.result = []
        # modified to the local model path
        self.model1 = AttentionThreeCategoryModel(40, 20, 10)
        self.model1.load_model('/data/model_save')
        self.model2 = AttentionModel(40, 20, 1)
        self.model2.load_model('/data/model_save')

        self.sl = []
        for i in range(8000):
            # generated data to predict
            self.sl.append(np.random.random((1, 40, 20)))
        return

    def start(self):
        pool = []
        for i in range(min(self.thread_num, len(self.sl))):
            t = threading.Thread(target=self.predict_thread, args=(i,))
            pool.append(t)
            t.start()
        for t in pool:
            t.join()
        return

    def predict_thread(self, tid):
        loop = tid
        cnt = 0
        print('Predict thread %d starting...' % tid)
        while loop < len(self.sl):
            cnt += 1
            try:
                self.predict(self.sl[loop])
                if cnt % 10 == 0:
                    print("Thread %d, predict progress: %d/%d" % (tid, loop, len(self.sl)))
                loop += self.thread_num
            except Exception as e:
                print('Fail to predict stock: %s'%self.sl[loop].stock_sym)
                print(e)
                continue
    
        print('Predict thread %d completed, finished task number %d.' % (tid, cnt))   

    def predict(self, x):
        try:
            pred1 = self.model2.model.predict(x)
            pred2 = self.model1.model.predict(x)
        except Exception as ex:
            print(ex)
            exit(0)
        return (pred1, pred2) 

def main():
    p = PredictTask(8)
    p.start()

if __name__ == "__main__":
    main()
``` @henghamao You should use multi processing to utilize all your resources as multi threading is not recommended mostly. Loading several models in several threads have its own overhead. Could you try increasing the batch size?  

Thank you! In the code, we didnot load models in several threads. We loaded the model in the main process, and used multi-thread to do the predictions.
And we found out a simple workaround is to retry model prediction while the exception raised. With this method, we could get all correct prediction results, though we still observed exception raised sometimes.
```
def predict(self, x, tf_server=False, port=8501, model_path='', step=0):
        if tf_server:
            return self.predict_tf_server_grpc(x, port, step)
        pred = None
        fail_cnt = 0
        max_fail = 5
        while fail_cnt < max_fail:
            try:
                if not self.model_trained:
                    print('try to load model ...\n')
                    self.load_model(model_path)
                    self.model_trained = True
                if step == 0:
                    pred = self.model.predict(x)
                else:
                    pred = self.model.predict(x, steps=step)
                break
            except Exception as ex:
                print(ex)
                fail_cnt += 1
        return pred
``` @henghamao Could you please downgrade the python version and retry the training as it is not replicating the reported error there. TF v2.13 is also compatible with that. Thank you! Downgrade python to 3.8, we did not observe the issue. Hi, 

Thanks for reporting the issue.

Tensorflow in general is not considered as Thread safe, since it has the [GIL](https://wiki.python.org/moin/GlobalInterpreterLock) concept, which prevents threads from executing Python code simultaneously in a single process.

Even if your code has executed successfully in other python version, it does not guarantee or makes a valid use case for Tensorflow to work thread safe. Thanks for the clarifications.
For multi-thread model predictions, we would use tf serving.
And we could close the issue. 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/61298">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/61298">No</a>

### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.13.0

### Custom code

Yes

### OS platform and distribution

Linux CentOS 7.9

### Mobile device

_No response_

### Python version

3.11.4

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

We executed model.predict() in multi-thread. And sometimes, the code raised the exception: Functional' object has no attribute 'predict_function.

### Standalone code to reproduce the issue

```shell
def predict(self, x, tf_server=False, port=8501, model_path='', step=0):
        if tf_server:
            return self.predict_tf_server_grpc(x, port, step)
        pred = None
        try:
            if not self.model_trained:
                print('try to load model ...\n')
                self.load_model(model_path)
                self.model_trained = True
            if step == 0:
                pred = self.model.predict(x)
            else:
                pred = self.model.predict(x, steps=step)
        except Exception as ex:
            print(ex)
        return pred
```
While the exception raised, we executed the code "self.model.predict(x)" in debug window again, and it returned the correct prediction results.
```


### Relevant log output

_No response_