Convert a TensorFlow model in a format that can be served

There is multiple ways of doing this, and other methods could be required for more complex models. I am currently using the method described here, which works great for tf.keras.models.Model and tf.keras.Sequential models (not sure for tensorflow subclassing?).

Below is a minimal working example, including creating a model using python (it seems like you have already completed this by your folder structure and can ignore the first step)

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
import tensorflow.keras.backend as K

inputs = Input(shape=(2,))
x = Dense(128, activation='relu')(inputs)
x = Dense(32, activation='relu')(x)
outputs = Dense(1)(x)

model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='mse')

# loading existing weights, model architectural must be the same as the existing model
#model.load_weights(".//MODEL_WEIGHT_PATH//WEIGHT.h5") 

export_path = 'SAVE_PATH//tensorflow_model//1'

with K.get_session() as sess:
    tf.saved_model.simple_save(
            sess,
            export_path,
            inputs={'inputs': model.input}, # for single input
            #inputs={t.name[:-5]: t for t in model.input}, # for multiple inputs
            outputs={'outputs': model.output})

I suggest you use folder name "tensorflow_model" instead of "tensorflow model", to avoid possible problems with spaces.

Then we can build the docker image in terminal by (for windows, use ^ instead of \ for line brake, and use //C/ instead of C:\ in path):

docker run -p 8501:8501 --name tfserving_test \
  --mount type=bind,source="SAVE_PATH/tensorflow_model",target=/models/tensorflow_model \
  -e MODEL_NAME=tensorflow_model -t tensorflow/serving

Now the container should be up and running, and we can test the serving with python

import requests
import json
#import numpy as np

payload = {
  "instances": [{'inputs': [1.,1.]}]
}

r = requests.post('http://localhost:8501/v1/models/tensorflow_model:predict', json=payload)
print(json.loads(r.content))
# {'predictions': [[0.121025]]}

The container is working with our model, giving the prediction 0.121025 for the input [1., 1.]

I hope this helps:

import tensorflow as tf
from tensorflow.contrib.keras import backend as K
from tensorflow.python.client import device_lib

K.set_learning_phase(0)
model = tf.keras.models.load_model('my_model.h5')


export_path = './'
with K.get_session() as sess:
    tf.saved_model.simple_save(
        sess,
        export_path,
        inputs={'input_image': model.input},
        outputs={t.name: t for t in model.outputs}
    )
    print('Converted to SavedModel!!!')

From your question, do you mean you no more have access to Model and you have only Check Point files and .meta files?

If that is the case, you can refer the below links which has the code for converting those files into '.pb' file.

Tensorflow: How to convert .meta, .data and .index model files into one graph.pb file

https://github.com/petewarden/tensorflow_makefile/blob/master/tensorflow/python/tools/freeze_graph.py

If you have access to the Trained Model, then I guess you are saving it currently using tf.train.Saver. Instead of that, you can Save the Model and Export it using any of the three (commonly used) functions mentioned below:

1. tf.saved_model.simple_save => In this case, only Predict API is supported during Serving. Example of this is mentioned by KrisR89 in his answer.

2. tf.saved_model.builder.SavedModelBuilder => In this case, you can define the SignatureDefs, i.e., the APIs which you want to access during Serving. You can find example on how to use it in the below link, https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/mnist_saved_model.py

3. Third way is shown below:

   classifier = tf.estimator.DNNClassifier(config=training_config, feature_columns=feature_columns,hidden_units=[256, 32], optimizer=tf.train.AdamOptimizer(1e-4),n_classes=NUM_CLASSES,dropout=0.1, model_dir=FLAGS.model_dir)

   classifier.export_savedmodel(FLAGS.saved_dir,

   serving_input_receiver_fn=serving_input_receiver_fn)

The Example on how to save model using Estimators can be found in the below link. This supports Predict and Classification APIs.

https://github.com/yu-iskw/tensorflow-serving-example/blob/master/python/train/mnist_premodeled_estimator.py

Let me know if this information helps or if you need any further help.