tensorflow-serving

This is probably a very basic question... But how do I convert checkpoint files into a single .pb file. My goal is to serve the model using probably C++ These are the files that I'm trying to convert. As a side note I'm using tflearn with tensorflow. Edit 1: I found an article that explains how to do this: https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc The problem is that I'm stuck with the following error KeyError: "The name 'Adam' refers to an Operation not in the graph." How do I fix this? Edit 2: Maybe this will shed some light on the

I am trying to export my local tensorflow model to use it on Google Cloud ML and run predictions on it. I am following the tensorflow serving example with mnist data . There is quite a bit of difference in the way they have processed and used their input/output vectors and it is not what you find in typical examples online. I am unsure how to set the parameters of my signatures : model_exporter.init( sess.graph.as_graph_def(), init_op = init_op, default_graph_signature = exporter.classification_signature( input_tensor = "**UNSURE**" , scores_tensor = "**UNSURE**"), named_graph_signatures = {

I know how to load a model into a container and also I know that we can create a static config file and when we run a tensorflow serving container pass it to the container and later use one the models inside that config files but I want to know if there is any way to hot load a completely new model (not a newer version of the previous model) into a running tensorflow serving container. What I mean is we run the container with model-A and later we load model-B into the container and use it, can we do this? If yes how? You can. First you need to copy the new model files to model_base_path you

Tensorflow Serving server (run with docker) responds to my GET (and POST) requests with this: { "error": "Malformed request: POST /v1/models/saved_model/" } Precisely the same problem was already reported but never solved (supposedly, this is a StackOverflow kind of question, not a GitHub issue): https://github.com/tensorflow/serving/issues/1085 https://github.com/tensorflow/serving/issues/1095 Any ideas? Thank you very much. I verified that this does not work pre-v12 and does indeed work post-v12. > docker run -it -p 127.0.0.1:9000:8500 -p 127.0.0.1:9009:8501 -v /models/55:/models/55 -e MODEL

Having seen this github issue and this stackoverflow post I had hoped this would simply work. It seems as though passing in the environment variable MODEL_CONFIG_FILE has no affect. I am running this through docker-compose but I get the same issue using docker-run . The error: I tensorflow_serving/model_servers/server.cc:82] Building single TensorFlow model file config: model_name: model model_base_path: /models/model I tensorflow_serving/model_servers/server_core.cc:461] Adding/updating models. I tensorflow_serving/model_servers/server_core.cc:558] (Re-)adding model: model E tensorflow

I create a lookup table from tf.contrib.lookup , using the training data (as input). Then, I pass every input through that lookup table, before passing it through my model. This works for training, but when it comes to online prediction from this same model, it raises the error: Table not initialized I'm using SavedModel to save the model. I run the prediction from this saved model. How can I initialize this table so that it stays initialized? Or is there a better way to save the model so that the table is always initialized? You can specify an "initialization" operation when you add a meta