Get info of exposed models in Tensorflow Serving

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tensorflow serving getmodelstatus
tf serving endpoints

Once I have a TF server serving multiple models, is there a way to query such server to know which models are served?

Would it be possible then to have information about each of such models, like name, interface and, even more important, what versions of a model are present on the server and could potentially be served?

It is really hard to find some info about this, but there is possibility to get some model metadata.

request = get_model_metadata_pb2.GetModelMetadataRequest() = 'your_model_name'
response = stub.GetModelMetadata(request, 10)


Hope it helps.

TensorFlow Serving with Docker, One of the easiest ways to get started using TensorFlow Serving is with Docker. -X POST http://localhost:8501/v1/models/half_plus_two:predict Port 8500 exposed for gRPC; Port 8501 exposed for the REST API; Optional environment More information on using the RESTful API can be found here. Get info of exposed models in Tensorflow Serving. Ask Question import model_service_pb2_grpc from tensorflow_serving.apis import get_model_status_pb2 from

It is possible to get model status as well as model metadata. In the other answer only metadata is requested and the response, response.metadata['signature_def'] still needs to be decoded.

I found the solution is to use the built-in protobuf method MessageToJson() to convert to json string. This can then be converted to a python dictionary with json.loads()

import grpc
import json
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
from tensorflow_serving.apis import model_service_pb2_grpc
from tensorflow_serving.apis import get_model_status_pb2
from tensorflow_serving.apis import get_model_metadata_pb2
from google.protobuf.json_format import MessageToJson

PORT = 8500
model = "your_model_name"

channel = grpc.insecure_channel('localhost:{}'.format(PORT))

request = get_model_status_pb2.GetModelStatusRequest() = model
result = stub.GetModelStatus(request, 5)  # 5 secs timeout
print("Model status:")

stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = get_model_metadata_pb2.GetModelMetadataRequest() = model
result = stub.GetModelMetadata(request, 5)  # 5 secs timeout
result = json.loads(MessageToJson(result))
print("Model metadata:")

Serving a TensorFlow Model, This tutorial shows you how to use TensorFlow Serving components to export a trained TensorFlow model and use the standard tensorflow_model_server to Before getting started, first install Docker. For details on the SavedModel format , please see the documentation at SavedModel by Gaurav Kaila How to deploy an Object Detection Model with TensorFlow serving Object detection models are some of the most sophisticated deep learning models. They’re capable of localizing and classifying objects in real time both in images and videos. But what good is a model if it cannot be used for production? Thanks to the wonderful guys at TensorFlow, we have TensorFlow serving that

To continue the decoding process, either follow Tyler's approach and convert the message to JSON, or more natively Unpack into a SignatureDefMap and take it from there

signature_def_map = get_model_metadata_pb2.SignatureDefMap()

Exposing model version for models loaded by model_server � Issue , My use case is that I would like to access the version for the models loaded by if we want to get all version of serving models from model server? those version labels (for details see� Deep Learning Model Deployment with TensorFlow Serving running in Docker and consumed by Flask App. In this tutorial, we will deploy a pre-trained TensorFlow model with the help of TensorFlow Serving with Docker, and will also create a visual web interface using Flask web framework which will serve to get predictions from the served TensorFlow model and enable end-users to consume through API

tensorflow/serving, I described how to serve trained tensorflow models with tensorflow As a result, we can get the class IDs, probabilities to each class and so on. Tensorflow Serving expects models to be in numerically ordered directory structure to manage model versioning. In this case, the directory 1/ corresponds to model version 1, which contains the model architecture saved_model.pb along with snapshot of the model weights (variables).

Introduction to RESTful API with Tensorflow Serving, Deploy Keras Models using TensorFlow Serving — TF 2.x You can find my notebook here: (You can also run it in Google Colab). [If you already have You can read more details about it in the Guide here. We won't We are mapping it to the 8501 port exposed for REST API calls by TensorFlow Serving. Port 8500 exposed for gRPC; This will run the docker container and launch the TensorFlow Serving Model Server, bind the REST API port 8501, and map our desired

Deploy Keras Models using TensorFlow Serving — TF 2.x, Deploying Deep Learning Models using TensorFlow Serving with Docker Let's have a look at the directory structure to get a better understanding of the model structure and Flask web app. The details of the command line parameters are: and the port after colon is exposed by the docker for serving. Browse other questions tagged tensorflow-serving multiple-models or ask your own question. The Overflow Blog Podcast 247: Paul explains it all

  • Any idea on how to decode the bytes contained in response.metadata['signature_def'] here?