Serving Deep Learning Models with Tensorflow Server – part 1
Now we already had our model ready to be serve with our Tensorflow Server!
Let’s install the docker image of Tensorflow Server
docker pull tensorflow/serving
Almost there! now we need just running the container with some parameters! offering the source path of our model(source), the path that it gonna be exposed (target) and the model name
docker run -t --rm -p 8501:8501 --mount type=bind,source=I:PROJECTStensorflow_servinglinear_model,target=/models/linear_model -e MODEL_NAME=linear_model -t tensorflow/serving
Voila! our model can be requested accessing the url: http://localhost:8509/v1/models/linear_model:predict
OBS: To request the prediction of our model is necessary to request using the protocol POST and the meta-data Content-Type as application/json and data has to be organized inside the data-tag instances. I created a yaml file that can be used as example using the VSCode and the extension apitester
