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1. LOGICWORKS 5 MODEL 00000001 CODE
2. LOGICWORKS 5 MODEL 00000001 FREE

I've added this Flask application to convert REST requests into gPRC requests.

It came as a part of TF-Serving standard distribution.

This is jupyter notebook which we are using right now. When this Docker Image started, it run example_jupyter/setup.sh. rw-r-r- 1 root root 159 Mar 10 10:29 export.index rw-r-r- 1 root root 119 Mar 10 10:29 checkpoint To demonstrate how it's working, we are going to use the typical MNIST example from the official TF tutorial page:ĭrwxr-xr-x 2 root root 4.0K Mar 10 10:29 00000001 Once it's running, please navigate to (Use different port if using Kitematic)įrom here it's best to continue from within Jupyter Notebook! You can start Docker Container from Kitematic, or use this command from console:ĭocker run -rm -it -p 8888:8888 -p 9000:9000 -p 5000:5000 quay.io/avloss/tensorflow-serving-rest

LOGICWORKS 5 MODEL 00000001 FREE

I've uploaded finished result to DockerHub manually, but please feel free to pull from in case you want to make sure it's what's it says it is. You can use Kitematic to start the image: avloss/tensorflow-serving-rest.Īt first, I tried building it on "DockerHub" - but it hit the limit of 2 hours, so I had to use. It will be scalable, and you will be able to query it via REST. You will have your self-built model running inside TF-Serving by the end of this tutorial. Here I present an easiest possible way to deploy your models with TensorFlow Serving. That process takes forever!Īfter all, it worked just fine.

LOGICWORKS 5 MODEL 00000001 CODE

Tutorial examples have C++ code (which I don't know).Few things that I've found particularly hard were:

Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models.