Skip to main content

TensorFlow Serving Tutorial for Beginners


TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs.

Basic Serving Tutorial

See the basic tutorial on the TensorFlow Serving site to learn how to export a trained TensorFlow model and build a server to serve the exported model.

Advanced Serving Tutorial

See the advanced tutorial on the TensorFlow Serving site to learn how to build a server that dynamically discovers and serves new versions of a trained TensorFlow model.

Serving Inception Model Tutorial

See the serving inception tutorial on the TensorFlow Serving site to learn how to serve the inception model with TensorFlow Serving and Kubernetes.

Comments

Popular posts from this blog

Integrating Tensor flow API into ASP.net Web applications for Image detection

Introduction Tensor flow provides advanced Machine Learning API. For the example here we will provide a solution for the Image recognition. The same can be applied to other use cases such as Text recognition or any other data set that needs to solve the problems of AI and Machine Learning such as: Predictive Analytics Cognitive Processes and its Automation e.g. NLP Virtual Agents (Bot’s) Camera Image detection and processing e.g. Industrial Robotics and Drones Text Knowledge Analytics Application Development Steps In the first step we create an ASP.NET application for connecting the camera feed or input data. In the next step Install Tensor flow as per instructions on the website –>  Click here To begin using Tensor flow we need to decide on the model to use for processing input data – There is an option to use either ready-made models or develop on own depending on the problem at hand. For simplicity we will use ready-made model called a deep  convolut...

PyTorch vs TensorFlow — spotting the difference

In this post I want to explore some of the key similarities and differences between two popular deep learning frameworks: PyTorch and TensorFlow. Why those two and not the others? There are many deep learning frameworks and many of them are viable tools, I chose those two just because I was interested in comparing them specifically. Origins TensorFlow is developed by Google Brain and actively used at Google both for research and production needs. Its closed-source predecessor is called DistBelief. PyTorch is a cousin of lua-based Torch framework which is actively used at Facebook. However, PyTorch is not a simple set of wrappers to support popular language, it was rewritten and tailored to be fast and feel native. The best way to compare two frameworks is to code something up in both of them. I’ve written a companion jupyter notebook for this post and you can  get it here . All code will be provided in the post too. First, let’s code a simple approximator for t...