Martin  Soit

Martin Soit


Getting started with TensorFlow Serving

TensorFlow Serving is a part of TensorFlow Extended(TFX) that makes deploying your machine learning model to a server more comfortable than ever. Before Google released TensorFlow Serving, your model has to be deployed into production using Docker. Using Docker to deploy your model is tedious, time-consuming, and prone to many errors. TensorFlow Serving provides us with an API that can be called upon using HTTP requests to run inference on the server. In this blog, we will serve an emotion recognition model and, through that, understand the basics of TensorFlow Serving.

Why serve a model?

Once you have trained your model, it has to be deployed into production so that it can be used. Various methods can be used to deploy the model like deploying locally on phones using TFlite, deploying on a website using TFjs, creating a docker container to deploy your model on the cloud, etc. TensorFlow Serving has an advantage over the other methods for the following reasons.

  1. It is much easier to deploy your model using TensorFlow Serving than with Docker, and it saves you time and prevents unnecessary errors.
  2. It is easier to manage different versions of the model as compared to TFlite or TFjs.
  3. When the model is updated, all the clients will be using the same version of the model, and the result will thus be uniform.
  4. Since the model will be running on the server, you can use powerful computational resources like GPUs or TPUs to run inference faster.
  5. Since the model is served an API, it can be used by different programming languages that TensorFlow does not support.

#artificial-intelligence #deep-learning #tensorflow #machine-learning

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Buddha Community

Getting started with TensorFlow Serving
Dominic  Feeney

Dominic Feeney


Getting started with Tensorflow

Learn the basics through examples


Ok, let’s discuss the elephant in the room. Should you learn Tensorflow or PyTorch?

Honestly, there is no right answer. Both platforms have a large open source community behind them, are easy to use, and capable of building complex deep learning solutions. If you really want to shine as a deep learning researcher you will have to know both.

Let’s now discuss how Tensorflow came about and how to use it for deep learning.

#artificial-intelligence #machine-learning #tensorflow #deep-learning #data-science #getting started with tensorflow

Adam Carter

Adam Carter


Serving TensorFlow models with TensorFlow Serving

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. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.

📖 Introduction

Currently there are a lot of different solutions to serve ML models in production with the growth that **MLOps **is having nowadays as the standard procedure to work with ML models during all their lifecycle. Maybe the most popular one is TensorFlow Serving developed by TensorFlow so as to server their models in production environments.

This post is a guide on how to train, save, serve and use TensorFlow ML models in production environments. Along the GitHub repository linked to this post we will prepare and train a custom CNN model for image classification of The Simpsons Characters Data dataset, that will be later deployed using TensorFlow Serving.

So as to get a better understanding on all the process that is presented in this post, as a personal recommendation, you should read it while you check the resources available in the repository, as well as trying to reproduce it with the same or with a different TensorFlow model, as “practice makes the master”.


#deep-learning #tensorflow-serving #tensorflow

Martin  Soit

Martin Soit


How to Serve Different Model Versions using TensorFlow Serving

This article explains how to manage multiple models and multiple versions of the same model in TensorFlow Serving using configuration files along with a brief understanding of batching.

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You have TensorFlow deep learning models with different architectures or have trained your models with different hyperparameters and would like to test them locally or in production. The easiest way is to serve the models using a Model Server Config file.

A Model Server Configuration file is a protocol buffer file(protobuf), which is a language-neutral, platform-neutral extensible yet simple and faster way to serialize the structure data.

#deep-learning #python #tensorflow-serving #tensorflow

Condo Mark

Condo Mark


Deployment of a TensorFlow model to Production using TensorFlow Serving

Learn step by step deployment of a TensorFlow model to Production using TensorFlow Serving.

You created a deep learning model using Tensorflow, fine-tuned the model for better accuracy and precision, and now want to deploy your model to production for users to use it to make predictions.

TensorFlow Serving allows you to

  • Easily manage multiple versions of your model, like an experimental or stable version.
  • Keep your server architecture and APIs the same
  • Dynamically discovers a new version of the TensorFlow flow model and serves it using (remote procedure protocol) using a consistent API structure
  • Consistent experience for all clients making inferences by centralizing the location of the model

The key components of TF Serving are

  • Servables: A Servable is an underlying object used by clients to perform computation or inference**. TensorFlow serving represents the deep learning models as one ore more Servables.
  • LoadersManage the lifecycle of the Servables as Servables cannot manage their own lifecycle. Loaders standardize the APIs for loading and unloading the Servables, independent of the specific learning algorithm.
  • Source: Finds and provides Servables and then supplies one Loader instance for each version of the servable.
  • Managers: Manage the full lifecycle of the servable: Loading the servable, Serving the servable, and Unloading the servable
  • TensorFlow Core: Manages lifecycle and metrics of the Servable by making the Loader and servable as opaque objects

#tensorflow-serving #deep-learning #mnist #tensorflow #windows-10

Carmen  Grimes

Carmen Grimes


How to start an electric scooter facility/fleet in a university campus/IT park

Are you leading an organization that has a large campus, e.g., a large university? You are probably thinking of introducing an electric scooter/bicycle fleet on the campus, and why wouldn’t you?

Introducing micro-mobility in your campus with the help of such a fleet would help the people on the campus significantly. People would save money since they don’t need to use a car for a short distance. Your campus will see a drastic reduction in congestion, moreover, its carbon footprint will reduce.

Micro-mobility is relatively new though and you would need help. You would need to select an appropriate fleet of vehicles. The people on your campus would need to find electric scooters or electric bikes for commuting, and you need to provide a solution for this.

To be more specific, you need a short-term electric bike rental app. With such an app, you will be able to easily offer micro-mobility to the people on the campus. We at Devathon have built Autorent exactly for this.

What does Autorent do and how can it help you? How does it enable you to introduce micro-mobility on your campus? We explain these in this article, however, we will touch upon a few basics first.

Micro-mobility: What it is


You are probably thinking about micro-mobility relatively recently, aren’t you? A few relevant insights about it could help you to better appreciate its importance.

Micro-mobility is a new trend in transportation, and it uses vehicles that are considerably smaller than cars. Electric scooters (e-scooters) and electric bikes (e-bikes) are the most popular forms of micro-mobility, however, there are also e-unicycles and e-skateboards.

You might have already seen e-scooters, which are kick scooters that come with a motor. Thanks to its motor, an e-scooter can achieve a speed of up to 20 km/h. On the other hand, e-bikes are popular in China and Japan, and they come with a motor, and you can reach a speed of 40 km/h.

You obviously can’t use these vehicles for very long commutes, however, what if you need to travel a short distance? Even if you have a reasonable public transport facility in the city, it might not cover the route you need to take. Take the example of a large university campus. Such a campus is often at a considerable distance from the central business district of the city where it’s located. While public transport facilities may serve the central business district, they wouldn’t serve this large campus. Currently, many people drive their cars even for short distances.

As you know, that brings its own set of challenges. Vehicular traffic adds significantly to pollution, moreover, finding a parking spot can be hard in crowded urban districts.

Well, you can reduce your carbon footprint if you use an electric car. However, electric cars are still new, and many countries are still building the necessary infrastructure for them. Your large campus might not have the necessary infrastructure for them either. Presently, electric cars don’t represent a viable option in most geographies.

As a result, you need to buy and maintain a car even if your commute is short. In addition to dealing with parking problems, you need to spend significantly on your car.

All of these factors have combined to make people sit up and think seriously about cars. Many people are now seriously considering whether a car is really the best option even if they have to commute only a short distance.

This is where micro-mobility enters the picture. When you commute a short distance regularly, e-scooters or e-bikes are viable options. You limit your carbon footprints and you cut costs!

Businesses have seen this shift in thinking, and e-scooter companies like Lime and Bird have entered this field in a big way. They let you rent e-scooters by the minute. On the other hand, start-ups like Jump and Lyft have entered the e-bike market.

Think of your campus now! The people there might need to travel short distances within the campus, and e-scooters can really help them.

How micro-mobility can benefit you


What advantages can you get from micro-mobility? Let’s take a deeper look into this question.

Micro-mobility can offer several advantages to the people on your campus, e.g.:

  • Affordability: Shared e-scooters are cheaper than other mass transportation options. Remember that the people on your campus will use them on a shared basis, and they will pay for their short commutes only. Well, depending on your operating model, you might even let them use shared e-scooters or e-bikes for free!
  • Convenience: Users don’t need to worry about finding parking spots for shared e-scooters since these are small. They can easily travel from point A to point B on your campus with the help of these e-scooters.
  • Environmentally sustainable: Shared e-scooters reduce the carbon footprint, moreover, they decongest the roads. Statistics from the pilot programs in cities like Portland and Denver showimpressive gains around this key aspect.
  • Safety: This one’s obvious, isn’t it? When people on your campus use small e-scooters or e-bikes instead of cars, the problem of overspeeding will disappear. you will see fewer accidents.

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