An introduction on how to develop REST APIs in Go using Gorilla Mux
REST APIs are an integral part of today’s programmer’s life. These days, most of the communication between various software components happen via REST APIs. Prominent programming languages have a wide variety of libraries and frameworks which help programmers to reduce time to build API services from scratch.
In this article, we are going to explore how to develop REST APIs in Go language using Gorilla Mux.
Gorilla Mux is a package from the Gorilla Web Toolkit. It provides simple ways to create HTTP routers. …
A quick help on how to create and execute a simple client-server application using gRPC
gRPC is a an open source RPC (Remote Procedure Call) framework that can be used to create high performance distributed applications and services. If you would like to have an introduction to gRPC, refer to my earlier article here, which gives a brief introduction to gRPC, it’s features and suitable usecases.
In this article, we’ll cover how to create and execute a simple client-server application using gRPC.
This article is intended for those who are relatively new to gRPC, but, may have some…
A quick introduction to gRPC
gRPC is a an open source RPC (Remote Procedure Call) framework that can be used to create high performance distributed applications and services. It allows a client application to directly call methods of a server application, which is deployed in another machine, as if it were available in client machines. It can also be used to connect clients, (such as devices, mobile apps and browsers) to backend services.
Since gRPC supports multiple programming languages and platforms, a client written in one language (e.g. Java) can communicate with a server written another language (e.g. Go) seamlessly…
A quick overview on how to setup local development environment and run your first Go program.
As someone who is taking baby steps into programming with Go, you may be uncertain on how to get started. Purpose of this article is to provide a helping hand in those initial steps. We’ll discuss about how to install Go’s binary distribution, setup local development environment and write a basic program. We’ll also look into how you can perform basic debugging activities along with building a binary and executing the same. Without much ado, let’s get started.
This article is intended…
A look back at the beginning of Go programming language
In November 2020, Go, the programming language, celebrated it’s eleventh birthday. Though Go has come a long way since it’s inception, compared to its peers, it is still considered as a newcomer in many circles. This article is an attempt to look back and recollect how the journey of Go began and provide a quick glance towards its design goals and a few features. Later, we’ll explore Go deeper in the articles that would follow.
This article is intended for those who are relatively new to Go programming…
When we think of designing ways to interact with users, Graphical User Interface (GUI) is the de-facto choice. In cases where our area of focus is cloud/infrastructure tools and users are programmers themselves, Command Line Interface (CLI) becomes a preferred choice. Though CLIs are text based, its simplicity and automation abilities make them a popular option among power-users. Ability to chain with other command line tools improves overall usability as well.
Most of the popular cloud platforms, such as AWS, Azure and Google Cloud, provide CLIs to interact with their underlying services. …
A brief introduction to TensorFlow
A question often asked when one gets started in the Machine Learning (ML) journey is, what tools and frameworks should we use in order to minimize the starting time. Follow-up question to that is, how much do we build from scratch and which pieces in the pipeline should we try to develop with already available libraries. Not surprisingly, the advice usually is that use as much libraries as possible.
In the present-day ML landscape, there are many tools, libraries and frameworks available which can do the heavy lifting, allowing us to focus on the problem…
As you might already know, TensorFlow 2.0 is in beta now. Like any other major upgrade, compared to 1.x, 2.0 has changed in many ways.
I’ve had the opportunity to play around with TensorFlow 2.0 from the alpha days. With the APIs being finalised now, this series is an attempt to share my experience so far.
( This is part 1 of the series. More parts are on the way. :-) )
Let’s start with the installation. If you did not get a chance to install TensorFlow 2.0 yet, here is how you could do it.
pip install tensorflow==2.0.0-beta1