If you are doing frontend development nowadays, you may have heard about ReactJS or may be actively using it in your projects. Introduced to the public five years ago, React has transformed into a library of choice for a lot of frontend developers that is easily certified by the enormous stars at its Github page (more than 100,000 stars). React was relicensed into MIT license almost a year ago, which only catapulted its popularity into a new high. The MIT license is a more commercial friendly license compared to the BSD + patents license that was previously used by React.
Creating a frontend project is easy with the help of scaffolding tools and boilerplates. Among the available choices is create-react-app, a React bootstrapping utility that takes care the laborious tasks of setting up a React project without much intervention about how the project should be structured. Given this nature, create-react-app is less assumed a boilerplate and more of a toolkit. Continue reading
Few years ago, I developed a WordPress plugin named Amikelive Adsense Widget. I was enthralled to see thousands of downloads for the plugin despite its simplicity. This post is not an announcement about a new release of the plugin despite the absence of simple-yet-powerful Google Adsense widget in the plugin repository. It’s rather a guide on how to display Google Adsense on the WordPress blog sidebar without using any Adsense plugin or widget. The steps are fairly simple, without any coding experience needed.
If you happen to be using Amikelive Adsense widget, it can be necessary to note that the plugin is not actively developed anymore and I don’t have any plan to release newer version anytime soon or sometimes later. Such widget functionality is now built into WordPress, which we can simply take advantage of without reinventing the wheel.
Now on to the ad configuration part.
When developing a deep-learning system, especially during the modeling stage, a lot of trials and errors can be involved in evolving the codebase. The easy remedy to reduce errors will be by using a robust IDE that provides productivity-boosting features such as code completion, method definition, codestyle suggestion, advanced debugging, user-friendly UI, and so forth.
In this article, we will go into more details about Jupyter Notebook installation and configuration on Ubuntu 16.04. However, it’s important to note that the configuration depends on some pre-requisites. This article is the continuation of the previous article about TensorFlow installation. Please make sure you have read the article to understand the pre-requisites, otherwise some steps explained in this article may not work. Continue reading
What is interesting in the deep learning ecosystem is the plentiful choices of deep learning frameworks. On the other side, of course there is another equation; more options equate to more confusion, especially in choosing the most appropriate framework for the entire gamut of the problems. At the end of the day, instead of using one, we may need to stick with multiple deep learning frameworks with each usage depending on the nature of the problem to solve.
TensorFlow is one of the popular (de facto most popular in terms of Github stars) deep learning frameworks. TensorFlow comes with excellent documentation. This also includes the documentation for installation. If you go to the official documentation page for installation, you will be provided with elaborate installation guide for multiple OS platforms. Then why this post?
The latest version of TensorFlow with GPU support (version 1.8 at the time this post is published) is built against CUDA 9.0. However, NVIDIA has released CUDA 9.1 and there is possibility of newer version release in the near future. Given that TensorFlow is lagging behind the CUDA GA version, the publicly released TensorFlow bundle cannot immediately work on the system having only the latest CUDA version installed. A remedy for this is by installing from source, which can be non-trivial especially for those who are not so familiar with the source build mechanism.
The final system setup after completing the installation steps explained in the posts will be as follows.
|NVIDIA driver version||390.48
|Python install method||virtualenv
Note that the components will be updated in the future. This implies version upgrade for the components. It is expected that this post will still be valid even after version upgrade. Under the circumstances where this post becomes invalid, the content will be updated or another post will be written. Yet, this would be realized with sufficient comments or feedback regarding existing content. Continue reading
NVIDIA Collective Communications Library (NCCL) is a library developed to provide parallel computation primitives on multi-GPU and multi-node environment. The idea is to enable GPUs to collectively work to complete certain computing task. This is especially helpful when the computation is complex. With multiple GPUs working together, the task will be completed in less time, rendering a more performing system. People with background or experience in distributed system, such as Hadoop, may immediately relate this concept with similar model applied in the traditional distributed system. Hadoop, for example, supports MapReduce programming model that splits a compute job into chunks that are spread into the slave nodes and collected back by the master to produce the final output. Continue reading