Alternatives to cnvrg.io

We use Debian and its derivative Ubuntu because the apt ecosystem and toolchain for Debian packages is far superior to the yum-based system used by Fedora and RHEL. This is large part due to a huge amount of investment into tools like debhelper/dh over the years by the Debian community. I haven't dealt with RPM in the last couple years, but every experience I've had with RPM is that the RPM tools are slower, have less useful options, and it's more work to package software for them (and one makes more compromises in doing so).

I think everyone has seen the better experience using Ubuntu in the shift of prevalence from RHEL to Ubuntu in what most new companies are deploying on their servers, and I expect that trend to continue as long as Red Hat is using the RPM system (and I don't really see them as having a path to migrate).

The experience with Ubuntu and Debian stable releases is pretty similar: A solid release every 2 years that's supported for a few years. (While Ubuntu in theory releases every 6 months, their non-LTS releases are effectively betas: They're often unstable, only have 9 months of support, etc. I wouldn't recommend them to anyone not actively participating in Ubuntu the development community). Ubuntu has better integration of non-free drivers, which may be important if you have hardware that requires them. But it's also the case that most bugs I experience when using Ubuntu are Ubuntu-specific issues, especially on servers (in part because Ubuntu has a bunch of "cloud management" stuff pre-installed that is definitely a regression if you're not using Canonical's cloud management products).

There is a question coming... I am using Oracle VirtualBox to spawn 3 Ubuntu Linux virtual machines (VM). VM1 is being used as a data lake - just a place to store flat files. VM2 hosts Apache NiFi. VM3 hosts PostgreSQL. I have built a NiFi pipeline that reads flat files on VM1 and then pipes the data over to and inserts it into the Postgresql database. I left this setup alone for a while, and then something hiccupped on VM3, and I had to rebuild it. Now I cannot make a remote connection to Postgresql on VM3. I was using pgAdmin3 on VM3, but it kept throwing errors - I found out it went end-of-life in 2018 and uninstalled it. pgAdmin4 is out, but for some reason, I cannot get the APT utility to find/install it. I am trying to figure out the pgAdmin4 install problem and looking for a good alternative for pgAdmin4 that I can use to diagnose the remote database connection problem. Does anyone have any suggestions? Thanks in advance.

At labinator.com, we use HTML5, CSS 3, Sass, Vanilla.JS and PHP when building our premium WordPress themes and plugins. When writing our codes, we use Sublime Text and Visual Studio Code depending on the project. We run Manjaro and Debian operating systems in our office. Manjaro is a great desktop operating system for all range of tasks while Debian is a solid choice for servers.

WordPress became a very popular choice when it comes to content management systems and building websites. It is easy to learn and has a great community behind it. The high number of plugins as well that are available for WordPress allows any user to customize it depending on his/her needs.

For development, HTML5 with Sass is our go-to choice when building our themes.

Main Advantages Of Sass:

• It's CSS syntax friendly
• It offers variables
• It uses a nested syntax
• It includes mixins
• Great community and online support.
• Great documentation that is easy to read and follow.

As for PHP, we always thrive to use PHP 7.3+. After the introduction of PHP 7, the WordPress development process became more stable and reliable than before. If you a developer considering PHP 7.3+ for your project, it would be good to note the following benefits.

The Benefits Of Using PHP:

• Open Source.
• Highly Extendible.
• Easy to learn and read.
• Platform independent.
• Compatible with APACHE.
• Low development and maintenance cost.
• Great community and support.
• Detailed documentation that has everything you need!

Why PHP 7.3+?

• Flexible Heredoc & Nowdoc Syntaxes - Two key methods for defining strings within PHP. They also became easier to read and more reliable.
• A good boost in performance speed which is extremely important when it comes to WordPress development.

We use Debian and its derivative Ubuntu because the apt ecosystem and toolchain for Debian packages is far superior to the yum-based system used by Fedora and RHEL. This is large part due to a huge amount of investment into tools like debhelper/dh over the years by the Debian community. I haven't dealt with RPM in the last couple years, but every experience I've had with RPM is that the RPM tools are slower, have less useful options, and it's more work to package software for them (and one makes more compromises in doing so).

I think everyone has seen the better experience using Ubuntu in the shift of prevalence from RHEL to Ubuntu in what most new companies are deploying on their servers, and I expect that trend to continue as long as Red Hat is using the RPM system (and I don't really see them as having a path to migrate).

The experience with Ubuntu and Debian stable releases is pretty similar: A solid release every 2 years that's supported for a few years. (While Ubuntu in theory releases every 6 months, their non-LTS releases are effectively betas: They're often unstable, only have 9 months of support, etc. I wouldn't recommend them to anyone not actively participating in Ubuntu the development community). Ubuntu has better integration of non-free drivers, which may be important if you have hardware that requires them. But it's also the case that most bugs I experience when using Ubuntu are Ubuntu-specific issues, especially on servers (in part because Ubuntu has a bunch of "cloud management" stuff pre-installed that is definitely a regression if you're not using Canonical's cloud management products).

Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

Hello guys

I am confused between choosing CentOS7 or centos8 for OpenStack tripleo undercloud deployment. Which one should I use? There is another option to use OpenStack, Ubuntu, or MicroStack.

We wanted to use this deployment to build our home cloud or private cloud infrastructure. I heard that centOS is always the best choice through a little research, but still not sure. As centos8 from Redhat is not supported for OpenStack tripleo deployments anymore, I had to upgrade to CentosStream.

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

Personal Dotfiles management

Given that they are all “configuration management” tools - meaning they are designed to deploy, configure and manage servers - what would be the simplest - and yet robust - solution to manage personal dotfiles - for n00bs.

Ideally, I reckon, it should:

• be containerized (Docker?)
• be versionable (Git)
• ensure idempotency
• allow full automation (tests, CI/CD, etc.)
• be fully recoverable (Linux/ macOS)
• be easier to setup/manage (as much as possible)

Does it make sense?

There is a question coming... I am using Oracle VirtualBox to spawn 3 Ubuntu Linux virtual machines (VM). VM1 is being used as a data lake - just a place to store flat files. VM2 hosts Apache NiFi. VM3 hosts PostgreSQL. I have built a NiFi pipeline that reads flat files on VM1 and then pipes the data over to and inserts it into the Postgresql database. I left this setup alone for a while, and then something hiccupped on VM3, and I had to rebuild it. Now I cannot make a remote connection to Postgresql on VM3. I was using pgAdmin3 on VM3, but it kept throwing errors - I found out it went end-of-life in 2018 and uninstalled it. pgAdmin4 is out, but for some reason, I cannot get the APT utility to find/install it. I am trying to figure out the pgAdmin4 install problem and looking for a good alternative for pgAdmin4 that I can use to diagnose the remote database connection problem. Does anyone have any suggestions? Thanks in advance.

Server side

We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

• Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

• Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

• Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

Client side

• UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

• State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

• Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

Cache

• Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

Database

• Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

Infrastructure

• Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

Other Tools

• Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

• Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)