The last time that I created a playground for experimenting with machine learning using Apache Spark and an InterSystems data platform,  see Machine Learning with Spark and Caché, I installed and configured everything directly on my laptop: Caché, Python, Apache Spark, Java, some Hadoop libraries, to name a few. It required some effort, but eventually it worked. Paradise. But, I worried. Would I ever be able to reproduce all those steps? Maybe. Would it be possible for a random Windows or Java update to wreck the whole thing in an instant? Almost certainly.

Terminal scripts can be used to run pre-designed commands on the terminal, like a batch file.  You can write anything that can be executed on terminal, like for loop, if else and so on,  inside Terminal scripts. In this article, I will show you how to call Terminal scripts, how to use parameters in Terminal scripts and how to avoid session disconnected when running Terminal scripts. If you have any information about how to use Terminal scripts or you have any feedback, please feel free to leave a comment

Now that the InterSystems IRIS Data Platform Community Edition is available on Docker Hub (https://hub.docker.com/_/intersystems-iris-data-platform), it seems like a great time to try InterSystems IRIS in a container.

I am planning to implement Business Intelligence based on the data in my instances. What is the best way to set up my databases and environment to use DeepSee?

Keywords:   Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP,  and HealthShare    

1. Purpose and Objectives

In  previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

So let's try a Keras "deep learning MLP" with standard demo data that everybody in AI/NN community has been using. It is a kind of so called "supervised learning". We will see how simple to run it on the Keras level.

We could later touch on its history and on why it's called "deep learning" the buzz word - what actually evolved over the recent 20 years.�

InterSystems Data Platform includes utilities and tools for system monitoring and alerting, however System Administrators new to solutions built on the InterSystems Data Platform (a.k.a Caché) need to know where to start and what to configure.

This guide shows the path to a minimum monitoring and alerting solution using references from online documentation and developer community posts to show you how to enable and configure the following;

1. Caché Monitor: Scans the console log and sends emails alerts.

2. System Monitor: Monitors system status and resources, generating notifications (alerts and warnings) based on fixed parameters and also tracks overall system health.

3. Health Monitor: Samples key system and user-defined metrics and compares them to user-configurable parameters and established normal values, generating notifications when samples exceed applicable or learned thresholds.

4. History Monitor: Maintains a historical database of performance and system usage metrics.

5. pButtons: Operating system and Caché metrics collection scheduled daily.

Remember this guide is a minimum configuration, the included tools are flexible and extensible so more functionality is available when needed. This guide skips through the documentation to get you up and going. You will need to dive deeper into the documentation to get the most out of the monitoring tools, in the meantime, think of this as a set of cheat sheets to get up and running.

In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:

• Git 101
• Git flow (development process)
• GitLab installation
• GitLab Workflow
• Continuous Delivery
• GitLab installation and configuration
• GitLab CI/CD

In the first article, we covered Git basics, why a high-level understanding of Git concepts is important for modern software development, and how Git can be used to develop software.

In the second article, we covered GitLab Workflow - a complete software life cycle process and Continuous Delivery

Hi,

This is a quick tutorial how to install and use TFS in Atelier. It is based on my self experience and some tricks that I 've noted.

If you are used to using visual studio maybe you feel that is a bit slow and heavy, but you have the same TFS panel as you have in Visual Studio, so don't need any special "training" to use it 

It's important don't store the file .buildpath because it has the server definition that you are working in a team, each one have the personal configuration, so the name of the server could be different.

To prevent it, create a file called .tfignore with this conten

Recently I needed a classmethod that returns annotation value based on a name of a activity.

As doing it at runtime seemed inefficient, I wrote compile-time utility that iterates over all business process activities and generates relevant code.

This code could be used in a variety of situations when you need to iterate over business process activities, just add it as a secondary superclass to your BPL processes.

The following post outlines an architectural design of intermediate complexity for DeepSee. As in the previous example, this implementation includes separate databases for storing the DeepSee cache, DeepSee implementation and settings. This post introduces two new databases: the first to store the globals needed for synchronization, the second to store fact tables and indices.

InterSystems products (IRIS, Caché, Ensemble) already include a built-in Apache web server. But the built-in server is designed for the development and administration tasks and thus has certain limitations. Though you may find some useful workarounds for these limitations, the more common approach is to deploy a full-scale web server for your production environment. This article describes how to set up Apache to work with InterSystems products and how to provide HTTPS access. We will be using Ubuntu, but the configuration process is almost the same for all Linux distributions.

This article was written as an attempt to share the experience of installing the InterSystems Caché DBMS for production environment.
We all know that the development configuration of a DBMS is very different from real-life conditions.
As a rule, development is carried out in “hothouse conditions” with a bare minimum of security measures, but when we publish our project online, we must ensure its reliable and uninterrupted operation in a very aggressive environment.

The process of installing the InterSystems Caché DBMS with maximum security settings

OS security settings

The first step is the operating system. You need to do the following:

Everybody has a testing environment.

Some people are lucky enough to have a totally separate environment to run production in.

-- Unknown

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In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:

• Git 101
• Git flow (development process)
• GitLab installation
• GitLab WorkFlow
• GitLab CI/CD
• CI/CD with containers

This first part deals with the cornerstone of modern software development - Git version control system and various Git flows.

Ensemble supports publish and subscribe message delivery. Publish and subscribe refers to the technique of routing a message to one or more subscribers based on the fact that those subscribers have previously registered to be notified about messages on a specific topic.

This article demonstrates how several Ensemble capabilities can work together:

• Publish/Subscribe
• Email
• Workflow
• Alerts
• PPG (that's general capability though)

In this article we would send emails about:

• New workflow tasks
• Unassigned workflow tasks
• Uncompleted workflow tasks
• Ensemble alerts

Email recipients would be determined using Publish/Subscribe operation and each user would receive only digest email whenever possible.