#Docker

When it comes to build an iris image, we can use the cpf merge files.

Here is an cpf merge example:

[Actions]
CreateDatabase:Name=IRISAPP_DATA,Directory=/usr/irissys/mgr/IRISAPP_DATA

CreateDatabase:Name=IRISAPP_CODE,Directory=/usr/irissys/mgr/IRISAPP_CODE

CreateNamespace:Name=IRISAPP,Globals=IRISAPP_DATA,Routines=IRISAPP_CODE,Interop=1

ModifyService:Name=%Service_CallIn,Enabled=1,AutheEnabled=48

CreateApplication:Name=/frn,NameSpace=IRISAPP,DispatchClass=Formation.REST.Dispatch,AutheEnabled=48

ModifyUser:Name=SuperUser,PasswordHash=a31d24aecc0bfe560a7e45bd913ad27c667dc25a75cbfd358c451bb595b6bd52bd25c82cafaa23ca1dd30b3b4947d12d3bb0ffb2a717df29912b743a281f97c1,0a4c463a2fa1e7542b61aa48800091ab688eb0a14bebf536638f411f5454c9343b9aa6402b4694f0a89b624407a5f43f0a38fc35216bb18aab7dc41ef9f056b1,10000,SHA512

I am just recently announced my project isc-tar. But sometimes it is not less interesting what’s behind the scene: how it was built, how it works and what happens around the project. Here is the story:

• How to develop this project
• How to test it
• How to release new versions for publishing
• And finally how to automate all above
• Continuous integration

So, I would like to tell all about it.

This is a continuation of my story about the development of my project isc-tar started in the first part.

Just having tests is not enough, it does not mean that you will run tests after all changes. Running tests should be automated, and when you cover all your functionality with tests, everything should work well after any change in any place. And Continuous Integration (CI) helps to keep the code and deployment procedure with as fewer bugs as possible and automates the routine procedures, like publishing releases.

I use GitHub to store the source code. And some time ago GitHub started to work on its own CI/CD platform and named it GitHub Actions. It is not widely available, yet. You have to be signed as a beta tester for this feature, as I did. GitHub Actions uses quite a different way how to deal with a build workflow. What is important that Github Actions allows to use Docker, and it’s quite easy to customize available actions. And interesting that GitHub Actions is really much bigger than any classic CI like we have in Travis, Circle or Gitlab CI and so on. You can find more in the official documentation.

Hi developers!

ZPM Package Manager is getting more and more popular and thus the library of packages is growing.

And thus some solutions or samples can be built with InterSystems IRIS and one or two packages installed.

I want to share with you iris-docker-zpm-usage-template - a template that can be used exactly for this purpose.

In this article, we will cover below topics:

• What is Kubernetes?
• Main Kubernetes (K8s) Components

What is Kubernetes?

Kubernetes is an open-source container orchestration framework developed by Google. In essence, it controls container speed and helps you manage applications consisting of multiple containers. Additionally, it allows you to operate them in different environments, e.g., physical machines, virtual machines, Cloud environments, or even hybrid deployment environments.

Hi All,

With this article, I would like to show you how easily and dynamically System Alerting and Monitoring (or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation).

Is anyone like me, and felt really jealous that they didn't have enough points to acquire the IRIS-based Raspberry Pi system when it was offered? Do you have a spare Raspberry Pi 4 handy? If so, I'll walk you through setting up Docker and IRIS on your Raspberry Pi so you can have the smallest IRIS computer in town!

Things you'll need:

After >40 years of writing in-countable lines of code in M*/COS/ISOS (and a bunch of archaic languages)
I decided for myself to set a strong signal for the future. We have Embedded Python available
(still pre-release)! I just felt it as a sacrilege to ignore this excellent NEW opportunity and
stay with the old sermon that I had used for decades.

I have recently started making more use of Docker Desktop on my Windows 11 workstation, particularly in conjunction with VS Code's Remote - Containers extension and the iris-python-template repo from @Guillaume Rongier

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
• Why containers?
• GitLab CI/CD using containers

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.

In the third article, we covered GitLab installation and configuration and connecting your environments to GitLab

In the fourth article, we wrote a CD configuration.

In this article, let's talk about containers and how (and why) they can be used.

Hi,

I have wrote an article about how to install the intersystems cache driver in a Docker container, and then deploy it using Azure Functions:

How to run a (Python) Azure Function as a Docker container & Deploy it using Bicep | Victor Sanner

This might be useful to others, especially the dockerfile which I have copied below. This builds a debian docker container and installs the Intersystems Cache driver, which python can then use :)

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

• Step1: Install required libraries

• Step2: Create patterns and responses file

• Step3: Train the Model

• Step4: Create ChatBot Application based on the trained model

So Let us start.