In this GitHub based on this InterSystems community rest api template Guillaume and I have created this example of all the import CRUD operations usable using ONLY Python on IRIS and using Flask.

Using the IRIS ORM or by simply doing SQL requests as both methods are seen in the GitHub.

Hi Community,


In this article, I will introduce Python Flask Web Framework. Together we will create a minimal web application to connect to IRIS and get data from it.

Below you can find the steps we will need to follow:

• Step 1 : Introduction to Python Flask Web Framework
• Step 2 : Installation of Flask module
• Step 3 : Creation of web application using Flask
• Step 4 : Use of HTML Templates 
• Step 5 : Installation of IRIS Python Native module
• Step 6 : Establishment of a connection with IRIS 
• Step 7 : Transferring data from IRIS to Flask and displaying it

So Let's start with step 1

Step1-Introduction to Python Flask Web Framework 

Flask is a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python easier. It gives developers flexibility and is a more accessible framework for new developers since it allows to build a web application quickly using only a single Python file. Flask is also extensible and doesn’t requires a particular directory structure or complicated boilerplate code before getting started.

For more details please view Flask Documentations

Hello everyone, I’m a French student that just arrived in Prague for an academical exchange for my fifth year of engineering school and here is my participation in the interop contest.

I hadn’t much time to code since I was moving from France to Prague and I’m participating alone, so I decided to make a project that’s more like a template rather than an application.

1. Integrated ML Demonstration

This repository is a demonstration of IntegratedML and Embedded Python.

• 1. Integrated ML Demonstration
• 2. Building the demo
  • 2.1. Architecture
  • 2.2. Building the nginx container
• 3. Running the demo
• 4. Python back-end
  • 4.1. Embedded Python
  • 4.1.1. Setting up the container
  • 4.1.2. Using Embedded Python
  • 4.1.3. Side By Side Comparaison
  • 4.2. Launching the server
• 5. IntegratedML
  • 5.1. Exploring both datasets
  • 5.2. Managing models
  • 5.2.1. Creating a model
  • 5.2.2. Training a model
  • 5.2.3. Validating a model
  • 5.2.4. Making predictions
• 6. Using COS
• 7. More explainability with DataRobot
• 8. Conclusion

This GitHub is the simplest way to scrap using IRIS and Python, all of that already incorporated in an IRIS PRODUCTION.

From here you can build any IRIS production in full Python or in ObjectScript as this module is interoperable.

See for more information

In this GitHub we fine tune a bert model from HuggingFace on review data like Yelp reviews.

The objective of this GitHub is to simulate a simple use case of Machine Learning in IRIS :
We have an IRIS Operation that, on command, can fetch data from the IRIS DataBase to train an existing model in local, then if the new model is better, the user can override the old one with the new one.
That way, every x days, if the DataBase has been extended by the users for example, you can train the model on the new data or on all the data and choose to keep or let go this new model.

On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

In the first part, I've shown how to start a new project on Django, as well as define new models and add already existing models.

This time, I'll introduce an admin panel, available out of the box and how it can be useful.

Important note: do not expect that if you try to repeat actions from this post it will work for you, it does not. During the article, I had to do some fixes in the django-iris project, and even in DB-API driver made by InterSystems to fix some issues there as well, and I think this driver is still in development, and we will get more stable driver in future. Let's decide that this article only explains how it could be if we would have all done.

Hi developers! In this article I’m going to explore the use of Iris Python Native API in a specific problem: large strings to store.

Some time ago I introduced a new driver for Django for IRIS. Now, let's see how to use Django with IRIS in practice.

Jupyter Notebook is an interactive environment consisting of cells that allow executing code in a great number of different markup and programming languages.

To do this Jupyter has to connect to an appropriate kernel. There was no ObjectScript Kernel, that is why I decided to create one.

You can try it out here.

Here's a sneak peek of the results:

InterSystems Native SDK for Python is a lightweight interface to InterSystems IRIS APIs that were once available only through ObjectScript.

I'm especially interested in the ability to call ObjectScript methods, class methods, to be precise. It works, and it works great, but by default, calls only support scalar arguments: strings, booleans, integers, and floats.

But if you want to:
- Pass or return structures, such as dicts or lists
- Pass or return streams

You'll need to write some glue code or take this project (installs with pip install edpy). edpy package gives you one simple signature:

call(iris, class_name, method_name, args)

which allows you to call any ObjectScript method and get results back.

Motivation

This project was thought of when I was thinking of how to let Python code deal naturally with the scalable storage and efficient retrieving mechanism given by IRIS globals, through Embedded Python.

My initial idea was to create a kind of Python dictionary implementation using globals, but soon I realized that I should deal with object abstraction first.

So, I started creating some Python classes that could wrap Python objects, storing and retrieving their data in globals, i.e., serializing and deserializing Python objects in IRIS globals.

This is a benchmark built in python and objectscript in InterSystems IRIS.

The objective is to compare the speed for sending back and forth a thousand request/message from a BP to a BO in python and in objectscript.

See https://github.com/LucasEnard/benchmark-python-objectscript for more information.