Forem: Rym

Stop duct-taping your Python scripts: Handle Scheduling and Versioning natively

Rym — Thu, 19 Feb 2026 17:14:05 +0000

TL;DR

Building a great model in Python is fast. But turning that script into a reliable, multi-user application usually involves writing manual "glue code," duct-taping Linux cron jobs, and crossing your fingers during updates.

What if you could bypass the "Day 2" operational wall and handle background scheduling and versioning entirely natively?

Enter Taipy: an open-source Python framework that lets you build production-ready data applications without forcing you to become a full-time DevOps engineer.

If you find this open-source project helpful, don't forget to show your support!

Star ⭐ the repo

The Need for an Alternative to "Glue Code"

We all know the pattern. You build a fast prototype (sales forecasting, optimization, etc.) and it runs perfectly on your machine. Then, the business team needs it every day.

Suddenly, your simple script turns into an operational monster:

• The Scheduling Nightmare:
You tie the model execution to a button click, which freezes the UI. So, you resort to external orchestrators or messy cron jobs.

• The Versioning Chaos:
You start creating files like model_final_v3.py. You become terrified to deploy a new version because you might break the dashboard that executives are looking at.

This is the transition from a prototype to a shared application. Let's look at how Taipy solves these architectural bottlenecks natively.

1. The Native Cron Scheduler ⏱️

Executing Machine Learning or optimization models takes time. Taipy now lets you run background computations automatically through a clean, built-in scheduling API.

🦾 How it helps:

Instead of relying on an external system, scheduling becomes part of your application model. You can set your scenarios to run overnight or hourly without blocking the app.

🛠️ Key features:

• Total Decoupling: The UI stays lightning-fast even if a 4-hour computation is running in the background.

• Overnight Updates: Forecasts and simulations refresh automatically, so decision-makers start the day with up-to-date insights.

• Zero "Glue Code": Reduce manual run steps and brittle external scripts.

2. Built-In Version Management 🗂️

Once people depend on your app, updates start to feel risky. Taipy provides built-in version management so you can track exactly what changed and why, right from the core.

🦾 How it helps:

It preserves traceability between your development, testing, and production environments. You avoid the situation where the safest option is doing nothing because nobody wants to break what already works.

🛠️ Key features:

• Absolute Traceability: You preserve the link between your logic, the data ingested, and the results produced.

• Stress-Free Deployments: Safely evolve your applications as usage grows and more users rely on the results.

• Built-in Governance: Seamlessly move from an isolated "experiment script" to a governed system.

🚀 See the Code in Action (No Sales Pitch)

Reading about architecture is great, but seeing it in code is better.
On April 9th, the Taipy technical team is hosting a Developer Webinar dedicated exactly to this transition: From Python models to trusted decision applications.

👉 Register for the April 9 Webinar here

This is a 100% technical, developer-first session shaped directly by questions from our Discord community. No marketing slides, no sales pitch.

What we will break down live:

• The Code: How to implement the Automated Scheduler and Built-In Version Management.

• End-to-End Tracking: How to monitor scenario and data node events so you aren't guessing how people use your app.

• Real-World Demos: We'll look at high-performance decision applications used by Taipy customer teams who handle massive datasets without browser bottlenecks.

👉 Register for the April 9 Webinar here

[Boost]

Rym — Tue, 14 Jan 2025 07:38:06 +0000

are you cool? do you like building with ai? well come build something :P

Ibrohim Abdivokhidov ・ Jan 13

#opensource #tutorial #ai #hackathon

[Boost]

Rym — Mon, 13 Jan 2025 08:06:19 +0000

Resume AI - Polish your Resume for New Beginning or Fresh Start - Powered by Taipy, Langchain, Gemini, and Github Copilot

Agung Sidharta So ・ Jan 12

#devchallenge #githubchallenge #webdev #ai

👋🏻Goodbye Power BI! 📊 In 2025 Build AI/ML Dashboards Entirely Within Python 🤖

Rym — Tue, 17 Dec 2024 14:54:55 +0000

TL;DR

The rise of data-driven decision-making has pushed businesses to adopt tools to create dynamic dashboards and applications.

Many of these solutions, like Power BI, require users to learn proprietary languages such as DAX or PowerQuery.

What if you could avoid the hassle and stay entirely within Python —the language you already use for data science and machine learning?

Enter Taipy Designer & Taipy: a game-changing, open-source solution that lets you build dashboards and applications without ever leaving the Python ecosystem. And customization is your best friend here.

The Need for an Alternative

Popular tools like Power BI and Tableau dominate the market for business intelligence (BI) and visualization. But they often fall short for AI/ML practitioners due to:

Proprietary languages: DAX, PowerQuery, or other complex syntaxes require additional learning and reduce flexibility.
Limited integration: While some tools support Python scripts, their integration is often cumbersome and not designed for seamless ML workflows.
Fragmented ecosystems: Moving data between BI tools and Python-based ML workflows can lead to inefficiencies and potential data handling errors.

A 100% Python Solution for Data Applications

As an open-source tool, Taipy is free to use and customize, making it a cost-effective alternative to proprietary BI solutions. Its Python foundation ensures that users are not locked into a specific vendor or technology, providing both freedom and flexibility.

Star ⭐ the repo

In addition, they developed a new product, Taipy Designer, the first no-code GUI builder specifically for Python users.

Fully integrated with the Taipy Enterprise Suite, this tool empowers businesses to create interactive, AI-driven applications seamlessly.

Whether you’re building dashboards to visualize complex Machine Learning models or implementing Real-Time AI analysis for operational data, Taipy Designer enables you to:

Leverage Python’s vast ecosystem without switching to other tools.
Implement advanced algorithms and seamlessly integrate machine learning workflows.
Build user-friendly dashboards without writing a single line of code—but with the flexibility to add Python-based customizations when needed.

Taipy Designer eliminates all current tools issues by offering a native Python environment, ensuring your workflows remain streamlined and efficient.

Key Benefits of Taipy Designer

1. For Business Analysts & BI Teams

No coding expertise needed A drag-and-drop interface that democratizes Python’s capabilities, enabling analysts to build applications and dashboards without coding expertise.
Faster application delivery to improved productivity.
Seamless integration with Python’s robust libraries, unlocking endless possibilities for data manipulation and visualization.

2. For Data Scientists & AI Practitioners

The ability to visualize and operationalize machine learning models in intuitive, interactive dashboards.
Tools for rapid experimentation, making it easier to iterate and present results to stakeholders.
Direct integration with Python’s ML & AI frameworks like TensorFlow, PyTorch, or Scikit-learn.

Real-World Impact: Why Businesses Are Switching to Taipy Designer?

The adoption of new tools often depends on their ability to drive tangible benefits. Taipy Designer has already demonstrated its transformative potential by empowering businesses to overcome technical barriers, enhance productivity, and innovate seamlessly.

Here's what leaders have to say about its impact:

Gilles Corde, Program Manager at IFP Energies Nouvelles (IFP-EN), one of Avaiga’s partners, highlighted how Taipy Designer has transformed their workflows.

Beyond Visualization: A Complete Solution

Taipy’s ecosystem doesn’t stop at dashboards. With Taipy you can orchestrate data workflows and create advanced user interfaces. Besides, the platform supports every stage of building enterprise-grade applications. Additionally, Taipy’s integration with leading platforms such as Databricks, Snowflake, IBM WatsonX, and Amazon SageMaker ensures compatibility with your existing data infrastructure.

Ready to Transform Your AI/ML Dashboards?

If you’re looking for a solution that allows you to:

Build scalable, interactive dashboards.
Stay entirely within the Python ecosystem.
Integrate machine learning and AI seamlessly.

Then, it’s time to say goodbye to Power BI and embrace Taipy.

Explore the GitHub repo and discover how this open-source alternative is redefining what’s possible in the world of business intelligence and AI-driven applications.

Have you tried building AI/ML dashboards in Python?

Let me know your thoughts or challenges in the comments below!

Build a 2025 Stock Dashboard in less than 40 lines of Python code!🤓

Rym — Thu, 05 Dec 2024 08:18:36 +0000

Building interactive data dashboards can seem intimidating.

Especially if you're unfamiliar with frontend technologies like HTML/CSS/ JS.

But what if you could create a fully functional, production-ready data science dashboard using just Python?

Enter Taipy, an open-source library that simplifies the process of creating data apps.

Star ⭐ Taipy repo

In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle.

Our app will dynamically filter data, display graphs, and handle user inputs—all from scratch.

Ready to dive in? Let’s get started!

Step 1: Setting Up Your Environment

First, we need to create a new Python environment. If you use Conda, you can set it up as follows:

conda create -n ds_env python=3.11
conda activate ds_env
pip install taipy pandas plotly

Clone the resources for this project:

git clone https://github.com/MariyaSha/data_science_dashboard.git
cd data_science_dashboard/starter_files

This will serve as our project root directory. Inside, you’ll find images, a wireframe, and a Python file (main.py) to start.

Step 2: Designing the GUI with Taipy

Let’s add a header and a logo to our app. Open main.py and start coding:

import taipy.gui as tgb

with tgb.page("Stock Dashboard"):
    # Add a logo
    tgb.image("images/icons/logo.png", width="10vw")

    # Add a title
    tgb.text("# S&P 500 Stock Value Over Time", mode="md")

Run your app:

taipy run main.py

Navigate to http://localhost:5000, and you’ll see your basic app!

Step 3: Adding User Inputs

To filter data by date, add a date range selector:

import datetime

dates = [datetime.date(2023, 1, 1), datetime.date(2024, 1, 1)]

with tgb.page("Stock Dashboard"):
    # Existing elements...

    # Add date range selector
    tgb.date_range(
        value="{dates}",
        label_start="Start Date",
        label_end="End Date",
    )

Step 4: Dynamic Data Handling with Taipy

Let’s load our dataset and filter it dynamically based on user inputs.

import pandas as pd

# Load the stock data
stock_data = pd.read_csv("data/sp500_stocks.csv")

def filter_data(state, name, value):
    if name == "dates":
        start, end = state.dates
        filtered_data = stock_data[
            (stock_data["Date"] >= str(start)) & 
            (stock_data["Date"] <= str(end))
        ]
        state.filtered_data = filtered_data

tgb.add_callback("filter_data", filter_data)

Step 5: Visualizing the Data

Finally, let’s plot the data with Plotly:

import plotly.graph_objects as go

def create_chart(data):
    fig = go.Figure()
    fig.add_trace(
        go.Scatter(
            x=data["Date"],
            y=data["High"],
            name="Stock Value",
            mode="lines"
        )
    )
    return fig

with tgb.page("Stock Dashboard"):
    # Existing elements...

    # Display the chart
    tgb.chart(figure="{create_chart(filtered_data)}")

Final Thoughts

And voilà!
You’ve built a stock dashboard with Taipy, handling dynamic user inputs and data visualization—all without writing a single line of HTML, CSS, or JavaScript.

Want to take it further?

Explore Taipy Scenarios to enable even more dynamic backend interactions. Check out the official Taipy GitHub repository and contribute to their open-source initiatives!

PS: you can watch the video tutorial here.

[Boost]

Rym — Tue, 03 Dec 2024 13:58:23 +0000

9 cutting-edge open-source tools to build next-gen AI apps 🔮💡

Ayush Thakur for Composio ・ Dec 3

#ai #programming #python #webdev

🤓 Top 12 Open Source Repositories to Watch in 2025 to Become the Ultimate Developer

Rym — Tue, 03 Dec 2024 07:26:48 +0000

If you're a developer, staying updated with the best open-source tools is a surefire way to boost productivity and deliver impactful projects.

Here, I've curated a list of 12 open-source tools that are game-changers for various aspects of modern software development—from AI integration to app building. Whether you’re working on data-driven apps, AI tools, or collaboration software, these are some of the most exciting tools you should explore in 2025.

1. Taipy - Build Python Data & AI web applications

Taipy is an open-source Python library that helps you build AI & Data web application including data-driven GUIs and automate scenario management.

➕ It provides a complete solution for building data apps, saving developers significant time and reducing complexity. It combines an intuitive GUI tool for designing dashboards with comprehensive scenario management capabilities, perfect for data scientists and analysts.

Key features:

Test multiple data simulations seamlessly.
Build fully customizable dashboards entirely in Python.
Real-Time Data Visualization

Star ⭐ the repo

2. Composio - Access +150 Tools in Just One Line of Code

For those aiming to build AI applications, you know that it's a nightmare to accomplish complex automation. You have to pass with success the connection of external tools such as GitHub, Jira, Notion, Discord... to your AI agent.

➕ Composio makes it super easy to connect over 150 tools, from system tools to SaaS apps and applications with AI models to accomplish agentic automation.

Key features:

Managed Auth
90+ Tools - Ready to Connect
Powerful RPA tools

Star ⭐ the repo

3. Postiz - Schedule your posts like a pro with AI.

Postiz is an open-source tool for automating and optimizing social media post scheduling.

➕ It offers a straightforward way to manage and debug APIs, improving collaboration and reducing development time. It provides a centralized platform to manage and debug APIs without hassle.

Key features:

Optimal Timing Analysis
Hashtag Generation
Team Collaboration

Star ⭐ the repo

4. LLMWare - Deploy & manage large language models with ease

LLMWare provides the tools necessary to integrate, deploy, and monitor large language models in production environments.

➕ It makes integrating AI models simple, enabling developers to create smarter applications with minimal effort. It focuses on simplifying the way these models interact with user data.

Key features:

Optimize models for environments with limited resources.
Utilize dashboards to monitor model performance in real time.
Implement models quickly with a straightforward API.

Star ⭐ the repo

5. Copilot Kit - Add a layer of AI assistance to your projects with just a few lines of code.

Copilot Kit is a modular development kit designed to integrate AI-powered assistants into your applications or systems.

➕ It enhances productivity by providing real-time coding suggestions, reducing bugs and speeding up development. It utilizes open AI models to assist coding and provide real-time suggestions

Key features:

Multi-language support for optimal customization.
Easy Integration with OpenAI and other large language models.
Comprehensive Documentation with interactive examples.

Star ⭐ the repo

6. Julep AI - Build scalable AI workflows in minutes

Julep is a minimalist yet powerful framework for building fast and lightweight web applications.

➕ It improves data reliability by offering quality monitoring, making data workflows more efficient and robust. It provides a user-friendly interface to manage, monitor, and improve data workflows.

Key features:

A lightweight and performant routing system.
Built-in support for RESTful APIs.
Extensible architecture through a robust plugin ecosystem.

Star ⭐ the repo

7. Traceloop - Debug distributed applications in real time.

Traceloop is a real-time debugging tool for distributed applications, offering clear data flow visualizations.

➕ It helps developers solve issues faster by providing real-time tracing without disrupting services. It traces processes in real time, helping you understand what’s going wrong without disrupting services.

Key features:

Real-time tracing of distributed systems.
Visualizes data interactions for better insight.
Supports multiple cloud environments for flexibility.

Star ⭐ the repo

8. Classiq - Easily design and simulate quantum circuits

Classiq simplifies quantum circuit design, enabling researchers and developers to simulate quantum operations.

➕ Python developers appreciate its robust integration with libraries like Scikit-learn and TensorFlow. It simplifies quantum computing, allowing developers to create quantum algorithms without deep expertise. It helps developers design quantum algorithms without deep quantum expertise.

Key features:

Intuitive tools for designing quantum circuits.
Integrates with major quantum platforms for a seamless workflow.
Real-Time simulation and analysis capabilities.

Star ⭐ the repo

9. ToolJet- Build custom Business Applications. Fast

ToolJet is an open-source low-code platform for creating business applications, compatible with various databases and APIs.

➕ It enables rapid development of internal tools without extensive coding, making it ideal for non-developers. It integrates with different data sources and provides a visual builder to create interfaces without coding from scratch.

Key features:

Drag-and-Drop Builder for quick UI creation.
API Integrations with external APIs for extended functionality.
Role-Based Access Control for user management

Star ⭐ the repo

10. Keep- Swiss-knife for managing alerts/events at scale

Keep offers a secure way to manage secrets and configurations for modern applications.

➕ It ensures critical alerts are never missed, improving incident response times and team coordination. It helps ensure you never miss critical notifications by integrating with various services.

Key features:

Robust encryption for sensitive data.
Integration with popular cloud platforms.
Versioning and auditing of configurations.

Star ⭐ the repo

11. Digger- Deploy cloud infrastructure quickly with simplified IaC.

Digger simplifies infrastructure-as-code workflows, enabling rapid cloud resource deployment.

➕ It streamlines infrastructure deployment, reducing manual tasks and speeding up the CI/CD process. It offers out-of-the-box compatibility with major cloud providers.

Key features:

Terraform integration for infrastructure automation.
Pre-configured templates for common use cases.
Collaboration tools for team-based deployments.

Star ⭐ the repo

12. Skyvern- Automate browser-based workflows with LLMs and vision

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows on a large number of websites, replacing brittle or unreliable automation solutions.

➕ It relies on prompts in addition to computer vision and LLMs to parse items in the viewport in real-time, create a plan for interaction and interact with them.

Key features:

Automated data extraction from web pages.
Customizable workflows using AI.
Integration with headless browsers for scaling.

Star ⭐ the repo

These 12 tools offer something for everyone, whether you're focusing on AI integration, app development, or workflow automation. Which tools have you used or are excited to try? Let’s keep the conversation going in the comments!

💭 By contributing to and integrating these tools into your projects, you not only advance your own skills but also support the vibrant open-source community that drives continuous progress in our industry.

9 Open-Source Tools to Build Better Data Apps in 2025

Rym — Mon, 18 Nov 2024 12:45:31 +0000

Python developer always lookout for tools that streamline workflow, help ship faster, and make data apps more powerful.

Here are 9 incredible open-source tools that will help you build better data applications. Whether you're into data visualization, scenario management, or full-on data orchestration, these tools are must-haves for your 2025 toolbox.

1. Taipy - Simplify Complex Data Workflows

Taipy is an open-source Python library that helps you build AI & Data web application including data-driven GUIs and automate scenario management.

🦾 It’s perfect for developers who need to create custom analytics dashboards without dealing with frontend headaches. Plus, it integrates easily with other tools like Databricks or IBM Watson, so no need to change your coding environment, and uses other libraries charts and components like Matplotlib, Plotly... saving you loads of development time.

Scenario Management
User-Friendly GUI
Python Integration

Star ⭐ the repo

2. Composio - Access +150 Tools in Just One Line of Code

🦾 Composio makes it super easy to connect over 150 tools, from system tools to SaaS apps and applications with AI models to accomplish agentic automation.

Managed Auth
90+ Tools - Ready to Connect
Powerful RPA tools

Star ⭐ the repo

3. Shadcn - Streamline Your Environment

Beautiful, ready-to-use components for your applications
Creating visually appealing custom components can be a time-consuming task. Shadcn simplifies this by offering beautifully designed, reusable components built with Radix and Tailwind. You can easily copy, paste, and customize them to fit your apps.

🦾 This saves significant time compared to building similar components from scratch. Just find the component you need in Shadcn, copy it, paste it into your app, and you're all set.

Shadcn supports most popular front-end libraries and frameworks, including React, Next.js, Astro, Gatsby, and Remix.

Consistent environments across machines
Easy packaging of applications and dependencies
Simplifies deployment and setup

Star ⭐ the repo

4. FastAPI - API Builder in Python

FastAPI is a high-performance framework for building APIs with Python.

🦾 If you need to serve your machine learning models or any backend functionality, this is the fastest, developer-friendly option out there.

High Performance
Automatic Documentation
Ease of Use

Star ⭐ the repo

5. Postman - API Testing Made Easy

APIs are the backbone of any full-stack application, and Postman makes testing them a breeze.

🦾 With its clean interface, you can easily send requests and verify responses. Whether you’re working with complex authorization flows or just testing a simple GET request, Postman keeps everything organized and easy to manage. It’s essential for quick API development and testing.

Clean and intuitive interface for testing APIs
Supports complex authorization flows
Organizes and saves requests for easy management

Star ⭐ the repo

6. GitHub Copilot - Your AI Coding Buddy

Ever wish you had a coding buddy to help with boilerplate code or suggest the best way to refactor a function? GitHub Copilot is here to do just that. This AI-powered tool integrates with VS Code and gives you smart code suggestions in real time.

🦾 It can suggest entire code blocks or help you find the right function name, cutting down on repetitive tasks and making your coding sessions much more productive.

Real-time code suggestions
Helps write boilerplate and repetitive code
Integrates seamlessly with VS Code

Star ⭐ the repo

7. DVC (Data Version Control) - Collaborative Design Tool

🦾 Version control is vital for managing machine learning projects, and DVC brings Git-like capabilities to data. Whether you’re tracking datasets or sharing results across teams, DVC integrates perfectly with your usual Python tools.

Data Versioning
Pipeline Management
Storage Agnostic

Star ⭐ the repo

8. MLflow - End-to-End Machine Learning Lifecycle Management

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It covers everything from experimentation and reproducibility to deployment.

🦾 Python developers appreciate its robust integration with libraries like Scikit-learn and TensorFlow.

Experiment Tracking
Model Registry
Integration with ML Libraries

Star ⭐ the repo

9. Airflow - Automate your Workflow

Apache Airflow is a powerful workflow automation tool.

🦾 While it takes more setup than some other tools, it offers incredible flexibility and is ideal for orchestrating complex data applications.

Directed Acyclic Graphs (DAGs)
Extensibility
Scheduler and Monitoring

Star ⭐ the repo

These tools, especially when used alongside Taipy, make it easier for Python developers to move fast and create sophisticated, production-ready data applications. Whether you're a data scientist, a backend developer, or just curious about building great data-driven experiences, these tools will save you time and make your projects more impactful.

💭 Which of these tools have you already tried? Did I miss your favorite time-saving tool?
Drop it in the comments, and let’s help each other code smarter, not harder!

🎉 HacktoberFest 2024: How to Contribute & Win Exclusive Prizes? 🚀

Rym — Mon, 07 Oct 2024 09:24:58 +0000

TL;DR

Have you heard about Hacktoberfest? 🤔
This month-long celebration of open-source contributions is the perfect opportunity for developers of all skill levels to give back, learn, and snag some awesome swag! 🤯

What is HacktoberFest?

HacktoberFest is an annual month-long event where developers contribute to open-source projects throughout October.
The goal? To grow and improve the open-source ecosystem while helping contributors learn new skills, meet fellow coders, and, yes, earn awesome rewards!🎁🎁🎁

Why Should You Join Hacktoberfest? 🚀

Hacktoberfest is all about collaborating, learning, and having fun while contributing to the projects you care about. Plus, you get to expand your GitHub profile and win swag as a reward for your hard work! It’s an awesome way to improve your coding skills and boost your portfolio while being part of a global event.

Which Repository Should You Contribute To?

One great way to get started with Hacktoberfest is by contributing to one of the best-prepared open-source projects. And one stands among others: Taipy! With more than 50 issues of all levels available, there are plenty of opportunities to dive in and help out while earning exclusive Taipy swag.

Star ⭐ the Taipy repository

What’s in It for You? 🎁

As you contribute, you’ll accumulate points based on the issues you solve. These points can be exchanged for exclusive Taipy swag!

Difficulty	Number of points	Rewards
Easy	100 💎	Stickers + Bottle
Intermediate	200 💎💎	Stickers + Scott Hoodie + Mousepad
Advanced	300 💎💎💎	Stickers + Jack & Jones Hoodie + Bag

➕➕➕ if you give Taipy a star on our ⭐GitHub Repo⭐, you could be one of 10 randomly selected stargazers to win an additional swag! 🎁🎁🎁

Taipy is Proud to Sponsor Quira’s Hacktoberfest Event! 🎮

This year, Taipy is excited to be one of 15 organizations that have partnered with Quira for Hacktoberfest 2024. Quira, a company that focuses on building sustainable communities for next-gen dev tools, is helping to turn open source into a positive-sum game 🎮 by organizing events that encourage individuals to improve their coding skills in a fun and engaging way.

In addition, Quira is one of the three official sponsors of HACKTOBERFEST, making this year’s event even more special!

How Do You Do?

Here’s how you can participate:

➡️ Step 1: Choose an Issue

Visit Taipy GitHub Repository.
Look for issues tagged with "hacktoberfest" and choose the issue that best matches your skills and interests.

Difficulty	Number of points	Rewards
Easy	100 💎	Beginner-friendly issues
Intermediate	200 💎💎	Mid-level tasks
Advanced	300 💎💎💎	Challenging tasks for experienced coders

➡️ Step 2: Fork the Repository

Once you’ve selected an issue, start by forking the Taipy repository to your GitHub account. This creates a copy of the repository that you can work on without affecting the original codebase.

git clone https://github.com/YourGitHubUsername/taipy.git

➡️ Step 3: Clone Your Fork Locally

After forking, clone the repository to your local machine by running the following command in your terminal:

git checkout -b issue-#<issue-number>-description

➡️ Step 4: Create a New Branch

Always create a new branch for your work. It’s good practice to name your branch based on the issue you're working on. For example:

git checkout -b bug/#<issue#>-comment

➡️ Step 5: Work on the Issue

Start coding and solving the issue! Follow the guidelines mentioned in the issue, and make sure to test your changes locally. If you have any questions, feel free to ask for help in the issue’s comment section or reach out to our community.

➡️ Step 6: Commit & Push Your Changes

Once you’re satisfied with your work, commit your changes and push the branch to your fork:

git add
git commit -m "description of the fix"
git push origin your-branch-name

➡️ Step 7: Submit a Pull Request (PR)

Go back to the original Taipy repository. You’ll see an option to submit a pull request. Make sure your PR description clearly references the issue you worked on and provides a brief explanation of what you fixed.

Important Guidelines to Follow

Stick to the issues labeled "hacktoberfest" and with a difficulty tag to ensure they count towards the event.
Do not submit PRs on newly created issues unless they have been approved by the Taipy team.

Follow these simple rules to ensure your contributions are counted, and you'll be on your way to grabbing that cool swag! 🎁

Event Timeline 📅

Starts: October 1, 2024
Ends: October 31, 2024

Get Started Now! 🎯

Head over to the Taipy GitHub Repository, choose an issue, and start contributing. Don’t forget to star our project for a chance to win more rewards!

Together, let’s make HacktoberFest 2024 an epic open-source celebration! 🌍✨

🚀Top 5 Open Source Projects to Contribute to During Hacktoberfest 2️⃣0️⃣2️⃣4️⃣

Rym — Fri, 04 Oct 2024 08:37:45 +0000

Hacktoberfest is an exciting event for developers to contribute to open-source projects, sharpen their skills, and earn awesome swag.
However, it's always so difficult to find the right project, the one that suits their level.

If you're looking for great projects to get involved with, here are five fantastic repositories offering swag and welcoming contributors of all levels.

1. Taipy

Taipy is a Python-based platform that simplifies the creation of production-ready AI & Data web applications.

By contributing to Taipy during Hacktoberfest 2024, you not only get to work with state-of-the-art tools but also have the chance to earn some 🎁🎁🎁very cool swag!🎁🎁🎁

Star ⭐ the Taipy repository

🎁🎁🎁 Prizes 🎁🎁🎁:

Taipy offers contributors some unique and exciting swag during Hacktoberfest 2024, based on the difficulty of merged pull requests
that solve hacktoberfest-tagged issues

Difficulty	Number of points	Rewards
Easy	100 points 💎	Stickers + Bottle
Intermediate	200 points 💎💎	Stickers + Scott Hoodie + Mousepad
Advanced	300 points 💎💎💎	Stickers + Jack & Jones Hoodie + Bag

⭐⭐⭐ Bonus ⭐⭐⭐

Star the repo to be among the 10 stargazers winning a special prize.

2. Appwrite

Appwrite is an open-source backend-as-a-service (BAAS) platform that allows developers to quickly build secure web and mobile applications. Appwrite is a big supporter of Hacktoberfest 2024 and offers a variety of swags, such as hoodies and stickers, to contributors who make valid pull requests.

Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

The top team will each walk away with The Appwriter, an exclusive award for the best overall project.
2nd and 3rd-place teams will receive the Appwrite swag kit.

3. CopilotKit

CopilotKit is an open-source tool that extends GitHub Copilot's capabilities, giving developers more flexibility with AI-assisted coding. This is an ideal project for developers who are interested in AI-powered development environments. CopilotKit is actively participating in Hacktoberfest 2024 with swag offerings for valid contributions.
Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

A bit harder with this library. You have to create a whole application to win some surprise swag (we know nothing about it yet)
Demos will be ranked from 0 to 100 based on:

Criteria	Points
Innovation & Creativity	0-25
Usefulness & Practicality	0-25
Quality & Completeness	0-25
User Experience & Design	0-25

Bonus Points

4 Ways to Earn 10 bonus points for each (Swag is only available if you complete a demo) :

⭐ Write and share a tutorial about your app on dev.to that receives up to 30 or more reactions.
⭐ Post about your project on Twitter and get at least 10 reactions.
⭐ Solve good first issues that get merged.
⭐ Create a YouTube video promoting your CopilotKit project that receives up to 100 views.

4. Auth0 (by Okta)

Auth0 by Okta provides authentication and authorization services. Developers can work on issues related to security and identity management during Hacktoberfest 2024.
Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

Contributors who submit two pull requests to the Auth0 projects can earn a limited-edition t-shirt as part of their “Authtoberfest” campaign.

5. Hyperswitch

Hyperswitch is a powerful open-source payment processor created by Juspay. It simplifies integrations with various payment gateways. During Hacktoberfest 2024, Hyperswitch offers exclusive swag, including stickers for 1 merged pull request and a special swag kit with t-shirts or hoodies for 3+ merged PRs.

Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

Contributors who submit a PR that solves a hacktoberfest-tagged issue will earn some nice shirts and hoodies.

Final Thoughts

Hacktoberfest 2024 is a great opportunity to grow your skills and connect with the open-source community. Not only will you have a meaningful impact by contributing to these projects, but you’ll also get rewarded with some exciting swag and most important, if you're a student this will help you in your future career. Make sure to register, track your contributions, and have fun hacking this October 2024!

🚀 Turning all Databricks Jobs Into Something Your Non-Tech-Savvy Team Can Actually Use 🤖

Rym — Tue, 24 Sep 2024 08:31:05 +0000

If you’re a Databricks user, you know the struggle is real.

You’ve got your data pipelines humming, your machine learning models crunching numbers, and your notebooks looking flawless. But then comes the dreaded part: turning all that backend magic into something your non-tech-savvy team can actually use.

Building interactive applications on top of Databricks? Not exactly a walk in the park.

Cue Taipy, your new best friend. We’re now a Databricks Technology Partner (yes, we’ve got the badge to prove it! 👇🏻👇🏻👇🏻) and we’re here to make your life easier.🦾🦾🦾

The Struggles You’re Facing

🤦🏻‍♂️ Building a Front-End: Databricks is awesome at backend stuff, but making that data accessible to non-tech folks? That’s a headache.
🤦🏻‍♂️ "What-If" Analysis: Your boss wants to tweak some variables and see different outcomes… but building a tool for that is a whole new project.
🤦🏻‍♂️ Collaboration Across Teams: Sharing insights across departments while keeping things easy for everyone? Easier said than done.

How Taipy Saves the Day

📊 No More Front-End Drama: Taipy’s low-code platform lets you turn those Databricks notebooks into slick, user-friendly web apps in no time. No need to be a front-end wizard!
📉📈 Scenario Management for the Win: Your boss can finally run those “What-If” scenarios with ease. Taipy lets you orchestrate Databricks jobs and visualize different outcomes like a pro.
🫱🏼‍🫲🏼 Teamwork, Dreamwork: Multi-user support means your whole team can jump in, collaborate, and make data-driven decisions together.

So How Does It Work?🤔

🦾 Run Databricks Jobs Through Taipy: You keep doing what you do best—setting up jobs in Databricks. Taipy handles the rest, linking those jobs to its super-friendly scenarios.
🤖 Automated Awesomeness: Taipy orchestrates all the heavy lifting, running your Databricks tasks and giving you (and your team) real-time results in a clean interface.

Go try it yourself and let us know what you think about it!

No more fussing with front-end code or manual workflows. With Taipy, you’ll be turning your Databricks magic into interactive apps faster than you can say "big data."

Ready to give it a go? Check out more details in this article, our tutorial, and video guide to see how easy it is to integrate Taipy with Databricks.

What-if I told you complex Data Integration makes good Task Orchestration?

Rym — Thu, 12 Sep 2024 15:24:17 +0000

No matter how complex your data problem is, you can always Divide & Conquer it. 🦾🦾🦾

In this article, we’ll break down how to solve intricate data challenges, data treatment, data management using a fully open-source Python library: Taipy. Whether you're cleaning data, orchestrating tasks, or exploring different outcomes, Taipy's intuitive design empowers you to handle it all.

Taipy's Scenario Management basis

Let’s go through three steps to show how Taipy can help streamline your data workflow:

1. Data Integration: Get the Data You Need

Every AI, ML, and data-driven project starts with data, obviously!
And it’s rarely as simple as a single, clean dataset. In most cases, you'll be pulling data from databases, APIs, flat files, or other external sources, and sometimes, all in the same project. This is where Taipy's data integration comes in—gathering and unifying data from these different sources.

In Taipy, this process is simplified with a key abstraction: the Data Node.

A Data Node represents your data but doesn’t store it directly. Instead, it holds all the necessary metadata to read and write the actual data, whether it’s a CSV file, a database table, or even an API response.

Here’s a quick example of defining a Data Node for a CSV:

from taipy import Config
initial_dataset_cfg = Config.configure_data_node(id="initial_dataset",
                                                 storage_type="csv",
                                                 path="data/dataset.csv",
                                                 scope=Scope.GLOBAL)

With this abstraction, Taipy takes care of data management, allowing you to focus on transforming and processing your data.

2. Task Orchestration: What Are You Doing with All That Data?

Now that your data is in place, what do you do with it? In any data workflow, the next step involves defining tasks that process and transform the data. This is what we call the Task Orchestration

A Task in Taipy is like a function, taking Data Node(s) as inputs, performing transformations, and then outputting Data Node(s).

For instance, you may want to filter some data or calculate new metrics. Here's an example of creating a Task to calculate the sum of a column:

clean_data_task_cfg = Config.configure_task(id="clean_data",
                                            function=clean_data,
                                            input=initial_dataset_cfg,
                                            output=cleaned_dataset_cfg,
                                            skippable=True)

Once you've defined your tasks, you can arrange them into a pipeline to ensure the steps execute in the correct order. This lets you easily build workflows that transform data, build models, generate reports, and more.

3. What-If Analysis: Explore Different Scenarios

Once your data and workflows are set up, you can begin exploring different scenarios. This is where the What-if analysis comes into play.

A Scenario in Taipy represents a specific instance of a problem you’re trying to solve, allowing you to test different parameters and see how they impact your results. By adjusting input data or assumptions, you can model various scenarios without starting over from scratch.

Here's how you can define a Scenario in Taipy:

scenario_cfg = Config.configure_scenario(id="scenario", task_configs=[clean_data_task_cfg, predict_task_cfg,evaluate_task_cfg], frequency=Frequency.MONTHLY)
tp.Core().run()
my_first_scenario = create_and_run_scenario(dt.datetime(2021, 1, 25))
predictions = my_first_scenario.predictions.read()
print("Predictions\n", predictions)

This makes it easy to perform sensitivity analysis or optimize outcomes, all within the same framework. Want to test different discount rates on your sales model? Just create new scenarios, tweak the parameters, and rerun them.

Taipy vs. Other Pipeline Management Tools

You may be wondering, how does Taipy compare to other popular pipeline orchestration tools like Apache Airflow, Luigi, or Prefect? While those tools are great for managing task scheduling across distributed environments, Taipy stands out by focusing on Python simplicity, especially when it comes to scenario management and what-if analysis.

Airflow/Luigi/Prefect: Generally focus on orchestrating ETL processes, scheduling, and monitoring workflows.
Taipy: It not only provides workflow orchestration but also simplifies what-if analysis with its unique scenario abstraction, allowing you to model various outcomes seamlessly.

For developers looking to handle complex data workflows in Python with minimal setup, Taipy offers a more straightforward, code-first approach.

Conclusion: Divide and Conquer with Taipy

The Divide and Conquer strategy wins every time, no matter the size or complexity of your data problems! With Taipy, you can handle everything from data integration to task orchestration and what-if analysis, all in one place. And you can finalize with data visualization as well.

Ready to give Taipy a try? Check out the GitHub repo and see how it can streamline your data workflows today!

Star Taipy ⭐️

Don’t forget to leave a star ⭐ and share your feedback or scenarios you've worked on in the comments below!