Rusty  Shanahan

Rusty Shanahan

1598165880

Plotly and NVIDIA Partner to Integrate Dash and RAPIDS

We’re pleased to announce that Plotly and NVIDIA are partnering to bring GPU-accelerated Artificial Intelligence (AI) & Machine Learning (ML) to a vastly wider audience of business users. By integrating the Plotly Dash frontend with the NVIDIA RAPIDS backend, we are offering one of the highest performance AI & ML stacks available in Python today. This is all open-source and accessible in a few lines of Python code.

On the Enterprise side, Dash Enterprise Kubernetes (DEK)now ships with out-of-the-box support for horizontally scalable GPU acceleration through RAPIDS and Dask. Once you’ve created a Dash + RAPIDS app on your desktop, get it into the hands of business users by uploading it to DEK. No IT or devops team required 🙅‍♀️.

NVIDIA’s CEO Jensen Huang mentioned some of the early fruits of this partnership in the first minute of his GTC 2020 Kitchen Keynote last week, and today we’re more formally announcing our partnership.

Image for post

A typical business intelligence (BI) dashboard or analytical application combines graphs, maps, and controls to provide interactive access to queries and AI models running on large, complex datasets. Any organization delivering goods or services at scale will have millions of records to analyze, spread out in time and space and across various more-abstract dimensions. Building a performant application on top of such a dataset usually requires a multi-team, multi-week effort and results in a complex, multi-tiered architecture. New technologies like Dash and RAPIDS are changing this landscape, empowering individual Python developers to easily and quickly build analytical applications that are more performant than their complex counterparts.

#machine-learning #business-intelligence #artificial-intelligence #gpu #data-visualization #deep learning

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Buddha Community

Plotly and NVIDIA Partner to Integrate Dash and RAPIDS
Jamie  Graham

Jamie Graham

1619672100

How to integrate your Python Class into Plotly Dash dashboards

A brief tutorial on Object Oriented Programming in Dash

Introduction

When developing your Python application, using classes and general Object Oriented Programming (OOP) concepts allows you to tailor and craft your code to allow for maximum flexibility and usability.

Even though you can very well use Python to a great extent and never worry about declaring your own objects, doing so can take your code and applications to the next level, and overall make you a more aware and complete developer.

If you have previously never used classes in Python and would like to know more, please find below a previous article of mine where I give an overview of projects you can get started with in order to develop foundational knowledge of how classes work.

In this article, I am going to provide an example of embedding flexible class objects into a simple Plotly-Dash python application. Plotly-Dash  is a set of libraries and framework which is UI-oriented, and used for various data-visualization use cases.

#python-class #python #object-oriented #dash #plotly

Riyad Amin

Riyad Amin

1620031478

From Plotly Dash 1.0 to Dash 2.0

Learn about the new enhancements of Dash Labs, which will develop into Dash 2.0 in the future. And help shape Dash 2.0 through feedback and suggestions.

Video layout:
00:00​ - What you will learn and why
03:14​ - Install necessary libraries: jup-short-app
04:06​ - Template Layout System (light overview)
06:16​ - Data and components
07:57​ - Callback Enhancements (light overview)
13:05​ - Jup-powerful-app
14:21​ - Template Layout System (Deep Dive)
16:49​ - Your Feedback Please
17:49​ - Callback Enhancements (Deep Dive)
24:14​ - DbcSidebarTabs
27:58​ - Call to action!

Feedback Forum Post:

https://community.plotly.com/t/introd…

My GitHub Code:

https://github.com/Coding-with-Adam/D…

Dash Labs GitHub Code:

https://github.com/plotly/dash-labs/t…

Dash Themes and Templates:

https://hellodash.pythonanywhere.com/…

Subscribe: https://www.youtube.com/c/CharmingData/featured

#plotly #dash

plotting multiple figures with live data using Dash and Plotly

whenever it comes to visualizations for data plotly comes out as a standout package to choose among multiple options especially when it comes to interactivity also plotly comes with Dash which is really a handy framework for creating powerful Dashboards. when there are times when you have live data and that too with multiple figures.

Image for post

And well its sometimes easy to set an interval for each figure and make Dash update the figure in that interval but consider if the data you are using is shared between all the figures. so you have to process the data in each callback, yep!.. you have to do the same computation multiple times and you can not update the data outside the callback because than it won’t be updated also you can’t use global variables because it will break your app especially when you are having user inputs.

now the one way is to create a hidden div and process and store the data in that and then use it as an input for your callbacks you can find this example in here

But what if this is the case

Image for post

that is for each figure you have to process the data from the main live data. In this case, you have to process read the data from JSON string in each callback which is again computationally expensive and you might get bugs.

thus a better way to do is to use multiple outputs

#data-visualization #livedata #plotly #dashboard #dash #data analysis

Rusty  Shanahan

Rusty Shanahan

1598165880

Plotly and NVIDIA Partner to Integrate Dash and RAPIDS

We’re pleased to announce that Plotly and NVIDIA are partnering to bring GPU-accelerated Artificial Intelligence (AI) & Machine Learning (ML) to a vastly wider audience of business users. By integrating the Plotly Dash frontend with the NVIDIA RAPIDS backend, we are offering one of the highest performance AI & ML stacks available in Python today. This is all open-source and accessible in a few lines of Python code.

On the Enterprise side, Dash Enterprise Kubernetes (DEK)now ships with out-of-the-box support for horizontally scalable GPU acceleration through RAPIDS and Dask. Once you’ve created a Dash + RAPIDS app on your desktop, get it into the hands of business users by uploading it to DEK. No IT or devops team required 🙅‍♀️.

NVIDIA’s CEO Jensen Huang mentioned some of the early fruits of this partnership in the first minute of his GTC 2020 Kitchen Keynote last week, and today we’re more formally announcing our partnership.

Image for post

A typical business intelligence (BI) dashboard or analytical application combines graphs, maps, and controls to provide interactive access to queries and AI models running on large, complex datasets. Any organization delivering goods or services at scale will have millions of records to analyze, spread out in time and space and across various more-abstract dimensions. Building a performant application on top of such a dataset usually requires a multi-team, multi-week effort and results in a complex, multi-tiered architecture. New technologies like Dash and RAPIDS are changing this landscape, empowering individual Python developers to easily and quickly build analytical applications that are more performant than their complex counterparts.

#machine-learning #business-intelligence #artificial-intelligence #gpu #data-visualization #deep learning

Riyad Amin

Riyad Amin

1622433526

The Upload Component - Plotly Dash

Build an app that allows users to view their own data, by using the Dash Upload component. We will also give users the capability to analyze their data by adding a bar graph to the app.

Video layout:
00:00 - What you will learn and why
02:17 - Code and Support
03:57 - App Layout
06:11 - DataTable/Dropdown Callback
09:24 - Parse_contents function
13:34 - Bar Graph Callback
18:05 - More Code and tutorials

My GitHub Code:

https://github.com/Coding-with-Adam/D…

Data Sheet:

https://drive.google.com/file/d/1Solf…

Dash Upload Docs:

https://dash.plotly.com/dash-core-com…

Subscribe: https://www.youtube.com/c/CharmingData/featured

#plotly #dash