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What is Streamlit?

Streamlit serves as an incredibly efficient solution for the creation and dissemination of data applications. With this platform, users can convert their data scripts into easily shareable web apps in a matter of minutes, leveraging Python without incurring any costs, and it removes the barriers that come with needing front-end development expertise. The platform is anchored by three foundational principles: it promotes the use of Python scripting for application creation; it allows users to build applications with minimal code by utilizing a user-friendly API that automatically updates upon saving the source file; and it enhances user interaction by enabling the inclusion of widgets as effortlessly as declaring a variable, all without the need to handle backend development, define routes, or manage HTTP requests. Furthermore, applications can be deployed instantly through Streamlit’s sharing platform, which streamlines the processes of sharing, managing, and collaborating on projects. This straightforward framework allows for the development of powerful applications, such as the Face-GAN explorer that integrates Shaobo Guan’s TL-GAN project and utilizes TensorFlow and NVIDIA’s PG-GAN for generating attribute-based facial images. Another compelling example is a real-time object detection application designed as an image browser for the Udacity self-driving car dataset, demonstrating impressive capabilities in real-time object processing and recognition. Overall, Streamlit is not only beneficial for developers but also serves as a vital resource for data enthusiasts, enabling them to explore innovative projects with ease. Each of these features highlights why Streamlit has become a preferred choice for many in the data community.

What is Horovod?

Horovod, initially developed by Uber, is designed to make distributed deep learning more straightforward and faster, transforming model training times from several days or even weeks into just hours or sometimes minutes. With Horovod, users can easily enhance their existing training scripts to utilize the capabilities of numerous GPUs by writing only a few lines of Python code. The tool provides deployment flexibility, as it can be installed on local servers or efficiently run in various cloud platforms like AWS, Azure, and Databricks. Furthermore, it integrates well with Apache Spark, enabling a unified approach to data processing and model training in a single, efficient pipeline. Once implemented, Horovod's infrastructure accommodates model training across a variety of frameworks, making transitions between TensorFlow, PyTorch, MXNet, and emerging technologies seamless. This versatility empowers users to adapt to the swift developments in machine learning, ensuring they are not confined to a single technology. As new frameworks continue to emerge, Horovod's design allows for ongoing compatibility, promoting sustained innovation and efficiency in deep learning projects.

Media

Media

Integrations Supported

Python
Azure Marketplace
Beam Cloud
Broxi AI
DemoGPT
Gradient
JarvisLabs.ai
NewsData.io
Positron
Streamoku
TrueFoundry

Integrations Supported

Python
Azure Databricks
Flyte
Keras
MXNet
Microsoft Azure
PyTorch
TensorFlow

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Not specified

Company Facts

Organization Name

Streamlit

Company Location

United States

Company Website

www.streamlit.io

Company Facts

Organization Name

Horovod

Company Website

horovod.ai/

Categories and Features

Data Science

Not specified

Categories and Features

AI/ML Model Training

Not specified

Deep Learning

Not specified

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