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What is Tornado Web Server?

Tornado is a Python web framework that doubles as an asynchronous networking library, originally developed at FriendFeed. With its non-blocking network I/O, it can handle tens of thousands of concurrent connections, making it ideal for applications that rely on persistent user connections like long polling and WebSockets. Distinct from most Python web frameworks, Tornado does not adhere to the WSGI standard and typically functions on a single thread per process. While the tornado.wsgi module provides a degree of WSGI compatibility, it is not the primary focus of the framework’s development, and it is recommended that developers utilize Tornado’s dedicated interfaces, such as tornado.web, when creating applications. Additionally, it is crucial to understand that Tornado's code is generally not thread-safe. Since the release of version 5.0, Tornado has been integrated with Python's standard library asyncio module, allowing it to share the same default event loop. This integration facilitates the use of libraries built for asyncio alongside Tornado, significantly enhancing its versatility and overall functionality. Consequently, developers can construct highly efficient applications that harness both the strengths of Tornado and the capabilities of asyncio, leading to more robust and responsive user experiences. The adaptability of Tornado has made it a popular choice among developers who require high performance in their web applications.

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
Activeeon ProActive
Amazon Web Services (AWS)
Azure Databricks
Flyte
Keras
MXNet
Microsoft Azure
New Relic
PyTorch
Swoole
TensorFlow

Integrations Supported

Python
Activeeon ProActive
Amazon Web Services (AWS)
Azure Databricks
Flyte
Keras
MXNet
Microsoft Azure
New Relic
PyTorch
Swoole
TensorFlow

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Tornado

Date Founded

2009

Company Location

United States

Company Website

www.tornadoweb.org/en/stable/

Company Facts

Organization Name

Horovod

Company Website

horovod.ai/

Categories and Features

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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