Find and compare the best Artificial Intelligence software for Horovod in 2024
Sort:
Use the comparison tool below to compare the top Artificial Intelligence software for Horovod on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
Need help deciding?
Talk to one of our software experts for free. They will help you select the best software for your business.
-
1
TensorFlow
TensorFlow
Free 2 RatingsOpen source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test. -
2
Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.
-
3
TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.
-
4
Flyte
Union.ai
FreeThe workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone. -
5
MXNet
The Apache Software Foundation
The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.
- Previous
- You're on page 1
- Next