Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • RunPod Reviews & Ratings
    206 Ratings
    Company Website
  • Qloo Reviews & Ratings
    23 Ratings
    Company Website
  • Fraud.net Reviews & Ratings
    56 Ratings
    Company Website
  • Google Cloud Speech-to-Text Reviews & Ratings
    361 Ratings
    Company Website
  • Gemini Enterprise Agent Platform Reviews & Ratings
    961 Ratings
    Company Website
  • PackageX OCR Scanning Reviews & Ratings
    46 Ratings
    Company Website
  • ClickLearn Reviews & Ratings
    67 Ratings
    Company Website
  • myACI Reviews & Ratings
    481 Ratings
    Company Website
  • Auvik Reviews & Ratings
    668 Ratings
    Company Website
  • Coursebox AI Reviews & Ratings
    98 Ratings
    Company Website

What is NVIDIA DIGITS?

The NVIDIA Deep Learning GPU Training System (DIGITS) enhances the efficiency and accessibility of deep learning for engineers and data scientists alike. By utilizing DIGITS, users can rapidly develop highly accurate deep neural networks (DNNs) for various applications, such as image classification, segmentation, and object detection. This system simplifies critical deep learning tasks, encompassing data management, neural network architecture creation, multi-GPU training, and real-time performance tracking through sophisticated visual tools, while also providing a results browser to help in model selection for deployment. The interactive design of DIGITS enables data scientists to focus on the creative aspects of model development and training rather than getting mired in programming issues. Additionally, users have the capability to train models interactively using TensorFlow and visualize the model structure through TensorBoard. Importantly, DIGITS allows for the incorporation of custom plug-ins, which makes it possible to work with specialized data formats like DICOM, often used in the realm of medical imaging. This comprehensive and user-friendly approach not only boosts productivity but also empowers engineers to harness cutting-edge deep learning methodologies effectively, paving the way for innovative solutions in various fields.

What is MXNet?

A versatile front-end seamlessly transitions between Gluon’s eager imperative mode and symbolic mode, providing both flexibility and rapid execution. The framework facilitates scalable distributed training while optimizing performance for research endeavors and practical applications through its integration of dual parameter servers and Horovod. It boasts impressive compatibility with Python and also accommodates languages such as Scala, Julia, Clojure, Java, C++, R, and Perl. With a diverse ecosystem of tools and libraries, MXNet supports various applications, ranging from computer vision and natural language processing to time series analysis and beyond. Currently in its incubation phase at The Apache Software Foundation (ASF), Apache MXNet is under the guidance of the Apache Incubator. This essential stage is required for all newly accepted projects until they undergo further assessment to verify that their infrastructure, communication methods, and decision-making processes are consistent with successful ASF projects. Engaging with the MXNet scientific community not only allows individuals to contribute actively but also to expand their knowledge and find solutions to their challenges. This collaborative atmosphere encourages creativity and progress, making it an ideal moment to participate in the MXNet ecosystem and explore its vast potential. As the community continues to grow, new opportunities for innovation are likely to emerge, further enriching the field.

Media

Media

Integrations Supported

AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon Elastic Inference
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Caffe
Cameralyze
Dask
Flower
Google Cloud Deep Learning VM Image
Gradient
Guild AI
Horovod
LeaderGPU
MLReef
NVIDIA Triton Inference Server
NetApp AIPod
TensorFlow
Torch

Integrations Supported

AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon Elastic Inference
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Caffe
Cameralyze
Dask
Flower
Google Cloud Deep Learning VM Image
Gradient
Guild AI
Horovod
LeaderGPU
MLReef
NVIDIA Triton Inference Server
NetApp AIPod
TensorFlow
Torch

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

NVIDIA DIGITS

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/digits

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

mxnet.apache.org

Categories and Features

Deep Learning

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

Categories and Features

Deep Learning

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

Popular Alternatives

Popular Alternatives