Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The NVIDIA Deep Learning GPU Training System (DIGITS) empowers engineers and data scientists by making deep learning accessible and efficient. With DIGITS, users can swiftly train highly precise deep neural networks (DNNs) tailored for tasks like image classification, segmentation, and object detection. It streamlines essential deep learning processes, including data management, neural network design, multi-GPU training, real-time performance monitoring through advanced visualizations, and selecting optimal models for deployment from the results browser. The interactive nature of DIGITS allows data scientists to concentrate on model design and training instead of getting bogged down with programming and debugging. Users can train models interactively with TensorFlow while also visualizing the model architecture via TensorBoard. Furthermore, DIGITS supports the integration of custom plug-ins, facilitating the importation of specialized data formats such as DICOM, commonly utilized in medical imaging. This comprehensive approach ensures that engineers can maximize their productivity while leveraging advanced deep learning techniques.
Description
NVIDIA's DeepStream SDK serves as a robust toolkit for streaming analytics, leveraging GStreamer to facilitate AI-driven processing across various sensors, including video, audio, and image data. It empowers developers to craft intricate stream-processing pipelines that seamlessly integrate neural networks alongside advanced functionalities like tracking, video encoding and decoding, as well as rendering, thereby enabling real-time analysis of diverse data formats. DeepStream plays a crucial role within NVIDIA Metropolis, a comprehensive platform aimed at converting pixel and sensor information into practical insights. This SDK presents a versatile and dynamic environment catered to multiple sectors, offering support for an array of programming languages such as C/C++, Python, and an easy-to-use UI through Graph Composer. By enabling real-time comprehension of complex, multi-modal sensor information at the edge, it enhances operational efficiency while also providing managed AI services that can be deployed in cloud-native containers managed by Kubernetes. As industries increasingly rely on AI for decision-making, DeepStream's capabilities become even more vital in unlocking the value embedded within sensor data.
API Access
Has API
No
API Access
Has API
Yes
Integrations
TensorFlow
Yes
C
No
C++
No
Caffe
Yes
Dask
Yes
Helm
No
Kubernetes
No
NVIDIA Jetson
No
NVIDIA Metropolis
No
NVIDIA TensorRT
No
Integrations
TensorFlow
Yes
C
Yes
C++
Yes
Caffe
No
Dask
No
Helm
Yes
Kubernetes
Yes
NVIDIA Jetson
Yes
NVIDIA Metropolis
Yes
NVIDIA TensorRT
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
NVIDIA DIGITS
Founded
1993
Country
United States
Website
developer.nvidia.com/digits
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/deepstream-sdk
Product Features
Deep Learning
Convolutional Neural Networks
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No
Product Features
Deep Learning
Convolutional Neural Networks
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No