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
GPUs excel at swiftly transferring data but suffer from limited locality of reference due to their relatively small caches, which makes them better suited for scenarios that involve heavy computation on small datasets rather than light computation on large ones. Consequently, the networks optimized for GPU architecture tend to run in layers sequentially to maximize the throughput of their computational pipelines (as illustrated in Figure 1 below). To accommodate larger models, given the GPUs' restricted memory capacity of only tens of gigabytes, multiple GPUs are often pooled together, leading to the distribution of models across these units and resulting in a convoluted software framework that must navigate the intricacies of communication and synchronization between different machines. In contrast, CPUs possess significantly larger and faster caches, along with access to extensive memory resources that can reach terabytes, allowing a typical CPU server to hold memory equivalent to that of dozens or even hundreds of GPUs. This makes CPUs particularly well-suited for a brain-like machine learning environment, where only specific portions of a vast network are activated as needed, offering a more flexible and efficient approach to processing. By leveraging the strengths of CPUs, machine learning systems can operate more smoothly, accommodating the demands of complex models while minimizing overhead.
API Access
Has API
No
API Access
Has API
No
Integrations
Caffe
Yes
Dask
Yes
NetApp AIPod
Yes
TensorFlow
Yes
Torch
Yes
Ultralytics
No
Unleash live
Yes
Integrations
Caffe
No
Dask
No
NetApp AIPod
No
TensorFlow
No
Torch
No
Ultralytics
Yes
Unleash live
No
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
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
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
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
NVIDIA DIGITS
Founded
1993
Country
United States
Website
developer.nvidia.com/digits
Vendor Details
Company Name
Neural Magic
Founded
2018
Country
United States
Website
neuralmagic.com
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
Artificial Intelligence
Chatbot
No
For Healthcare
No
For Sales
No
For eCommerce
No
Image Recognition
No
Machine Learning
No
Multi-Language
No
Natural Language Processing
No
Predictive Analytics
No
Process/Workflow Automation
No
Rules-Based Automation
No
Virtual Personal Assistant (VPA)
No
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
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No