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Average Ratings 0 Ratings
Description
Deep learning frameworks like TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have significantly enhanced the accessibility of deep learning by simplifying the design, training, and application of deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) offers a standardized method for deploying these deep-learning frameworks as a service on Kubernetes, ensuring smooth operation. The architecture of FfDL is built on microservices, which minimizes the interdependence between components, promotes simplicity, and maintains a stateless nature for each component. This design choice also helps to isolate failures, allowing for independent development, testing, deployment, scaling, and upgrading of each element. By harnessing the capabilities of Kubernetes, FfDL delivers a highly scalable, resilient, and fault-tolerant environment for deep learning tasks. Additionally, the platform incorporates a distribution and orchestration layer that enables efficient learning from large datasets across multiple compute nodes within a manageable timeframe. This comprehensive approach ensures that deep learning projects can be executed with both efficiency and reliability.
Description
The NVIDIA GPU-Optimized AMI serves as a virtual machine image designed to enhance your GPU-accelerated workloads in Machine Learning, Deep Learning, Data Science, and High-Performance Computing (HPC). By utilizing this AMI, you can quickly launch a GPU-accelerated EC2 virtual machine instance, complete with a pre-installed Ubuntu operating system, GPU driver, Docker, and the NVIDIA container toolkit, all within a matter of minutes.
This AMI simplifies access to NVIDIA's NGC Catalog, which acts as a central hub for GPU-optimized software, enabling users to easily pull and run performance-tuned, thoroughly tested, and NVIDIA-certified Docker containers. The NGC catalog offers complimentary access to a variety of containerized applications for AI, Data Science, and HPC, along with pre-trained models, AI SDKs, and additional resources, allowing data scientists, developers, and researchers to concentrate on creating and deploying innovative solutions.
Additionally, this GPU-optimized AMI is available at no charge, with an option for users to purchase enterprise support through NVIDIA AI Enterprise. For further details on obtaining support for this AMI, please refer to the section labeled 'Support Information' below. Moreover, leveraging this AMI can significantly streamline the development process for projects requiring intensive computational resources.
API Access
Has API
API Access
Has API
Integrations
AWS Marketplace
Amazon Web Services (AWS)
Azure Marketplace
Caffe
Gaia
Kubernetes
NVIDIA NGC
OpenLIT
PyTorch
TensorFlow
Integrations
AWS Marketplace
Amazon Web Services (AWS)
Azure Marketplace
Caffe
Gaia
Kubernetes
NVIDIA NGC
OpenLIT
PyTorch
TensorFlow
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$3.06 per hour
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
IBM
Founded
1911
Country
United States
Website
developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/marketplace/pp/prodview-7ikjtg3um26wq
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization