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Average Ratings 0 Ratings
Description
Exafunction enhances the efficiency of your deep learning inference tasks, achieving up to a tenfold increase in resource utilization and cost savings. This allows you to concentrate on developing your deep learning application rather than juggling cluster management and performance tuning. In many deep learning scenarios, limitations in CPU, I/O, and network capacities can hinder the optimal use of GPU resources. With Exafunction, GPU code is efficiently migrated to high-utilization remote resources, including cost-effective spot instances, while the core logic operates on a low-cost CPU instance. Proven in demanding applications such as large-scale autonomous vehicle simulations, Exafunction handles intricate custom models, guarantees numerical consistency, and effectively manages thousands of GPUs working simultaneously. It is compatible with leading deep learning frameworks and inference runtimes, ensuring that models and dependencies, including custom operators, are meticulously versioned, so you can trust that you're always obtaining accurate results. This comprehensive approach not only enhances performance but also simplifies the deployment process, allowing developers to focus on innovation instead of infrastructure.
Description
Quickly set up a virtual machine on Google Cloud for your deep learning project using the Deep Learning VM Image, which simplifies the process of launching a VM with essential AI frameworks on Google Compute Engine. This solution allows you to initiate Compute Engine instances that come equipped with popular libraries such as TensorFlow, PyTorch, and scikit-learn, eliminating concerns over software compatibility. Additionally, you have the flexibility to incorporate Cloud GPU and Cloud TPU support effortlessly. The Deep Learning VM Image is designed to support both the latest and most widely used machine learning frameworks, ensuring you have access to cutting-edge tools like TensorFlow and PyTorch. To enhance the speed of your model training and deployment, these images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers, as well as the Intel® Math Kernel Library. By using this service, you can hit the ground running with all necessary frameworks, libraries, and drivers pre-installed and validated for compatibility. Furthermore, the Deep Learning VM Image provides a smooth notebook experience through its integrated support for JupyterLab, facilitating an efficient workflow for your data science tasks. This combination of features makes it an ideal solution for both beginners and experienced practitioners in the field of machine learning.
API Access
Has API
API Access
Has API
Integrations
PyTorch
TensorFlow
Chainer
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
JupyterLab
MXNet
NVIDIA DRIVE
Integrations
PyTorch
TensorFlow
Chainer
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
JupyterLab
MXNet
NVIDIA DRIVE
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
Exafunction
Website
exafunction.com
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/deep-learning-vm
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