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What is Google Cloud Deep Learning VM Image?
Rapidly establish a virtual machine on Google Cloud for your deep learning initiatives by utilizing the Deep Learning VM Image, which streamlines the deployment of a VM pre-loaded with crucial AI frameworks on Google Compute Engine. This option enables you to create Compute Engine instances that include widely-used libraries like TensorFlow, PyTorch, and scikit-learn, so you don't have to worry about software compatibility issues. Moreover, it allows you to easily add Cloud GPU and Cloud TPU capabilities to your setup. The Deep Learning VM Image is tailored to accommodate both state-of-the-art and popular machine learning frameworks, granting you access to the latest tools. To boost the efficiency of model training and deployment, these images come optimized with the most recent NVIDIA® CUDA-X AI libraries and drivers, along with the Intel® Math Kernel Library. By leveraging this service, you can quickly get started with all the necessary frameworks, libraries, and drivers already installed and verified for compatibility. Additionally, the Deep Learning VM Image enhances your experience with integrated support for JupyterLab, promoting a streamlined workflow for data science activities. With these advantageous features, it stands out as an excellent option for novices and seasoned experts alike in the realm of machine learning, ensuring that everyone can make the most of their projects. Furthermore, the ease of use and extensive support make it a go-to solution for anyone looking to dive into AI development.
What is GPUonCLOUD?
Previously, completing tasks like deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling could take days or even weeks. However, with GPUonCLOUD's specialized GPU servers, these tasks can now be finished in just a few hours. Users have the option to select from a variety of pre-configured systems or ready-to-use instances that come equipped with GPUs compatible with popular deep learning frameworks such as TensorFlow, PyTorch, MXNet, and TensorRT, as well as libraries like OpenCV for real-time computer vision, all of which enhance the AI/ML model-building process. Among the broad range of GPUs offered, some servers excel particularly in handling graphics-intensive applications and multiplayer gaming experiences. Moreover, the introduction of instant jumpstart frameworks significantly accelerates the AI/ML environment's speed and adaptability while ensuring comprehensive management of the entire lifecycle. This remarkable progression not only enhances workflow efficiency but also allows users to push the boundaries of innovation more rapidly than ever before. As a result, both beginners and seasoned professionals can harness the power of advanced technology to achieve their goals with remarkable ease.
Integrations Supported
MXNet
PyTorch
TensorFlow
Chainer
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
JupyterLab
NVIDIA DRIVE
API Availability
API Availability
Pricing Information
Pricing not provided
Free Trial Offered?
Pricing Information
$1 per hour
Supported Platforms
SaaS
Supported Platforms
SaaS
Customer Service / Support
24 Hour Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Training Options
Documentation Hub
Company Facts
Organization Name
Date Founded
1998
Company Location
United States
Company Website
cloud.google.com/deep-learning-vm
Company Facts
Organization Name
GPUonCLOUD
Company Location
India
Company Website
gpuoncloud.com/gpu-as-a-service/
Categories and Features
AI Infrastructure
Not specified
Deep Learning
Not specified