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Description

Bayesforge™ is a specialized Linux machine image designed to assemble top-tier open source applications tailored for data scientists in need of sophisticated analytical tools, as well as for professionals in quantum computing and computational mathematics who wish to engage with key quantum computing frameworks. This image integrates well-known machine learning libraries like PyTorch and TensorFlow alongside open source tools from D-Wave, Rigetti, and platforms like IBM Quantum Experience and Google’s innovative quantum language Cirq, in addition to other leading quantum computing frameworks. For example, it features our quantum fog modeling framework and the versatile quantum compiler Qubiter, which supports cross-compilation across all significant architectures. Users can conveniently access all software through the Jupyter WebUI, which features a modular design that enables coding in Python, R, and Octave, enhancing flexibility in project development. Moreover, this comprehensive environment empowers researchers and developers to seamlessly blend classical and quantum computing techniques in their workflows.

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

Screenshots View All

Screenshots View All

Integrations

PyTorch
TensorFlow
AWS Marketplace
Chainer
D-Wave
Google Cirq
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
IBM Quantum
JupyterLab
MXNet
NVIDIA DRIVE
Octave
Python
R
Rigetti Quantum Cloud Services (QCS)

Integrations

PyTorch
TensorFlow
AWS Marketplace
Chainer
D-Wave
Google Cirq
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
IBM Quantum
JupyterLab
MXNet
NVIDIA DRIVE
Octave
Python
R
Rigetti Quantum Cloud Services (QCS)

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

Quantum Programming Studio

Website

artiste-qb.net/bayesforge/

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

cloud.google.com/deep-learning-vm

Product Features

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Alternatives

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