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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.
Description
Scikit-image is an extensive suite of algorithms designed for image processing tasks. It is provided at no cost and without restrictions. Our commitment to quality is reflected in our peer-reviewed code, developed by a dedicated community of volunteers. This library offers a flexible array of image processing functionalities in Python. The development process is highly collaborative, with contributions from anyone interested in enhancing the library. Scikit-image strives to serve as the definitive library for scientific image analysis within the Python ecosystem. We focus on ease of use and straightforward installation to facilitate adoption. Moreover, we are judicious about incorporating new dependencies, sometimes removing existing ones or making them optional based on necessity. Each function in our API comes with comprehensive docstrings that clearly define expected inputs and outputs. Furthermore, arguments that share conceptual similarities are consistently named and positioned within function signatures. Our test coverage is nearly 100%, and every piece of code is scrutinized by at least two core developers prior to its integration into the library, ensuring robust quality control. Overall, scikit-image is committed to fostering a rich environment for scientific image analysis and ongoing community engagement.
API Access
Has API
API Access
Has API
Integrations
Akira AI
Chainer
Cython
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
JupyterLab
Label Studio
MLReef
MXNet
Integrations
Akira AI
Chainer
Cython
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
JupyterLab
Label Studio
MLReef
MXNet
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
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
Founded
1998
Country
United States
Website
cloud.google.com/deep-learning-vm
Vendor Details
Company Name
scikit-image
Country
United States
Website
scikit-image.org
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
