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Moonglow
Overview :
Moonglow is a service that allows users to run local Jupyter notebooks on remote GPUs without the hassle of managing SSH keys or installing packages. The service was founded by Leila and Trevor, with Leila having built high-performance infrastructure at Jane Street, and Trevor conducting machine learning research at Stanford's Hazy Research Lab.
Target Users :
Moonglow is designed for technical professionals and data scientists who require high-performance computing resources, especially those working in machine learning and deep learning fields. It simplifies the use of GPU resources, allowing users to focus more on model training and data analysis rather than on configuring and managing underlying hardware.
Use Cases
Use Moonglow to train a ResNet model on the CIFAR-10 dataset, completing it in under 1 minute and 15 seconds from scratch.
Data scientists utilize Moonglow to seamlessly switch GPU resources in the IDE for large-scale data processing and model training.
Enterprise teams use the enterprise version of Moonglow to share GPU resources for collaborative completion of complex machine learning projects.
Features
Seamlessly switch from CPU to GPU runtime in seconds
No need to manage SSH keys, package installations, or other DevOps challenges
Offers multiple GPU options, including A40, A100, H100, etc.
Directly manage GPUs in the IDE, including starting, stopping, and restarting them
The personal version supports simultaneous connection to 3 notebooks and integrated file storage
The enterprise version supports connection to any infrastructure with unlimited notebook sharing
How to Use
Visit the Moonglow official website and register an account
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Select the appropriate GPU type and connect to Runpod
Start, stop, or restart GPU resources directly in the IDE
Connect your local Jupyter notebook to the remote GPU
Manage notebook data using the integrated file storage feature
For enterprise users, schedule a demo to learn how to effectively share and collaborate within your team
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