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What is supercomputer?

Supercomputer is a cloud-based computational platform that provides sessions equipped with both GPU and CPU resources, tailored specifically for machine-learning applications and other intensive computational tasks. It empowers developers, researchers, and teams to start and manage sessions using either a command-line interface or a Python SDK, which allows them to execute their code on adjustable GPU instances while preserving their workspace files for future sessions. This service supports a wide range of activities including model training, fine-tuning, inference, experiments, evaluations, batch processing, hyperparameter tuning, and various data-processing operations. Users can conveniently upload their scripts and datasets, execute commands in persistent sessions, and either download results or store them in shared volumes for team access. Importantly, users are charged only for active sessions, and any inactive sessions retain their workspace for future use, enhancing operational efficiency. The pricing model is based on prepaid credits, with costs calculated by the second and varying according to the selected GPU specifications. Furthermore, this adaptable structure not only allows users to streamline their workflows but also assists in managing expenses effectively, making it a valuable resource for any computational project. Overall, the combination of flexibility and efficiency positions Supercomputer as a leading solution in the realm of cloud-based computing services.

What is Ray?

You can start developing on your laptop and then effortlessly scale your Python code across numerous GPUs in the cloud. Ray transforms conventional Python concepts into a distributed framework, allowing for the straightforward parallelization of serial applications with minimal code modifications. With a robust ecosystem of distributed libraries, you can efficiently manage compute-intensive machine learning tasks, including model serving, deep learning, and hyperparameter optimization. Scaling existing workloads is straightforward, as demonstrated by how Pytorch can be easily integrated with Ray. Utilizing Ray Tune and Ray Serve, which are built-in Ray libraries, simplifies the process of scaling even the most intricate machine learning tasks, such as hyperparameter tuning, training deep learning models, and implementing reinforcement learning. You can initiate distributed hyperparameter tuning with just ten lines of code, making it accessible even for newcomers. While creating distributed applications can be challenging, Ray excels in the realm of distributed execution, providing the tools and support necessary to streamline this complex process. Thus, developers can focus more on innovation and less on infrastructure.

Media

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Media

Integrations Supported

Integrations Supported

Amazon EC2 Trn2 Instances
Amazon SageMaker
Amazon Web Services (AWS)
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Databricks
Feast
Flyte
Google Cloud Platform
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
MLflow
PyTorch
Python
Snowflake
TensorFlow
Union Cloud
io.net

API Availability

Has API

API Availability

Has API

Pricing Information

Compute is billed by the second from a prepaid balance. Stopped sessions incur no compute charges.

Pricing Information

Free
Open source. Consumption-based.
Free Version
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

supercomputer

Company Website

supercomputer.sh/

Company Facts

Organization Name

Anyscale

Date Founded

2019

Company Location

United States

Company Website

ray.io

Categories and Features

Cloud GPU

Not specified

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

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