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What is Modal?

We created a containerization platform using Rust that focuses on achieving the fastest cold-start times possible. This platform enables effortless scaling from hundreds of GPUs down to zero in just seconds, meaning you only incur costs for the resources you actively use. Functions can be deployed to the cloud in seconds, and it supports custom container images along with specific hardware requirements. There's no need to deal with YAML; our system makes the process straightforward. Startups and academic researchers can take advantage of free compute credits up to $25,000 on Modal, applicable to GPU computing and access to high-demand GPU types. Modal keeps a close eye on CPU usage based on fractional physical cores, where each physical core equates to two vCPUs, and it also monitors memory consumption in real-time. You are billed only for the actual CPU and memory resources consumed, with no hidden fees involved. This novel strategy not only simplifies deployment but also enhances cost efficiency for users, making it an attractive solution for a wide range of applications. Additionally, our platform ensures that users can focus on their projects without worrying about resource management complexities.

What is Google Deep Learning Containers?

Speed up the progress of your deep learning initiative on Google Cloud by leveraging Deep Learning Containers, which allow you to rapidly prototype within a consistent and dependable setting for your AI projects that includes development, testing, and deployment stages. These Docker images come pre-optimized for high performance, are rigorously validated for compatibility, and are ready for immediate use with widely-used frameworks. Utilizing Deep Learning Containers guarantees a unified environment across the diverse services provided by Google Cloud, making it easy to scale in the cloud or shift from local infrastructures. Moreover, you can deploy your applications on various platforms such as Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm, offering you a range of choices to align with your project's specific requirements. This level of adaptability not only boosts your operational efficiency but also allows for swift adjustments to evolving project demands, ensuring that you remain ahead in the dynamic landscape of deep learning. In summary, adopting Deep Learning Containers can significantly streamline your workflow and enhance your overall productivity.

Media

Media

Integrations Supported

Hermes Agent
Muse Spark Open
Python

Integrations Supported

Google Cloud Platform
Google Cloud Run
Google Compute Engine
Google Kubernetes Engine (GKE)
Kubernetes

API Availability

Has API

API Availability

Pricing Information

$0.192 per core per hour

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Modal Labs

Company Location

United States

Company Website

modal.com

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

cloud.google.com/ai-platform/deep-learning-containers

Categories and Features

AI Cloud Providers

Not specified

AI Infrastructure

Not specified

Serverless

Not specified

Categories and Features

AI Cloud Providers

Not specified

AI Infrastructure

Not specified

Deep Learning

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

Machine Learning

Not specified

Neural Network

Not specified

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