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Description

Accelerate the development of your deep learning project on Google Cloud: Utilize Deep Learning Containers to swiftly create prototypes within a reliable and uniform environment for your AI applications, encompassing development, testing, and deployment phases. These Docker images are pre-optimized for performance, thoroughly tested for compatibility, and designed for immediate deployment using popular frameworks. By employing Deep Learning Containers, you ensure a cohesive environment throughout the various services offered by Google Cloud, facilitating effortless scaling in the cloud or transitioning from on-premises setups. You also enjoy the versatility of deploying your applications on platforms such as Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm, giving you multiple options to best suit your project's needs. This flexibility not only enhances efficiency but also enables you to adapt quickly to changing project requirements.

Description

Oracle Cloud Infrastructure Container Registry is a managed Docker registry service that adheres to open standards, allowing for the secure storage and sharing of container images. Engineers can utilize the well-known Docker Command Line Interface (CLI) and API to efficiently push and pull Docker images. The Registry is designed to facilitate container lifecycles by integrating seamlessly with Container Engine for Kubernetes, Identity and Access Management (IAM), Visual Builder Studio, as well as various third-party development and DevOps tools. Users can manage Docker images and container repositories by employing familiar Docker CLI commands and the Docker HTTP API V2. With Oracle handling the operational aspects and updates of the service, developers are free to concentrate on creating and deploying their containerized applications. Built on a foundation of object storage, Container Registry guarantees data durability and high availability of service through automatic replication across different fault domains. Notably, Oracle does not impose separate fees for the service; users are only billed for the storage and network resources utilized, making it an economical choice for developers. This model allows for a streamlined experience in managing container images while ensuring robust performance and reliability.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Kubernetes Yes 
Docker No 
Google Cloud Platform Yes 
Google Cloud Run Yes 
Google Compute Engine Yes 
Google Kubernetes Engine (GKE) Yes 
Kasm Workspaces No 

Integrations

Kubernetes Yes 
Docker Yes 
Google Cloud Platform No 
Google Cloud Run No 
Google Compute Engine No 
Google Kubernetes Engine (GKE) No 
Kasm Workspaces Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

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

Vendor Details

Company Name

Oracle

Founded

1977

Country

United States

Website

www.oracle.com/cloud-native/container-registry/

Product Features

Deep Learning

Convolutional Neural Networks Yes 
Document Classification Yes 
Image Segmentation Yes 
ML Algorithm Library No 
Model Training Yes 
Neural Network Modeling Yes 
Self-Learning Yes 
Visualization Yes 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

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