Marketplace For Buyers For Vendors For Partners

Deep Learning AMI DLAMI Ubuntu 16.04 LTS | Support by SupportedImages

This product has charges associated with it for seller support. The Deep Learning AMI DLAMI Ubuntu 16.04 LTS is designed for developers and data scientists looking to streamline the deployment of deep learning applications on AWS EC2. This AMI comes pre-installed with popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet, enabling rapid experimentation and model training. With GPU support for accelerated performance, users can efficiently process large datasets, making it ideal for tasks ranging from image recognition to natural language processing. Additionally, the image is optimized for easier integration with AWS services like S3 and SageMaker. Whether you're prototyping ML models or running production workloads, the DLAMI Ubuntu 16 provides a versatile, scalable solution for all your deep learning needs.

Purchase this listing from Webvar in AWS Marketplace using your AWS account. In AWS Marketplace, you can quickly launch pre-configured software with just a few clicks. AWS handles billing and payments, and charges on your AWS bill.

About

This is a repackaged open source software wherein additional charges apply for extended support with a 24 hour response time.

The Deep Learning AMI DLAMI on Ubuntu 16.04 LTS is optimized for deep learning applications, providing a powerful and flexible platform for developing and deploying machine learning models. This AMI comes pre-configured with a rich set of deep learning frameworks, including TensorFlow, PyTorch, and Apache MXNet, enabling developers to easily build and train neural networks right out of the box.

Key Features:

Pre-installed Frameworks: Start immediately with popular frameworks that are fully optimized for the EC2 environment.

NVIDIA GPU Support: Leverage high-performance GPU options for accelerated training, reducing time required for model development and experimentation.

Customizable Environment: Modify and adjust the runtime environment to fit specific project requirements or preferences, promoting flexibility in development.

Comprehensive Documentation: Access to detailed documentation and tutorials to accelerate the learning curve and facilitate effective use of the AMI.

Benefits:

Time-efficient: Rapidly set up deep learning environments without the need for complex installation processes.

Cost-effective: Pay only for what you use, scalable resources to match your workloads, ensuring you manage costs effectively.

Production-ready: Suitable for deploying applications into production, with built-in features to enhance reliability and performance.

Use Cases:

Research & Development: Ideal for researchers and data scientists looking to prototype and test machine learning algorithms.

Education: Perfect for institutions providing deep learning courses, offering students hands-on experience with state-of-the-art technology.

Business Applications: Utilize deep learning for advanced analytics, customer segmentation, and predictive modeling in various industries.

Unlock the potential of deep learning with the Deep Learning AMI DLAMI on Ubuntu 16, designed to accelerate your projects while maintaining a robust and user-friendly experience.

Try our most popular AMIs on AWS EC2

Ubuntu 24.04 AMI on AWS EC2

Ubuntu 22.04 AMI on AWS EC2

Ubuntu 20.04 AMI on AWS EC2

Ubuntu 18.04 AMI on AWS EC2

CentOS 9 AMI on AWS EC2

CentOS 8 AMI on AWS EC2

CentOS 7 AMI on AWS EC2

Debian 12 AMI on AWS EC2

Debian 11 AMI on AWS EC2

Debian 10 AMI on AWS EC2

Debian 9 AMI on AWS EC2

Red Hat Enterprise Linux 9 (RHEL 9) AMI on AWS EC2

Red Hat Enterprise Linux 8 (RHEL 8) AMI on AWS EC2

Red Hat Enterprise Linux 7 (RHEL 7) AMI on AWS EC2

Oracle Linux 9 AMI on AWS EC2

Oracle Linux 8 AMI on AWS EC2

Oracle Linux 7 AMI on AWS EC2

Amazon Linux 2023 AMI on AWS EC2

Windows 2022 Server AMI on AWS EC2

Windows 2019 Server AMI on AWS EC2

Docker on Ubuntu 20 AMI on AWS EC2

Docker on CentOS 7 AMI on AWS EC2

Related Products

Sedai for ECS: Autonomous Optimization & Remediation

Sedai for ECS is an autonomous cloud management platform powered by AI/ML delivering continuous optimization for cloud operations teams to reduce costs by 40%, improve performance by 35%, reduce FCIs by 50% and improve ops productivity 6x

AI based Intelligent Document Processing

Our product is an advanced artificial intelligence solution designed to streamline document processing workflows for businesses of all sizes. Leveraging cutting-edge machine learning algorithms, it automates the extraction, analysis, and interpretation of valuable information from a variety of documents, including invoices, contracts, forms, and more. With this powerful tool, businesses can significantly reduce manual effort, minimize errors, and accelerate decision-making processes.

Automated SAS to Pyspark Conversion

SAS workloads come with high costs, scalability challenges, and limited flexibility. Hexaware’s Amaze® accelerates SAS-to-PySpark migration using Gen AI and LLM-powered automation, ensuring a 70-80% conversion accuracy, 3x-5x faster execution, and up to 40% cost savings.

Vectra Cognito for Service Delivery Partners (Billed Monthly)

Vectra Threat Detection and Response for Partner Provided Consulting Engagements

Real-time & Historical Datafeeds | Global Post-War Contemporary Auctions

This dataset is prepared for statistical factor pricing models and standardized across variables including country, region, currency, vendor, artist for seamless data filtering. It contains 20+ years of all items in the Post-War & Contemporary art category sold on auction by Christie’s, Sotheby’s, Bonhams and Phillips from 2000 to date.

Philter

Philter deidentifies and redacts sensitive information, such as Personally Identifiable Information (PII) and Protected Health Information (PHI), in text.

Tuberculosis - Total number of cases in the US | CDC

Centers for Disease Control and Prevention provides free and open access to various health related data. This release contains total number of Tuberculosis cases reported in the United States, by region and by states, in accordance with the current method of displaying WONDER data. Data on United States will exclude counts from US territories. The data is available for past 2 years.

AWS Security Review

An AWS Security Review is an opportunity to ensure that your cloud infrastructure is optimized for performance, security, reliability, and cost-effectiveness. Our certified AWS architects will identify any potential issues or areas for improvement in your AWS environment and make recommendations to get your back on track.