Marketplace For Buyers For Vendors For Partners

DLAMI Base Prop Nvidia Driver Amazon Linux | Support by SupportedImages

This product has charges associated with it for seller support. The Deep Learning Base Proprietary Nvidia Driver AMI (Amazon Linux 2) Version 64.9 is engineered for seamless deployment of deep learning applications in the Amazon EC2 cloud. Pre-configured with the latest Nvidia drivers, this AMI ensures optimized GPU performance, making it ideal for both research and production environments. Users can effortlessly launch and scale their deep learning models using popular frameworks such as TensorFlow, PyTorch, and MXNet. With built-in support for CUDA and cuDNN, developers can accelerate their workloads and reduce time to market. This AMI is particularly beneficial for data scientists, machine learning engineers, and organizations looking to harness the full potential of NVIDIA GPUs while leveraging the flexibility of the AWS infrastructure.

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 Base Proprietary Nvidia Driver AMI (Amazon Linux 2) Version 64.9 is expertly designed for developers and data scientists seeking to leverage the power of NVIDIA GPUs for deep learning applications. This AMI comes pre-installed with proprietary NVIDIA drivers and optimized libraries that ensure high performance, streamlined GPU utilization, and efficient training cycles.

Key Features:

NVIDIA GPU Support: Optimized for the latest NVIDIA GPUs, providing superior computational power for deep learning workflows.

Amazon Linux 2 Compatibility: Seamlessly integrates with Amazon's ecosystem, leveraging the stability and security of Amazon Linux 2.

Pre-configured Environment: Comes pre-packaged with essential deep learning frameworks such as TensorFlow, PyTorch, and MXNet, minimizing setup time.

Enhanced Performance: Utilizing NVIDIA's cuDNN and TensorRT, it offers accelerated training and inference performance for neural networks.

User-friendly: Ready-to-use configurations and easy access to NVIDIA utilities for quick troubleshooting.

Benefits:

Accelerated Development: Reduce the time-to-market for deep learning projects with a ready-to-use environment.

Cost-effective Scaling: Elastic Cloud Compute (EC2) capabilities allow for scalable compute resources tailored to your workload demands.

Robust Support: Optional extended support with a 24-hour response time ensures you can focus on your project without worrying about downtime.

Use Cases:

Research and Development: Ideal for academic institutions and research organizations experimenting with cutting-edge deep learning models.

Commercial Applications: Perfect for businesses deploying machine learning applications such as image recognition, natural language processing, and predictive analytics.

Prototyping and Testing: An excellent choice for rapid prototyping, allowing teams to test model performance without extensive infrastructure investment.

Unlock the potential of NVIDIA GPU acceleration for your deep learning projects with the Deep Learning Base Proprietary Nvidia Driver AMI, and take the next step in AI innovation.

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.