Sold by: Supported Images
AWS Free Tier
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.
Sold by: Supported Images
Overview
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
Highlights
- The Deep Learning Base Proprietary Nvidia Driver AMI offers a robust environment for developers and researchers engaging in deep learning projects. With pre-installed essential libraries, such as TensorFlow and PyTorch, this AMI streamlines the setup process, allowing users to focus on model development rather than configuration. Leveraging the latest Nvidia drivers ensures optimal GPU performance, unlocking powerful compute capabilities necessary for scalable training.
- This AMI is optimized for Amazon EC2 instances equipped with Nvidia GPUs, such as the P3 and G4 families. It supports varying levels of GPU resources, making it suitable for diverse tasks from small experiments to large-scale model training. Users benefit from elastic compute resources that can be adjusted on-demand, enhancing both cost-effectiveness and flexibility to meet project needs efficiently.
- With the availability of an Amazon Linux 2 base, this AMI provides enhanced security features, stability, and seamless integration with other AWS services. Users can leverage Amazon S3 for data storage and Amazon SageMaker for end-to-end machine learning workflows. This synergy allows teams to accelerate their development cycles while harnessing advanced cloud capabilities, delivering innovative solutions more rapidly.
Details
Sold by
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux 2
Typical total price
This estimate is based on use of the seller's recommended configuration (g3.4xlarge) in the US East (N. Virginia) Region. View pricing details
$2.26/hour
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covering your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
- ...
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
|---|---|---|---|
t1.micro | $0.07 | $0.02 | $0.09 |
t2.nano | $0.07 | $0.006 | $0.076 |
t2.micro AWS Free Tier | $0.21 | $0.012 | $0.222 |
t2.small | $0.07 | $0.023 | $0.093 |
t2.medium | $0.14 | $0.046 | $0.186 |
t2.large | $0.14 | $0.093 | $0.233 |
t2.xlarge | $0.28 | $0.186 | $0.466 |
t2.2xlarge | $0.56 | $0.371 | $0.931 |
t3.nano | $0.07 | $0.005 | $0.075 |
t3.micro AWS Free Tier | $0.07 | $0.01 | $0.08 |
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp3) volumes | $0.08/per GB/month of provisioned storage |
Vendor refund policy
Fees for this product are not refundable. The instance can be terminated at any time to stop incurring charges.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
System Updates
Additional details
Usage instructions
SSH to the instance and login as 'ec2-user' using the key specified at launch.
OS commands via SSH: SSH as user 'ec2-user' to the running instance and use sudo to run commands requiring root access.
More on using Deep Learning AMI with Conda: https://docs.thinkwithwp.com/dlami/latest/devguide/tutorial-conda.html https://awsdocs-neuron.readthedocs-hosted.com/en/latest/dlami/index.html
Resources
Vendor resources
Support
Vendor support
Email support for this AMI is available through the following: https://supportedimages.com/support/ OR support@supportedimages.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
Deep Learning AMI DLAMI Ubuntu 16.04 LTS | Support by SupportedImages
By Supported Images
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.
Deep Learning AMI DLAMI Ubuntu 18.04 LTS | Support by SupportedImages
By Supported Images
This product has charges associated with it for seller support. The Deep Learning AMI DLAMI Ubuntu 18.04 LTS is a powerful and flexible Amazon Machine Image designed for deep learning and AI development in the cloud. Pre-configured with popular frameworks such as TensorFlow, PyTorch, and MXNet, this AMI enables developers to streamline their machine learning workflows. It supports GPU instances for accelerated performance, making it ideal for training complex models quickly. With extensive documentation and community support, users can easily deploy and manage their deep learning applications. Suitable for data scientists, researchers, and developers, this AMI accelerates experimentation and innovation in machine learning projects, providing a robust environment to tackle various data-driven challenges effectively. Leverage the power of AWS to scale your AI initiatives with ease.
This is a repackaged software product wherein additional charges apply for support by Bansir with Deep Learning AMI (DLAMI) Amazon Linux 2 and includes support.
DLAMI ARM Base OSS Nvidia GPU Ubuntu 22 | Support by SupportedImages
By Supported Images
This product has charges associated with it for seller support. The Deep Learning ARM64 Base OSS Nvidia Driver GPU AMI (Ubuntu 22.04) is designed for developers and researchers who require a robust environment for advanced machine learning tasks. This Amazon Machine Image (AMI) leverages ARM64 architecture for optimized performance and is pre-configured with essential deep learning frameworks, including TensorFlow and PyTorch, as well as the latest Nvidia drivers. Users can quickly launch multiple EC2 instances to scale their deep learning experiments and leverage the power of GPU acceleration, enhancing training times and processing capabilities. Ideal for data scientists, AI developers, and businesses looking to deploy AI solutions, this AMI simplifies the setup process and ensures a seamless experience for demanding computational workloads. Explore deep learning without the hassle of manual configurations.
DLAMI ARM OSS Nvidia GPU PyTorch Ubuntu 22 | Support by SupportedImages
By Supported Images
This product has charges associated with it for seller support. The Deep Learning ARM64 AMI is optimized for running graphics-intensive applications on EC2 using Nvidia GPUs. Pre-configured with PyTorch 2.3.0, this AMI is designed for developers and researchers in machine learning, enabling rapid experimentation and deployment of deep learning models. It leverages the capabilities of Ubuntu 22.04 for an agile development environment and includes essential libraries and tools, ensuring seamless integration into existing workflows. Ideal for use in academic research, AI-driven startups, or enterprise projects, this AMI helps reduce setup time and maximizes productivity. By choosing this AMI, users can scale their deep learning applications efficiently while benefiting from the power of ARM64 architecture and Nvidia's GPU acceleration.
Customer reviews
No customer reviews yet
Be the first to write a review for this product.