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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.
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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.
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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
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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
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.aws.amazon.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.
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