Sold by: AMD XilinxÂ
Overview
This AMI is a Ubuntu Linux image provided by AMD Xilinx Inc. The AMI is pre-built with FPGA and SoC development and runtime tools to program the latest and greatest AMD Xilinx devices. Vivado ML offers machine learning based optimization along with collaborative design environment and new advanced DFX features. Vivado supplies design teams with the tools and methodology needed to leverage C-based design and optimized reuse, IP sub-system reuse, integration automation and accelerated design closure. Users targeting AWS FPGA Instances should use the AWS FPGA developer AMI: https://aws.amazon.com/marketplace/pp/B06VVYBLZZ?ref=cns_srchrowÂ
Highlights
- Seamless AMD Xilinx device development environment with no additional licensing requirements
- Improved Runtime and Ease of use capabilities as listed in the release notes
- Access Vivado 2023.1 ML development environments from anywhere
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 20.04
Deployed on AWS
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Features and programs
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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 covers 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.
Dimension | Cost/hour |
|---|---|
m6a.xlarge Recommended | $0.36 |
t3.micro AWS Free Tier | $0.36 |
c6i.2xlarge | $0.36 |
c5ad.12xlarge | $0.36 |
g4ad.8xlarge | $0.36 |
c3.8xlarge | $0.36 |
x2idn.16xlarge | $0.36 |
m6id.metal | $0.36 |
x1.16xlarge | $0.36 |
c3.large | $0.36 |
Vendor refund policy
We do not currently support refunds, but you can cancel at any time.
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)Â .
Content disclaimer
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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
Additional details
Usage instructions
Please follow the instructions below to get started: https://xilinx-documentation.s3.amazonaws.com/How+to+Launch+a+Ubuntu+OS+based+Xilinx+AMI+Instance.pdfÂ
Support
Vendor support
Please allow 24 hours forums.xilinx.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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Development Environment
Pre-configured Ubuntu Linux image with integrated FPGA and SoC development tools
Machine Learning Optimization
Vivado ML offers machine learning based optimization techniques for design processes
Design Acceleration
Supports C-based design, IP sub-system reuse, and integration automation for accelerated design closure
Advanced Design Features
Includes collaborative design environment with new advanced Dynamic Function eXchange (DFX) capabilities
Runtime Toolchain
Comprehensive runtime tools for programming latest AMD Xilinx FPGA and SoC devices
GPU Acceleration
Includes NVIDIA drivers, CUDA, and cuDNN for high-performance GPU-based deep learning computations
Deep Learning Framework
Pre-installed PyTorch 2.0.1 with support for advanced machine learning model development
Operating System
Built on Amazon Linux 2, providing a lightweight and secure cloud-optimized environment
Machine Learning Libraries
Pre-configured with essential libraries including TorchVision, NumPy, and PyTorch-related packages
Cloud Instance Compatibility
Supports multiple GPU-based EC2 instances for scalable AI training and inference tasks
Machine Learning Framework Support
Provides foundational platform for deep learning with support for multiple machine learning frameworks and tools
GPU Acceleration Infrastructure
Includes NVIDIA CUDA, cuDNN, NCCL, and GPU drivers for high-performance GPU computing
Containerization Support
Integrates Docker and NVIDIA-Docker for building and running GPU-accelerated containers
Performance Libraries
Includes Intel MKL-DNN for optimized machine learning and deep learning performance
Advanced Networking Capabilities
Supports AWS Elastic Fabric Adapter (EFA) and AWS Neuron for enhanced computational networking
Standard contract
No
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