Sold by: Galaxys CloudÂ
Deployed on AWS
Free Trial
AWS Free Tier
This is a repackaged software product wherein additional charges apply for support provided by Galaxys. Harness the synergy of cutting-edge technologies with the "Deep Learning OSS NVIDIA Driver AMI GPU PyTorch 2.0.1 on Amazon Linux 2." This Amazon Machine Image (AMI) provides a meticulously configured environment, blending the Open Source Software (OSS) NVIDIA driver, GPU acceleration, and the latest PyTorch 2.0.1, all on the robust foundation of Amazon Linux 2.
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Overview
This is a repackaged software product wherein additional charges apply for support provided by Galaxys. Harness the synergy of cutting-edge technologies with the "Deep Learning OSS NVIDIA Driver AMI GPU PyTorch 2.0.1 on Amazon Linux 2." This Amazon Machine Image (AMI) provides a meticulously configured environment, blending the Open Source Software (OSS) NVIDIA driver, GPU acceleration, and the latest PyTorch 2.0.1, all on the robust foundation of Amazon Linux 2.
Key Highlights:
Open Source Software (OSS) NVIDIA Driver: Experience the unparalleled performance of NVIDIA GPUs with the Open Source Software (OSS) NVIDIA driver, ensuring optimal compatibility and efficiency for deep learning tasks. GPU acceleration takes your computations to the next level, significantly reducing training times.
Amazon Linux 2 Stability and Optimization: Built on the solid base of Amazon Linux 2, this AMI offers stability, security, and compatibility for your deep learning workflows. Take advantage of the well-established Amazon Linux ecosystem, optimized for seamless integration with AWS services.
PyTorch 2.0.1 for Cutting-Edge Deep Learning: Dive into the world of deep learning with PyTorch 2.0.1, a major update featuring enhanced performance, new features, and improved developer experience. Leverage PyTorch's dynamic computational graph for flexible and efficient model development.
You can also deploy the following complementary products:
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PyTorch 2.1 with CUDA 12.1 - Optimized Deep Learning AMI https://aws.amazon.com/marketplace/pp/prodview-nbndtjeqywg32Â
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TensorFlow 2.15 with Keras 3.0 Deep Learning Stack https://aws.amazon.com/marketplace/pp/prodview-dnuw5pmugjrj6Â
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Deep Learning Base GPU AMI On Ubuntu 24.04 with Tesla T4 https://aws.amazon.com/marketplace/pp/prodview-6qacpepfhww7wÂ
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Deep Learning OSS Nvidia Driver AMI GPU TensorFlow 2.13 https://aws.amazon.com/marketplace/pp/prodview-dd2v7zz5562zcÂ
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Deep Learning OSS Nvidia Driver AMI GPU PyTorch 1.13.1 https://aws.amazon.com/marketplace/pp/prodview-52f2pzevpizueÂ
Highlights
- Pre-configured Environment for Seamless Deployment: Save valuable time with a pre-configured environment that eliminates the hassle of setting up deep learning dependencies. Start your projects with confidence, knowing that this AMI is meticulously designed to meet the demands of modern deep learning practices.
- GPU Acceleration for Rapid Training: Utilize the power of GPU acceleration to expedite model training, enabling you to iterate and experiment more quickly. GPU support enhances the efficiency of deep learning workflows, making complex tasks more accessible and achievable.
- PyTorch 2.0.1 for Cutting-Edge Deep Learning: Dive into the world of deep learning with PyTorch 2.0.1, a major update featuring enhanced performance, new features, and improved developer experience. Leverage PyTorch's dynamic computational graph for flexible and efficient model development.
Details
Sold by
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux Amazon Linux 2
Deployed on AWS
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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
Free trial
Try this product free for 5 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
Dimension | Cost/hour |
|---|---|
t2.large Recommended | $0.10 |
t3.micro | $0.05 |
m7a.metal-48xl | $2.40 |
i4i.24xlarge | $3.20 |
c6a.2xlarge | $0.40 |
c5n.metal | $2.40 |
m5zn.metal | $2.40 |
m5zn.xlarge | $0.20 |
m5.8xlarge | $1.60 |
c5n.xlarge | $0.20 |
Vendor refund policy
For this offering, Galaxys Cloud does not offer refund, you may cancel at anytime.
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
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
Latest Version: 09-01-2024
Additional details
Usage instructions
For more information: https://docs.aws.amazon.com/es_es/dlami/latest/devguide/what-is-dlami.htmlÂ
Support
Vendor support
Remote support seller@galaxys.cloudÂ
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.
Updated weekly
AI generated from product descriptions
Machine Learning Framework
"Includes PyTorch 2.0.1 with dynamic computational graph for deep learning model development"
GPU Acceleration
"Integrated Open Source Software (OSS) NVIDIA driver with GPU acceleration capabilities for computational performance"
Operating System
"Built on Amazon Linux 2 providing stable and optimized environment for deep learning workflows"
Driver Support
"Native NVIDIA GPU driver integration for enhanced computational efficiency"
Deployment Configuration
"Pre-configured development environment with pre-installed deep learning dependencies"
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
Includes pre-configured Tensorflow 2.15 and PyTorch 2.2 for deep learning model development
GPU Acceleration
Automatic GPU support with Nvidia CUDA 12.3 and CUDNN 8 for accelerated computational processing
Development Environment
Jupyter notebook server pre-configured with Python 3.11 for interactive coding and experimentation
Scientific Computing Libraries
Integrated Scikit Learn, Matplotlib, and Numpy for comprehensive data science and machine learning workflows
Computational Flexibility
Supports both GPU and CPU execution environments with automatic instance type detection
Standard contract
No
Customer reviews
reviewer2778747
Accelerated medical image models have improved training speed and streamlined delivery
Reviewed on Dec 09, 2025
Review from a verified AWS customer
What is our primary use case?
I am using Deep Learning Base GPU AMI on Ubuntu 24.04 with Tesla T4 for medical image recognition. The Tesla T4 GPU accelerates model training significantly. Ubuntu 24.04 is stable and easy to work with. Integration with AWS services is seamless. Overall, it is a great choice for deep learning tasks.
How has it helped my organization?
Deep Learning Base GPU AMI on Ubuntu 24.04 with Tesla T4 has greatly improved our organization by accelerating our model training times and increasing our productivity. The powerful Tesla T4 GPU enables us to process large datasets quickly, allowing us to develop and deploy models faster. This has enabled us to deliver results to our clients more efficiently and effectively.
What is most valuable?
The Tesla T4 GPU acceleration and Ubuntu 24.04 stability have been most valuable. The GPU acceleration enables fast model training, while the stable OS lets me focus on development, not environment configuration.
What needs improvement?
Improved compatibility with more frameworks, such as Keras or OpenCV, would be beneficial. Enhanced network configuration options would also be valuable. Support for additional instance types, like P3 or G5, could expand usability. More detailed documentation would help new users get started. Integration with AWS services like SageMaker could be tighter.
For how long have I used the solution?
I have used the solution for one year.
What's my experience with pricing, setup cost, and licensing?
The pricing for Deep Learning Base GPU AMI on Ubuntu 24.04 with Tesla T4 is competitive, considering the power and capabilities it offers. I would advise others to consider the cost-effectiveness of using this AMI, especially for large-scale projects or long-term use cases, as it can lead to significant savings compared to on-premises infrastructure.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)