Sold by: SmartAMI
Deployed on AWS
Preconfigured PyTorch 2.12 with TorchVision, TorchServe and JupyterLab. CUDA 13.1 optimized for NVIDIA T4G. Ready-to-use on AWS g5g.xlarge, g5g.2xlarge, g5g.4xlarge, g5g.8xlarge.
Overview
Preconfigured PyTorch 2.12, TorchVision, TorchServe and JupyterLab. CUDA 13.1 optimized for NVIDIA T4G. Ready-to-use on AWS g5g.xlarge, g5g.2xlarge, g5g.4xlarge, g5g.8xlarge. Secure Jupyter via SSH tunneling
Highlights
- Preconfigured PyTorch 2.12, TorchVision, TorchServe and JupyterLab with CUDA 13.1. Launch and start building immediately on NVIDIA T4G-powered AWS g5g instances.
- Built for NVIDIA T4G (sm_75) on AWS g5g ARM64 instances, delivering lower-cost GPU acceleration for notebooks, inference and model development.
- JupyterLab runs securely through SSH tunneling with no public notebook ports exposed by default. Safe, production-friendly developer experience out of the box.
Details
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Delivery method
Delivery option
64-bit (Arm) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 26.04
Deployed on AWS
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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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
g5g.2xlarge Recommended | $0.40 |
g5g.4xlarge | $0.25 |
g5g.xlarge | $0.60 |
g5g.8xlarge | $0.15 |
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Delivery details
64-bit (Arm) 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
20260602
Additional details
Usage instructions
To connect to the operating system, use SSH and the username: ubuntu. Example: ssh -i <your-key.pem> ubuntu@<public-ip>. Verify the environment. The PyTorch environment activates automatically after login. Run: pytorch-test. This validates: PyTorch, CUDA, NVIDIA GPU. Start JupyterLab. Run: smartami-jupyter. The command will: Detect the instance public IP, Print the exact SSH tunnel command to run on your laptop, Start JupyterLab securely. Open a second terminal on your laptop and run the displayed SSH tunnel command. Then open in your browser: http://127.0.0.1:8888 . Validate the full AI/ML stack. Run: smartami-test. This validates the installed environment, including: PyTorch, TorchVision, CUDA GPU support, JupyterLab, Core ML libraries.
Resources
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Support
Vendor support
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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