Sold by: Intuz
Deployed on AWS
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This product has charges associated with it for pre-configuration and packaging by Intuz. It includes a pre-installed deep learning environment featuring PyTorch, TensorFlow, and JupyterLab for rapid AI/ML development on AWS.
Overview
This is a repackaged open source software product wherein additional charges apply for pre-configuration and packaging by Intuz.
The PyTorch and TensorFlow Deep Learning Stack AMI provides a ready-to-use machine learning environment optimized for AWS EC2. It comes with PyTorch, TensorFlow, and JupyterLab pre-installed and configured, enabling data scientists and AI researchers to focus on building and training models without the need for time-consuming setup. GPU-ready and secure, the stack allows you to instantly launch an ML workspace for experiments, prototyping, and scalable development.
With browser-based JupyterLab access and support for GPU instances, this AMI is designed to accelerate your deep learning workflows, all in a controlled and isolated AWS environment.
Highlights
- Fully Packaged AI/ML Stack: Pre-configured with PyTorch, TensorFlow, and JupyterLab-repackaged by Intuz
- Browser-Based Development: Access JupyterLab instantly via public IP without local setup
- GPU-Optimized on AWS: Compatible with EC2 GPU instances for fast, scalable model training
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
Deployed on AWS
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
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. 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 |
|---|---|
t3a.small Recommended | $0.09 |
t3.micro AWS Free Tier | $0.09 |
t2.micro AWS Free Tier | $0.09 |
m6a.48xlarge | $0.09 |
m5a.16xlarge | $0.09 |
m6a.2xlarge | $0.09 |
m6a.16xlarge | $0.09 |
m5a.12xlarge | $0.09 |
m6a.xlarge | $0.09 |
m6a.32xlarge | $0.09 |
Vendor refund policy
Intuz will not refund money in any case.However, you can cancel your subscription 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) .
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
Pre-installed TensorFlow and PyTorch for deep learning and machine learning workloads. Jupyter Lab configured to run on port 8888 for an interactive development environment. Optimized for performance on AWS EC2 instances, supporting CPU-based computations. Includes essential dependencies and Python libraries for AI/ML development. Secure configuration with minimal pre-opened ports for better security.
Additional details
Usage instructions
Open Jupyter Lab by navigating to <your-instance-public-ip>:8888 in your browser and enter the EC2 instance ID as the password. You can also access your instance via SSH using the username 'ubuntu' and your Amazon private key.
Support
Vendor support
We provide best effort technical support for this product. We will do our best to respond to your questions within the next 24 hours in business days. For any technical support or query, you can drop an email here: cloudsupport@intuz.com or fill up this form:
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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