Sold by: Hanwei Software Technology
Deployed on AWS
AWS Free Tier
Pre-configured Deep Learning AMI with TensorFlow 2.21, PyTorch 2.5.1, CUDA 12.6 on Ubuntu 24.04 LTS. GPU-ready with Jupyter Notebook, out-of-the-box.
Overview
Production-ready Deep Learning AMI built on Ubuntu 24.04 LTS with long-term support until 2034. Pre-installed with TensorFlow 2.21 (GPU), PyTorch 2.5.1 (GPU), CUDA 12.6, cuDNN 9.x, and NVIDIA Driver 595. All frameworks are installed in an isolated Python 3.12 virtual environment for clean dependency management. Includes Jupyter Notebook with auto-start on port 8888 - the default password is the EC2 Instance ID for immediate secure access. Bundled with essential scientific computing libraries (NumPy, SciPy, scikit-learn, Matplotlib). Optimized for NVIDIA T4 GPUs on g4dn instances. Launch and start working immediately, no setup required.
Highlights
- Out-of-the-box: Jupyter Notebook auto-starts with Instance ID as password, no SSH setup needed
- Ubuntu 24.04 LTS with security support until 2034, Python 3.12 venv isolation, versions pinned for stability
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
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.
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 |
|---|---|
g4dn.4xlarge Recommended | $0.25 |
t2.micro | $0.00 |
t3.micro | $0.00 |
g4ad.16xlarge | $0.12 |
t3.2xlarge | $0.18 |
p3dn.24xlarge | $0.20 |
g5.48xlarge | $0.12 |
g5.12xlarge | $0.12 |
g4dn.8xlarge | $0.25 |
t3.medium | $0.08 |
Dimensions summary
You pay by the hour for this deep learning software, billed per EC2 instance type you run. The many dimensions map to specific AWS instance sizes, not feature tiers. Prices scale with the compute power of the instance you select. General-purpose t2 and t3 instances cover lighter workloads, while g-series and p-series GPU instances support heavier machine learning tasks. Larger instances within each family carry higher hourly rates. You choose the instance that fits your needs and pay software charges on top of standard AWS infrastructure costs. There is no upfront commitment; billing follows actual usage hours.
Top-of-mind questions for buyers
You are billed for each hour the chosen instance runs, matched to that instance type's hourly software rate. The rate reflects the compute and GPU capacity of that AWS instance. Each instance you launch is metered separately, so running two instances bills both concurrently.
Software charges meter running hours only. A fully stopped instance does not accrue software fees. Note that underlying AWS storage or reserved resources may still incur AWS infrastructure costs, which are separate from this product's hourly software charge.
Rates track the capacity of each AWS instance type. General-purpose t2 and t3 instances suit lighter workloads and carry lower hourly rates. The g-series and p-series GPU instances provide graphics and compute acceleration for heavier machine learning work, so their hourly rates are higher.
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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
Version 2.0.0 - 2026-04-30
Major Changes
- Upgraded OS from Ubuntu 20.04 LTS (EOL) to Ubuntu 24.04 LTS (supported until 2034)
- Upgraded NVIDIA Driver from 525 to 595
- Upgraded CUDA Toolkit from 11.7 to 12.6
- Upgraded cuDNN from 8.9 to 9.x
- Upgraded TensorFlow from 2.11 to 2.21 (GPU enabled)
- Upgraded PyTorch from 1.13.1 to 2.5.1 (GPU enabled)
- Upgraded Python from 3.8 to 3.12
New Features
- Out-of-the-box Jupyter access: Default password is the EC2 Instance ID, no SSH setup required
- Python venv isolation: All frameworks installed in /home/ubuntu/dl-env, avoiding system package conflicts
- Version pinning: CUDA and NVIDIA driver locked to prevent unintended upgrades during apt upgrade
Security Fixes
- Resolved all known vulnerabilities from Ubuntu 20.04 EOL by migrating to Ubuntu 24.04 LTS
- IMDSv2 support for secure instance metadata access
Known Issues
- TensorFlow and PyTorch ship their own CUDA runtime libraries inside the venv. System CUDA 12.6 is used for nvcc compilation only.
- First page load of Jupyter may take a few seconds after instance boot.
Upgrade Notes
- This is a full OS migration, not an in-place upgrade.
- Existing models and code using TensorFlow or PyTorch should be tested for compatibility with the new framework versions.
- Users who pinned specific package versions should verify compatibility with Python 3.12.
Additional details
Usage instructions
Getting Started
This AMI is ready to use out of the box. No SSH setup required.
Step 1: Launch an EC2 Instance Launch an instance using this AMI. Make sure your security group allows inbound traffic on port 22 (SSH) and port 8888 (Jupyter Notebook).
Step 2: Access Jupyter Notebook Open your browser and go to: http://(your-instance-public-ip):8888
Default password: your EC2 Instance ID (example: i-0a1b2c3d4e5f6g7h8) You can find the Instance ID in the AWS Console under EC2 - Instances.
Step 3: Start Working You now have a fully configured deep learning environment with TensorFlow, PyTorch, and GPU acceleration. Create a new notebook and start coding.
Recommended Instance Types
g4dn.xlarge (Best Value) GPU: 1x NVIDIA T4, 16 GB GPU Memory vCPU: 4, RAM: 16 GB Best for: Development, inference, light training On-Demand: approximately USD 0.526/hr
g4dn.2xlarge GPU: 1x NVIDIA T4, 16 GB GPU Memory vCPU: 8, RAM: 32 GB Best for: Larger datasets, CPU-intensive preprocessing On-Demand: approximately USD 0.752/hr
g5.xlarge GPU: 1x NVIDIA A10G, 24 GB GPU Memory vCPU: 4, RAM: 16 GB Best for: Larger models, faster training On-Demand: approximately USD 1.006/hr
g5.2xlarge GPU: 1x NVIDIA A10G, 24 GB GPU Memory vCPU: 8, RAM: 32 GB Best for: Production inference, medium-scale training On-Demand: approximately USD 1.212/hr
p3.2xlarge GPU: 1x NVIDIA V100, 16 GB GPU Memory vCPU: 8, RAM: 61 GB Best for: Serious training workloads On-Demand: approximately USD 3.06/hr
Prices are approximate and may vary. Please check AWS pricing for the latest rates. Use Spot Instances to save up to 70%.
Cost Saving Tips
- Use Spot Instances for development and non-critical workloads
- Stop the instance when not in use, you only pay for EBS storage when stopped
- Start with g4dn.xlarge and scale up only if needed
SSH Access (Optional)
ssh -i your-key.pem ubuntu@(your-instance-public-ip) source ~/dl-env/bin/activate
Changing the Jupyter Password
source ~/dl-env/bin/activate jupyter notebook password sudo systemctl restart jupyter.service
Pre-installed Frameworks
TensorFlow 2.21 (GPU enabled) PyTorch 2.5.1 (GPU enabled) NumPy, SciPy, scikit-learn Matplotlib, Pillow, h5py Jupyter Notebook
Support
For framework-specific questions, refer to the official documentation: TensorFlow: https://www.tensorflow.org/ PyTorch: https://pytorch.org/
Resources
Vendor resources
Support
Vendor support
prosupport@hanweie.com If you encounter problems in the process of using the system, please feel free to contact us by email, thank you!
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
By Hanwei Software Technology
By Elm Computing
AI generated from product descriptions
Pre-installed Deep Learning Frameworks
TensorFlow 2.21 (GPU), PyTorch 2.5.1 (GPU), and cuDNN 9.x with NVIDIA Driver 595 pre-configured for GPU acceleration
GPU Compute Toolkit
CUDA 12.6 optimized for NVIDIA T4 GPUs on g4dn instances
Isolated Python Environment
Python 3.12 virtual environment with pinned dependency versions for clean package management and stability
Integrated Development Environment
Jupyter Notebook with auto-start functionality on port 8888 and Instance ID-based authentication
Scientific Computing Libraries
NumPy, SciPy, scikit-learn, and Matplotlib bundled for data processing and visualization tasks
Deep Learning Framework Support
Includes TensorFlow 2.15 and PyTorch 2.2 for model training and experimentation
GPU Acceleration
Automatic GPU support with Nvidia CUDA 12.3 and CUDNN 8 on compatible instances (g3/g5), with CPU fallback capability
Jupyter Notebook Environment
Pre-configured Jupyter notebook server running on HTTPS port 8888 with instance ID-based authentication
Python Runtime and Dependencies
Python 3.11 with included libraries including Scikit Learn, Matplotlib, and Numpy
Rapid Development Setup
Pre-installed and configured environment enabling deep learning model development without additional setup steps
Multi-User Notebook Server
Pre-configured JupyterHub server capable of serving Jupyter notebooks to multiple concurrent users
Multiple Python Versions
Support for multiple Python versions installed and available within the environment
Pre-installed Data Science Libraries
Popular data science Python packages and SageMath pre-installed and configured
Extensive Python Package Ecosystem
Hundreds of Python packages available for data science, scientific computing, and development workflows
Standard contract
No
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