Sold by: Sigmodata
Deployed on AWS
AWS Free Tier
This AMI provides a jupyter notebook instance for quick experimentation with the latest software and GPU support
Overview
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JupyterLab
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Sign in
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Setup wizard - administrator account
Jupyter notebook instance ready to train deep learning models
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Start coding in minutes
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Browser-based setup wizard on first launch over HTTPS (port 443). You choose the administrator password during setup.
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Sign in with a local account, single sign-on (OIDC: Okta, Microsoft Entra ID, Google Workspace, Auth0), or LDAP / Active Directory. SSO and LDAP only control who can open this instance. Jupyter is one shared notebook, not JupyterHub, and does not isolate users from each other.
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Optional trusted TLS certificate from Let's Encrypt, with or without a domain name
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Built-in admin console for GPU, storage, certificate and service status
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Runs on GPU automatically if available. Recommended instance: g6.xlarge (NVIDIA L4). Also g4dn.xlarge (cheaper T4), g6.2xlarge (more RAM), g6e.xlarge (L40S, more VRAM), or m7i.xlarge for CPU-only.
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Python version 3.12
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Latest Tensorflow and PyTorch versions
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Scikit Learn, Matplotlib, Numpy included as dependencies
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Nvidia CUDA 12.9 + cuDNN (only if running on GPU instance)
Highlights
- Jupyter with current TensorFlow, PyTorch, and CUDA - start training instead of building an AMI
- Local accounts or OIDC/LDAP control who can open a shared Jupyter instance
- Browser setup over HTTPS; GPU on automatically. Recommended GPU instance: g6.xlarge
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux Amazon Linux 2003 - January 2026
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 |
|---|---|
g5.xlarge Recommended | $0.10 |
t2.micro | $0.00 |
t3.micro | $0.075 |
r3.8xlarge | $0.10 |
t2.nano | $0.05 |
r5d.24xlarge | $0.10 |
m3.xlarge | $0.10 |
r4.16xlarge | $0.10 |
r5a.2xlarge | $0.10 |
m5.xlarge | $0.10 |
Dimensions summary
You pay by the hour based on the EC2 instance type you launch. Each dimension maps to one instance size, and the software runs inside your own AWS account. Pricing scales with the instance you pick: small general-purpose types cost less, while GPU types (g-series, p-series) and large memory or compute types (r, m, c, x, i, d, z families) cost more. Hourly charges apply only while an instance runs. You choose the type at launch to match your compute, RAM, VRAM, or CPU-only needs, and can change instances anytime.
Top-of-mind questions for buyers
Each unit is one running EC2 instance of the type you pick, billed per hour. The instance runs a Jupyter notebook AMI with guided browser setup, GPU support, and optional cloud storage. You launch it in your own AWS account and choose the instance size at launch.
Hourly software charges apply only while an instance runs. Stopping the instance stops the software charges. AWS may still bill underlying storage for a stopped instance, but the notebook software meters running time only. Subscribing without launching does not incur charges.
Yes. You pick the type at launch and can change instances later. Each type maps to its own hourly rate, so your charge follows whichever instance is running. For example, GPU types serve deep learning work, while CPU-only types handle lighter tasks at a different rate.
www.sigmodata.com
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No refunds, but you may cancel at any time.
Legal
Vendor terms and conditions
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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
- Security updates for January 2026
- Updated CUDA version to 12.9
- Updated Python version to 3.12
- Updated to latest Tensorflow version
- Updated to latest PyTorch version
- Updated to latest Nvidia drivers version
Additional details
Usage instructions
- Launch the product via 1-click.
- Access the application via web browser at https://<instance-ip>:8888/
- Accept self-signed SSL certificate warning (a free certificate is generated by the instance unless you provide your own - see below for instructions)
- Login using the EC2 instance ID as the password (ex i-xxxxxxxxxxxxxxxxx)
- Click new > Python3 to create a new notebook. From then on you can experiment with Tensorflow, Keras and Pytorch
- When selecting an ec2 instance type, pick an instance with GPUs (ex g5.xlarge) to automatically enable faster model training in Tensorflow and Pytorch thanks to GPU acceleration
Optional settings via User Data:
- The instance can be configured to map a S3 bucket, and/or custom SSL certificates for the https connection (instead of the auto-generated ones)
- Provide the values in the User Data section of the EC2 launch screen
- S3_BUCKET set this user data if you wish to use S3 as storage for your notebooks. Add a line such as S3_BUCKET=your-s3-bucket-name and the instance will try to mount the bucket as the notebook directory (and also independently as /home/ec2-user/s3). This requires the right IAM role with S3 access to the bucket
- SSL_CERT, SSL_KEY set this user data if you wish to use your own SSL certificate. Add SSL_CERT=/home/ec2-user/s3/path-to-cert.crt and SSL_KEY=/home/ec2-user/s3/path-to-cert.key to let the instance copy the certificate and private key. This can be useful if you don't want a self-signed certificate to be generated.
- PORT set this optional value to a different port number than the default (8888). For example, to run on port 443 add a user-data line like "PORT=443"
- DISABLE_SSL (not recommended) this will disable SSL as well as traffic encryption between your browser and the server. To disable SSL, add the user-data line "DISABLE_SSL=true". You will have to set the url to http instead of https, for example http://<instance ip>:8888
Resources
Vendor resources
Support
Vendor support
contact: aws-support@sigmodata.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.
Updated weekly
By Sigmodata
By Hanwei Software Technology
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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
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
Multi-User Access Management
Industry-standard KeyCloak-based user management system with support for external LDAP and Kerberos federation
Multiple IDE Support
Includes JupyterLab, Jupyter Classic Notebook, RStudio IDE, and VSCode for diverse development environments
GPU and CUDA Support
GPU support with multiple CUDA versions installed for optimized PyTorch and TensorFlow usage
Pre-configured Development Environment
Multiple Python and R versions with core data science packages pre-installed and configured
Operating System Foundation
Built on Ubuntu 22.04 with SSL termination capability through custom DNS configuration
Standard contract
No
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