Sold by: Sigmodata
Deployed on AWS
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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 2023
Deployed on AWS
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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 |
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g6.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 is one instance size, and the software price scales with the compute class you choose. Options include GPU instances (like g5, g6, g4dn, and p4d families) for deep learning work, plus general-purpose and memory-optimized CPU instances (m6i, m6a, r6i, m7i, and older families) for lighter or CPU-only use. Larger sizes within a family cost more per hour. You are charged only while an instance runs; subscribing alone incurs no charge.
Top-of-mind questions for buyers
Each dimension maps to one EC2 instance type running the notebook software. You pay a per-hour software charge for the size you launch, on top of the AWS compute cost for that instance. The wizard runs one shared JupyterLab notebook, so all signed-in users share the same instance and GPU.
Software charges accrue only while the instance runs. Subscribing on AWS Marketplace starts nothing and incurs no charge. If you stop the instance, the hourly software charge stops. Note that stopped instances may still incur AWS storage fees for attached volumes, billed separately by AWS.
Your per-hour rate follows whichever instance you launch. GPU families suit deep learning; CPU-only families suit lighter work. You can start on one size, stop it, and relaunch on another. The hourly charge changes to match the new instance type from the moment it runs.
www.sigmodata.com
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Vendor refund policy
No refunds, but you may cancel at 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
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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
- BREAKING CHANGE: configuration via EC2 user-data has been removed. The S3_BUCKET, SSL_CERT, SSL_KEY, PORT and DISABLE_SSL keys are no longer applied. Configure these in the setup wizard or the admin console instead. If the old keys are present in your launch template, the wizard lists them next to the setting that replaces each one. Instances running earlier versions are unaffected.
- BREAKING CHANGE: the notebook is now served on port 443 instead of 8888, and port 8888 is closed. Update any bookmarks, security groups or load balancers.
- The instance ID is no longer the notebook password. On first launch a setup wizard asks for the instance ID to confirm ownership, then you choose an administrator password.
- New: single sign-on via OIDC (Okta, Microsoft Entra ID, Google Workspace, Auth0) and LDAP / Active Directory. A local administrator account is always configured as a fallback if the identity provider is unreachable.
- New: admin console at /admin showing GPU, driver, memory, disk, certificate expiry, S3 mount and service status, and applying most changes without a restart.
- New: optional Let's Encrypt certificates, with a domain name or for a bare public IP address. Validation happens over port 443, so port 80 stays closed. Any issuance or renewal failure falls back to the self-signed certificate rather than leaving the notebook unreachable.
- Fixed: the self-signed certificate now covers the instance's public IP address and hostname, not only its private IP.
- Fixed: a failed S3 mount no longer prevents the notebook server from starting.
- The notebook server now listens only on loopback, behind an authenticating gateway.
Additional details
Usage instructions
- Launch g6.xlarge (recommended) with inbound TCP 443 allowed. Alternatives: g4dn.xlarge (cheaper), g6.2xlarge (more RAM), g6e.xlarge (more VRAM), or m7i.xlarge (CPU-only).
- Open https://<instance-address>/ in a browser. The certificate is self-signed at this point, so your browser will warn you once.
- Enter the instance ID shown in the EC2 console to confirm ownership.
- Complete the setup wizard: choose an administrator password, a certificate option, and optionally an S3 bucket and single sign-on. SSO/LDAP only control who can sign in. Jupyter is one shared notebook on this instance, not per-user JupyterHub.
- The notebook starts immediately - no reboot. Sign in with the administrator account you created.
Port 80 is not required and stays closed. Certificate validation for Let's Encrypt happens over port 443.
Manage the instance later at https://<instance-address>/admin, or over SSH with sigmodata-notebook status.
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
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