Sold by: Sigmodata
Deployed on AWS
Free Trial
Secure Jupyter notebook for data science teams, with single sign-on (OIDC or LDAP), local accounts and automatic GPU support. Live in minutes from a browser setup wizard, running inside your AWS account.
Overview
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JupyterLab
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Sign in
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Setup wizard - administrator account
Runs inside your own AWS account: notebooks, data and models stay on an instance you control, inside your VPC.
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 in minutes from a browser setup wizard
- Runs inside your own AWS account - sign in with local accounts, OIDC single sign-on or LDAP
- GPU turns on automatically; offered on 14 GPU and xlarge CPU instance types (recommended g6.xlarge), $0.10 per hour software fee
Details
Sold by
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux 2023
Deployed on AWS
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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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
g6.xlarge Recommended | $0.10 |
m5.xlarge | $0.10 |
g4dn.2xlarge | $0.10 |
m5.2xlarge | $0.10 |
g4dn.xlarge | $0.10 |
g5.xlarge | $0.10 |
g5.2xlarge | $0.10 |
m6i.xlarge | $0.10 |
m6i.2xlarge | $0.10 |
g6.2xlarge | $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
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
Security update: fixes CVE-2026-111497 (Brotli decoder buffer overflow) with the patched Amazon Linux 2023 Brotli packages. Includes the latest operating system and Python package security updates.
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.
Where your data is stored
- Accounts and API tokens: /etc/sigmodata/identity.sqlite3. Passwords and tokens are stored hashed, never in clear text.
- Service configuration: /etc/sigmodata/
- TLS certificate and private key: /var/lib/sigmodata/certs/
- Your notebooks, datasets, and any credentials you save from inside a
- notebook: /home/ec2-user. Anything you write there persists on the EBS
- volume and is not managed by the product. All of it stays on the instance EBS volume in your own AWS account. Nothing is sent to Sigmodata.
Encryption
- In transit: HTTPS on port 443, self-signed by default or Let's Encrypt from the setup wizard.
- At rest: AWS Marketplace requires the published AMI snapshot to be unencrypted. If you need encryption at rest, enable EBS encryption when you launch (Advanced details -> EBS encryption) or launch from an encrypted copy. The product needs no change either way.
Rotation
- The TLS certificate renews automatically on a systemd timer. Renew it or switch certificate type on demand at /admin.
- Revoke and re-issue account passwords and API tokens at /admin at any time. Rotate any credential that may have been exposed.
Checking that the product is healthy
- In the EC2 console, choose Instances, select this instance, and check the Status checks tab for passed system and instance checks.
- Request https://<instance-address>/_gw/healthz for the service state.
- On the instance, run: sudo systemctl status sigmodata-notebook and sudo journalctl -u sigmodata-notebook for logs.
Backup and recovery
- Take EBS snapshots of the instance volume for full-instance recovery.
- Notebooks under /home/ec2-user are ordinary files; copy them to S3 or to version control for off-instance backup.
Service quotas and cost
- GPU instances (g4dn/g5/g6) draw on the "Running On-Demand G and VT instances" vCPU quota, which is 0 on a new AWS account. Request an increase in Service Quotas -> Amazon EC2 before you launch.
- You pay the AWS Marketplace hourly charge for this product plus the AWS resources it uses: EC2 instance hours, the EBS root volume, any S3 storage you configure, and data transfer out.
Resources
Vendor resources
Support
Vendor support
Support contact: Email: support@sigmodata.com Support URL: https://www.sigmodata.com/contact
Support description: Email support@sigmodata.com or https://www.sigmodata.com/contact . We reply within one business day, Monday through Friday, US Pacific time.
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
By Galaxys Cloud
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AI generated from product descriptions
Authentication and Access Control
Support for multiple authentication methods including local accounts, OIDC-based single sign-on (Okta, Microsoft Entra ID, Google Workspace, Auth0), and LDAP/Active Directory integration
GPU Acceleration
Automatic GPU detection and support with NVIDIA CUDA 12.9 and cuDNN, compatible with multiple GPU instance types including g6.xlarge (NVIDIA L4), g4dn.xlarge (T4), g6.2xlarge, and g6e.xlarge (L40S)
Pre-installed Deep Learning Frameworks
Latest versions of TensorFlow and PyTorch with Python 3.12, along with Scikit Learn, Matplotlib, and Numpy libraries
Network Isolation and Security
Deployment within customer-controlled AWS VPC with HTTPS-based browser setup wizard, optional trusted TLS certificates from Let's Encrypt, and administrator password configuration
Built-in Management Console
Admin console for monitoring and managing GPU status, storage allocation, TLS certificate configuration, and service health
Deep Learning Framework Integration
TensorFlow 2.21 and PyTorch 2.5.1 integrated with CUDA 12.6, cuDNN 9.x, and NVIDIA driver 595 as a coordinated image configuration
Isolated Python Environment
Python 3.12 virtual environment with frameworks and scientific libraries installed separately from the operating-system Python environment to define dependency boundaries
GPU Hardware Optimization
Software stack configured and tested for NVIDIA T4 GPUs on g4dn instances with verification of driver loading, CUDA visibility, framework GPU detection, and tensor operations
Scientific Computing Libraries
NumPy, SciPy, scikit-learn, and Matplotlib included for numerical computing, machine-learning utilities, and visualization workflows
Notebook Service Configuration
Jupyter Notebook installed and configured as a managed service on port 8888 with authentication requiring unique, unpredictable secrets and support for TLS or protected access paths
Deep Learning Framework
PyTorch 2.0.1 with dynamic computational graph support for flexible and efficient model development
GPU Acceleration
NVIDIA GPU acceleration with Open Source Software (OSS) NVIDIA driver for optimized deep learning computations
Pre-configured Environment
Pre-configured AMI with CUDA, cuDNN, and essential ML/DL libraries eliminating manual dependency setup
Operating System Foundation
Amazon Linux 2 base operating system providing stability, security, and seamless integration with AWS services
Security and Management
Security hardening, firewall policies, monitoring tools, and built-in management utilities for secure deployment
Standard contract
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