Sold by: Elm Computing
Deployed on AWS
Free Trial
AWS Free Tier
JupyterHub Server with multiple Python versions, published by Elm Computing.
Overview
This is a repackaged open source software product wherein additional charges apply for support by Elm Computing.
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This JupyterHub Server AMI equipped with multiple Python versions, popular data science Python packages and SageMath is best suited to serve Jupyter notebook for multiple users. For details, please visit docs.elmcomputing.io .
Highlights
- Pre-configured JupyterHub
- Popular data science packages
- Hundreds of Python packages
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
Free trial
Try this product free for 7 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 |
|---|---|
c5.2xlarge Recommended | $0.10 |
t2.micro | $0.10 |
t3.micro | $0.10 |
x1e.32xlarge | $0.10 |
c5a.24xlarge | $0.10 |
g4dn.8xlarge | $0.10 |
m7a.large | $0.10 |
m5dn.16xlarge | $0.10 |
c6a.12xlarge | $0.10 |
z1d.xlarge | $0.10 |
Dimensions summary
You pay by the hour for the software running on a chosen Amazon EC2 instance type. Each dimension maps to one instance size, so your rate depends on the hardware you select. Smaller general-purpose instances bill at lower hourly rates. Compute-optimized, memory-optimized, storage-optimized, and GPU-equipped instances bill at higher rates that reflect their added CPU, memory, or accelerator capacity. There is no upfront commitment; charges accrue only while an instance runs. This lets you match spending to workload size by picking the instance that fits your data science needs.
Top-of-mind questions for buyers
Each dimension maps to one Amazon EC2 instance type, such as g4dn.8xlarge or m7a.large. You pick the instance size that fits your workload. Billing counts each hour the instance runs. The rate reflects that instance's CPU, memory, and any GPU capacity.
Software charges accrue only while the instance runs. A stopped instance does not incur the hourly software rate. Underlying AWS storage fees for attached volumes may still apply while stopped, but the software meters running time only.
You get a JupyterHub platform with GPU support, preloaded with hundreds of Python packages. It supports multiple programming languages and collaborative development. Elm Computing adds support charges on top of the repackaged open source software. GPU-equipped instance types let you run accelerated data science workloads.
docs.elmcomputing.io+1
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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
JupyterHub 5.2.1; Python 3.10 and data science packages such as tensorflow, torch, datascience, mlpack, mlflow and xgboost; OS: Ubuntu 24.04
SageMath has been removed. It can be added upon request.
Additional details
Usage instructions
The details can be found at https://docs.elmcomputing.io/ami/x86/ami.html listing all the installed python packages.
Resources
Vendor resources
Support
Vendor support
Please contact us at support@elmcomputing.io if any assistance is needed.
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 Elm Computing
By netCUBED
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AI generated from product descriptions
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
Browser-Based Remote Access
Full-featured Ubuntu 22.04 LTS Desktop environment accessible through a web browser without requiring client software installation.
Remote Desktop Protocol Support
Native Remote Desktop client connectivity enabling access to the workspace through remote desktop protocol.
Preinstalled Development Tools
Includes latest versions of Google Chrome, Anaconda, Jupyter, RStudio Server, Visual Studio Code, and Docker.
Multi-User Environment
Supports multiple concurrent users on appropriately sized instances with capability to add additional users to the system.
Integrated Development Interfaces
Secure browser-based access to Jupyter notebooks, RStudio Server, and Terminal sessions with copy-paste and drag-and-drop file upload functionality between virtual desktop and client.
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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