Sold by: Elm Computing
Deployed on AWS
Free Trial
AWS Free Tier
This product has charges associated with it for seller support.
Overview
This is a repackaged open source software product wherein additional charges apply for support by Elm Computing.
Disclaimer: All trademarks referenced in this listing belong to their respective owners. Their use does not imply any affiliation with or endorsement by the trademark holders.
This workbench, powered by a JupyterHub server and pre-configured with popular machine learning Python packages, is designed to efficiently serve Jupyter notebooks for both single and multiple users.
For a complete list of installed Python packages, please visit https://docs.elmcomputing.io
Once the EC2 instance is running, you can access the JupyterHub server using its public IP address. The default username is elm, and the password is the EC2 instance ID (e.g., i-0c7fadc4dc193eca7).
Highlights
- Tensorflow, torch
- Elm Computing Support
Details
Sold by
Categories
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
Deployed on AWS
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Features and programs
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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 |
|---|---|
t3a.xlarge Recommended | $0.10 |
t2.micro | $0.10 |
t3.micro | $0.10 |
c5a.2xlarge | $0.10 |
r5dn.4xlarge | $0.10 |
r6idn.2xlarge | $0.10 |
r6in.large | $0.10 |
r6a.24xlarge | $0.10 |
m5zn.12xlarge | $0.10 |
m7i.48xlarge | $0.10 |
Dimensions summary
You pay by the hour for the Machine Learning Workbench, based on the AWS EC2 instance type you run it on. Each dimension maps to a single instance size, and the price scales with that instance's compute, memory, and storage capacity. Smaller shared instances cost less per hour than larger memory- or compute-optimized ones. The hourly rate covers the software plus maintenance support from Elm Computing on top of your AWS infrastructure. You pick the instance that fits your workload, and billing follows actual usage with no upfront commitment.
Top-of-mind questions for buyers
Each dimension bills per hour that one AWS EC2 instance of that type runs the workbench. The rate reflects that instance's compute, memory, and storage capacity. You are billed for actual running hours on the instance you launch, with no user-count or data-volume metering.
The hourly software charge meters running time only. A fully stopped instance does not accrue the workbench fee. You may still pay AWS separately for attached storage while the instance is stopped, but that is AWS infrastructure cost, not the software charge.
The rate covers the packaged Machine Learning Workbench software plus maintenance support from Elm Computing. The listings repackage open source software, and the hourly charge adds vendor support on top of your AWS infrastructure cost. Support is provided through documentation and direct email.
docs.elmcomputing.io+1
Helpful?
Vendor refund policy
Refunds are generally not available. Instances are billed hourly based on actual usage and can be terminated at any time to stop charges.
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
First Release: Installation of Python 3.10.12 and Popular Machine Learning Packages
This initial release includes the installation of Python 3.10.12 along with a curated selection of widely-used machine learning libraries. These libraries are pre-installed to streamline the development process and support various machine learning workflows.
Additional details
Usage instructions
After the EC2 instance is started, you can access the JupyterHub server using its public IP address. For example, if the IP address is 10.11.12.13, make sure to enter http://10.11.12.13 (note the use of http, not https) in your browser to reach the JupyterHub server login page. Initially, you may see an nginx 502 Bad Gateway message; if this happens, please wait a few moments and reload the page.
The default username is elm, and the password is the EC2 instance ID (e.g., i-0c7fadc4dc193eca7).
Support
Vendor support
If you have any questions, please don't hesitate to contact us at support@elmcomputing.io . If you need specific software packages included, we can integrate them into the product.
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.
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