Sold by: Techlatest.net
Deployed on AWS
AWS Free Tier
This product has charges associated with it for seller support. This VM offers a hassle-free setup with <b>Jupyter</b> for projects, <b>Jupyterhub</b> for multiuser collaboration, a <b>Jupyter AI extension</b> for for <b>LLM and Generative AI development</b>, and preloaded popular libraries like TensorFlow and PyTorch. It also comes with pre-configured <b>NVIDIA GPU drivers and CUDA libraries</b>.
Overview
This is a repackaged open source software product wherein additional charges apply for support by TechLatest.net.
Important: For step by step guide on how to setup this vm , please refer to our Getting Started guide
If you are AI/ML practitioner or someone who is starting their AI/ML journey but do not want to spend hours setting up the right environment , this VM is for you. It includes :
- Jupyter : Your AI/ML Playground
- Jupyterhub: Making your AI/ML projects more collaborative by providing multi-user environment and enabling easy code and data sharing 3. Jupyter AI extension : your gateway to generative AI within Jupyter
- Provides better data privacy and control as your data, models, code & other information is stored on the VM
- Preinstalled popular AI/ML libraries such as TensorFlow, PyTorch, scikit-learn and many more
- Pre-configured NVIDIA GPU drivers & CUDA libraries
The preinstalled Juputer and AI/ML libraries jump-start your AI/ML development by saving you hours of installation time.
Jupyterhub gives you the collaboration capabilities by allowing a multi-user environment within the same VM. This not only makes it easy to share the AI/ML work , but makes it more cost efficient in a team setup by allowing multiple users/team members to share the same VM infrastructure instead of each user creating their own VM/notebooks.
With the Jupyter AI extension, you can seamlessly integrate with 100+ widely used LLMs from 10+ model providers such as OpenAI for ChatGPT, Anthropic, Hugging Face, AI21, SageMaker to name a few. Complete list of supported LLM Model providers is available here .
The JupyterAI extension comes with built-in LLM Chat UI for seamless collaboration for generative AI. Enjoy flexibility with support for diverse models and providers, seek code suggestions, debugging tips, or even have code snippets generated for you by interacting with the chat UI.
In addition to the Chat UI, the JupyterAI extension comes with %ai and %%ai magic commands turning your Jupyter into a generative AI playground anywhere the IPython kernel runs!
The VM also has pre-configured NVIDIA GPU drivers & CUDA libraries saving you hours of driver setup and configuration hassle so you can harness the power of GPU resources for your AI/ML workload and conduct advanced data analysis with ease.
Highlights
- Multi User Jupyter notebook with AI/ML setup for LLM & Generative-AI
- Unleash the Power of Python, Jupyter & GPU: Accelerate Your Machine Learning Journey on the Cloud
- AI & ML Collaboration & Innovation : Unleash the Power of Multi-User Jupyter with GPU for AI & ML development, training & inference
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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Features and programs
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
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 |
|---|---|
t2.large Recommended | $0.15 |
t3.micro | $0.15 |
t2.micro | $0.15 |
m3.2xlarge | $0.15 |
r5ad.2xlarge | $0.15 |
h1.16xlarge | $0.15 |
r5ad.24xlarge | $0.15 |
t3.large | $0.15 |
r5.4xlarge | $0.15 |
r5ad.12xlarge | $0.15 |
Dimensions summary
You pay by the hour based on the AWS EC2 instance type you choose to run this software. The software cost is the same across instances; your total scales with the compute size you select. Options range from small burstable instances for light development to large memory-optimized, compute-optimized, and storage-heavy instances for demanding workloads. GPU-accelerated instances, such as the g4dn family, support faster AI/ML training and inference. Larger instance sizes provide more vCPUs, memory, and throughput, so hourly costs rise as you scale up. You are billed only for the hours each instance runs.
Top-of-mind questions for buyers
Each hour maps to one running EC2 virtual machine of the type you choose. That instance carries a set number of vCPUs, memory, and storage. For example, the default t2.large gives you 2 vCPUs and 8 GB of memory. Larger types provide more resources per hour.
Software charges accrue only while the instance runs. Fully stopped instances do not accrue hourly software fees. Stopped instances may still incur underlying AWS storage costs for attached volumes, billed separately by AWS. To stop software charges, shut the instance down.
Select an instance from the g4dn family to run this software with GPU acceleration. GPU instances speed up training and inference workloads. If a GPU deployment fails with a quota error, you may need to request a quota increase through AWS before launching.
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Vendor refund policy
Will be charged for usage, can be canceled anytime and usage fee is non refundable.
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Vendor terms and conditions
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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
jupyterhub upgraded to version 5.4.3 on host ubuntu 2404
Additional details
Usage instructions
Getting started guide:
- Login to instance using SSH via key based authentication. Use "ubuntu" as userid (refer https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/putty.html for details on how to connect using putty/ssh)
- Once connected using ssh/putty, run below command to set the password for "ubuntu" user on the terminal sudo passwd ubuntu
- Once the password is set for ubuntu user, from your local windows machine, goto start menu.
- On the start menu, search & select "remote desktop connection" .
- In the "remote desktop connection" wizard, provide public IP of your instance & click connect.
- In the displayed window, provide "ubuntu" as userid & password set in step 2 above.
- Now you are connected to the desktop environment of the VM where you can access out of box environment for python AI & machine learning .
- You can use the remote desktop you connected in above step for using the VM, however, more convenient & better method is to use the Jupyter,Ipython notebook which comes with the VM . The Notebook is available on the same public IP you used for remote desktop & accessible via any browser. Just open the browser & type the public IP address http://yourpublicip & you will get screen for login . Use "ubuntu" as username & the password you set in step 2 to login. Make sure you use http & not https in the url visit http://www.techlatest.net/support/python_ai_machine_learning_support/aws_gettingstartedguide for more details
Resources
Vendor resources
Support
Vendor support
email info@techlatest.net
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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