Sold by: RELEVANCE LAB
Deployed on AWS
Launch a preconfigured Windows GPU workstation with RStudio, JupyterLab, PyTorch, and TensorFlow. No software charge - pay only for AWS infrastructure.
Overview
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DCV Login Page
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Desktop Page
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RStudio Icon
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RStudio Homepage
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Jupyter Homepage
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Visual Studio 2022
Windows Data Science Workstation by Relevance Labs
Eliminate hours of environment setup and driver troubleshooting. This preconfigured Windows Server 2022 AMI delivers a complete, GPU-accelerated data science workstation that is ready to use from the moment you connect via Amazon DCV remote desktop. Unlike Linux-only alternatives, this workstation unifies Python and R ecosystems with full Windows GUI support - ideal for data scientists, researchers, and developers who need a familiar desktop experience with enterprise-grade compute.
Pricing
This AMI carries no additional software charge. You pay only for the underlying AWS infrastructure costs (EC2 instances, EBS storage, and data transfer). There is no hourly software fee on top of your chosen instance type.
Why This Workstation
- Windows-native GPU stack: The only Marketplace AMI combining a full Windows desktop, NVIDIA GPU acceleration, and both R and Python data science ecosystems in a single image with no software fee.
- Zero-configuration remote desktop: Amazon DCV provides encrypted, high-performance remote access with no additional licensing - connect and start working immediately.
- Unified development environment: Switch seamlessly between RStudio for statistical analysis, JupyterLab for interactive notebooks, and Visual Studio for application development without managing multiple servers.
Key Features and Buyer Outcomes
Accelerate Deep Learning Without Driver Headaches Pre-installed NVIDIA drivers, CUDA Toolkit, and cuDNN eliminate the most common pain point in GPU computing setup. PyTorch and TensorFlow are pre-configured to leverage GPU acceleration out of the box.
Complete Development Stack
- Visual Studio 2022 and VS Code for application development
- PyCharm Community Edition for Python workflows
- RStudio for R-based statistical analysis
- JupyterLab and Jupyter Notebook for interactive exploration
- Python 3.x and R pre-installed with essential packages
Production-Ready Data Science Frameworks
- PyTorch and TensorFlow for deep learning
- scikit-learn for classical machine learning
- PySpark and Dask for distributed data processing
- Vowpal Wabbit for online learning
- Anaconda for reproducible environment management
Containerization and DevOps Docker and Docker Compose are pre-installed, enabling containerized workflows and reproducible experiments directly on the workstation.
Productivity Tools Google Chrome, Git, AWS CLI, 7-Zip, and LibreOffice (Writer, Calc, Impress) are included for day-to-day productivity.
Example Use Case
A financial analyst needs to train gradient-boosted models on transaction data using PySpark for preprocessing and scikit-learn for modeling. They launch this AMI on a GPU-enabled instance, connect via Amazon DCV, open JupyterLab, and begin processing data immediately - skipping the typical multi-hour setup of drivers, frameworks, and IDE configuration.
AWS Integration
Designed to work alongside AWS services including Amazon SageMaker for managed training jobs, Amazon Bedrock for generative AI, and Amazon OpenSearch for search and analytics workloads. The pre-installed AWS CLI enables direct interaction with S3, EC2, and other services.
Recommended Instance Types
- GPU workloads (deep learning, model training): g4dn.xlarge, g5.xlarge, p3.2xlarge
- CPU-only workloads (data analysis, R development): m5.xlarge, m5.2xlarge
- Minimum recommended: 4 vCPUs, 16 GB RAM, 100 GB EBS storage
Getting Started
- Launch the AMI from AWS Marketplace on a supported instance type
- Configure your security group to allow TCP port 8443 (Amazon DCV)
- Connect via Amazon DCV client using your instance credentials
- Begin working in JupyterLab, RStudio, or your preferred IDE
Need help validating the workstation for your use case? Contact Relevance Labs at rlcloudsupport@relevancelab.com to schedule a guided setup walkthrough.
Security Considerations
Amazon DCV sessions are encrypted in transit. Buyers should configure security groups to restrict port 8443 access to trusted IP ranges and follow AWS best practices for key pair management and IAM role assignment.
Technical Details
- Operating System: Windows Server 2022
- Remote Access: Amazon NICE DCV (encrypted)
- GPU Support: NVIDIA drivers, CUDA Toolkit, cuDNN
- Languages: Python 3.x, R
- IDEs: Visual Studio 2022, VS Code, PyCharm CE, RStudio
- Notebooks: JupyterLab, Jupyter Notebook
- Frameworks: PyTorch, TensorFlow, scikit-learn, PySpark, Dask, Vowpal Wabbit
- Environment Management: Anaconda, Docker, Docker Compose
Highlights
- Windows Native GPU Computing Without Setup Hassle: Pre installed NVIDIA drivers, CUDA Toolkit, and cuDNN eliminate the multi hour process of configuring GPU acceleration on Windows. PyTorch and TensorFlow are validated to leverage GPU compute immediately on g4dn, g5, and p3 instance families with no driver compatibility troubleshooting required.
- Unified R and Python Workstation With Encrypted Remote Desktop: Combines RStudio, JupyterLab, Visual Studio 2022, VS Code, and PyCharm in a single Windows environment accessible via Amazon DCV encrypted remote desktop. Unlike Linux only data science AMIs, this workstation provides a familiar Windows GUI experience for analysts who need both R statistical computing and Python ML frameworks without managing separate servers or VNC configurations.
- Production Ready Frameworks With Container and AWS Integration: Includes PyTorch, TensorFlow, scikit learn, PySpark, Dask, and Vowpal Wabbit pre configured alongside Docker, Docker Compose, and Anaconda for reproducible experiments. The pre installed AWS CLI enables direct integration with Amazon SageMaker, Bedrock, and OpenSearch, letting teams move from local experimentation to scalable cloud workflows without rebuilding their environment.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Win 2022
Deployed on AWS
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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
This product is available free of charge. Free 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.
Vendor refund policy
NA
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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
Quick Usage Summary
Subscribe to the AWS Marketplace product and launch an instance. Please ensure you keep 100GB as the size of the EBS Volume.
- Connect via NICE DCV
- Open a browser and navigate to: https://:8443
- Log in using yourWindows Administrator username and password.
- You will gain access to the Windows desktop directly through your browser.
Note: Make sure TCP port 8443 is open in the EC2 security group and Windows firewall.
- RStudio Server
- From theStart Menu, click on theRStudio icon to open the application.
- RStudio will launch, and you can log in using your Windows credentials.
- Use RStudio for statistical analysis and data visualization.
- Visual Studio Code / Visual Studio 2022 / PyCharm
- From theStart Menu, select the appropriate icon to launchVisual Studio Code,Visual Studio 2022, orPyCharm.
- Start new files or open existing projects.
- These tools are ideal for Python, R, .NET, and full-stack application development.
- Anaconda
- OpenAnaconda Navigator from the Start Menu to manage environments and packages.
- Alternatively, you can useAnaconda Prompt to access the CLI: bash conda list
- ML & Data Science Libraries
- You can access various libraries via Jupyter, Python, or your preferred IDE. Example: python import torch, tensorflow, sklearn, pyspark, dask
- Popular frameworks likePyTorch,TensorFlow, andscikit-learn are ready for use.
Additional details
Usage instructions
Quick Usage Summary
Subscribe to the AWS Marketplace product and launch an instance. Please ensure you keep 200GB as the size of the EBS Volume and select g4dn.2xlarge instance type minimum.
- Connect via NICE DCV
- Open a browser and navigate to: https://<your-public-dns-or-IP>:8443
- Log in using yourWindows Administrator username and password.
- You will gain access to the Windows desktop directly through your browser.
Note: Make sure TCP port 8443 is open in the EC2 security group and Windows firewall.
- RStudio Server
- From theStart Menu, click on theRStudio icon to open the application.
- RStudio will launch, and you can log in using your Windows credentials.
- Use RStudio for statistical analysis and data visualization.
- Visual Studio Code / Visual Studio 2022 / PyCharm
- From theStart Menu, select the appropriate icon to launchVisual Studio Code,Visual Studio 2022, orPyCharm.
- Start new files or open existing projects.
- These tools are ideal for Python, R, .NET, and full-stack application development.
- Anaconda
- OpenAnaconda Navigator from the Start Menu to manage environments and packages.
- Alternatively, you can useAnaconda Prompt to access the CLI: bash conda list
- ML & Data Science Libraries
- You can access various libraries via Jupyter, Python, or your preferred IDE. Example: python import torch, tensorflow, sklearn, pyspark, dask
- Popular frameworks likePyTorch,TensorFlow, andscikit-learn are ready for use.
Resources
Vendor resources
Support
Vendor support
Support for Windows Data Science Workstation
Relevance Labs provides support for this preconfigured Windows Data Science Workstation AMI.
Contact
Support Scope Relevance Labs provides setup documentation and usage guidance for this preconfigured workspace, including assistance with initial configuration, connectivity via Amazon DCV, and troubleshooting of pre-installed software components.
For issues related to the AMI configuration, installed software, or initial setup, please contact the support email above with a description of your issue, your instance type, and relevant instance details.
Guided Setup Walkthrough If you need help validating the workstation for your specific use case or require assistance onboarding your team, contact the support email to schedule a guided walkthrough session.
Refund Requests For billing questions or refund requests, contact rlcloudsupport@relevancelab.com with your AWS account ID and the relevant subscription details.
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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