Sold by: RELEVANCE LAB
Deployed on AWS
Preconfigured Amazon Linux 2023 AMI with LLM tools, Hugging Face, LangChain, RStudio, and JupyterLab. Build and scale Generative AI using AWS Bedrock, SageMaker, and OpenSearch.
Overview
Overview
This Amazon Linux 2023 AMI comes fully pre-installed with essential tools for Generative AI, data science, and machine learning workloads. It is optimized for scalable cloud deployments, AWS-native AI integrations, and secure remote access, delivering a seamless out-of-the-box experience for researchers, developers, and enterprise teams.
Key Features
Remote Access: High-performance remote desktop access via Amazon NICE DCV. Web & Utility Tools: Google Chrome, Git, and AWS CLI preinstalled. Container-Ready Environment: Docker and Docker Compose configured for scalable deployments and microservices-based AI applications. Development Environments: Visual Studio Code, PyCharm Community Edition, JupyterLab, and RStudio for end-to-end development. Generative AI Frameworks: Hugging Face Transformers, LangChain, LlamaIndex, FAISS, and LoRA/PEFT for fine-tuning LLMs. Sample Workflows: RAG chatbot with Bedrock + OpenSearch, multimodal pipelines, and MONAI for medical imaging use cases. ML & Data Science Frameworks: PyTorch, TensorFlow, scikit-learn, PySpark, Dask, and Vowpal Wabbit. Productivity Tools: LibreOffice for document editing and reporting. AWS-Native Integrations: Bedrock SDK, SageMaker SDK, and OpenSearch connectors for building scalable Generative AI applications.
Technical Details
Operating System: Amazon Linux 2023 Remote Access: Amazon NICE DCV Browsers & Utilities: Google Chrome, Git, AWS CLI Programming Languages: Python 3.x, R
IDEs & Authoring Tools: Visual Studio Code PyCharm Community Edition RStudio Desktop & Server
Notebook Interfaces: Jupyter Notebook JupyterLab
GenAI & Machine Learning Frameworks: Hugging Face Transformers, LangChain, LlamaIndex FAISS for vector search and RAG pipelines PyTorch, TensorFlow, scikit-learn PySpark, Dask, Vowpal Wabbit MONAI for medical imaging LoRA and PEFT for fine-tuning LLMs
Environment & Containerization: Anaconda Docker Docker Compose
Office Tools: LibreOffice Writer, Calc, Impress
Highlights
- Preconfigured Generative AI Environment on Amazon Linux 2023 with Hugging Face, LangChain, FAISS, and LoRA for LLM fine tuning.
- AWS Native Integrations with Bedrock, SageMaker, and OpenSearch for building RAG chatbots, multimodal workflows, and scalable AI applications.
- Developer Productivity with Amazon NICE DCV remote access, JupyterLab, RStudio, Visual Studio Code, and a complete data science and AI development stack.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux 2023
Deployed on AWS
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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
Legal
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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
NA
Additional details
Usage instructions
"Quick Usage Summary
- Subscribe to the AWS Marketplace product and launch an instance.
- Use at least 200 GB for the EBS Volume and select g4dn.4xlarge for smooth performance.
- AI and ML libraries are installed in the conda environment named ""genai"". To use them: a. Open Anaconda Prompt. b. Run: conda activate genai c. Run: python --version d. Import packages such as torch, transformers, langchain, faiss, scikit-learn, pyspark, dask, vowpalwabbit, monai, peft.
Connect via NICE DCV
- Open a browser and navigate to https://<your-public-dns-or-IP>:8443
- Log in using your Windows Administrator username and password.
- You will gain access to the Windows desktop in your browser. Note: Ensure that TCP port 8443 is allowed in the EC2 security group and Windows firewall.
Development IDEs
- Launch Visual Studio Code, Visual Studio 2022, or PyCharm from the Start Menu.
- Create new files or open existing projects.
- Suitable for Python, R, .NET, and full-stack development.
Anaconda
- Open Anaconda Navigator from the Start Menu to manage environments and packages.
- Alternatively, use Anaconda Prompt to run commands such as: conda list
JupyterLab and Python
- Access JupyterLab from the desktop or Start Menu.
- Run Python or R notebooks and import preinstalled libraries.
- Activate the genai environment for AI and ML workflows.
Machine Learning and AI Libraries
The environment includes preinstalled libraries for data science, machine learning, and generative AI. Examples:
- Transformers version 4.56.2
- LangChain version 0.3.27
- LlamaIndex version 0.14.3
- FAISS version 1.9.0
- PyTorch version 2.5.1 with CUDA 12.1 (GPU enabled)
- scikit-learn version 1.7.2
- PySpark version 4.0.1
- Dask version 2025.9.1
- VowpalWabbit version 9.10.0
- MONAI version 1.5.1
- PEFT version 0.17.1
GPU Support
- Verified with nvidia-smi: Tesla T4 GPU available
- PyTorch GPU acceleration is enabled"
Support
Vendor support
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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