Fast.ai : Complete Deep Learning Environment for Beginners. Pre-installed PyTorch 2.9.1 Fast.ai 2.8.5 on Ubuntu 22.04. Start training AI models in 5 minutes with our simplified setup. Perfect for students, developers, and researchers beginning their ML journey. Includes Jupyter Lab, example notebooks, and AWS optimization guides. Works on CPU instances for learning, ready for GPU acceleration when needed. Step-by-step tutorials for image classification, NLP, and tabular data projects. All dependencies pre-configured in isolated environment. Reduce setup time from days to minutes. Deploy, train, and experiment with deep learning immediately. AWS-specific guidance for cost optimization and performance tuning. Everything you need to go from zero to production-ready models. Additional charges apply to this product, as it is a pre-configured, security-hardened PyTorch 2.9.1 Fast.ai 2.8.5 image. It ships with compliance-ready configurations (PCI DSS, NIST) ClamAV, audit logging.
Fast.ai Complete Deep Learning Environment - AWS Marketplace Edition
The perfect starting point for beginners entering deep learning. Specifically designed for students, career changers, and developers who want to learn AI without complex setup headaches.
Additional charges apply to this product, as it is a pre-configured, security-hardened PyTorch 2.9.1 Fast.ai 2.8.5 on Ubuntu 22.04 image. It ships with compliance-ready configurations (PCI DSS, NIST), firewall rules, ClamAV, audit logging.
Why Perfect for Beginners:
Traditional deep learning requires days of configuration. Our solution eliminates barriers with zero configuration needed. Train your first model in under 5 minutes with no prior experience required.
Complete Package Includes:
Pre-installed PyTorch 2.9.1 and Fast.ai 2.8.5
Jupyter Lab with interactive notebooks
Essential libraries: NumPy, Pandas, Matplotlib, OpenCV
AWS optimized for CPU and GPU instances
Isolated virtual environment
Ideal For:
Students and self-learners avoiding setup struggles
Career changers needing portfolio projects
Developers exploring AI capabilities
Educators requiring consistent classroom environments
Key Differentiators:
15-minute setup versus days of configuration
Pre-configured and tested environment
Beginner-focused guidance included
AWS performance optimization
Regular updates and maintenance
Ready Projects:
Image classification for object recognition
Sentiment analysis for text emotions
Price prediction models
Style transfer applications
Recommendation systems
AWS Advantages:
Start with t3.micro for learning
Scale to g4dn.xlarge for serious training
Use Spot Instances for cost savings
Pay only for compute time used
Learning Timeline:
Week 1: First model in 5 minutes
Week 2: Complete first project
Week 3: Advanced techniques
Week 4: Production deployment
Technical Specifications:
Ubuntu 22.04 LTS base image
Python 3.10 plus
Jupyter Lab and Notebook
Git and AWS CLI included
GPU ready for NVIDIA CUDA
Security updates maintained
Educational Value:
Progressive difficulty structure
Real-world dataset examples
Industry best practices
Community learning access
Business Applications:
Quick AI prototyping
Team training efficiency
Cost-effective experimentation
Production transition ready
Educational Use:
Standardized lab setup
Reduced IT support needs
Classroom scalability
Industry-relevant curriculum
Simple Start Process:
Launch from AWS Marketplace
Connect via browser
Activate environment
Train first model
Follow documentation
DIY vs Our Solution:
Days of setup vs 15 minutes
Configuration issues vs pre-configured
No guidance vs complete tutorials
Unoptimized vs AWS-tuned
For Beginners Who Think:
AI seems too complicated
Setup always fails
What to do after installing
Need practical projects
Transparent Pricing:
No hidden fees
AWS compute costs only
Free tier eligible starts
Budget-friendly scaling
Start Today:
Begin your AI journey in minutes, not days. Everything included for deep learning success from first model to production deployment. Perfect for absolute beginners with growth for advanced projects.
Launch now from AWS Marketplace. Complete documentation and support provided. Your path to AI mastery starts here with our Fast.ai Complete Environment.
Highlights
Beginner-Friendly AI Launchpad: Start training deep learning models in under 5 minutes with zero configuration. Our pre-installed Fast.ai environment eliminates complex setup, allowing complete beginners to focus on learning AI concepts rather than troubleshooting dependencies. Includes step-by-step tutorials and ready-to-run project templates.
Complete Educational Ecosystem: More than just software a guided learning path with comprehensive documentation. Access 30 days of structured curriculum, real-world project examples, troubleshooting guides, and best practices for production deployment. Perfect for students, career changers, and educators building AI skills.
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 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. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
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.
You pay by the hour for the Amazon EC2 instance you run this software on. There is no upfront commitment. Each dimension maps to a specific EC2 instance type, and the hourly rate changes with that instance's size and family. Smaller general-purpose instances cost less per hour, while larger memory, compute, storage, and accelerated GPU instances cost more. You pick the instance that fits your workload, from light learning setups to heavy model training. Your total cost depends on which instance you select and how many hours you keep it running.
Top-of-mind questions for buyers
What does one hourly unit cover, and am I charged when the instance is stopped?
Each hourly unit covers one running EC2 instance of the type you selected. The software meters running time only. When you stop the instance, software charges stop. Stopped instances may still incur separate AWS storage fees for attached disks, which are billed by AWS, not by this software.
Do I need a GPU instance, or can I run this on a standard CPU instance?
The software runs on CPU instances for learning and light work. For serious model training, GPU-accelerated instance types shorten training time. You pick the instance that fits your workload. The hourly rate rises with larger memory, compute, storage, or GPU instances.
If I switch to a different instance type, how does my bill change?
You are billed at the hourly rate tied to whichever instance type is running. Switching to a larger instance raises your hourly cost; switching to a smaller one lowers it. The change takes effect when you launch the new instance. You pay only for hours each instance actually runs.
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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
ver2025
Additional details
Usage instructions
Subscribe and Launch: Navigate to this product in AWS Marketplace. Click Continue to Subscribe, accept terms, then Continue to Configuration. Select region and instance type.
Configure Access: Set up security group to allow SSH (port 22) and HTTP (port 8888). Create or select existing key pair for authentication.
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.
The NVIDIA GPU-Optimized AMI is an environment for running the GPU-accelerated deep learning and HPC containers from the NVIDIA NGC catalog. The deep learning containers from NGC catalog require this AMI for GPU acceleration on AWS P5d, P4d, P3, G4dn, G5 GPU instances.
NVIDIA AI Enterprise is an end-to-end, cloud-native software platform that accelerates data science pipelines and streamlines development and deployment of production-grade AI applications, including generative AI.
This product has charges associated with it because it is delivered as a pre-configured and hardened Deep Learning AMI on Ubuntu 20.04. It includes PyTorch 2.0.1, NVIDIA GPU drivers, CUDA, cuDNN, and essential ML/DL libraries, along with security hardening, firewall policies, audit logging, monitoring agents, and built-in management utilities. Designed for data scientists and ML engineers, it enables rapid training, inference, and deployment of deep learning workloads on AWS. Extended support and customer attention are also included. The Deep Learning AMI GPU PyTorch 2.0.1 Ubuntu 20.04 is a preconfigured AWS machine image optimized for deep learning projects.
This product has charges associated with it for Deep Learning AMI (Amazon Linux 2023) Pytorch and TensorFlow Pre-configured and CIS Level 1 hardening, pre-configured compliance (PCI DSS, NIST), firewall policies, Monitoring, Wazuh SIEM agent, and seller support.
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