CloudWorkstation

Academic Research Computing Platform - Pre-configured cloud environments in seconds

What is CloudWorkstation?

CloudWorkstation provides researchers with pre-configured cloud workstations for data analysis, machine learning, and computational research. Launch production-ready environments in seconds, not hours.

From individual researchers to institutional deployments - research computing made simple, scalable, and cost-effective.

🎯 Core Design Principles

• 🎯 Default to Success: Every template works out of the box in every supported region
• ⚡ Optimize by Default: Smart instance sizing and cost-performance optimization
• 🔍 Transparent Fallbacks: Clear communication when configurations change
• 💡 Helpful Warnings: Gentle guidance for optimal choices
• 🚫 Zero Surprises: Users always know what they're getting
• 📈 Progressive Disclosure: Simple by default, detailed when needed

🚀 Installation

macOS

Homebrew (Recommended)

brew install scttfrdmn/tap/cloudworkstation

Manual Installation

# Download and extract
curl -L https://github.com/scttfrdmn/cloudworkstation/releases/latest/download/cloudworkstation_0.5.4_darwin_arm64.tar.gz | tar xz

# Install binaries
sudo mv cws cwsd /usr/local/bin/

Linux

Debian/Ubuntu

wget https://github.com/scttfrdmn/cloudworkstation/releases/download/v0.5.4/cloudworkstation_0.5.4_linux_amd64.deb
sudo dpkg -i cloudworkstation_0.5.4_linux_amd64.deb

RHEL/CentOS/Fedora

wget https://github.com/scttfrdmn/cloudworkstation/releases/download/v0.5.4/cloudworkstation_0.5.4_linux_amd64.rpm
sudo rpm -i cloudworkstation_0.5.4_linux_amd64.rpm

Alpine Linux

wget https://github.com/scttfrdmn/cloudworkstation/releases/download/v0.5.4/cloudworkstation_0.5.4_linux_amd64.apk
sudo apk add --allow-untrusted cloudworkstation_0.5.4_linux_amd64.apk

Windows

Scoop

scoop bucket add scttfrdmn https://github.com/scttfrdmn/scoop-bucket
scoop install cloudworkstation

Manual Installation

# Download from GitHub releases
# https://github.com/scttfrdmn/cloudworkstation/releases/latest
# Extract and add to PATH

🚀 Quick Start

1. Configure AWS Credentials

# If you already have AWS CLI configured, skip to step 2
aws configure

CloudWorkstation automatically discovers credentials from:

• Environment variables (AWS_PROFILE, AWS_ACCESS_KEY_ID)
• AWS CLI configuration (~/.aws/credentials)
• CloudWorkstation profiles (for multi-account management)

2. Launch Your First Workstation

# View available templates
cws templates

# Launch a Python ML environment
cws launch python-ml my-research

# Connect via SSH
cws connect my-research

# View running instances
cws list

What happens automatically:

• ✅ Daemon starts if not running
• ✅ Optimal instance type selected
• ✅ Security groups configured
• ✅ SSH keys generated and managed
• ✅ Template provisioned and ready

🌟 Key Features

💰 Cost Optimization

• Hibernation: Preserve state while reducing costs by 90%
• Idle Detection: Automated hibernation policies with configurable thresholds
• Budget Management: Project-level cost tracking and alerts
• Cost Analytics: Real-time spending reports and forecasts

🏗️ Research Templates

• 21+ Pre-configured Environments: Python ML, R, bioinformatics, web dev, and more
• Template Inheritance: Compose complex environments from simple building blocks
• Smart Defaults: Optimal instance sizing and cost-performance ratios
• Regional Fallbacks: Automatic handling of availability constraints

🏢 Enterprise & Collaboration

• Project-Based Organization: Multi-user projects with role-based access
• Research User System: Persistent identities across instances
• Multi-Account Support: Manage multiple AWS profiles seamlessly
• Template Marketplace: Share and discover community templates

📱 Multi-Modal Access

• CLI: Fast, scriptable command-line interface
• TUI: Interactive terminal interface with keyboard navigation
• GUI: Desktop application (available when building from source)
• REST API: Complete HTTP API on port 8947

📦 Example Templates

CloudWorkstation includes 21+ pre-configured templates for research computing:

• Python ML: Jupyter, scikit-learn, TensorFlow, PyTorch
• R Research: RStudio, tidyverse, Bioconductor
• Bioinformatics: BLAST, bowtie2, samtools, bedtools
• Web Development: Node.js, Docker, nginx
• Deep Learning: GPU-optimized environments with CUDA

# View all templates
cws templates

# Get detailed template info
cws templates info python-ml

💻 Usage Examples

Basic Instance Management

# Launch an instance
cws launch python-ml my-project

# List running instances
cws list

# Connect via SSH
cws connect my-project

# Stop instance
cws stop my-project

Cost Optimization

# Hibernate to preserve state while saving costs
cws hibernate my-instance
cws resume my-instance

# Automated idle policies
cws idle profile list
cws idle instance my-gpu --profile gpu

Project Management

# Create project with budget
cws project create ml-research --budget 500

# Add team members
cws project member add ml-research user@example.com --role member

# Launch instance in project
cws launch python-ml analysis --project ml-research

Multi-Modal Access

# Command line
cws templates

# Terminal UI
cws tui

# REST API
curl http://localhost:8947/api/v1/instances

📖 Documentation

cws --help                      # Show all commands
cws templates                   # List available templates
cws templates info <template>   # Detailed template info
cws doctor                      # System health check

Guides:

• AWS Setup Guide - AWS account and credential configuration
• Installation Guide - Comprehensive installation instructions
• Changelog - Version history and release notes

🗓️ Version History

v0.5.4 (Current) - Universal Version System

• Dynamic OS Versions: Choose OS versions at launch time with --version flag
• Version Aliases: Support for latest, lts, previous-lts
• AMI Freshness Checking: cws ami check-freshness validates static AMI IDs
• AWS SSM Integration: Automatic latest AMI discovery for major distributions
• Package Management: Available via Homebrew (macOS), Scoop (Windows), deb, rpm, apk

v0.5.3 - Research User System & Template Marketplace

• Multi-User Architecture: Persistent research identities across instances
• SSH Key Management: Complete key generation and distribution
• Template Registry: Multi-registry support with community templates
• Policy Framework: Institutional governance and access control

v0.4.5 - Enterprise Research Platform

• Project-Based Organization: Multi-user projects with role-based access
• Budget Management: Real-time cost tracking and automated controls
• Hibernation Ecosystem: Manual + automated idle detection policies
• Template Inheritance: Stackable template system

🚀 Roadmap

Phase 5 (Current): Multi-user collaboration and template marketplace Phase 6: Advanced storage (FSx, S3 integration) and AWS research services Phase 7: Enterprise authentication (OAuth, LDAP, SAML) and TUI enhancements

🤝 Contributing

CloudWorkstation is open source and welcomes contributions!

• Issues: Report bugs or request features
• Pull Requests: Submit code improvements
• Templates: Contribute research environment templates
• Documentation: Help improve guides

Development:

git clone https://github.com/scttfrdmn/cloudworkstation.git
cd cloudworkstation
make build
make test

📄 License

Apache License 2.0 - Free for academic and commercial use

🆘 Support

• Documentation: cws --help or browse docs
• System Check: cws doctor
• Issues: GitHub Issues
• AWS Setup: See AWS Setup Guide

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CloudWorkstation v0.5.4 - Pre-configured cloud environments for research computing