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🔷 prism

Interactive research workspaces in the cloud

Visit prismcloud.io → | GitHub →

Overview

prism provides pre-configured cloud workspaces for data science, machine learning, and computational research. Launch GPU-powered environments in minutes with common tools, frameworks, and libraries pre-installed.

Key Features

Pre-Configured Environments

  • Machine Learning - PyTorch, TensorFlow, JAX, scikit-learn
  • Data Science - pandas, NumPy, Matplotlib, Seaborn
  • Deep Learning - CUDA, cuDNN, GPU-optimized frameworks
  • Development Tools - VSCode, Jupyter, terminals

GPU Support

  • NVIDIA GPUs - T4, V100, A10G, A100 instances
  • CUDA Ready - Pre-installed CUDA toolkit and drivers
  • Multi-GPU - Support for multi-GPU training
  • Cost Options - On-demand or spot instances

Workspace Management

  • Quick Launch - Start workstations in under 2 minutes
  • Persistent Storage - EBS volumes for data persistence
  • Snapshots - Save workspace state for later resumption
  • Auto-Shutdown - Automatic shutdown to control costs

Collaboration

  • Shared Workspaces - Collaborate with team members
  • Access Control - IAM-based permissions
  • Session Sharing - Share terminal sessions
  • Data Sharing - Shared S3 buckets for datasets

Use Cases

Interactive Machine Learning

Develop and train ML models with GPU acceleration without managing infrastructure.

Data Exploration

Analyze large datasets interactively with pre-installed data science tools.

Model Prototyping

Quickly prototype deep learning models with access to latest frameworks.

Teaching and Workshops

Provide students with consistent, pre-configured environments for courses.

Architecture

graph TB
    User[Researcher] -->|Launch| CW[cloudworkspaces]
    CW -->|Create| EC2[GPU EC2 Instance]
    EC2 -->|Mount| EBS[Persistent EBS Volume]
    EC2 -->|Access| S3[S3 Data Buckets]

    EC2 -->|VSCode| Browser[Web Browser]
    EC2 -->|Jupyter| Browser
    EC2 -->|SSH| Terminal[Local Terminal]

    style CW fill:#fff4e1
    style EC2 fill:#e1f5ff

Available Workspace Types

ML Development (ml.*)

  • ml.g4dn.xlarge - 1x NVIDIA T4, 4 vCPU, 16 GB RAM
  • ml.g5.xlarge - 1x NVIDIA A10G, 4 vCPU, 16 GB RAM
  • ml.p3.2xlarge - 1x NVIDIA V100, 8 vCPU, 61 GB RAM

Deep Learning Training (dl.*)

  • dl.g5.2xlarge - 1x A10G, 8 vCPU, 32 GB RAM
  • dl.p3.8xlarge - 4x V100, 32 vCPU, 244 GB RAM
  • dl.p4d.24xlarge - 8x A100, 96 vCPU, 1152 GB RAM

Data Science (ds.*)

  • ds.m5.2xlarge - 8 vCPU, 32 GB RAM (CPU-only)
  • ds.r5.4xlarge - 16 vCPU, 128 GB RAM (memory-optimized)

Development (dev.*)

  • dev.t3.large - 2 vCPU, 8 GB RAM (cost-optimized)

Getting Started

Launch a Workspace

# Install prism CLI
brew install prism

# List available workspace types
prism list-types

# Launch ML workspace
prism launch \
  --type ml.g4dn.xlarge \
  --name my-ml-workspace \
  --storage 100

# Connect via VSCode
prism connect my-ml-workspace --vscode

# Or via SSH
prism ssh my-ml-workspace

Manage Workspaces

# List running workspaces
prism list

# Stop workspace (preserves data)
prism stop my-ml-workspace

# Start stopped workspace
prism start my-ml-workspace

# Create snapshot
prism snapshot my-ml-workspace --name experiment-1

# Delete workspace
prism delete my-ml-workspace

Pre-Installed Software

Python Ecosystem

  • Python 3.11, 3.10, 3.9 (via conda)
  • PyTorch 2.1 (GPU)
  • TensorFlow 2.14 (GPU)
  • JAX (GPU)
  • scikit-learn, XGBoost, LightGBM

Development Tools

  • VSCode Server
  • Jupyter Lab
  • Git, GitHub CLI
  • Docker

Data Tools

  • pandas, Polars
  • DuckDB
  • Apache Arrow
  • Parquet tools

Utilities

  • tmux, screen
  • htop, nvtop
  • AWS CLI
  • S3 mount utilities

Integration with ResearchComputing

Data Sources

  • S3 - Direct access to S3 buckets for datasets
  • cargoship - Retrieve archived data for analysis

Workflow

  1. Launch prism for interactive development
  2. Prototype and test on small datasets
  3. Scale to atom for production HPC runs
  4. Archive results with cargoship

Account Management

  • Provision accounts via petri
  • Cost tracking and budget alerts

Documentation

Technology Stack

  • Language: Go (CLI), Python (utilities)
  • Infrastructure: EC2, EBS, CloudFormation
  • Networking: VPC, Security Groups
  • Storage: EBS, S3
  • Access: Systems Manager Session Manager, SSH

Project Status

Current Version: v1.2.0 Status: Production Ready License: Apache 2.0

Contributing

Contributions welcome! See the contribution guide.

Support

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