Installation

Choose the installation method that best fits your environment.

Conda (Recommended)

Clone and build IOWarp Core from source using the automated installer. This uses conda internally to manage dependencies and produces a full-featured build with all optional components.

git clone --recurse-submodules https://github.com/iowarp/clio-core.git
cd clio-core
./install.sh

Activate the environment in every new terminal:

conda activate iowarp

Build Variants

Pass a variant name to enable additional features:

Variant	Command	What it enables
Release (default)	./install.sh	CPU-only, all engines
Debug	./install.sh debug	Debug symbols, sanitizers
CUDA	./install.sh cuda	NVIDIA GPU acceleration
ROCm	./install.sh rocm	AMD GPU acceleration
MPI	./install.sh mpi	Distributed multi-node
Full	./install.sh full	CUDA + MPI + everything

Verify the installation

conda activate iowarp
chimaera --help

What's Included

The Conda install provides the complete IOWarp stack:

• All engines — CTE, CAE, CEE with full feature support
• Python API — import wrp_cee for context management
• CLI — chimaera command for runtime management
• GPU support — CUDA and ROCm variants available
• MPI support — Distributed multi-node deployments
• HDF5 support — Scientific data format ingestion

Docker

Pull and run the IOWarp Docker image:

docker pull iowarp/deploy-cpu:latest

Run the container:

docker run -d -p 9413:9413 --memory=8g --name iowarp iowarp/deploy-cpu:latest chimaera runtime start

Using Docker Compose

Create a docker-compose.yml:

services:
  iowarp:
    image: iowarp/deploy-cpu:latest
    container_name: iowarp
    hostname: iowarp
    volumes:
      - ./chimaera.yaml:/home/iowarp/.chimaera/chimaera.yaml:ro
    ports:
      - "9413:9413"
    mem_limit: 8g
    command: ["chimaera", "runtime", "start"]
    restart: unless-stopped

Start the service:

docker compose up -d

info

IOWarp uses memfd_create() for shared memory, so no special /dev/shm configuration is needed. Only mem_limit matters for resource control. See the Configuration Reference for details.

Spack

1. Install Spack (v0.22.3+ recommended):

git clone https://github.com/spack/spack.git -b v0.22.3
cd spack
. share/spack/setup-env.sh
echo ". ${PWD}/share/spack/setup-env.sh" >> ~/.bashrc

2. Add the IOWarp Spack repository (included in this repo):

git clone --recurse-submodules https://github.com/iowarp/clio-core.git
cd clio-core
spack repo add installers/spack

3. Install IOWarp:

spack install iowarp

Pip (Experimental)

Experimental

The pip package is experimental. It does not include GPU (CUDA/ROCm), MPI, or HDF5 support. For production use, prefer the Conda install method.

Install the IOWarp Python package:

pip install iowarp-core

Verify the installation:

import iowarp_core
print(iowarp_core.get_version())

Start the Runtime

IOWarp includes the chimaera CLI for managing the runtime:

chimaera runtime start

What's Included

The pip package is self-contained with all dependencies statically linked. It includes:

• Python API — import iowarp_core and import wrp_cee for context management
• CLI — chimaera command for runtime management
• Shared libraries — All IOWarp runtime libraries bundled in the package

info

No system dependencies are required beyond a standard C/C++ runtime (glibc). The package works on any Linux x86_64 or aarch64 system with Python 3.10+.

Next Steps

• Quick Start Tutorial — Start the runtime and run your first example
• Configuration Reference — Customize your deployment
• CLIO Kit — Explore MCP servers for AI agents