Installation

Install the Dynex SDK and configure your environment

System Requirements

Component	Minimum	Recommended
Python	3.11	3.11+
RAM	4 GB	8 GB+
Storage	2 GB	10 GB+
OS	Linux, macOS, Windows	—

Install

uv (recommended)

uv is a fast Python package manager. If you don’t have it:

curl -LsSf https://astral.sh/uv/install.sh | sh

Then:

uv add dynex

Or in a new project:

uv init my-project && cd my-project
uv add dynex
uv run python main.py

pip

pip install dynex

For development or latest features, install from source:

git clone https://github.com/Dynex-Development/py-sdk.git
cd py-sdk
uv sync --group dev   # or: pip install -e .

With optional dependencies

uv

uv add dynex python-dotenv
uv add dynex pennylane
uv add dynex torch

pip

pip install dynex python-dotenv   # .env file support
pip install dynex pennylane       # gate circuits
pip install dynex torch           # PyTorch neuromorphic layers

Core dependencies

The SDK automatically installs:

• grpcio ≥ 1.60.0 — gRPC communication
• protobuf ≥ 4.25.0 — protocol buffer serialization
• dimod ≥ 0.12.0 — quadratic model framework
• numpy ≥ 1.24.0 — numerical computing
• pydantic ≥ 2.0.0 — data validation

Configuration

Environment variables

export DYNEX_SDK_KEY="your_sdk_key"
export DYNEX_GRPC_ENDPOINT="quantum-router-engine-grpc.hz.dynex.co:3000"

.env file (recommended)

Create .env in your project root and install python-dotenv:

# .env
DYNEX_SDK_KEY=your_sdk_key
DYNEX_GRPC_ENDPOINT=quantum-router-engine-grpc.hz.dynex.co:3000

# Optional
DYNEX_COMPUTE_BACKEND=cpu
DYNEX_DEFAULT_TIMEOUT=300.0

pip install python-dotenv

The SDK automatically discovers .env files in the current directory and up to 3 parent directories.

Verify installation

import dynex
print(dynex.__version__)

from dynex import DynexConfig, ComputeBackend
import dimod

bqm = dimod.BinaryQuadraticModel({0: 1, 1: -1}, {(0, 1): 0.5}, 0.0, 'BINARY')
config = DynexConfig(compute_backend=ComputeBackend.LOCAL)
model = dynex.BQM(bqm)
sampler = dynex.DynexSampler(model, config=config)
sampleset = sampler.sample(num_reads=10)
print(f"Test passed. Best energy: {sampleset.first.energy}")

Platform notes

Linux

No additional setup required. All features work out of the box.

macOS

Install Xcode command line tools if not present:

xcode-select --install

Apple Silicon (M1/M2/M3) is fully supported.

Windows

• Install Microsoft Visual C++ Build Tools if compilation errors occur
• WSL (Windows Subsystem for Linux) recommended for production use
• PowerShell and Command Prompt both supported

Docker

FROM python:3.11-slim

RUN pip install dynex python-dotenv

ENV DYNEX_SDK_KEY=${DYNEX_SDK_KEY}
ENV DYNEX_GRPC_ENDPOINT=${DYNEX_GRPC_ENDPOINT}

COPY . /app
WORKDIR /app
CMD ["python", "main.py"]

docker build -t my-dynex-app .
docker run -e DYNEX_SDK_KEY=your_key -e DYNEX_GRPC_ENDPOINT=quantum-router-engine-grpc.hz.dynex.co:3000 my-dynex-app

Jupyter notebooks

pip install dynex jupyter   # or: uv add dynex jupyter
jupyter notebook

Add this boilerplate at the top of your notebooks:

import dynex
from dynex import DynexConfig, ComputeBackend, QPUModel
import dimod
import numpy as np

config = DynexConfig(
    compute_backend=ComputeBackend.GPU,
    use_notebook_output=True
)
print(f"Dynex SDK {dynex.__version__} ready")

Troubleshooting

ImportError: No module named 'grpc'

pip install grpcio

ModuleNotFoundError: No module named 'dynex'

Check that you are using the correct Python environment. Try:

pip uninstall dynex && pip install dynex

Connection errors (CPU/GPU/QPU backends)

• Verify credentials: DYNEX_SDK_KEY and DYNEX_GRPC_ENDPOINT
• Check that port 443 is not blocked by a firewall
• Test connectivity: grpcurl quantum-router-engine-grpc.hz.dynex.co:3000 list

ValueError: qpu_model is required when compute_backend='qpu'

QPU backend requires an explicit model. Use:

config = DynexConfig(
    compute_backend=ComputeBackend.QPU,
    qpu_model='apollo_rc1'
)

FileNotFoundError: Solver file not found in testnet mode

LOCAL backend requires the dynexcore binary in a testnet/ directory. Download it from the releases page or switch to ComputeBackend.CPU.

Upgrading from legacy SDK

pip uninstall dynex
pip install dynex

Notable changes from legacy SDK:

• Replace mainnet=True/False with explicit DynexConfig(compute_backend=...)
• Remove v2=True parameter from sampler calls
• Communication is now gRPC-based instead of REST