Installation

Requirements

Python 3.10+
Windows, Linux, or macOS
An R3AL platform account and API key (quantization runs on the platform)

Prerequisite: account + API key

Quantization runs on the R3AL platform, so you need a key before you can submit a job:

Create an account

Create an API key

On the SDK / API keys page, mint a key (r3l_live_...). Copy it immediately: the secret is shown only once.

See Using the platform for the full walkthrough.

Install from PyPI

pip install "r3alai[vision]"

The base package is a thin client (pydantic + numpy, no torch). The vision extra adds what you need to run and benchmark the downloaded quantized model locally.

Install from source

git clone https://github.com/R3AL-AI/SDK.git
cd SDK
pip install -e ".[vision]"

Extras

Extra	Installs	Needed for
`vision`	onnx, onnxruntime-gpu (Linux/Windows) or onnxruntime (macOS), pillow	Running and benchmarking the downloaded model locally (`benchmark()`, `load()`)
`export-torch`	torch, onnx, onnxscript	Exporting a PyTorch checkpoint or `nn.Module` to ONNX locally before upload
`yolo`	ultralytics, opencv, PyYAML, …	Ultralytics (YOLO) export and mAP validation
`export-tf`	tensorflow, tf2onnx	TensorFlow / Keras export
`export-paddle`	paddlepaddle, paddle2onnx	Paddle export
`export-tflite`	tflite2onnx	TFLite export
`export-hf`	optimum-onnx, transformers	Exporting a HuggingFace Hub model to ONNX with `r3al export`
`dev`	ruff	Contributing
`all`	vision + yolo + all export extras	Everything

The base install has no torch and no ONNX Runtime. Add vision to run the deliverable locally, and an export-* extra only if you need the SDK to convert framework weights to ONNX before upload. If you already have an .onnx file, vision alone is enough.

pip install "r3alai[vision]"

pip install "r3alai[vision,export-torch]"

pip install "r3alai[vision,yolo]"

pip install "r3alai[all]"

The `r3al` command

Installing the package also puts an r3al command on your PATH, covering the same workflow from a terminal or a coding agent:

r3al login
r3al inspect model.onnx

See the CLI documentation. Only quantize and qat consume a plan run, and both refuse to submit without --yes.

Verify

python -c "from r3alai.quant import Quantizer, QuantConfig; import r3alai.platform; print('OK')"

Then authenticate and confirm the platform sees your key:

import r3alai.platform as platform

platform.login("r3l_live_...")
print(platform.PlatformClient().usage())   # plan + remaining free runs

GPU support

Quantization always runs on R3AL GPU infrastructure, so you do not need a local GPU to quantize. Locally, the vision extra installs onnxruntime-gpu on Linux and Windows and the CPU build on macOS (onnxruntime-gpu has no macOS wheels). Local benchmark() and load() transparently fall back to CPU when no GPU is present. For CUDA inference with ONNX Runtime on Windows, ensure the NVIDIA runtime DLLs (installed under site-packages/nvidia/*/bin by the pip CUDA packages) are on PATH.

Get Started

Core Concepts

Guides

Requirements

Prerequisite: account + API key

Install from PyPI

Install from source

Extras

The `r3al` command

Verify

GPU support

​Requirements

​Prerequisite: account + API key

​Install from PyPI

​Install from source

​Extras

​The r3al command

​Verify

​GPU support

Requirements

Prerequisite: account + API key

Install from PyPI

Install from source

Extras

The `r3al` command

Verify

GPU support