Installation

One repository publishes two distributions. Choose the smallest install for your execution path:

I want to…	Install	Import
Call hosted Nori	`pip install synthefy`	`from synthefy import SynthefyNoriClient`
Call a SageMaker endpoint	`pip install "synthefy[aws]"`	`from synthefy import SynthefyNoriClient`
Run regression locally through the same client	`pip install synthefy-nori`	`from synthefy import SynthefyNoriClient`
Forecast through hosted Nori	`pip install "synthefy[forecasting]"`	`from synthefy.nori_ts import NoriTSForecaster`
Forecast locally	`pip install "synthefy-nori[forecasting]"`	`from synthefy.nori_ts import NoriTSForecaster`
Use the low-level local estimator	`pip install synthefy-nori`	`from synthefy_nori import NoriRegressor`

synthefy is the lightweight client and workflow package. Its base install never installs or imports Torch.
synthefy-nori adds the model runtime, weights, training, and local inference. It depends on a compatible synthefy release, so installing the local runtime also installs the shared client.

Both distributions are built from Synthefy/synthefy-nori. Hosted code needs only synthefy; local execution needs synthefy-nori. The dependency never points from the lightweight package to the heavy runtime.

Optional features

Choose extras on the distribution that owns the workflow:

# Hosted text preparation, with no local Nori runtime.
pip install "synthefy[text]"

# Local text features and local explainability.
pip install "synthefy-nori[text,interpretability]"

Forecasting

Forecasting now lives in the lightweight synthefy.nori_ts namespace and uses the same explicit client backend for every environment:

# Hosted forecasting.
pip install "synthefy[forecasting]"

# Local forecasting plus the Nori runtime.
pip install "synthefy-nori[forecasting]"

Import NoriTSForecaster from synthefy.nori_ts. See the Forecasting guide for complete local and hosted examples.

Contributor extras

Only needed if you are working on the package itself, not using it:

Extra	Adds	Needed for
`eval`	matplotlib, openml	reproducing the published benchmarks
`train`	wandb, xgboost	training a checkpoint from scratch
`dev`	build, pytest, ruff	running the test suite and linter

Develop from source

git clone https://github.com/Synthefy/synthefy-nori
cd synthefy-nori
uv sync --extra dev

Get Started

Guides

Integration

Optional features

Forecasting

Contributor extras

Develop from source

Next steps

Nori Quickstart

API key

​Optional features

​Forecasting

​Contributor extras

​Develop from source

​Next steps

Nori Quickstart

API key

Optional features

Forecasting

Contributor extras

Develop from source

Next steps