Installation

belfort-ml and belfort-ml-client import as belfort_ml and belfort_ml_client. Both depend on belfort-ml-bucket, the models bucket layout they share; neither depends on the other.

belfort-ml#

In a virtual environment with Python 3.12 or newer, e.g. created with uv venv --python 3.12:

uv pip install --torch-backend=cpu belfort-ml

With pip, take torch from PyTorch's CPU index; on Linux, the default index would pull the CUDA build:

pip install --extra-index-url https://download.pytorch.org/whl/cpu belfort-ml

It pulls in torch, belfort-torch-mlir, httpx and belfort-ml-bucket. Compiling runs on the CPU, on macOS on Apple Silicon or Linux (x86-64 or ARM).

An API key#

Sign in to the Belfort console at eml.belfortlabs.com, open API keys, and create one. The key (blf_live_...) is shown once. An account holds up to five active keys.

Keep the key out of source. The SDK reads it from the environment:

export BELFORT_API_KEY=blf_live_...

Check the key with belfort-ml:

import belfort_ml

with belfort_ml.Provider() as provider:
    account = provider.account.get()
    print(account["mp_id"], account["credits"]["available"])

A new account starts with a few credits; Deploys and credits has the details.

The Belfort ML client#

uv pip install belfort-ml-client
# or: pip install belfort-ml-client

To run the client yourself while testing, as the Quickstart does, install it next to belfort-ml.

The Belfort ML client compiles the client-side C++ generated for your model and links it against the Cyclops client library. It needs no GPU; the machine needs:

• macOS 14 or newer on Apple Silicon, or Linux (x86-64 or ARM) on a recent distribution. On ARM, encryption and decryption are slower than on x86-64.
• A C++20 compiler

On these platforms belfort-ml-client installs a platform wheel that contains the Cyclops client library and its headers, with libtommath linked in. Neither a Cyclops build nor libtommath needs to be installed.

The Belfort ML client covers running it.