---
id: truss-transfer
version: "0.0.43"
license: unclear
license_treatment: unclear
maintenance: active
---

# truss-transfer — Speed up file transfers with the baseten.co + baseten_fs.

License: unclear · Maintenance: active · Downloads: 389.4K/mo

## What it is and what it does

Truss-Transfer is a download utility that resolves Baseten Pointers—references to model files and data stored across cloud providers—into local directories. It wraps a Rust implementation with a Python API and CLI, allowing you to define which models or datasets to download from HuggingFace, Google Cloud Storage, AWS S3, or Azure, then fetch them on demand with optional caching and authentication via environment variables or secrets.

The package is designed for machine learning workflows where you need to download large model repositories at runtime. You define a manifest of models using PyModelRepo objects (specifying repo ID, revision, storage location, and file patterns), create a bptr manifest, then call lazy_data_resolve() to download files to a specified directory. It handles authentication through runtime secrets and supports filtering files by include/exclude patterns.

Use it for:

- Download HuggingFace model checkpoints to a local directory during model initialization in a serving container.
- Create a manifest of multiple models from different cloud providers and resolve them all to a shared volume.
- Set up a data loader that fetches model files on first use with automatic caching to avoid repeated downloads.
- Integrate with Baseten's b10fs caching layer to speed up repeated access to the same model files.
- Manage authentication for private model repositories using environment-based secrets.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Downloads and resolves Baseten Pointers (bptr) to local directories, supporting HuggingFace, Google Cloud Storage, AWS S3, and Azure sources with optional caching.

Yes, if you are working within the Baseten ecosystem or need to download models from multiple cloud providers at runtime. The package is actively maintained, has no known vulnerabilities, and handles the boilerplate of cloud authentication and file resolution. Install friction is moderate due to compiled wheels, but pre-built binaries are available for common platforms. Not necessary if you only download from a single source or use standard tools like huggingface-hub directly.

## Install

pip install truss-transfer
uv add truss-transfer
poetry add truss-transfer

## Installing truss-transfer

Before you install: Medium install friction due to compiled wheels across multiple platforms and Python versions. Package is actively maintained with recent releases and no known vulnerabilities.

Quickstart:

pip install truss-transfer

import truss_transfer

# Resolve a bptr to a local directory
result_dir = truss_transfer.lazy_data_resolve("/tmp/models")
print(f"Files resolved to: {result_dir}")

Requires Python >= 3.8. Compiled wheels are provided for Linux (x86_64, i686, ppc64le, aarch64, armv7l, musllinux variants) and macOS (x86_64, arm64); Windows support unclear from available wheels.

Verify before relying:

- Whether the package requires system libraries or build tools beyond what pip provides
- Performance characteristics and typical download speeds for large model repositories
- Compatibility with Windows systems (wheels shown are Linux/macOS focused)

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 389.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

baseten pointer resolution, model file download utility, huggingface model downloader, cloud storage file resolver, baseten fs integration, model-download, cloud-storage, baseten

[View on SkillFed](https://skillfed.io/packages/truss-transfer) · [View on PyPI](https://pypi.org/project/truss-transfer/)