truss-transfer 0.0.24rc11

Speed up file transfers with the baseten.co + baseten_fs.

Truss-Transfer

Python-optional download utility for resolving Baseten Pointers (bptr).

Installation

pip install truss-transfer
# pip install /workspace/model-performance/michaelfeil/truss/truss-transfer/target/wheels/truss_transfer-0.1.0-cp39-cp39-manylinux_2_34_x86_64.whl

How to Resolve a bptr

Via Python Package

import truss_transfer

# Resolve bptr using default download directory from environment
result_dir = truss_transfer.lazy_data_resolve()

# Resolve bptr with custom download directory
result_dir = truss_transfer.lazy_data_resolve("/custom/download/path")

# Example usage in a data loader
def lazy_data_loader(download_dir: str):
    print(f"download using {truss_transfer.__version__}")
    try:
        resolved_dir = truss_transfer.lazy_data_resolve(str(download_dir))
        print(f"Files resolved to: {resolved_dir}")
        return resolved_dir
    except Exception as e:
        print(f"Lazy data resolution failed: {e}")
        raise

Via CLI

# Using the compiled binary
./target/x86_64-unknown-linux-musl/release/truss_transfer_cli /tmp/download_dir

# Using the Python package CLI
python -m truss_transfer /tmp/download_dir

How to Build a bptr and Save it via Python

You can create Baseten Pointers from HuggingFace models using the Python API:

import truss_transfer
import json

# Define models to include in the bptr
models = [
    truss_transfer.PyModelRepo(
        repo_id="microsoft/DialoGPT-medium",
        revision="main",
        volume_folder="dialogpt",
        kind="hf",  # "hf" for HuggingFace, "gcs" for Google Cloud Storage
        runtime_secret_name="hf_access_token",
        allow_patterns=["*.safetensors", "*.json"],  # Optional: specific file patterns
        ignore_patterns=["*.txt"]  # Optional: patterns to ignore
    ),
    truss_transfer.PyModelRepo(
        repo_id="julien-c/dummy-unknown",
        revision="60b8d3fe22aebb024b573f1cca224db3126d10f3",
        volume_folder="julien_dummy",
        runtime_secret_name="hf_access_token_2"
    )
]

# Create the bptr manifest
bptr_manifest = truss_transfer.create_basetenpointer_from_models(models)

# Save to file
with open("/bptr/static-bptr-manifest.json", "w") as f:
    f.write(bptr_manifest)

# Or parse as JSON for programmatic use
manifest_data = json.loads(bptr_manifest)
print(f"Created bptr with {len(manifest_data)} pointers")

PyModelRepo Parameters

• repo_id: Repository identifier (e.g., "microsoft/DialoGPT-medium")
• revision: Git commit hash or branch name (e.g., "main", commit hash)
• volume_folder: Local folder name where files will be stored
• kind: Repository type - "hf" for HuggingFace, "gcs" for Google Cloud Storage
• runtime_secret_name: Name of the secret containing access token
• allow_patterns: Optional list of file patterns to include
• ignore_patterns: Optional list of file patterns to exclude

End-to-End Flow

Here's a complete example of creating and resolving a bptr:

Step 1: Create a bptr Manifest

import truss_transfer
import json
import os

# Create models configuration
models = [
    truss_transfer.PyModelRepo(
        repo_id="microsoft/DialoGPT-medium",
        revision="main",
        volume_folder="dialogpt",
        runtime_secret_name="hf_access_token"
    ),
    truss_transfer.PyModelRepo(
        repo_id="gs://llama-3-2-1b-instruct/",
        revision="",
        volume_folder="llama",
        # requires json in /secrets/gcs-service-account-jsn
        runtime_secret_name="gcs-service-account-jsn",
        kind="gcs"
    ),
    truss_transfer.PyModelRepo(
        repo_id="s3://bt-training-dev-org-b68c04fe47d34c85bfa91515bc9d5e2d/training_projects",
        revision="",
        volume_folder="training",
        # requires json in /secrets/aws
        runtime_secret_name="aws-secret-json",
        kind="s3"
    )
]

# Generate the bptr manifest
bptr_manifest = truss_transfer.create_basetenpointer_from_models(models)

# Ensure the directory exists
os.makedirs("/bptr", exist_ok=True)

# Save the manifest
with open("/bptr/static-bptr-manifest.json", "w") as f:
    f.write(bptr_manifest)

print("bptr manifest created successfully!")

Step 2: Set up Environment (Optional)

# Configure download location
export TRUSS_TRANSFER_DOWNLOAD_DIR="/tmp/my-models"

# Enable b10fs caching (optional)
export BASETEN_FS_ENABLED=1

# Set up authentication (if needed)
export HF_TOKEN="your-huggingface-token"
# Or use the official HuggingFace environment variable
export HUGGING_FACE_HUB_TOKEN="your-huggingface-token"

Step 3: Resolve the bptr

import truss_transfer

# Resolve the bptr - downloads files to the specified directory
resolved_dir = truss_transfer.lazy_data_resolve("/tmp/my-models")
print(f"Files downloaded to: {resolved_dir}")

# Now you can use the downloaded files
import os
files = os.listdir(resolved_dir)
print(f"Downloaded files: {files}")

Step 4: Use the Downloaded Files

# Example: Load a model from the resolved directory
model_path = os.path.join(resolved_dir, "dialogpt")
# Your model loading code here...

Complete Workflow

# Complete example combining creation and resolution
import truss_transfer
import json
import os

def create_and_resolve_bptr():
    # runtime_secret_name: best to be created with `-` in baseten.
    # 1. Create bptr manifest
    models = [
        truss_transfer.PyModelRepo(
            repo_id="NVFP4/Qwen3-235B-A22B-Instruct-2507-FP4",
            revision="main",
            # write to folder named
            volume_folder="dialogpt",
            # read secret from /secrets/hf-access-token
            runtime_secret_name="hf-access-token"
        ),
        # requires a gcs service account json
    ]
    root = "/tmp/my-models"
    bptr_manifest = truss_transfer.create_basetenpointer_from_models(models, root)

    # 2. Save manifest
    os.makedirs("/static-bptr", exist_ok=True)
    with open("/static-bptr/static-bptr-manifest.json", "w") as f:
        f.write(bptr_manifest)

    # 3. Resolve bptr. If we would set `root` above to "", we could define the base dir here.
    truss_transfer.lazy_data_resolve(root)

    # 4. Verify files were downloaded
    dialogpt_path = os.path.join(root, "dialogpt")
    if os.path.exists(dialogpt_path):
        files = os.listdir(dialogpt_path)
        print(f"Successfully downloaded {len(files)} files to {dialogpt_path}")
        return dialogpt_path
    else:
        raise Exception("Model files not found after resolution")

# Run the workflow
model_path = create_and_resolve_bptr()

Secrets

Preferably, use a - to and lowercase characters to add credentials in baseten.

AWS

{
  "access_key_id": "XXXXX",
  "secret_access_key": "adada/adsdad",
  "region": "us-west-2"
}

Google GCS

{
      "private_key_id": "b717a4db1dd5a5d1f980aef7ea50616584b6ebc8",
      "private_key": "-----BEGIN PRIVATE KEY-----\nMI",
      "client_email": "[e-mail]"
}

Huggingface

The Huggingface token.

Azure

(Untested)

{
    "account_key": "key",
}

Environment Variables and Settings

The following environment variables can be used to configure truss-transfer behavior:

Core Configuration

• TRUSS_TRANSFER_DOWNLOAD_DIR (default: /tmp/bptr-resolved)

  • Directory where resolved files will be downloaded
  • Used when no explicit download directory is provided
  • Can be overridden by passing a directory to the CLI or Python function

• RUST_LOG (default: info)

  • Controls logging level: error, warn, info, debug, trace
  • Example: RUST_LOG=debug for detailed logging

Authentication

• HF_TOKEN (optional)

  • HuggingFace access token for accessing private repositories
  • Takes precedence over HUGGING_FACE_HUB_TOKEN
  • Used when runtime_secret_name is hf_token or hf_access_token

• HUGGING_FACE_HUB_TOKEN (optional)

  • Official HuggingFace Hub token environment variable
  • Used as fallback if HF_TOKEN is not set
  • Allows access to private HuggingFace repositories

Baseten FS (b10fs) Configuration

• BASETEN_FS_ENABLED (default: false)

  • Enable/disable Baseten FS caching: 1/true to enable, 0/false to disable
  • When enabled, files are cached in /cache/org/artifacts/truss_transfer_managed_v1

• TRUSS_TRANSFER_B10FS_CLEANUP_HOURS (default: 96)

  • Hours after last access before deleting cached files from other tenants
  • Helps manage disk space by removing old cached files
  • Example: TRUSS_TRANSFER_B10FS_CLEANUP_HOURS=48 for 2 days

• TRUSS_TRANSFER_B10FS_DOWNLOAD_SPEED_MBPS (default: 350)

  • Expected download speed in MB/s for b10fs performance benchmarking
  • Used to determine if b10fs is faster than direct download
  • Lower values make b10fs more likely to be used

Example Configuration

# Basic setup
export TRUSS_TRANSFER_DOWNLOAD_DIR="/tmp/my-models"
export RUST_LOG=info

# With b10fs enabled and authentication
export BASETEN_FS_ENABLED=1
export TRUSS_TRANSFER_B10FS_CLEANUP_HOURS=48
export TRUSS_TRANSFER_B10FS_DOWNLOAD_SPEED_MBPS=100
export HF_TOKEN="your-huggingface-token"

Development

Running Tests

# Run all tests
cargo test

# Run tests without network dependencies
cargo test --lib

# Run Python tests
python -m pytest tests/

Running the CLI as binary

Compiling the libary as musl-linux target for cross-platform usage.

# Add one-time installations
# apt-get install -y musl-tools libssl-dev libatomic-ops-dev
# rustup target add x86_64-unknown-linux-musl

# To build with cargo:
cargo build --release --target x86_64-unknown-linux-musl --features cli --bin truss_transfer_cli

# To run the binary
./target/x86_64-unknown-linux-musl/release/truss_transfer_cli /tmp/ptr

Building a wheel from source

Prerequisites:

# apt-get install patchelf
# Install rust via Rustup https://www.rust-lang.org/tools/install
pip install maturin==1.8.1

This will build you the wheels for your current python3 --version. The output should look like this:

maturin build --release
🔗 Found pyo3 bindings
🐍 Found CPython 3.9 at /workspace/model-performance/michaelfeil/.asdf/installs/python/3.9.21/bin/python3
    Finished `dev` profile [unoptimized + debuginfo] target(s) in 0.75s
🖨  Copied external shared libraries to package truss_transfer.libs directory:
    /usr/lib/x86_64-linux-gnu/libssl.so.3
    /usr/lib/x86_64-linux-gnu/libcrypto.so.3
📦 Built wheel for CPython 3.9 to /workspace/model-performance/michaelfeil/truss/truss-transfer/target/wheels/truss_transfer-0.1.0-cp39-cp39-manylinux_2_34_x86_64.whl

Release a new version and make it the default version used in the serving image builder for new deploys

truss-transfer gets bundled with truss in the context-builder phase. In this phase, the truss-transfer version gets installed. To make truss-transfer bundeable, it needs to be published to pypi and github releases.

1. Open a PR with rust changes
2. Change the version to x.z.y+1.rc0 in Cargo.toml and push change to branch a.
3. Run a `Buid and Release truss-transfer" action https://github.com/basetenlabs/truss/actions with "release to pypi = true" on this branch a.
4. Make x.z.y+1.rc0 as truss pyproject.toml, and templates/server/requirements.txt dependency
5. Edit truss to a new truss.rcX, publish truss.rcX to pypy.org (main.yml action)
6. pip install truss=truss.rcX locally and truss push (on example that uses python truss)
7. Merge PR
8. Wait for CLI binary to be released under assets as part of a new tag (https://github.com/basetenlabs/truss/releases)
9. add the CLI to the server.Dockerfile.jinja to have it available for trussless.