truss-transfer

import truss_transfer import os # Example: Define a Hugging Face model repository using PyModelRepo # For actual use, 'hf_access_token' would be an environment variable or Baseten secret. # Replace 'your-hf-model' with an actual model ID and 'main' with a valid revision. hf_model_repo = truss_transfer.PyModelRepo( repo_id=os.environ.get('HF_MODEL_ID', 'microsoft/DialoGPT-medium'), revision=os.environ.get('HF_MODEL_REVISION', 'main'), volume_folder='dialogpt_model_cache', runtime_secret_name='hf_access_token' ) print(f"Defined Hugging Face model repository: {hf_model_repo.repo_id}") # Example: Define a GCS model repository # 'gcs-service-account-jsn' would be a Baseten secret containing GCS service account JSON. gcs_model_repo = truss_transfer.PyModelRepo( repo_id=os.environ.get('GCS_BUCKET_PATH', 'gs://my-bucket/my-model/'), revision='', volume_folder='gcs_model_cache', runtime_secret_name='gcs-service-account-jsn', kind='gcs' ) print(f"Defined GCS model repository: {gcs_model_repo.repo_id}") # In a typical Truss deployment, these PyModelRepo objects are often used # within a `config.yaml` or a `model.py` for model loading and data transfer. # This example primarily shows how to instantiate the objects.