How to load model weights

Serving a model requires access to model files, such as model weights. These files are often many gigabytes.

For many models, these files are loaded from Hugging Face. However, model files can come from other sources or be stored directly in the Truss. Model weights and other model data can be:

• Public on Hugging Face (default, example here)
• Private on Hugging Face
• Bundled directly with the Truss
• Public cloud storage like S3
• Private cloud storage like S3

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Bundling model weights in Truss

You can bundle model data directly with your model in Truss. To do so, use the Truss’ data folder to store any necessary files.

Here’s an example of the data folder for a Truss of Stable Diffusion 2.1.

data/
    scheduler/
        scheduler_config.json
    text_encoder/
        config.json
        diffusion_pytorch_model.bin
    tokenizer/
        merges.txt
        special_tokens_map.json
        tokenizer_config.json
        vocab.json
    unet/
        config.json
        diffusion_pytorch_model.bin
    vae/
        config.json
        diffusion_pytorch_model.bin
    model_index.json

To access the data in the model, use the self._data_dir variable in the load() function of model/model.py:

class Model:
    def __init__(self, **kwargs) -> None:
        self._data_dir = kwargs["data_dir"]

    def load(self):
        self.model = StableDiffusionPipeline.from_pretrained(
            str(self._data_dir), # Set to "data" by default from config.yaml
            revision="fp16",
            torch_dtype=torch.float16,
        ).to("cuda")

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Loading public model weights from S3

Bundling multi-gigabyte files with your Truss can be difficult if you have limited local storage and can make deployment slow. Instead, you can store your model weights and other files in cloud storage like S3.

Using files from S3 requires four steps:

1. Uploading the content of your data directory to S3
2. Setting external_data in config.yaml
3. Removing unneeded files from the data directory
4. Accessing data correctly in the model

Here’s an example of that setup for Stable Diffusion, where we have already uploaded the content of our data/ directory to S3.

First, add the URLs for hosted versions of the large files to config.yaml:

external_data:
  - url: https://baseten-public.s3.us-west-2.amazonaws.com/models/stable-diffusion-truss/unet/diffusion_pytorch_model.bin
    local_data_path: unet/diffusion_pytorch_model.bin
  - url: https://baseten-public.s3.us-west-2.amazonaws.com/models/stable-diffusion-truss/text_encoder/pytorch_model.bin
    local_data_path: text_encoder/pytorch_model.bin
  - url: https://baseten-public.s3.us-west-2.amazonaws.com/models/stable-diffusion-truss/vae/diffusion_pytorch_model.bin
    local_data_path: vae/diffusion_pytorch_model.bin

Each URL matches with a local data path that represents where the model data would be stored if everything was bundled together locally. This is how your model code will know where to look for the data.

Then, get rid of the large files from your data folder. The Stable Diffusion Truss has the following directory structure after large files are removed:

data/
    scheduler/
        scheduler_config.json
    text_encoder/
        config.json
    tokenizer/
        merges.txt
        special_tokens_map.json
        tokenizer_config.json
        vocab.json
    unet/
        config.json
    vae/
        config.json
    model_index.json

The code in model/model.py does not need to be changed and will automatically pull the large files from the provided links.

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Loading private model weights from S3

If your model weights are proprietary, you’ll be storing them in a private S3 bucket or similar access-restricted data store. Accessing these model files works exactly the same as above, but first uses secrets to securely authenticate your model with the data store.

First, set the following secrets in config.yaml. Set the values to null, only the keys are needed here.

secrets:
  aws_access_key_id: null
  aws_secret_access_key: null
  aws_region: null # e.g. us-east-1
  aws_bucket: null

Then, add secrets to your Baseten account for your AWS access key id, secret access key, region, and bucket. This time, use the actual values as they will be securely stored and provided to your model at runtime.

In your model code, authenticate with AWS in the __init__() function:

def __init__(self, **kwargs) -> None:
    self._config = kwargs.get("config")
    secrets = kwargs.get("secrets")
    self.s3_config = (
        {
            "aws_access_key_id": secrets["aws_access_key_id"],
            "aws_secret_access_key": secrets["aws_secret_access_key"],
            "aws_region": secrets["aws_region"],
        }
    )
    self.s3_bucket = (secrets["aws_bucket"])

You can then use the boto3 package to access your model weights in load().

When you’re ready to deploy your model, make sure to pass is_trusted=True to baseten.deploy():

import baseten
import truss

my_model = truss.load("my-model")
baseten.deploy(
    my_model,
    model_name="My model",
    is_trusted=True
)

For further details, see docs on using secrets in models.