Using model weights in Tigris anywhere with Beam

The most common way to deploy AI models in production is by using “serverless” inference. This means that every time you get a request, you don’t know what state the underlying hardware is in. You don’t know if you have your models cached, and in the worst case you need to do a cold start and download your model weights from scratch.

A couple fixable problems arise when running your models on serverless or any frequently changing infrastructure:

• Model distribution that's not optimized for latency causes needless GPU idle time as the model weights are downloaded to the machine on cold start. Tigris behaves like a content delivery network by default and is designed for low latency, saving idle time on cold start.
• Compliance restrictions like data sovereignty and GDPR increase complexity quickly. Tigris makes regional restrictions a one-line configuration, guide here.
• Reliance on third party caches for distributing models creates an upstream dependency and leaves your system vulnerable to downtime. Tigris guarantees 99.99% availability with public availability data.

Beam

Defining HTTP endpoints for AI things is annoyingly complicated. There's a lot of opinionated frameworks and layers that get in the way of you just running the bit of code you need to get your app working. Beam is all about simplifying down the experence so that all you need to do to get an endpoint working is define a single function:

from beam import endpoint, Image


@endpoint(
    name="quickstart",
    cpu=1,
    memory="1Gi",
    image=Image().add_python_packages(["numpy"]),
)
def predict(**inputs):
    x = inputs.get("x", 256)
    return {"result": x**2}

This lets you unify your code and configuration into the same file, allowing you to glance at a file and instantly understand what the endpoints are and do. They also support GPU compute, allowing you to do scale-to-zero inference seamlessly.

Usecase

You can put AI model weights into Tigris so that they are cached and fast no matter where you’re inferencing from. This allows you to have cold starts be faster and you can take advantage of Tigris' globally distributed architecture, enabling your workloads to start quickly no matter where they are in the world.

For this example, we’ll set up SDXL Lightning by ByteDance for inference with the weights stored in Tigris.

Getting Started

Download the sdxl-in-tigris template from GitHub:

git clone https://github.com/tigrisdata-community/sdxl-in-tigris

Prerequisite tools

In order to run this example locally, you need these tools installed:

• Python 3.11
• pipenv
• The AWS CLI

Also be sure to configure the AWS CLI for use with Tigris: Configuring the AWS CLI.

To build a custom variant of the image, you need these tools installed:

• Mac/Windows: Docker Desktop app, alternatives such as Podman Desktop will not work.
• Linux: Docker daemon, alternatives such as Podman will not work.
• Replicate's cog tool
• jq

To install all of the tool depedencies at once, clone the template repo and run brew bundle.

Create a new bucket for generated images, it’ll be called generated-images in this article.

aws s3 create-bucket --acl private generated-images

Optional: upload your own model

If you want to upload your own models, create a bucket for this. It'll be called model-storage in this tutorial.

Both of these buckets should be private.

Then activate the virtual environment with pipenv shell and install the dependencies for uploading a model:

pipenv shell --python 3.11
pip install -r requirements.txt

Run the prepare_model script to massage and upload a Stable Diffusion XL model or finetune to Tigris:

python scripts/prepare_model.py ByteDance/SDXL-Lightning model-storage

info

Want differently styled images? Try finetunes like Kohaku XL! Pass the Hugging Face repo name to the prepare_model script like this:

python scripts/prepare_model.py KBlueLeaf/Kohaku-XL-Zeta model-storage

Access keys

Create a new access key in the Tigris Dashboard. Don't assign any permissions to it.

Copy the access key ID and secret access keys into either your notes or a password manager, you will not be able to see them again. These credentials will be used later to deploy your app in the cloud. This keypair will be referred to as the workload-keypair in this tutorial.

Limit the scope of this access key to only the model-storage-demo (or a custom bucket if you're uploading your own models) and generated-images buckets.

Deploying it to Beam

Install the Beam SDK and CLI into your python environment according to their directions. Be sure to run beam config create to authenticate with an API key.

As a reminder, this example is configured with environment variables. Set the following secrets in your deployments:

Envvar name	Value
AWS_ACCESS_KEY_ID	The access key ID from the workload keypair
AWS_SECRET_ACCESS_KEY	The secret access key from the workload keypair
AWS_ENDPOINT_URL_S3	https://fly.storage.tigris.dev
AWS_REGION	auto
MODEL_PATH	ByteDance/SDXL-Lightning
MODEL_BUCKET_NAME	model-storage-demo (Optional: replace with your own bucket name)
PUBLIC_BUCKET_NAME	generated-images (replace with your own bucket name)

You will need to run the beam secret create command for each of these:

beam secret create AWS_ENDPOINT_URL_S3 https://fly.storage.tigris.dev

Then deploy it with beam deploy:

beam deploy beamcloud.py:generate

You'll get a URL back that you can use to generate images. Do a test generation with this curl command:

curl "https://url-you-were-given-v1.app.beam-cloud" \
  -X PUT \
  -H "Content-Type: application/json" \
  -H 'Authorization: Bearer put-your-beam-auth-token-here' \
  --data-binary '{
    "prompt": "The space needle in Seattle, best quality, masterpiece",
    "aspect_ratio": "1:1",
    "guidance_scale": 3.5,
    "num_inference_steps": 4,
    "max_sequence_length": 512,
    "output_format": "png",
    "num_outputs": 1
}'

If all goes well, you should get an image like this:

Beam will automatically scale the deployment down when it's not in use. You can fully destroy your deployment with beam deployment delete:

beam deployment list # to find the UUID of the deployment
beam deployment delete uuid-of-deployment