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Saving Models to a Beam Volume

You can mount a Volume in your app and save your TF models inside of it.

from beam import App, Runtime, Image, Volume

import tensorflow as tf
import tensorflow_hub as hub


beam_cache_path = "./models"

app = App(
    name="tf-example",
    runtime=Runtime(
        cpu=1,
        memory="4Gi",
        image=Image(
            python_version="python3.8",
            python_packages=["tensorflow_hub", "tensorflow"],
        ),
    ),
    volumes=[Volume(path=beam_cache_path, name="models")],
)


@app.rest_api()
def save_model():
    model = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
    # Save models to Beam Volume
    tf.saved_model.save(model, beam_cache_path)

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Loading Models from a Beam Volume

You can easily read a file saved in the volume by passing in the path to the volume using the hub.load() method:

@app.rest_api()
def load_saved_model():
    # Load the model from the Beam Volume
    loaded_model = hub.load(beam_cache_path)
    # Run the saved model
    embeddings = loaded_model(["This is an example sentence."])
    # Print model output
    print(embeddings)

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Deploying Tensorflow Models

You can deploy your code as a REST API, Task Queue, or Scheduled Job using the beam deploy command:

beam deploy app.py:your-function-name

When you run this command, your browser window will open the Beam Dashboard. You can copy the cURL or Python request to call the API:

After making a request, you’ll see metrics appear in the dashboard:

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