Deploy a Hugging Face model

In this example we will show how to deploy a Hugging Face model to SlashMLO. We will use the Hugging Face Transformers and deploy it to SlashML. Then we will use the endpoint exposed by SlashML to make predictions on our model.

from slashml import ModelDeployment
import time

# you might have to install transfomers and torch
from transformers import pipeline

def train_model():
    # Bring in model from huggingface
    return pipeline('fill-mask', model='bert-base-uncased')

my_model = train_model()

# Replace `API_KEY` with your SlasML API token. Fetch the api key from https://slashml.com/settings/api-key

model = ModelDeployment(api_key=None)

# deploy model
response = model.deploy(model_name='my_model_3', model=my_model)

# wait for it to be deployed
time.sleep(2)
status = model.status(model_version_id=response.id)

while status.status != 'READY':
    print(f'status: {status.status}')
    print('trying again in 5 seconds')
    time.sleep(5)
    status = model.status(model_version_id=response.id)

    if status.status == 'FAILED':
        raise Exception('Model deployment failed')

# submit prediction
input_text = 'Steve jobs is the [MASK] of Apple.'
prediction = model.predict(model_version_id=response.id, model_input=input_text)
print(prediction)