Deploy instance of a class

Deploy instance of a class as service using servicefoundry

In this guide, we will learn how to deploy an instance of class as a service using servicefoundry.

Before we begin,

  1. You need to have the servicefoundry
    library installed and login using the servicefoundry login command. If you do not have the library installed follow the instructions here.

  2. Select a workspace on the the workspace page. Copy the workspace FQN. We will use the workspace FQN to deploy the service in that workspace.

Writing the class that we will deploy

File Structure:

└── requirements.txt

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

class Model:
    def __init__(self, model_fqn: str):
        self.tokenizer = AutoTokenizer.from_pretrained(model_fqn)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_fqn)

    def infer(self, input_text: str) -> str:
        input_ids = self.tokenizer(input_text, return_tensors="pt").input_ids
        outputs = self.model.generate(input_ids)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)




  • Here we will use the FunctionService class from servicefoundry library to define and deploy the service.
  • We can use the register_class method to deploy the instance of the class.
  • All the public methods of the instance will be deployed as HTTP POST APIs.

    NOTE: The instance properties, attributes, and methods starting with _ (Ex:- def _infer(self,...)) will not be deployed.

  • While deploying, we automatically install the requirements defined in the requirements.txt file.

File Structure:

├── requirements.txt

# with the actual value.
import logging

from servicefoundry.function_service import FunctionService
from servicefoundry import Resources

from inference import Model


service = FunctionService(
    resources=Resources(memory_request=1000, memory_limit=1500),

service.register_class(Model, init_kwargs={"model_fqn": "t5-small"}, name="t5-small")

# NOTE:- You can run the service locally using the code snippet below,

You can deploy using the below command,


NOTE: The above command will only upload the contents of the current directory. Therefore, when you import the class/module for registration, ensure that the import statement is relative to the directory from where you are deploying.

After you deploy the service, you will get an output like below,

INFO:servicefoundry:Method 'infer' from `Model:t5-small` will be deployed on path 'POST /t5-small/infer'.
INFO:servicefoundry:Deploying application 't5-small' to 'v1:local:my-ws-2'
INFO:servicefoundry:Uploading code for service 't5-small'
INFO:servicefoundry:Uploading contents of '/Users/debajyotichatterjee/work/truefoundry-examples/deployment/function/hf_inference_function_deployment'
INFO:servicefoundry:.tfyignore not file found! We recommend you to create .tfyignore file and add file patterns to ignore
INFO:servicefoundry:Deployment started for application 't5-small'. Deployment FQN is 'v1:local:my-ws-2:t5-small:v5'
INFO:servicefoundry:Service 't5-small' will be available at
after successful deployment
INFO:servicefoundry:You can find the application on the dashboard:- ''

You can find the host of the deployed service in the following section in the above logs.

INFO:servicefoundry:Service 't5-small' will be available at
after successful deployment

NOTE: Swagger API documentation will be available on the root path. Click on the host link to open the docs.

You can send requests to the deployed service by using the code snippet below. Pass the host using the --host command line argument.

File Structure:

├── requirements.txt

import argparse
from urllib.parse import urljoin

import requests

parser = argparse.ArgumentParser()
parser.add_argument("--host", required=True, type=str)
args = parser.parse_args()

response =
    urljoin(, "/t5-small/infer"),
    json={"input_text": "translate English to German: Hello world."},