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Stochastic Acceleration Platform aims to simplify the life cycle of a Deep Learning model. From uploading and versioning the model, through training, compression and acceleration to putting it into production.
This example goes over how to use LangChain to interact with StochasticAI models. Get the API_KEY and API_URL from your Stochastic AI workspace profile settings. 
from getpass import getpass

STOCHASTICAI_API_KEY = getpass()

 ········

import os

os.environ["STOCHASTICAI_API_KEY"] = STOCHASTICAI_API_KEY

YOUR_API_URL = getpass()

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from langchain_classic.chains import LLMChain
from langchain_community.llms import StochasticAI
from langchain_core.prompts import PromptTemplate

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

llm = StochasticAI(api_url=YOUR_API_URL)

llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain.run(question)

"\n\nStep 1: In 1999, the St. Louis Rams won the Super Bowl.\n\nStep 2: In 1999, Beiber was born.\n\nStep 3: The Rams were in Los Angeles at the time.\n\nStep 4: So they didn't play in the Super Bowl that year.\n"
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