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from azureml.core.model import InferenceConfig
inference_config = InferenceConfig(environment=deploy_env,
                                   entry_script="score.py")

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from azureml.core.webservice import AciWebservice, Webservice
from azureml.core.model import Model

aspect_lex = Model(ws, 'aspect_lex')
opinion_lex = Model(ws, 'opinion_lex')    

service = Model.deploy(workspace=ws,
                       name='absa-srvc', 
                       models=[aspect_lex, opinion_lex],
                       inference_config=inference_config, 

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########### Python Form Recognizer Async Analyze #############
import json
import time
from requests import get, post

# Endpoint URL
endpoint = r"<endpoint>"
apim_key = "<subsription key>"
model_id = "<model_id>"
post_url = endpoint + "/formrecognizer/v2.0-preview/custom/models/%s/analyze" % model_id

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import json
import time
import pandas as pd
from requests import get, post

def extract_value(value):
    """
    Helper Method to Extract Cell Value from Response
    """
    if value['type'] == 'number':

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def set_environment_variables_for_nccl_backend(single_node=False, master_port=6105):
    if not single_node:
        master_node_params = os.environ["AZ_BATCH_MASTER_NODE"].split(":")
        os.environ["MASTER_ADDR"] = master_node_params[0]

        # Do not overwrite master port with that defined in AZ_BATCH_MASTER_NODE
        if "MASTER_PORT" not in os.environ:
            os.environ["MASTER_PORT"] = str(master_port)
    else:
        os.environ["MASTER_ADDR"] = os.environ["AZ_BATCHAI_MPI_MASTER_NODE"]

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import os
from azureml.core import ScriptRunConfig, Experiment
from azureml.core.runconfig import MpiConfiguration

cluster = ws.compute_targets[cluster_name]

src = ScriptRunConfig(
    source_directory=source_dir,
    script=script_name,
    arguments=["--max_epochs", 5, "--gpus", 2,  "--num_nodes", 2, "--accelerator", "ddp"],

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class LitModel(pl.LightningModule):
    def __init__(self, encoder: nn.Module, coeff_x: float = 0.2, lr: float = 1e-3)

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def forward(...):
    embeddings = self.encoder(x)

def training_step(...):
    x, y = ...
    z = self.encoder(x)
    pred = self.decoder(z)
    ...

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class DataModule(pl.LightningDataModule):

  def __init__(self, data_dir: str = PATH, batch_size):
    # Initalize 

  def prepare_data(self):
    # Download and Preprocess Raw Data to File System or In Memory

  def setup(self, stage=None):
     # Load, Transform and Split Data 

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! pip install lightning-flash