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import typer

from src.utils.mlflow_run_decorator import mlflow_run


@mlflow_run
def preprocess_data(input_folder, output_folder):
    ...

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from functools import wraps

import mlflow

from src.constants import PROJECT_EXPERIMENT_NAME


def mlflow_run(wrapped_function):
    @wraps(wrapped_function)
    def wrapper(*args, **kwargs):

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run_pipeline:
	export MLFLOW_RUN_ID=`python src/utils/start_pipeline.py $(RUN_NAME)`; \
	dvc repro

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import mlflow
import typer

from src.constants import PROJECT_ROOT_PATH, PROJECT_EXPERIMENT_NAME


def start_pipeline(run_name):
    mlflow.set_experiment(PROJECT_EXPERIMENT_NAME)
    with mlflow.start_run(run_name=run_name):
        print(mlflow.active_run().info.run_id)

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stages:
  preprocess_data:
    foreach:
      - train
      - val
      - test
    do:
      cmd: python scripts/preprocess_data.py --input-folder datasets/raw/${item} --output-folder datasets/preprocessed/${item}
      deps:
        - scripts/preprocess_data.py

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from pathlib import Path

import tensorflow as tf
from build_big_model import build_big_model

MODEL_NAME = 'big_model'
MODEL_VERSION = '1'

model = build_big_model()
tf.saved_model.save(model, str(Path('models') / MODEL_NAME / MODEL_VERSION))

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import threading
import time
from queue import Empty, Queue

import numpy as np
from flask import Flask, request as flask_request

from build_big_model import build_big_model

BATCH_SIZE = 20

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import numpy as np
from locust import HttpLocust, TaskSet, task, between

INPUT_WIDTH = 10


class PredictTaskSet(TaskSet):
    @task(1)
    def predict(self):
        payload = {

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import numpy as np
from flask import Flask, request

from build_big_model import build_big_model

model = build_big_model()

app = Flask(__name__)

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from tensorflow.keras.layers import Dense
from tensorflow.keras import Model, Input


def build_big_model(
    # small input and output in order to avoid latency due to data transfer through the internet during our tests
    input_width=10,
    output_width=10,
    hidden_layers_width=10**4,
    nb_hidden_layers=200,