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main.py
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main.py
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import json
import mlflow
import tempfile
import os
import wandb
import hydra
from omegaconf import DictConfig
_steps = [
"download",
"basic_cleaning",
"data_check",
"data_split",
"train_random_forest",
# NOTE: We do not include this in the steps so it is not run by mistake.
# You first need to promote a model export to "prod" before you can run this,
# then you need to run this step explicitly
"test_regression_model"
]
# This automatically reads in the configuration
@hydra.main(config_name='config')
def go(config: DictConfig):
# Setup the wandb experiment. All runs will be grouped under this name
os.environ["WANDB_PROJECT"] = config["main"]["project_name"]
os.environ["WANDB_RUN_GROUP"] = config["main"]["experiment_name"]
# Steps to execute
steps_par = config['main']['steps']
active_steps = steps_par.split(",") if steps_par != "all" else _steps
# Move to a temporary directory
with tempfile.TemporaryDirectory() as tmp_dir:
if "download" in active_steps:
_ = mlflow.run(
f"{config['main']['components_repository']}/get_data",
"main",
version="main",
parameters={
"sample": config["etl"]["sample"],
"artifact_name": "sample.csv",
"artifact_type": "raw_data",
"artifact_description": "Raw file as downloaded"
}
)
if "basic_cleaning" in active_steps:
_ = mlflow.run(
os.path.join(hydra.utils.get_original_cwd(), "src", "basic_cleaning"),
"main",
parameters={
"input_artifact": "sample.csv:latest",
"output_artifact": "clean_sample.csv",
"output_type": "clean_sample",
"output_description": "Data with outliers and null values removed",
"min_price": config['etl']['min_price'],
"max_price": config['etl']['max_price']
}
)
if "data_check" in active_steps:
_ = mlflow.run(
os.path.join(hydra.utils.get_original_cwd(), "src", "data_check"),
"main",
parameters={
"csv": "clean_sample.csv:latest",
"ref": "clean_sample.csv:reference",
"kl_threshold": config["data_check"]["kl_threshold"],
"min_price": config['etl']['min_price'],
"max_price": config['etl']['max_price']
}
)
if "data_split" in active_steps:
_ = mlflow.run(
f"{config['main']['components_repository']}/train_val_test_split",
"main",
#version="main",
parameters={
"input": "clean_sample.csv:latest",
"test_size": config['modeling']['test_size'],
"random_seed": config['modeling']['random_seed'],
"stratify_by": config['modeling']['stratify_by']
}
)
if "train_random_forest" in active_steps:
# serialize the random forest configuration into JSON
rf_config = os.path.abspath("rf_config.json")
with open(rf_config, "w+") as fp:
json.dump(dict(config["modeling"]["random_forest"].items()), fp) # DO NOT MODIFY
# use rf_config as rf_config parameter in train_random_forest
_ = mlflow.run(
os.path.join(hydra.utils.get_original_cwd(), "src", "train_random_forest"),
"main",
parameters={
"trainval_artifact": "trainval_data.csv:latest",
"val_size": config['modeling']['val_size'],
"random_seed": config['modeling']['random_seed'],
"stratify_by": config['modeling']['stratify_by'],
"rf_config": rf_config,
"max_tfidf_features": config['modeling']['max_tfidf_features'],
"output_artifact": "random_forest_export"
}
)
if "test_regression_model" in active_steps:
# This step is not run in the pipeline, me need to execute it manually:
_ = mlflow.run(
f"{config['main']['components_repository']}/test_regression_model",
"main",
parameters={
"mlflow_model": "random_forest_export:prod",
"test_dataset": "test_data.csv:latest"
}
)
if __name__ == "__main__":
go()