Open source MLOps stack

# valohai.yaml
---
- step:
    name: ingest_data
    image: python:3.9
    command:
      - python ingest_data.py
    outputs:
      - name: dataset
        path: dataset.csv

- step:
    name: train_model
    image: python:3.9
    command:
      - python train_model.py
    inputs:
      - name: dataset
        default: datum://ingest_data/dataset.csv
    outputs:
      - name: model
        path: model.pkl

- step:
    name: evaluate_model
    image: python:3.9
    command:
      - python evaluate_model.py
    inputs:
      - name: dataset
        default: datum://ingest_data/dataset.csv
      - name: model
        default: datum://train_model/model.pkl

# ingest_data.py
import pandas as pd

def ingest_data():
    df = pd.read_csv("data/dataset.csv")
    df.to_csv("/valohai/outputs/dataset.csv", index=False)

if __name__ == "__main__":
    ingest_data()

# train_model.py
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
import pickle

def train_model():
    df = pd.read_csv("/valohai/inputs/dataset.csv")
    X, y = df.drop("target", axis=1), df["target"]
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X, y)
    with open("/valohai/outputs/model.pkl", "wb") as f:
        pickle.dump(model, f)

if __name__ == "__main__":
    train_model()

# evaluate_model.py
import pandas as pd
from sklearn.metrics import mean_squared_error
import pickle

def evaluate_model():
    df = pd.read_csv("/valohai/inputs/dataset.csv")
    with open("/valohai/inputs/model.pkl", "rb") as f:
        model = pickle.load(f)
    X, y = df.drop("target", axis=1), df["target"]
    rmse = mean_squared_error(y, model.predict(X)) ** 0.5
    print(f"RMSE: {rmse}")

if __name__ == "__main__":
    evaluate_model()

# To run the pipeline:
# valohai execution run --adhoc ingest_data
# valohai execution run --adhoc train_model
# valohai execution run --adhoc evaluate_model