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Homework

Solution: https://www.loom.com/share/802c8c0b843a4d3bbd9dbea240c3593a

The goal of this homework is to create a simple training pipeline, use mlflow to track experiments and register best model, but use Mage for it.

We'll use the same NYC taxi dataset, the Yellow taxi data for March, 2023.

Question 1. Run Mage

First, let's run Mage with Docker Compose. Follow the quick start guideline.

What's the version of Mage we run?

(You can see it in the UI)

Question 2. Creating a project

Now let's create a new project. We can call it "homework_03", for example.

How many lines are in the created metadata.yaml file?

  • 35
  • 45
  • 55
  • 65

Question 3. Creating a pipeline

Let's create an ingestion code block.

In this block, we will read the March 2023 Yellow taxi trips data.

How many records did we load?

  • 3,003,766
  • 3,203,766
  • 3,403,766
  • 3,603,766

Question 4. Data preparation

Let's use the same logic for preparing the data we used previously. We will need to create a transformer code block and put this code there.

This is what we used (adjusted for yellow dataset):

def read_dataframe(filename):
    df = pd.read_parquet(filename)

    df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime)
    df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime)

    df['duration'] = df.tpep_dropoff_datetime - df.tpep_pickup_datetime
    df.duration = df.duration.dt.total_seconds() / 60

    df = df[(df.duration >= 1) & (df.duration <= 60)]

    categorical = ['PULocationID', 'DOLocationID']
    df[categorical] = df[categorical].astype(str)
    
    return df

Let's adjust it and apply to the data we loaded in question 3.

What's the size of the result?

  • 2,903,766
  • 3,103,766
  • 3,316,216
  • 3,503,766

Question 5. Train a model

We will now train a linear regression model using the same code as in homework 1.

  • Fit a dict vectorizer.
  • Train a linear regression with default parameters.
  • Use pick up and drop off locations separately, don't create a combination feature.

Let's now use it in the pipeline. We will need to create another transformation block, and return both the dict vectorizer and the model.

What's the intercept of the model?

Hint: print the intercept_ field in the code block

  • 21.77
  • 24.77
  • 27.77
  • 31.77

Question 6. Register the model

The model is trained, so let's save it with MLFlow.

If you run mage with docker-compose, stop it with Ctrl+C or

docker-compose down

Let's create a dockerfile for mlflow, e.g. mlflow.dockerfile:

FROM python:3.10-slim

RUN pip install mlflow==2.12.1

EXPOSE 5000

CMD [ \
    "mlflow", "server", \
    "--backend-store-uri", "sqlite:///home/mlflow_data/mlflow.db", \
    "--host", "0.0.0.0", \
    "--port", "5000" \
]

And add it to the docker-compose.yaml:

  mlflow:
    build:
      context: .
      dockerfile: mlflow.dockerfile
    ports:
      - "5000:5000"
    volumes:
      - "${PWD}/mlflow_data:/home/mlflow_data/"
    networks:
      - app-network

Note that app-network is the same network as for mage and postgres containers. If you use a different compose file, adjust it.

We should already have mlflow==2.12.1 in requirements.txt in the mage project we created for the module. If you're starting from scratch, add it to your requirements.

Next, start the compose again and create a data exporter block.

In the block, we

  • Log the model (linear regression)
  • Save and log the artifact (dict vectorizer)

If you used the suggested docker-compose snippet, mlflow should be accessible at http://mlflow:5000.

Find the logged model, and find MLModel file. What's the size of the model? (model_size_bytes field):

  • 14,534
  • 9,534
  • 4,534
  • 1,534

Note: typically we do last two steps in one code block.

Submit the results