BM2304 / nd0821-c2-build-model-workflow-starter

Starter Code for the Course 2 project of the Udacity ML DevOps Nanodegree Program

ML Pipeline for Short-Term Rental Prices in NYC

End-to-end ml pipeline for estimating the typical price for a given property based on the price of similar properties in NYC.

Link to Github: https://github.com/BM2304/nd0821-c2-build-model-workflow-starter Link to W&B: https://wandb.ai/bm23/nyc_airbnb

Create environment

Make sure to have conda installed and ready, then create a new environment using the environment.yml file provided in the root of the repository and activate it:

> conda env create -f environment.yml
> conda activate nyc_airbnb_dev

Get API key for Weights and Biases

Let's make sure we are logged in to Weights & Biases. Get your API key from W&B by going to https://wandb.ai/authorize and click on the + icon (copy to clipboard), then paste your key into this command:

> wandb login [your API key]

You should see a message similar to:

wandb: Appending key for api.wandb.ai to your netrc file: /home/[your username]/.netrc

The configuration

As usual, the parameters controlling the pipeline are defined in the config.yaml file defined in the root of the starter kit.

Running the entire pipeline or just a selection of steps

In order to run the pipeline when you are developing, you need to be in the root of the starter kit, then you can execute as usual:

>  mlflow run .

This will run the entire pipeline.

When developing it is useful to be able to run one step at the time. Say you want to run only the download step. The main.py is written so that the steps are defined at the top of the file, in the _steps list, and can be selected by using the steps parameter on the command line:

> mlflow run . -P steps=download

If you want to run the download and the basic_cleaning steps, you can similarly do:

> mlflow run . -P steps=download,basic_cleaning

You can override any other parameter in the configuration file using the Hydra syntax, by providing it as a hydra_options parameter. For example, say that we want to set the parameter modeling -> random_forest -> n_estimators to 10 and etl->min_price to 50:

> mlflow run . \
  -P steps=download,basic_cleaning \
  -P hydra_options="modeling.random_forest.n_estimators=10 etl.min_price=50"

• download: downloads the data (get_data). MLproject
• basic_cleaning: remove outliers and null values MLproject
• data_check: run data checks like distribution and expected columns MLproject
• data_split: segrgate the data (splits the data) MLproject
• train_random_forest: train random forest and upload fitted model MLproject
• test_regression_model: test a trained model on new test data MLproject

Train the model on a new data sample

> mlflow run https://github.com/BM2304/nd0821-c2-build-model-workflow-starter.git \
             -v [the version you want to use, like 1.0.1] \
             -P hydra_options="etl.sample='sample2.csv'"

License

License