Custom Container Propensity model training in Vertex AI.

This is a barebones solution to run model training in Vertex AI with training data either with a BQ table or a Vertex Managed Dataset.

The Python module syntax is:

python -m vertex_proptrainer.train --segment <SEGMENT> --dataset <DATASET>

where

• "SEGMENT" is the propensity segment to be trained
• "DATASET" is the BQ table with data for training and evaluation

Alternatively, if the dataset is already split into train and eval:

python -m vertex_proptrainer.train --segment <SEGMENT> --train_data <TRAIN> --eval_data <EVAL>

If training with Vertex Managed Dataset in Vertex Kubeflow Pipelines, dataset is passed as an argument directly to CustomContainerTrainingJobRunOp and neither of dataset, train_data or eval_data args is passed allowing the trainer package to pick the data from default environment variables - os.environ["AIP_TRAINING_DATA_URI"], os.environ["AIP_EVALUATION_DATA_URI"], and os.environ["AIP_TESTING_DATA_URI"] - set by Vertex AI.

• --dataset: BQ dataset with data for training and testing
• --max_depth: Maximum depth of the base learners
• --min_child_weight: Minimum sum of instance weight in a child tree
• --max_delta_step: Maximum delta step allowed for each tree's weight estimation
• --reg_lambda: L2 regularization
• --reg_alpha: L1 regularization
• --gamma: Pruning strength
• --lr: Learning rate

• --loss: Model loss function ('hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron')
• --penalty: Regularization term ('l2', 'l1', 'elasticnet')
• --l1_ratio: L1 & L2 Regularization mixing parameter if penalty is elasticnet
• --alpha: Regularization strength
• --tol: Stopping criterion (loss > best_loss - tol)
• --epsilon: Measure of epsilon-insensitive loss if loss is 'huber', 'epsilon_insensitive' or 'squared_epsilon_insensitive'
• --linear_lr: Learning rate schedule ('constant', 'optimal', 'invscaling', 'adaptive')
• --eta0: Initial learning rate. Not used if learning rate schedule is 'optimal'
• --no_intercept: Fit model without an intercept

Tuning (using cloudml-hypertune package) is enabled by passing the "--hypertune" argument:

python -m vertex_proptrainer.train --segment=<SEGMENT> --dataset <DATASET> --hypertune

Tuning does not save artifacts at this time; functionality to be added in subsequent release(s) to allow checkpointing, training resumption and tuning artifact saving to GCS.

• --rounds: Number of rounds without evaluation improvement to trigger early stopping

• --model_dir: GCS URI to save model artifacts. If not provided, package will use Vertex AI default environment variable os.environ["AIP_MODEL_DIR"], and, if provided, must end with "/model" i.e.
  ... --model_dir gs://<bucket>/<gcs_path>/model

Build package within src folder:

cd src
python setup.py sdist OR python setup.py sdist bdist_wheel