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Quick Start

Trainer Quick Start

Once we have our FeatureLabelSet calculated and loaded in cluster memory using Featurizer, let's use Trainer to train XGBoost model to predict mid-price 5 seconds ahead, validate the model, tune hyperparams and pick best model

  • Define config 
    xgboost:
  params:
    tree_method: 'approx'
    objective: 'reg:linear'
    eval_metric: [ 'logloss', 'error' ]
  num_boost_rounds: 10
  train_valid_test_split: [0.5, 0.3]
num_workers: 3
tuner_config:
  param_space:
    params:
      max_depth:
        randint:
          lower: 2
          upper: 8
      min_child_weight:
        randint:
          lower: 1
          upper: 10
  num_samples: 8
  metric: 'train-logloss'
  mode: 'min'
max_concurrent_trials: 3

  • Run Trainer

    svoe trainer run --config-path <config-path> --ray-address <addr>

    config = TrainerConfig.load_config(config_path)
trainer_manager = TrainerManager(config=config, ray_address=ray_address)
trainer_manager.run(trainer_run_id='sample-run-id', tags={})

  • Visualize predictions

    svoe trainer predictions --model-uri <model-uri>

  • Select best model

    svoe trainer best-model --metric-name valid-logloss --mode min

    best-model-uri = mlflow_client.get_best_checkpoint_uri(metric_name=metric_name, experiment_name=experiment_name, mode=mode)