model_search

Getting Started

Let us start with the simplest case. You have a csv file where the features are numbers and you would like to run let AutoML find the best model architecture for you.

Below is a code snippet for doing so:

import model_search
from model_search import constants
from model_search import single_trainer
from model_search.data import csv_data

trainer = single_trainer.SingleTrainer(
    data=csv_data.Provider(
        label_index=0,
        logits_dimension=2,
        record_defaults=[0, 0, 0, 0],
        filename="model_search/data/testdata/csv_random_data.csv"),
    spec=constants.DEFAULT_DNN)

trainer.try_models(
    number_models=200,
    train_steps=1000,
    eval_steps=100,
    root_dir="/tmp/run_example",
    batch_size=32,
    experiment_name="example",
    experiment_owner="model_search_user")

The above code will try 200 different models - all binary classification models, as the logits_dimension is 2. The root directory will have a subdirectory of all models, all of which will be already evaluated. You can open the directory with tensorboard and see all the models with the evaluation metrics.

The search will be performed according to the default specification. That can be found in: model_search/configs/dnn_config.pbtxt.

For more details about the fields and if you want to create your own specification, you can look at: model_search/proto/phoenix_spec.proto.