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80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
# Tests the following end-to-end:
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#
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# 1. Comet is imported
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# 2. Conflicting modules (i.e., TensorFlow) are not imported
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# 3. Overridden methods are called (train_init, train_model, etc.) and run without error
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#
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# This test runs in an isolated environment to ensure TensorFlow imports are not leaked
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# from previous tests.
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import argparse
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import os
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import sys
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import tempfile
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from unittest.mock import Mock, patch
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# Comet must be imported before the libraries it wraps
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import comet_ml # noqa
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from ludwig.api import LudwigModel
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from ludwig.constants import BATCH_SIZE, TRAINER
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from ludwig.contribs.comet import CometCallback
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# Bad key will ensure Comet is initialized, but nothing is uploaded externally.
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os.environ["COMET_API_KEY"] = "key"
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# Add tests dir to the import path
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PATH_HERE = os.path.abspath(os.path.dirname(__file__))
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PATH_ROOT = os.path.join(PATH_HERE, "..", "..", "..")
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sys.path.insert(0, os.path.abspath(PATH_ROOT))
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from tests.integration_tests.utils import category_feature, generate_data, image_feature # noqa
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parser = argparse.ArgumentParser()
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parser.add_argument("--csv-filename", required=True)
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def run(csv_filename):
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with tempfile.TemporaryDirectory() as tmpdir:
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# Image Inputs
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image_dest_folder = os.path.join(tmpdir, "generated_images")
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# Inputs & Outputs
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input_features = [image_feature(folder=image_dest_folder)]
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output_features = [category_feature(output_feature=True)]
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data_csv = generate_data(input_features, output_features, csv_filename)
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config = {
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"input_features": input_features,
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"output_features": output_features,
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"combiner": {"type": "concat", "output_size": 14},
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TRAINER: {"epochs": 2, BATCH_SIZE: 128},
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}
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callback = CometCallback()
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model = LudwigModel(config, callbacks=[callback])
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# Wrap these methods so we can check that they were called
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callback.on_train_init = Mock(side_effect=callback.on_train_init)
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callback.on_train_start = Mock(side_effect=callback.on_train_start)
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with patch("comet_ml.Experiment.log_asset_data") as mock_log_asset_data:
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# Training with csv
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_, _, _ = model.train(dataset=data_csv, output_directory=os.path.join(tmpdir, "output"))
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model.predict(dataset=data_csv)
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# Verify that the experiment was created successfully
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assert callback.cometml_experiment is not None
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# Check that these methods were called at least once
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callback.on_train_init.assert_called()
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callback.on_train_start.assert_called()
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# Check that we ran `train_model`, which calls into `log_assert_data`, successfully
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mock_log_asset_data.assert_called()
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if __name__ == "__main__":
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args = parser.parse_args()
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run(args.csv_filename)
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