def test_read_value_serialized_and_non_value_serialized_hyperparameters():
hyperparameters = {
k: json.dumps(v)
for k, v in SAGEMAKER_HYPERPARAMETERS.items()
}
hyperparameters.update(USER_HYPERPARAMETERS)
test.write_json(hyperparameters, environment.hyperparameters_file_dir)
assert environment.read_hyperparameters() == ALL_HYPERPARAMETERS
def main():
"""Training entry point
"""
hyperparameters = environment.read_hyperparameters()
env = environment.Environment(hyperparameters=hyperparameters)
user_hyperparameters = env.hyperparameters
# If the training job is part of the multiple training jobs for tuning, we need to append the training job name to
# model_dir in case they read from/write to the same object
if "_tuning_objective_metric" in hyperparameters:
model_dir = _model_dir_with_training_job(hyperparameters.get("model_dir"), env.job_name)
logger.info("Appending the training job name to model_dir: {}".format(model_dir))
user_hyperparameters["model_dir"] = model_dir
s3_utils.configure(user_hyperparameters.get("model_dir"), os.environ.get("SAGEMAKER_REGION"))
train(env, mapping.to_cmd_args(user_hyperparameters))
_log_model_missing_warning(MODEL_DIR)
def test_read_exception(loads):
loads.side_effect = ValueError("Unable to read.")
assert environment.read_hyperparameters() == {"a": 1}
def test_read_value_serialized_hyperparameters():
serialized_hps = {k: json.dumps(v) for k, v in ALL_HYPERPARAMETERS.items()}
test.write_json(serialized_hps, environment.hyperparameters_file_dir)
assert environment.read_hyperparameters() == ALL_HYPERPARAMETERS
def test_read_hyperparameters():
test.write_json(ALL_HYPERPARAMETERS, environment.hyperparameters_file_dir)
assert environment.read_hyperparameters() == ALL_HYPERPARAMETERS