def test_client_logs_expected_run_data():
client = MlflowAutologgingQueueingClient()
params_to_log = {
"param_key_{}".format(i): "param_val_{}".format(i)
for i in range((2 * MAX_PARAMS_TAGS_PER_BATCH) + 1)
}
tags_to_log = {
"tag_key_{}".format(i): "tag_val_{}".format(i)
for i in range((2 * MAX_PARAMS_TAGS_PER_BATCH) + 1)
}
metrics_to_log = {
"metric_key_{}".format(i): i
for i in range((4 * MAX_METRICS_PER_BATCH) + 1)
}
with mlflow.start_run() as run:
client.log_params(run_id=run.info.run_id, params=params_to_log)
client.set_tags(run_id=run.info.run_id, tags=tags_to_log)
client.log_metrics(run_id=run.info.run_id, metrics=metrics_to_log)
client.flush()
run_params, run_metrics, run_tags = get_run_data(run.info.run_id)
assert run_params == params_to_log
assert run_metrics == metrics_to_log
assert run_tags == tags_to_log
def test_client_truncates_tag_keys_and_values():
client = MlflowAutologgingQueueingClient()
tags_to_log = {
"a" * (MAX_ENTITY_KEY_LENGTH + 5): "b" * (MAX_PARAM_VAL_LENGTH + 5),
"c" * (MAX_ENTITY_KEY_LENGTH + 50): "d" * (MAX_PARAM_VAL_LENGTH + 50),
}
with mlflow.start_run() as run:
client.set_tags(run_id=run.info.run_id, tags=tags_to_log)
client.flush()
run_tags = get_run_data(run.info.run_id)[2]
assert run_tags == _truncate_dict(
tags_to_log, max_key_length=MAX_ENTITY_KEY_LENGTH, max_value_length=MAX_TAG_VAL_LENGTH,
)