def execute_task(task_module, task_name, inputs, output_prefix, test):
with _TemporaryConfiguration(_internal_config.CONFIGURATION_PATH.get()):
with _utils.AutoDeletingTempDir('input_dir') as input_dir:
# Load user code
task_module = _importlib.import_module(task_module)
task_def = getattr(task_module, task_name)
if not test:
local_inputs_file = input_dir.get_named_tempfile('inputs.pb')
# Handle inputs/outputs for array job.
if _os.environ.get('BATCH_JOB_ARRAY_INDEX_VAR_NAME'):
job_index = _compute_array_job_index()
# TODO: Perhaps remove. This is a workaround to an issue we perceived with limited entropy in
# TODO: AWS batch array jobs.
_flyte_random.seed_flyte_random("{} {} {}".format(
_random.random(), _datetime.datetime.utcnow(),
job_index))
# If an ArrayTask is discoverable, the original job index may be different than the one specified in
# the environment variable. Look up the correct input/outputs in the index lookup mapping file.
job_index = _map_job_index_to_child_index(
input_dir, inputs, job_index)
inputs = _os.path.join(inputs, str(job_index), 'inputs.pb')
output_prefix = _os.path.join(output_prefix,
str(job_index))
_data_proxy.Data.get_data(inputs, local_inputs_file)
input_proto = _utils.load_proto_from_file(
_literals_pb2.LiteralMap, local_inputs_file)
_engine_loader.get_engine().get_task(task_def).execute(
_literal_models.LiteralMap.from_flyte_idl(input_proto),
context={'output_prefix': output_prefix})
def test_isolated_random_state():
random.seed_flyte_random("abc")
r = random.random.random()
global_random.seed("abc")
assert random.random.random() != r
def test_seed():
random.seed_flyte_random("abc")
r = random.random.random()
random.seed_flyte_random("abc")
assert r == random.random.random()
