def test_failed_and_resumed_workflow(workflow_start_regular, tmp_path):
workflow_id = "simple"
error_flag = tmp_path / "error"
error_flag.touch()
@ray.remote
def simple():
if error_flag.exists():
raise ValueError()
return 0
with pytest.raises(workflow.WorkflowExecutionError):
workflow.run(simple.bind(), workflow_id=workflow_id)
workflow_metadata_failed = workflow.get_metadata(workflow_id)
assert workflow_metadata_failed["status"] == "FAILED"
error_flag.unlink()
assert workflow.resume(workflow_id) == 0
workflow_metadata_resumed = workflow.get_metadata(workflow_id)
assert workflow_metadata_resumed["status"] == "SUCCESSFUL"
# make sure resume updated running metrics
assert (workflow_metadata_resumed["stats"]["start_time"] >
workflow_metadata_failed["stats"]["start_time"])
assert (workflow_metadata_resumed["stats"]["end_time"] >
workflow_metadata_failed["stats"]["end_time"])
def test_nested_workflow(workflow_start_regular):
@workflow.options(name="inner", metadata={"inner_k": "inner_v"})
@ray.remote
def inner():
time.sleep(2)
return 10
@workflow.options(name="outer", metadata={"outer_k": "outer_v"})
@ray.remote
def outer():
time.sleep(2)
return workflow.continuation(inner.bind())
workflow.run(outer.bind(),
workflow_id="nested",
metadata={"workflow_k": "workflow_v"})
workflow_metadata = workflow.get_metadata("nested")
outer_step_metadata = workflow.get_metadata("nested", "outer")
inner_step_metadata = workflow.get_metadata("nested", "inner")
assert workflow_metadata["user_metadata"] == {"workflow_k": "workflow_v"}
assert outer_step_metadata["user_metadata"] == {"outer_k": "outer_v"}
assert inner_step_metadata["user_metadata"] == {"inner_k": "inner_v"}
assert (workflow_metadata["stats"]["end_time"] >=
workflow_metadata["stats"]["start_time"] + 4)
assert (outer_step_metadata["stats"]["end_time"] >=
outer_step_metadata["stats"]["start_time"] + 2)
assert (inner_step_metadata["stats"]["end_time"] >=
inner_step_metadata["stats"]["start_time"] + 2)
assert (inner_step_metadata["stats"]["start_time"] >=
outer_step_metadata["stats"]["end_time"])
def test_recovery_simple_3(workflow_start_regular):
@ray.remote
def append1(x):
return x + "[append1]"
@ray.remote
def append2(x):
return x + "[append2]"
@ray.remote
def simple(x):
x = append1.bind(x)
y = the_failed_step.bind(x)
z = append2.bind(y)
return workflow.continuation(z)
utils.unset_global_mark()
workflow_id = "test_recovery_simple_3"
with pytest.raises(workflow.WorkflowExecutionError):
# internally we get WorkerCrashedError
workflow.run(simple.bind("x"), workflow_id=workflow_id)
assert workflow.get_status(workflow_id) == workflow.WorkflowStatus.FAILED
utils.set_global_mark()
assert workflow.resume(workflow_id) == "foo(x[append1])[append2]"
utils.unset_global_mark()
# resume from workflow output checkpoint
assert workflow.resume(workflow_id) == "foo(x[append1])[append2]"
def test_successful_workflow(workflow_start_regular):
user_step_metadata = {"k1": "v1"}
user_run_metadata = {"k2": "v2"}
step_name = "simple_step"
workflow_id = "simple"
@workflow.options(name=step_name, metadata=user_step_metadata)
@ray.remote
def simple():
time.sleep(2)
return 0
workflow.run(simple.bind(),
workflow_id=workflow_id,
metadata=user_run_metadata)
workflow_metadata = workflow.get_metadata("simple")
assert workflow_metadata["status"] == "SUCCESSFUL"
assert workflow_metadata["user_metadata"] == user_run_metadata
assert "start_time" in workflow_metadata["stats"]
assert "end_time" in workflow_metadata["stats"]
assert (workflow_metadata["stats"]["end_time"] >=
workflow_metadata["stats"]["start_time"] + 2)
step_metadata = workflow.get_metadata("simple", "simple_step")
assert step_metadata["user_metadata"] == user_step_metadata
assert "start_time" in step_metadata["stats"]
assert "end_time" in step_metadata["stats"]
assert (step_metadata["stats"]["end_time"] >=
step_metadata["stats"]["start_time"] + 2)
def test_dedupe_serialization(workflow_start_regular_shared):
@ray.remote(num_cpus=0)
class Counter:
def __init__(self):
self.count = 0
def incr(self):
self.count += 1
def get_count(self):
return self.count
counter = Counter.remote()
class CustomClass:
def __getstate__(self):
# Count the number of times this class is serialized.
ray.get(counter.incr.remote())
return {}
ref = ray.put(CustomClass())
list_of_refs = [ref for _ in range(2)]
# One for the ray.put
assert ray.get(counter.get_count.remote()) == 1
single = identity.bind((ref, ))
double = identity.bind(list_of_refs)
workflow.run(gather.bind(single, double))
# One more for hashing the ref, and for uploading.
assert ray.get(counter.get_count.remote()) == 3
def test_nested_catch_exception_3(workflow_start_regular_shared, tmp_path):
"""Test the case where the exception is not raised by the output task of
a nested DAG."""
@ray.remote
def f3():
return 10
@ray.remote
def f3_exc():
raise ValueError()
@ray.remote
def f2(x):
return x
@ray.remote
def f1(exc):
if exc:
return workflow.continuation(f2.bind(f3_exc.bind()))
else:
return workflow.continuation(f2.bind(f3.bind()))
ret, err = workflow.run(
f1.options(**workflow.options(catch_exceptions=True)).bind(True)
)
assert ret is None
assert isinstance(err, ValueError)
assert (10, None) == workflow.run(
f1.options(**workflow.options(catch_exceptions=True)).bind(False)
)
def test_dedupe_indirect(workflow_start_regular_shared, tmp_path):
counter = Path(tmp_path) / "counter.txt"
lock = Path(tmp_path) / "lock.txt"
counter.write_text("0")
@ray.remote
def incr():
with FileLock(str(lock)):
c = int(counter.read_text())
c += 1
counter.write_text(f"{c}")
@ray.remote
def identity(a):
return a
@ray.remote
def join(*a):
return counter.read_text()
# Here a is passed to two steps and we need to ensure
# it's only executed once
a = incr.bind()
i1 = identity.bind(a)
i2 = identity.bind(a)
assert "1" == workflow.run(join.bind(i1, i2))
assert "2" == workflow.run(join.bind(i1, i2))
# pass a multiple times
assert "3" == workflow.run(join.bind(a, a, a, a))
assert "4" == workflow.run(join.bind(a, a, a, a))
def test_get_output_3(workflow_start_regular, tmp_path):
cnt_file = tmp_path / "counter"
cnt_file.write_text("0")
error_flag = tmp_path / "error"
error_flag.touch()
@ray.remote
def incr():
v = int(cnt_file.read_text())
cnt_file.write_text(str(v + 1))
if error_flag.exists():
raise ValueError()
return 10
with pytest.raises(workflow.WorkflowExecutionError):
workflow.run(incr.options(max_retries=0).bind(), workflow_id="incr")
assert cnt_file.read_text() == "1"
from ray.exceptions import RaySystemError
# TODO(suquark): We should prevent Ray from raising "RaySystemError",
# in workflow, because "RaySystemError" does not inherit the underlying
# error, so users and developers cannot catch the expected error.
# I feel this issue is a very annoying.
with pytest.raises((RaySystemError, ValueError)):
workflow.get_output("incr")
assert cnt_file.read_text() == "1"
error_flag.unlink()
with pytest.raises((RaySystemError, ValueError)):
workflow.get_output("incr")
assert workflow.resume("incr") == 10
def test_dynamic_output(workflow_start_regular_shared):
@ray.remote
def exponential_fail(k, n):
if n > 0:
if n < 3:
raise Exception("Failed intentionally")
return workflow.continuation(
exponential_fail.options(**workflow.options(
name=f"step_{n}")).bind(k * 2, n - 1))
return k
# When workflow fails, the dynamic output should points to the
# latest successful step.
try:
workflow.run(
exponential_fail.options(**workflow.options(name="step_0")).bind(
3, 10),
workflow_id="dynamic_output",
)
except Exception:
pass
from ray.workflow.workflow_storage import get_workflow_storage
wf_storage = get_workflow_storage(workflow_id="dynamic_output")
result = wf_storage.inspect_step("step_0")
assert result.output_step_id == "step_3"
def test_resume_different_storage(shutdown_only, tmp_path):
@ray.remote
def constant():
return 31416
ray.init(storage=str(tmp_path))
workflow.init()
workflow.run(constant.bind(), workflow_id="const")
assert workflow.resume(workflow_id="const") == 31416
def test_sleep_checkpointing(workflow_start_regular_shared):
"""Test that the workflow sleep only starts after `run` not when the step is
defined."""
sleep_step = workflow.sleep(2)
time.sleep(2)
start_time = time.time()
workflow.run(sleep_step)
end_time = time.time()
duration = end_time - start_time
assert 1 < duration
def test_user_metadata_not_dict(workflow_start_regular):
@ray.remote
def simple():
return 0
with pytest.raises(ValueError):
workflow.run_async(
simple.options(**workflow.options(metadata="x")).bind())
with pytest.raises(ValueError):
workflow.run(simple.bind(), metadata="x")
def test_user_metadata_not_json_serializable(workflow_start_regular):
@ray.remote
def simple():
return 0
class X:
pass
with pytest.raises(ValueError):
workflow.run_async(
simple.options(**workflow.options(metadata={"x": X()})).bind())
with pytest.raises(ValueError):
workflow.run(simple.bind(), metadata={"x": X()})
def test_recovery_simple_1(workflow_start_regular):
utils.unset_global_mark()
workflow_id = "test_recovery_simple_1"
with pytest.raises(workflow.WorkflowExecutionError):
# internally we get WorkerCrashedError
workflow.run(the_failed_step.bind("x"), workflow_id=workflow_id)
assert workflow.get_status(workflow_id) == workflow.WorkflowStatus.FAILED
utils.set_global_mark()
assert workflow.resume(workflow_id) == "foo(x)"
utils.unset_global_mark()
# resume from workflow output checkpoint
assert workflow.resume(workflow_id) == "foo(x)"
def test_dynamic_workflow_ref(workflow_start_regular_shared):
@ray.remote
def incr(x):
return x + 1
# This test also shows different "style" of running workflows.
assert workflow.run(incr.bind(0), workflow_id="test_dynamic_workflow_ref") == 1
# Without rerun, it'll just return the previous result
assert (
workflow.run(
incr.bind(WorkflowRef("incr")), workflow_id="test_dynamic_workflow_ref"
)
== 1
)
def test_wf_run(workflow_start_regular_shared, tmp_path):
counter = tmp_path / "counter"
counter.write_text("0")
@ray.remote
def f():
v = int(counter.read_text()) + 1
counter.write_text(str(v))
workflow.run(f.bind(), workflow_id="abc")
assert counter.read_text() == "1"
# This will not rerun the job from beginning
workflow.run(f.bind(), workflow_id="abc")
assert counter.read_text() == "1"
def test_wf_no_run(shutdown_only):
# workflow should be able to run without explicit init
ray.shutdown()
@ray.remote
def f1():
pass
f1.bind()
@ray.remote
def f2(*w):
pass
workflow.run(f2.bind(*[f1.bind() for _ in range(10)]))
def test_user_metadata_empty(workflow_start_regular):
step_name = "simple_step"
workflow_id = "simple"
@workflow.options(name=step_name)
@ray.remote
def simple():
return 0
workflow.run(simple.bind(), workflow_id=workflow_id)
assert workflow.get_metadata("simple")["user_metadata"] == {}
assert workflow.get_metadata("simple",
"simple_step")["user_metadata"] == {}
def test_dedupe_download_raw_ref(workflow_start_regular):
with tempfile.TemporaryDirectory() as temp_dir:
debug_store = DebugStorage(temp_dir)
utils._alter_storage(debug_store)
ref = ray.put("hello")
workflows = [identity.bind(ref) for _ in range(100)]
workflow.run(gather.bind(*workflows))
ops = debug_store._logged_storage.get_op_counter()
get_objects_count = 0
for key in ops["get"]:
if "objects" in key:
get_objects_count += 1
assert get_objects_count == 1
def test_partial(workflow_start_regular_shared):
ys = [1, 2, 3]
def add(x, y):
return x + y
from functools import partial
f1 = workflow.step(partial(add, 10)).step(10)
assert "__anonymous_func__" in f1._name
assert f1.run() == 20
fs = [partial(add, y=y) for y in ys]
@ray.remote
def chain_func(*args, **kw_argv):
# Get the first function as a start
wf_step = workflow.step(fs[0]).step(*args, **kw_argv)
for i in range(1, len(fs)):
# Convert each function inside steps into workflow step
# function and then use the previous output as the input
# for them.
wf_step = workflow.step(fs[i]).step(wf_step)
return wf_step
assert workflow.run(chain_func.bind(1)) == 7
def test_nested_workflow_no_download(workflow_start_regular):
"""Test that we _only_ load from storage on recovery. For a nested workflow
step, we should checkpoint the input/output, but continue to reuse the
in-memory value.
"""
@ray.remote
def recursive(ref, count):
if count == 0:
return ref
return workflow.continuation(recursive.bind(ref, count - 1))
with tempfile.TemporaryDirectory() as temp_dir:
debug_store = DebugStorage(temp_dir)
utils._alter_storage(debug_store)
ref = ray.put("hello")
result = workflow.run(recursive.bind([ref], 10))
ops = debug_store._logged_storage.get_op_counter()
get_objects_count = 0
for key in ops["get"]:
if "objects" in key:
get_objects_count += 1
assert get_objects_count == 1, "We should only get once when resuming."
put_objects_count = 0
for key in ops["put"]:
if "objects" in key:
print(key)
put_objects_count += 1
assert (
put_objects_count == 1
), "We should detect the object exists before uploading"
assert ray.get(result) == ["hello"]
def test_get_output_1(workflow_start_regular, tmp_path):
@ray.remote
def simple(v):
return v
assert 0 == workflow.run(simple.bind(0), workflow_id="simple")
assert 0 == workflow.get_output("simple")
def test_event_during_arg_resolution(workflow_start_regular_shared):
"""If a workflow's arguments are being executed when the event occurs, the
workflow should run immediately with no issues.
"""
class MyEventListener(workflow.EventListener):
async def poll_for_event(self):
while not utils.check_global_mark():
await asyncio.sleep(0.1)
utils.set_global_mark("event_returning")
@ray.remote
def triggers_event():
utils.set_global_mark()
while not utils.check_global_mark("event_returning"):
time.sleep(0.1)
@ray.remote
def gather(*args):
return args
event_promise = workflow.wait_for_event(MyEventListener)
assert workflow.run(gather.bind(event_promise, triggers_event.bind())) == (
None,
None,
)
def test_event_after_arg_resolution(workflow_start_regular_shared):
"""Ensure that a workflow resolves all of its non-event arguments while it
waiting the the event to occur.
"""
class MyEventListener(workflow.EventListener):
async def poll_for_event(self):
while not utils.check_global_mark():
await asyncio.sleep(0.1)
# Give the other step time to finish.
await asyncio.sleep(1)
@ray.remote
def triggers_event():
utils.set_global_mark()
@ray.remote
def gather(*args):
return args
event_promise = workflow.wait_for_event(MyEventListener)
assert workflow.run(gather.bind(event_promise, triggers_event.bind())) == (
None,
None,
)
def test_object_deref(workflow_start_regular_shared):
@ray.remote
def empty_list():
return [1]
@ray.remote
def receive_workflow(workflow):
pass
@ray.remote
def return_workflow():
return empty_list.bind()
@ray.remote
def return_data() -> ray.ObjectRef:
return ray.put(np.ones(4096))
@ray.remote
def receive_data(data: "ray.ObjectRef[np.ndarray]"):
return ray.get(data)
# test we are forbidden from directly passing workflow to Ray.
x = empty_list.bind()
with pytest.raises(ValueError):
ray.put(x)
with pytest.raises(ValueError):
ray.get(receive_workflow.remote(x))
with pytest.raises(ValueError):
ray.get(return_workflow.remote())
# test return object ref
obj = return_data.bind()
arr: np.ndarray = workflow.run(receive_data.bind(obj))
assert np.array_equal(arr, np.ones(4096))
def test_objectref_inputs(workflow_start_regular_shared):
@ray.remote
def nested_workflow(n: int):
if n <= 0:
return "nested"
else:
return workflow.continuation(nested_workflow.bind(n - 1))
@ray.remote
def deref_check(u: int, x: str, y: List[str], z: List[Dict[str, str]]):
try:
return (u == 42 and x == "nested"
and isinstance(y[0], ray.ObjectRef)
and ray.get(y) == ["nested"]
and isinstance(z[0]["output"], ray.ObjectRef) and ray.get(
z[0]["output"]) == "nested"), f"{u}, {x}, {y}, {z}"
except Exception as e:
return False, str(e)
output, s = workflow.run(
deref_check.bind(
ray.put(42),
nested_workflow.bind(10),
[nested_workflow.bind(9)],
[{
"output": nested_workflow.bind(7)
}],
))
assert output is True, s
def test_dataset_2(workflow_start_regular_shared):
ds_ref = gen_dataset_2.bind()
transformed_ref = transform_dataset_1.bind(ds_ref)
output_ref = sum_dataset.bind(transformed_ref)
result = workflow.run(output_ref)
assert result == 2 * sum(range(1000))
def test_checkpoint_dag_recovery_partial(workflow_start_regular_shared):
utils.unset_global_mark()
start = time.time()
with pytest.raises(workflow.WorkflowExecutionError):
workflow.run(checkpoint_dag.bind(False),
workflow_id="checkpoint_partial_recovery")
run_duration_partial = time.time() - start
utils.set_global_mark()
start = time.time()
recovered = workflow.resume("checkpoint_partial_recovery")
recover_duration_partial = time.time() - start
assert np.isclose(recovered, np.arange(SIZE).mean())
print(f"[partial] run_duration = {run_duration_partial}, "
f"recover_duration = {recover_duration_partial}")
def test_same_object_many_workflows(workflow_start_regular_shared):
"""Ensure that when we dedupe uploads, we upload the object once per workflow,
since different workflows shouldn't look in each others object directories.
"""
@ray.remote
def f(a):
return [a[0]]
x = {0: ray.put(10)}
result1 = workflow.run(f.bind(x))
result2 = workflow.run(f.bind(x))
print(result1)
print(result2)
assert ray.get(*result1) == 10
assert ray.get(*result2) == 10
def test_workflow_queuing_1(shutdown_only, tmp_path):
ray.init(storage=str(tmp_path))
workflow.init(max_running_workflows=2, max_pending_workflows=2)
import queue
import filelock
lock_path = str(tmp_path / ".lock")
@ray.remote
def long_running(x):
with filelock.FileLock(lock_path):
return x
wfs = [long_running.bind(i) for i in range(5)]
with filelock.FileLock(lock_path):
refs = [
workflow.run_async(wfs[i], workflow_id=f"workflow_{i}")
for i in range(4)
]
assert sorted(x[0] for x in workflow.list_all({workflow.RUNNING})) == [
"workflow_0",
"workflow_1",
]
assert sorted(x[0] for x in workflow.list_all({workflow.PENDING})) == [
"workflow_2",
"workflow_3",
]
with pytest.raises(queue.Full, match="Workflow queue has been full"):
workflow.run(wfs[4], workflow_id="workflow_4")
assert ray.get(refs) == [0, 1, 2, 3]
assert workflow.run(wfs[4], workflow_id="workflow_4") == 4
assert sorted(x[0] for x in workflow.list_all({workflow.SUCCESSFUL})) == [
"workflow_0",
"workflow_1",
"workflow_2",
"workflow_3",
"workflow_4",
]
for i in range(5):
assert workflow.get_output(f"workflow_{i}") == i
